U.S. Department of the Interior

U.S. Geological Survey

Professional Paper 1796

An Economic Value of Remote-Sensing Information—

Application to Agricultural Production and 

Maintaining Groundwater Quality
Front Cover

This image shows a rendition of the next satellite in the Landsat Program, the Landsat Data Continuity Mission, which is scheduled to launch in February 2013. The 

Landsat Program is a series of Earth-observing satellite missions jointly managed by the U.S. Geological Survey and NASA. Since 1972, Landsat satellites have collected information about Earth from space. This science, known as remote sensing, has matured with the Landsat Program. Landsat satellites have taken specialized 

digital photographs of Earth’s continents and surrounding coastal regions for more than three decades, enabling people to study many aspects of our planet and to 

evaluate the dynamic changes caused by both natural processes and human practices. (Modified NASA image.)

Back Cover

Image sources: Image of Landsat 7, Landsat image of Iowa study region, and photograph of Iowa cornfield (U.S. Geological Survey); Waverly, Iowa, water tower 

(Iowa Department of Natural Resources /Prior and others, 2003).
An Economic Value of Remote-Sensing 

Information—Application to Agricultural 

Production and Maintaining 

Groundwater Quality

By William M. Forney, Ronald P. Raunikar, Richard L. Bernknopf, and 

Shruti K. Mishra

Professional Paper 1796

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

For an overview of USGS information products, including maps, imagery, and publications, 

visit http://www.usgs.gov/pubprod

For more information on the USGS—the Federal source for science about the Earth,

its natural and living resources, natural hazards, and the environment, visit 

http://www.usgs.gov or call 1-888-ASK-USGS (1-888-275-8747).

To order this and other USGS information products, visit http://store.usgs.gov

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain

copyrighted materials as noted in the text. Permission to reproduce any copyrighted items must be

secured from the copyright owner.

Suggested citation:

Forney, W.M., Raunikar, R.P., Bernknopf, R.L., and Mishra, S.K., 2012, An economic value of remote-sensing information—Application to agricultural production and maintaining groundwater quality: U.S. Geological Survey Professional Paper 1796, 60 p.
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Background.......................................................................................................................................2

Conceptual Framework and Economic Model..........................................................................................3

Value of Information Theory ...........................................................................................................5

Empirical Application and Methods ...........................................................................................................6

Agricultural Production...................................................................................................................7

Nitrate Contamination of Aquifers.................................................................................................7

 Dynamic Nitrate Pollution......................................................................................................10

 Capture-Zone Delineation................................................................................................12

 Crop-Area Calculation Using MRLI................................................................................13

 Nitrogen-Leaching Estimation........................................................................................13

 Hazard Function and Groundwater Survival.......................................................................15

Value of Information Estimation...................................................................................................18

 Without MRLI Case.................................................................................................................18

 With MRLI Case: Enhanced Landscape Configuration ....................................................18

Materials and Data......................................................................................................................................19

Northeastern Iowa Study Region ................................................................................................21

MRLI: Cropland Data Layer...........................................................................................................21

Crop Price Data...............................................................................................................................24

Wells.................................................................................................................................................25

Hydrogeologic Characteristics ....................................................................................................26

Soil Characteristics........................................................................................................................28

Slope, Depressions, and Topographic Position.........................................................................28

Rivers, Watersheds, and Hydrologic Response Units..............................................................30

Results and Discussion...............................................................................................................................30

Agricultural Production.................................................................................................................30

Nitrate Contamination of Aquifers...............................................................................................30

 Dynamic Nitrate Pollution......................................................................................................30

 Leachate Estimation ...............................................................................................................32

 Groundwater Vulnerability.....................................................................................................37

Value of Information Results ........................................................................................................39

Additional Discussion....................................................................................................................43

Future Research .............................................................................................................................43

Summary and Conclusions.........................................................................................................................47

Acknowledgments.......................................................................................................................................47

References Cited..........................................................................................................................................48

Appendixes ...................................................................................................................................................54

 Appendix 1—Integrated Assessment Approach’s Assumptions and Their Types .............55

 Appendix 2—Nitrogen Cycle .......................................................................................................56

 Appendix 3—Kriging of Hydraulic Conductivity .......................................................................57
iv

Figures

 1. Diagram showing conceptual framework for the integrated assessment approach. Adapted 

from Antle and Just (1991) ..............................................................................................................4

 2. Diagram showing production possibilities frontiers (PPF) for crop production and the 

survivability of potable groundwater............................................................................................6

 3. Diagram showing conceptual factors of the decisionmakers’ framework across space and 

time for the empirical application of the integrated assessment approach ..........................8

 4. Diagram showing conceptual schematic for models and biophysical drivers of the empirical 

application of the integrated assessment approach. ................................................................9

 5. Graph showing trends in corn and soybeans in the 35-county northeastern Iowa study 

region, 2001–2010 ...........................................................................................................................10

 6. Diagram showing the nitrogen cycle ................................................................................................11

 7. Map showing ArcAEM/Split model output and capture zone result on the National 

Agricultural Statistics Service’s 2007 Cropland Data Layer....................................................14

 8. Map showing watersheds, rivers, and digital elevation model for the study region in 

northeastern Iowa. These geospatial datasets are also used as ArcSWAT inputs ...........15

 9. Diagram illustrating arbitrarily interval-censored data used for the proportional hazards 

model................................................................................................................................................18

10. Map showing case study location in reference to the full extent of National Agricultural 

Statistics Service’s 2007 Cropland Data Layer for the Midwest ............................................20

11. Map showing the distribution of capture zones (CZs) delineated using ArcAEM/Split for the 

northeastern Iowa study area, which includes 35 counties...................................................22

12. Map showing crop rotation patterns with the National Agricultural Statistics Service’s 

2007 Cropland Data Layer in a small part of the northeastern Iowa study region, which 

includes the City of Waterloo, 2000–2010...................................................................................23

13. Graph showing average annual real prices received for corn grain and soybeans by U.S. 

producers. The data are derived from the monthly Agricultural Prices reports of the 

National Agricultural Statistics Service, Agricultural Statistics Board, U.S. Department of 

Agriculture.......................................................................................................................................25

14. Map of Iowa showing a sample of National Water quality Assessment Program (NAWQA) 

wells in Silurian, Devonian, and Ordovician aquifers and cities in the northeastern Iowa 

study region ....................................................................................................................................26

15. Map showing interpolated well depths for the 35-county northeastern Iowa study region. ..27

16. Map showing derived Silurian and Devonian aquifer thicknesses in the northeastern Iowa 

study region. Source is Iowa Department of Natural Resources. .........................................28

17. Map showing shaded relief of topography for the 35-county northeastern 

Iowa study region...........................................................................................................................29

18. Subbasin-level yield maps for combined corn and soybean production estimated from 

ArcSWAT for the northeastern Iowa study region, 2001 to 2010............................................33

19. Maps showing subbasin-level nitrate leaching associated with corn and soybean 

production estimated from ArcSWAT for the northeastern Iowa study region, 2001 to 

2010. Avg., average; kg, kilograms...............................................................................................38

20. Graph showing groundwater-survival probability curves of three subbasins in the 35-county 

northeastern Iowa study region ..................................................................................................39

21. Map showing groundwater failure and subbasin probability of survival for the northeastern 

Iowa study region...........................................................................................................................40

22. Bar graph showing annual flow of benefits with and without moderate-resolution land 

imagery (MRLI) for the 35-county northeastern Iowa study region ......................................41
v

23. Map showing the net present value estimates for the value of information (VOI) calculations 

at the subbasin-level that overlap the 35-counties in the 

northeastern Iowa study region ..................................................................................................42

24. Image of regression kriging without residuals, using hydraulic conductivity for the 35-county 

northeastern Iowa study region as an example .......................................................................58

25. Image of kriging regression residuals, using hydraulic conductivity for the 35-county 

northeastern Iowa study region as an example .......................................................................59

26. Image of final regression kriged layer, using hydraulic conductivity for the 35-county 

northeastern Iowa study region as an example .......................................................................60

Tables

 1. Description of data used for estimation of economic value of remote-sensing information 

in the northeastern Iowa study region .......................................................................................17

 2. Shifting imaging sensors, accuracies, and metrics of the National Agricultural Statistics 

Service’s Cropland Data Layer, 2000–2010.................................................................................24

 3. Crop yield estimates and area calculations for four land-use/land-cover (LULC) types of 

the northeastern Iowa study region, 2001–2010 .......................................................................31

 4. Summary statistics for cumulative nitrate indicator (CNI) data .............................................34

 5. Ordinary-least-square estimates for cumulative nitrate indicator equations. ....................35

 6. Arrelano-bond dynamic panel-data estimation results for cumulative nitrate indicator 

equations .........................................................................................................................................35

 7. Arrelano-Bond dynamic panel-data estimations using three categories of thickness for 

Quaternary deposits.......................................................................................................................36

 8. Average nitrate leaching rates and total leached amount estimated from ArcSWAT for 

the northeastern Iowa study region, 2001–2010 .......................................................................37

 9. Proportional hazards model estimated effects of explanatory factors on probablitly of 

well survival (equation 20) ............................................................................................................37

 10. United States corn and soybean production estimates and projections, 2000 to 2026, from 

Food and Agricultural Policy Research Institue (2012)............................................................44

 11. Natural Resources Conservation Service (NRCS) land conservation programs summary 

for the 35-county northeastern Iowa study region, 2003 to 2011 ...........................................45

 12. United States biofuels production and feedstock source estimates and projections, 2005 

to 2025, from Food and Agricultural Policy Research Institue (2012) ....................................46

 13. Log-log regression equation for kriging of hydraulic conductivity in Devonian and 

Silurian aquifers in the northeastern Iowa study region.........................................................57
vi

Acronyms and Abbreviations

AEM Analytic element method

ArcGIS® ESRITM geographic information systems software

ArcSWAT Soil and Water Assessment Tool for use with ArcGIS

AWiFS Advance Wide Field Sensor

CDL Cropland Data Layer

CNI Cumulative nitrate indicator

CLU

CPI-U

Common Land Unit

Consumer price index for all urban consumers

CZ Capture zone

DEM Digital elevation model

DSS Decision support system

EAI Equivalent annual income

EPA Environmental Protection Agency

FAPRI Food and Agricultural Policy Research Institute

GMM Generalized method of moments

HC Hydraulic conductivity

HRU Hydrologic response unit

HUC Hydrologic unit code

IAA Integrated assessment approach

IDNR Iowa Department of Natural Resources

JAS June Agricultural Survey

LCDM Landsat Data Continuity Mission

L/min. Liters per minute

LULC Land use/land cover

MCL Maximum Contamination Level

mpd Meters per day

MODIS Moderate-resolution Imaging Spectroradiometer

MRLI Moderate-resolution land imagery

NASS National Agricultural Statistics Service

NAWQA National Water Quality Assessment Program

N2 Nitrogen gas

NH3 Ammonia

N2

O Nitrous oxide

NO3

- Nitrate

NHD National Hydrography Dataset

NPV Net present value

NRCS Natural Resources Conservation Service
vii

OK Ordinary kriging

OLS Ordinary least squares

PPF Production possibility frontier

RATS Estima, Inc., Regression Analysis of Time Series software

RFS Renewable Fuel Standard

RK Regression kriging

STATA StataCorp LP statistical software 

USDA U.S. Department of Agriculture

USGS U.S. Geological Survey

VOI Value of information

WGSC Western Geographic Science Center
This page intentionally left blank.
An Economic Value of Remote-Sensing Information—

Application to Agricultural Production and Maintaining 

Groundwater Quality

Abstract

Does remote-sensing information provide economic 

benefits to society, and can a value be assigned to those 

benefits? Can resource management and policy decisions be 

better informed by coupling past and present Earth observations with groundwater nitrate measurements? Using an 

integrated assessment approach, the U.S. Geological Survey 

(USGS) applied an established conceptual framework to 

answer these questions, as well as to estimate the value of 

information (VOI) for remote-sensing imagery. The approach 

uses moderate-resolution land-imagery (MRLI) data from the 

Landsat and Advanced Wide Field Sensor satellites that has 

been classified by the National Agricultural Statistics Service 

into the Cropland Data Layer (CDL). Within the constraint 

of the U.S. Environmental Protection Agency’s public health 

threshold for potable groundwater resources, the USGS modeled the relation between a population of the CDL’s land uses 

and dynamic nitrate (NO3

-

) contamination of aquifers in a 

case study region in northeastern Iowa. Employing various 

multiscaled, multitemporal geospatial datasets with MRLI to 

maximize the value of agricultural production, the approach 

develops and uses multiple environmental science models to 

address dynamic nitrogen loading and transport at specified 

distances from specific sites (wells) and at landscape scales 

(for example, across 35 counties and two aquifers). In addition to the ecosystem service of potable groundwater, this 

effort focuses on the use of MRLI for the management of the 

major land uses in the study region—the production of corn 

and soybeans, which can impact groundwater quality. Derived 

methods and results include (1) economic and dynamic 

nitrate-pollution models, (2) probabilities of the survival 

of groundwater, and (3) a VOI for remote sensing. For the 

northeastern Iowa study region, the marginal benefit of the 

MRLI VOI (in 2010 dollars) is $858 million ± $197 million 

annualized, which corresponds to a net present value of $38.1 

billion ± $8.8 billion for that flow of benefits in perpetuity. 

Given that these economic estimates are derived from one 

case study in a part of only one State, the estimates provide 

a lower estimate related to the potential value of the Landsat 

Data Continuity Mission.

Introduction

This study demonstrates the value of information (VOI) 

of moderate-resolution land imagery (MRLI)—which mostly 

includes Landsat imagery—using a case study of agricultural 

production and preservation of groundwater resources in the 

agricultural State of Iowa. The MRLI is used widely in several 

sectors; however, the societal value of the operational application of the scientific information provided by MRLI has not 

been quantified. In the analysis here, we use an integrated 

assessment approach (IAA) in the context of public health to 

estimate the value of MRLI. Our research has concentrated 

on documenting changes in two ecosystem services and the 

economic tradeoffs and impacts of those changes. Systematically, the analysis links spatiotemporal Earth observations to 

the maintenance of a certain level of groundwater quality over 

time, while maximizing the value of production on agricultural 

land. By linking the Landsat data archive to a groundwater 

vulnerability model, a probabilistic forecast can be made of 

when, where, and how long an aquifer system would retain its 

potability. This report comprehensively evaluates the coupling 

of archival Earth observation imagery and land uses to hydrogeologic and ecosystem-science process models, demonstrating an economic benefit and a positive value of scientific 

information in agricultural and natural-resource decisions.

The intent of the case study is to demonstrate the use of 

remote-sensing imagery to assist in natural-resource management of corn and soybean crops in 35 counties in eastern Iowa. 

MRLI can assist with identifying suitable places for types of 

agricultural production that may require nitrogen fertilizer, 

which can contaminate groundwater. Over a 10-year period of 

analysis, we found that some groundwater wells are threatened 

By William M. Forney1, Ronald P. Raunikar1, Richard L. Bernknopf2, and Shruti K. Mishra3

1

U.S. Geological Survey.

 2University of New Mexico, Albuquerque, New Mexico.

3

U.S. Geological Survey contractor.
2 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

by nitrate (NO3

-

) contamination and could fail to maintain 

drinking-water quality in the next 10 years and that other locations where the topography, soils, well characteristics (such 

as depth and operations), and surficial geology are less likely 

to transport the contaminant to the water supply in the future. 

Given that VOI estimates require an application in which the 

information in question is used, the case study has created a 

suite of integrated models that can be used as a decision support system (DSS) by interested stakeholders. For example, 

a regulator could decide whether to intervene to impose a 

reduction in fertilizer application or change the distribution of 

crop-production patterns to reduce the probability of a public 

or private well failure. 

This case study shows that a new application of MRLI 

potentially provides significant societal benefits from Landsat 

imagery. It is likely that many new applications of MRLI have 

the potential to become important additions to the enhanced 

management of land use/land cover (LULC).

Background

Currently, a question faces Congress and the Nation 

as to allocating and maintaining the resources necessary to 

a particular MRLI sensor, namely Landsat. As part of the 

Landsat Data Continuity Mission (LDCM) to maintain the 

series of satellites first launched in 1972, the launch of Landsat-8 is planned for February 2013. The output of Landsat is 

information and the U.S. Geological Survey (USGS) policy 

was established to provide minimally processed Landsat 

imagery at zero price (Landsat Science Team, 2008). The 

policy defines the information as a public good because a 

market for the information does not exist. As with any ambitious, high-cost endeavor, interested stakeholders will want 

to know, “Is it worth it?” A way to answer that question is to 

quantify the benefit of having the information available to 

public-sector decisionmaking versus a situation where it is 

not, which is one definition of VOI (Bernknopf and others, 

1997; Macauley, 2005). The analysis here is an input to the 

justification of the continuation of the Landsat mission as a 

public good that requires a quantitative economic approach 

and development of a method to estimate the societal benefits 

of providing the information. The MRLI and its classified 

derivatives provide a spatiotemporal LULC signal that can 

be used for making decisions in preservation and management of resources. This research estimates the economic VOI 

for a particular usage of MRLI. 

This research estimates the VOI of MRLI using an IAA 

to assess the impacts of environmental pollution based on 

farm activities in the agricultural sector of Iowa. The VOI is 

estimated as (1) the economic benefit stream of a net increase 

in agricultural production across a region without sacrificing 

groundwater resources and (2) how the characterization and 

management of agricultural production and its environmental 

impacts may change with or without the availability of MRLI. 

The analysis is an examination of the nitrate accumulation 

patterns in wells of Iowa. Benefits of MRLI accrue to society 

by avoiding the risk of exceeding the nitrate water-quality 

standard in groundwater as a result of efficient changes in 

land-use patterns. A second objective of the research examines 

the capability of MRLI to inform the management of agricultural production for the benefit of individual producers, as 

well as for the benefit of societal welfare. The widespread and 

diffuse nature of nonpoint source contamination of groundwater from crop and livestock production makes the determination of sources and opportunities for control and remediation 

very difficult (Rodvang and Simpkins, 2001). Thus, the ability 

to utilize MRLI to observe regional scale LULC and use it 

as an input into the characterization of its impact on natural 

resources across a region is appealing. Over time, the archive 

of Earth observation of land-use patterns can be used to monitor and document how producers’ decisions can affect ecosystem services in socially relevant and policy-relevant contexts. 

A number of social welfare considerations, policy drivers, and other factors helped to define the orientation of this 

research on this particular case study. First, applications in 

environmental science and management (40 percent), LULC 

(17 percent), planning and development (11 percent), and the 

agricultural sector (8 percent) have been documented as the 

four largest application areas of Landsat for all user sectors 

(Miller and others, 2011). In addition, Miller and others (2011) 

continued to identify agriculture and environmental science 

in the Federal sector as the largest application areas. Second, 

biofuel production from corn ethanol and other sources was 

incentivized by the Energy Policy Act of 2005, which set the 

Renewable Fuel Standard (RFS) to increase ethanol levels 

from 4.0 to 7.5 billion gallons per year by 2012. The Energy 

Independence and Security Act of 2007 increased those RFS 

levels to require the use of at least 36 billion gallons of biofuel by 2022—15 billion gallons being corn ethanol and the 

remainder being cellulosic ethanol (such as perennial grasses, 

biomass, and municipal solid waste) and other advanced biofuels. Third, the increase in renewable fuel production could 

not occur independent of constraining public health policies 

such as the Federal Clean Water Act of 1972 and Safe Drinking Water Act of 1974, State water-quality codes, Groundwater Protection Acts, and manure management plans. Most 

importantly for this work, the U.S. Environmental Protection 

Agency (EPA) established the threshold Maximum Contamination Level (MCL)4

 of 10 milligrams per liter (mg/L) 

nitrate (measured as nitrogen) for safe drinking water. Finally, 

the U.S. Department of Agriculture’s (USDA) and Natural 

Resources Conservation Service (NRCS) conservation programs (for example, Crop Reserve Program, Wetland Reserve 

Program, Environmental Quality Incentives Program) provide 

economic incentives in the form of voluntary, yet binding, 

cost-share programs to farmers to take their lands out of 

4

EPA list of contaminants and their Maximum Contaminant Levels: 

http://www.epa.gov/safewater/contaminants/index.html.
Conceptual Framework and Economic Model 3

production, which exacerbates the complexity of understanding changes in LULC and agricultural production. 

Drinking water exceeding the MCL of nitrate causes 

human health impacts such as methemoglobinemia, which in 

infants is also known as “blue baby syndrome.” Nolan and 

Hitt (2006) documented that adverse health impacts such as 

miscarriages and cancer risks were noted in populations with 

drinking water with nitrate levels higher than 2.46 mg/L. 

Nitrate is not a carcinogenic compound; however, it reacts 

with other chemicals to form carcinogenic compounds, such 

as nitrosamines and nitrosamides that are associated with 

multiple different types of cancers (Mirvish, 1995; Weyer and 

others, 2001; Ward and others, 2005). In addition, Mirvish 

(1995) stated that disruption of thyroid function, birth defects, 

and hypertension are other results of ingesting nitrate. It is also 

important to note that some countries such as Germany and 

South Africa have a much stricter standard for drinking-water 

nitrate levels set at 4.4 mg/L (Kross, 2002).

Conceptual Framework and Economic 

Model

The primary components of the conceptual framework 

(fig. 1) include decisions at the levels of the individual farmer 

and regulators both with and without spatiotemporal MRLI 

information and how MRLI data relates to agricultural production, environmental pollution, and the joint production of 

agricultural products (corn and soybeans) and groundwater 

contaminants (nitrates) in a landscape (Bernknopf and others, 

2012). Adapting an IAA from Antle and Just (1991), our IAA 

illustrates how MRLI can inform production and regulatory decisions. The decisions by individual producers in the 

site-specific production of the joint output of a marketable 

agricultural commodity (Antle and McGuckin, 1993) and 

a nonmarket service of groundwater quality are reflected in 

figure 1 (boxes 1 through 6). The decisions by public agencies and other stakeholders involve regional-scale land-use 

decisions and their impact on ecosystem services (fig. 1, boxes 

7 and 8). The MRLI observations of regional crop production and rotation when linked to the current level and future 

accumulation of nitrates in the groundwater, and the environmental risks associated with historical, current, and possible 

land uses, provides value by informing potential reallocation 

of regional land use in order to preserve ecosystem services 

associated with groundwater resources. The VOI is estimated 

for MRLI using equation 1 to maximize agricultural production for any given location within the region, while avoiding 

an increase in groundwater pollution from those agricultural 

nitrogen sources. 

The regional model incorporates both the producers’ (an 

individual’s or microeconomic perspective) and the regulators’ (a regional or macroeconomic perspective) priorities 

in accommodating the overall decisionmaking process. The 

regional economic model is based on an individual producer’s 

objective to maximize profit, while constraining risks of a 

marketable crop in equation 1 (fig. 1, boxes 1–6). Given a 

vector of regulations R, producers seek to maximize profit on 

each plot of land:

 , (1) dt

�(qt ,vt ,z

t 

,P(R)

t

W(R)t ,et)

∞

s.t. et+1=ez+∆e(qt ,vt ,zt)

Rqmin

t ≤ qt

 ≤ Rqmax

t

Rvmin

t ≤ vt

 ≤ Rvmax

t

Rzmin

t ≤ zt

 ≤ Rzmax

t

F (q,v,z,e;R) ≥ 0

 qt ,vt ,zt t=t0

maxΣ

where P represents prices of relevant crops and crop production (q) is the amount of each crop produced on a plot of 

land and is a function of variable inputs (v), farm management practices (z), and plot characteristics (e). Discounting 

is by factor d, π is the annual profit function for farmers in 

the region, W is input costs vector, and R is explicitly shown 

as minimum and maximum regulatory constraints (s.t.) on 

the decision variables and subsidies applied to the prices and 

costs the producer faces. (Note that in equations in this report, 

italic represents a scalar and bold represents a vector.) Current 

choice variables affect the future by the changes they cause 

to the physical properties of the plot by the function. Time, 

t, is discrete corresponding to planting decisions made each 

annual growing season. Planting decisions are based, in part, 

on past prices that form expectations for a new crop in the 

upcoming year. In terms of aggregating the estimation of joint 

output and conducting statistical analyses for regional policy 

analysis, we assume an annual basis is appropriate. 

Moving from the individual producer to the regional perspective, our study region is a limited fraction of world soybean 

and corn production so that marginal changes in production 

do not have a significant effect of crop prices, P. Thus, the 

partial equilibrium approximation of policies R not affecting P

is appropriate. Note that Q (aggregate, regional production of 

crops) and P are vectors of quantity and present discounted real 

price of corn and soybeans for each year of the analysis. Therefore, PQ is the present discounted value of the corn/soybean 

crop over that time period and subject to the regional risks. The 

regional production model in equation 2 represents activities 

associated with regulator choices (fig. 1). Like individual producer decisions, in the aggregation of joint output and conducting statistical analyses for regional (macroeconomic) policy 

analysis, we assume an annual basis is appropriate. Regulatory 

standards originate as regional decisions based on scientific 

analysis of the human health effects of specific agrochemical 

usage. We approximate the regulator’s problem as: 

 

max PQ R

s.t. risks ≤ α , (2)
4 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

Both the plot level and regional risks are related to the quality 

of information about crop production, variable inputs, farm 

management practices, plot characteristics, and α, which represents the probability of exceeding regulatory standards that 

cause damage to a natural resource (groundwater). 

A societal choice involves decisions at the regional scale 

and has an overarching effect on a variety of individual landowners and firms. As stated above, EPA has established the 

national standard for the regulation of nitrate contamination at 

10 mg/L, which can be applied regionally and locally to protect 

the public supply of drinking water. The regulator solves equation 2 acting as if α is given, but this acceptable risk is also the 

result of an optimization process (a risk standard is assumed 

to be the result of scientific trials) at the higher level of authority of the policymaker. The expected present discounted value 

of the policies to society, especially local residents and their 

aggregated improvement in public welfare, can be optimized by 

choosing a particular R* from possible policies Γ:

 max �s (R, α(R)) R ∈C. (3)

 The choices of policymakers reveal social preferences.5

We can infer α* from observed regulatory outcomes. This 

inferred α* is the risk constraint to which regulators have 

1. Prices and market 

mechanisms

2. Policies and 

regulations

3. Land characteristics

4. Management decisions: land use, input use

5. Output: yield / area 6. Output: nitrate in groundwater

7. Joint output: regional economic production

and environmental polluion

8. Regional regulatory and policy 

analysis

9. VOI for MRLI

Figure 1. Diagram showing conceptual framework for the integrated assessment approach. Adapted from Antle and Just 

(1991). VOI, value of information; MRLI, moderate-resolution land imagery.
Conceptual Framework and Economic Model 5

conformed, and we assume it is a reasonable risk constraint for 

the future to apply to marginal alterations of policy or to the 

case of improved information structure. Over long periods of 

time, production of corn—and to a lesser degree the production of soybeans—its associated pollution, and its cumulative 

environmental effects at regional scale can cause substantial 

changes to groundwater. The policy issue is whether the ecosystem service of potable groundwater becomes stressed and 

could be compromised, or even lost, because of changes in the 

land-use pattern, location, and intensity. 

The regional economic model has a number of assumptions that make the agricultural application more tractable for 

empirical demonstration. Appendix 1 lists the model assumptions for individual producers, regional-scale output, and the 

other models and analysis of the IAA. 

Value of Information Theory

The final calculation in the IAA (fig. 1, box 9) is the 

estimation of the economic value of the MRLI. In estimating a dollar value of the Earth observation information, it 

is assumed that the regional market is competitive and is 

operating efficiently. Because the requirements for a functioning competitive economy are achieved, the joint output of the 

economy for a production scenario is a point on the regional 

production possibility frontier (PPF) in figure 2. Using equation 3, different estimates of efficient outcomes of the joint 

output of crop production and the survivability of potable 

groundwater can be plotted along the PPF for each set of policies, R, in a LULC scenario. 

The MRLI input is a unique technological innovation 

that when applied to improve on a current land-use allocation, 

creates an outward shift in the PPF from PPF0 to PPF1 (Buckwell, 1989; Mas-Colell and others, 1995; Varian, 1999); that is, 

society could have a more valuable mix of agricultural production and greater survivability of potable groundwater. The VOI 

for the MRLI is derived from this outward shift in the PPF. In 

figure 2, points A and B on PPF0 are two efficient allocations 

of land for crop production and the survivability of potable 

groundwater. Point A represents a regional economy that has 

a high level of crop output and low survivability of potable 

groundwater. Point A maximizes crop production given a particular level of surviving, potable groundwater. Point A could 

be current and historical crop patterns as shown by the Landsat 

archive. Alternatively, point B represents a regional output that 

lowers the risk of losing potable groundwater by accepting a 

crop production of lower value as a tradeoff. Point B could be 

a highly regulated amount of crop production to sustain a large 

quantity of high-quality groundwater and could be considered a 

conservation alternative. Points A and B are alternative optimal 

solutions of regional land allocation without MRLI. Point C 

on PPF1 improves the regional allocation of land with MRLI 

by taking advantage of the technological advances in Earth 

observation. Point C indicates that a reallocation of land use 

would increase both crop production and retain the existing 

survivability of potable groundwater. Point C is a reallocation 

of land use based on the linkage between MRLI observation 

and classification of agricultural products and physical-process 

models of groundwater vulnerability to identify better locations 

for particular land uses. 

Using equation 4, we estimate the value of PΔQ for the 

application of estimating the benefits of MRLI. The existing 

regulations with the additional information from MRLI (ω(1)) 

would be R*(ω(1),α) and without additional information 

(ω(0)) would be R*( ω(0),α) for the probability of exceeding 

the regulatory standard for resource damage, α. The additional 

information may allow regulations to be better targeted so that 

the crop production will be different Q*R(ω(1),α) with the information (at point C on PPF1

 in fig. 2) than without Q*R(ω(0),α) (at 

either point A or point B on PPF0 in fig. 2). Therefore, the VOI 

to the regulator is stated explicitly as:

 VOIω(1)=P Q*

R(ω(1),α)

– Q*

R(ω(0),α) . (4)

The present discounted value, PQ, for the range of years 

analyzed is calculated by summing the quantities of corn and 

soybeans produced in each land unit into the time series Q and 

multiplying by the present discounted vector of real prices that 

prevailed during the period of analysis. A possible combination of cropping choices across the study region is eliminated 

if the environmental constraint is exceeded, and among those 

choices not eliminated, the optimization algorithm steps 

through cropping choices until a maximum PQ is identified 

and annualized. The present discounted value of the difference 

between optimal (with MRLI and associated modeling data) 

and baseline (without MRLI data) is PΔQ. The VOI expressed 

as an equivalent annual income (EAI) is:

 

EAI = P∆Q

r (1+r)t

(1+r)t −1 , (5)

 

where r is the discount rate. Assuming a similar flow of benefits into the indefinite future because of the continuation of 

the availability of MRLI, for this region the net present value 

(NPV) is calculated:

 NPV = EAI (1+r)

r

 . (6)

 This net present value is an estimate of the value of using 

MRLI based information for managing the corn/soybean crop 

patterns and groundwater resources in our study region into 

the indefinite future.

5

Others have used observed government actions to reveal social preferences. McFadden (1975) inferred the revealed value of indirect costs and 

benefits to highway route selectors, and Ross (1984) shows how revealed 

preference can be applied to infer the implied social-weighted sum of preferences of regulators. We are not using revealed preference to infer values, 

but rather to infer the optimal constraints implied by those values.
6 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

changes to LULC vary over seasons to a given year; the estimates of joint output occur on an annual basis; and a particular site’s land characteristics, the changes to energy policies 

and the duration of USDA and NRCS conservation program 

contracts are on the order of 5 to 10 years (although some land 

characteristics like soil texture can change more quickly from 

farm-management practices and some conservation program 

contracts such as those of the Wetland Reserve Program6 contain permanent easements). These scales influence the spatiotemporal units of analyses for the research.

In this research, the primary units of analyses include fields, 

counties, wells, capture zones (CZ), hydrologic response units, 

watersheds and aquifers. The empirical application is summarized in a schematic (fig. 4), which presents the joint output 

agricultural products and groundwater contamination, land-use 

transitions, leachate from the soil, surface areas that represent 

CZs for particular wells, accumulation of nitrate in a well in a 

time-dependent indicator, and an aquifer’s likelihood of survival 

Empirical Application and Methods

This section focuses on discussing the technical aspects 

necessary to apply empirically the conceptual framework (fig. 

1) and the methods derived for achieving that application in 

the case study region of northeastern Iowa. The application 

requires the consideration of the more germane driving factors 

of individual producers and government regulators (fig. 3). 

Along the continuum of space from smallest to largest, a field 

includes farm-management practices and land characteristics, a farm includes crop rotation patterns and the influence 

of lands in the USDA’s or NRCS’s conservation programs, a 

county is the level to which agricultural statistics are reported 

and the without MRLI VOI base case is estimated, a region 

is susceptible to distribution of climate (temperature and 

precipitation), and the State and Federal governments dictate 

overriding energy and regulatory policies. Along the temporal 

continuum from shortest to longest, commodity prices and 

weather can vary in a given week; farm management practices, 

regulatory policies, and accumulation of pollution can occur 

or be imposed over months and seasons; crop rotations and 

 

 

 

 

 

PPF0 without Landsat

C

Production Possibility

Frontiers (PPF)

Survivability of potable groundwater (megaliters) 

Crop production,

in dollars

Frontier (PPF)

B

A

PPF1 with Landsat

Figure 2. Diagram showing production possibility frontiers (PPF) for crop production and the survivability of 

potable groundwater.

6

For more information on the Wetland Reserve Program see

http://www.fs.fed.us/spf/coop/programs/loa/wrp.shtml.
Empirical Application and Methods 7

in the long run. The empirical aspects of the research are further 

described in the following sections on agricultural production, 

nitrate contamination of aquifers, and estimation of VOI.

Agricultural Production 

Crop production is a function of variable inputs (v), farm 

management practices (z), and plot characteristics (e) (eq. 6 in 

Bernknopf and others, 2012). Previous studies estimated crop 

production and fertilizer using a quadratic function, such as 

the von Leibig and Mitscherlich-Baule functions (Frank and 

others, 1990), apply agricultural production and yield data at 

the county level (Farajalla and others, 1993; Liao and others, 

2012), and have used annual tonnage of nitrogenous fertilizer 

shipped into a county as a proxy for direct measurement of 

application rates (Farajalla and others, 1993). This research in 

this report relies on the capability of remote sensing to classify 

agricultural production by crop type at a field-scale resolution 

and then to develop site-specific, model-based estimates of 

crop production and fertilizer application rates.

The largest percentage of Iowa’s farmland (76 percent) is 

devoted to croplands, with 92 percent of these crops dedicated to corn and soybeans (Causarano and others, 2008). The 

Cropland Data Layer (CDL) from the National Agricultural 

Statistics Service (NASS) provides estimates of land in corn 

and soybean production from 2001 to 2010, which shows a 

general trend toward increasing corn production and decreasing soybean production (fig. 5). Because of their significance 

in ethanol production and typical crop rotation patterns, the 

particular crops of interest are corn and soybeans. We considered fractionation of the types of corn into such commodities 

as grain, food, and silage; however, the LULC system we used 

did not provide such a distinction, and we assumed that the 

difference in nitrogenous fertilizer application and its resulting 

effects on groundwater quality would be minimal.

Corn and soybean yields were estimated with an ArcGIS® version of the Soil and Water Assessment Tool (ArcSWAT) (Neitsch and others, 2009). ArcSWAT is a physically 

based, nonpoint source model and was developed to estimate 

crop yields and predict impacts of land management on water, 

sediment, and agricultural chemical yields in complex, large 

watersheds with differing soils, land uses, and climate and 

landscape characteristics. ArcSWAT has been applied widely.7

In an improvement over the simplifications of previous economic models of crop production, ArcSWAT’s plant growth 

model is based on inhibition by temperature, water, nitrogen and 

phosphorous availability, accumulated heat units, the Monteith potential biomass method, and a harvest index to calculate 

yield.8 ArcSWAT categorizes plants into seven different types 

and varies their treatment on such factors as nitrogen fixation 

simulation, growth of roots, and dormancy. The seven types 

are (1) warm season annual legumes, (2) cold season annual 

legumes, (3) perennial legumes, (4) warm season annuals, (5) 

cold season annuals, (6) perennials, and (7) trees. As is suggested by Bock and Hergert (1991), Kapp (1986), and Bourg 

(1984), the model matches fertilizer application rate to the needs 

of the crops and climatic conditions by timing it with the accumulated heat index. Including a drying time needed for harvesting dry weight, the yield of a particular crop is defined as:

yld = bio × 1– (1+HI )

1 ( ) , (7)

 HI = HI

opt

× 

100×frPHU

(100×frPHU +exp [11.1–10×frPHU] )

, (8)

where, yld is the dry-weight crop yield in kilograms per hectare 

(kg/ha), bio is the harvested aboveground biomass, HI is the 

potential harvest index for a given day, HI

opt is the potential harvest index for a particular plant at maturity and with ideal growing conditions, and frPHU is the fraction of potential heat units 

accumulated throughout the growing season. Yield is estimated 

by ArcSWAT at the hydrologic response unit (HRU) level, of 

which there are a number for any given watershed or subbasin.

Nitrate Contamination of Aquifers 

Nitrate pollution in groundwater resources is a type of 

stock pollution, or a pollution that accumulates over time. 

Depending on the environmental characteristics of the groundwater system, there may be little capacity for the system to 

absorb additional nitrates. Nitrate loading from sources such 

as fertilizer, sewage, atmospheric deposition, and manure 

that leaches through the vadose zone, is transported through 

stratigraphic layers, and can accumulate in and contaminate aquifers over the years. Nitrate accumulation dynamics 

in groundwater is determined by nitrogenous compounds 

activities in the soil surface (for example, fertilizer and other 

nitrogen sources, land-use and management practices, properties of soil, and precipitation), movement of nitrate to aquifers 

(subsurface geology and age and recharge of the groundwater 

system), and denitrification of accumulated nitrate in aquifers. The following paragraphs describe the science, context, 

approach, and methods of nitrogen sources, fate, and transport 

in our research.

Figure 4 provides a conceptual schematic of the fate 

and transport of nitrate, as well as of the germane processes 

of groundwater contamination in this study. If the nitrogen 

source is an agricultural field, shallow and deep infiltration 

and transport of nitrate are observed in private wells (typically shallower) and municipal wells (typically deeper). The 

influx of uncontaminated groundwater from outside of the 

boundary condition of the case study region can dilute the 

concentration of nitrate. The two-dimensional (2D) surface 

7

For a sample of applications, please see http://swatmodel.tamu.edu/

applications/. 8

This harvest index is expected to be relatively stable across a range of 

environmental conditions.
8 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

 Field Farm County Regional State / Federal 

SPACE

Farm

management

USDA / NRCS Programs 

Crop rotation 

Land

charachteristics

Regulatory policies

Weather variation 

Energy policies

Weekly Monthly Seasonal Annual 5 to 10 Years 50 Years

Commodity prices

Farm managment practices

Weather variation 

Land

use / land cover

Crop rotations

Estimates of joint output 

Energy policies

USDA / NRCS Programs 

Land characteristics

TIME

PollutionRegulatory policies

Agricultural statistics

Figure 3. Diagram showing conceptual factors of the decisionmakers’ framework across space and time for the empirical application 

of the integrated assessment approach. The bars, tick marks, and arrow under the time and space axes represent the range of scales 

and intervals, respectively, to which the conceptual factors apply. USDA, U.S. Department of Agriculture; NRCS, Natural Resources 

Conservation Service.

represents the possible presence of an aquitard. The nitrogen 

deposited into the soil surface undergoes one or more of the 

processes of nitrogen cycle, depending on various physical, 

chemical, and biological properties (fig. 6 and appendix 2). The 

degree of leached-nitrate attenuation as the contaminant moves 

through the subsurface depends on the stratigraphic layers 

through which it passes (as represented by brown ovals in 

fig. 6), the time it takes to do so, and the distance the pollutant 

has to travel through unsaturated materials in the vadose zone 

to reach groundwater. In the unsaturated zone, the processes 

dictating fate and transport include infiltration, evapotranspiration, dilution, filtration, sorption, ion exchange, biochemical 

transformations and plant uptake, and volatilization. These 

processes are influenced by soil texture, permeability, temperature, thickness, pH, organic matter content, exchange capability, and acid neutralizing capacity. Some of these processes and 

characteristics also influence the degree of nitrate attenuation in 

the saturated zone. In the entire hydrogeologic system (in other 

words, unsaturated and saturated zones), additional factors 

related to vulnerability include net recharge from irrigation and 

(or) precipitation, depth to the water table, and the gradients of 

flow (Canter, 1997; Committee on Techniques for Assessing 

Groundwater Vulnerability, 1993). 

Earlier studies on groundwater nitrate pollution are 

primarily oriented toward static groundwater pollution models. 

Tesoriero (1997), Rupert (1998), and Eckhardt and Stackleberg (1995) estimated the probability of exceeding particular 

groundwater nitrate levels using well depth, groundwater 

recharge, soil hydrologic group, soil drainage, surficial geology type, land-use type, and population density. Nolan and 

others (2002) used a logistic regression to estimate aquifer 

susceptibility to nitrate pollution as a function of fertilizer 
Empirical Application and Methods 9

Contaminated

groundwater flow

Uncontaminated

groundwater flow

Agricultural field Private

well

Public

well

Surface capture zones

nitrogen loading, percent cropland-pasture, human population 

density, percent well-drained soils, depth to the water table, 

and presence or absence of a fracture zone within an aquifer. 

Other research on groundwater nitrate pollution focused on 

estimating nitrate concentration in wells instead of probability 

of pollution. Litchenberg and Shapiro (1997) estimated nitrate 

concentration in Maryland Community Water System wells 

as a function of hydrological conditions for well and vector of 

land-use activities in recharge zone of the well. A comprehensive study by Nolan and Hitt (2006) estimated groundwater 

nitrate concentration in shallow and deep aquifers as a function 

of different nitrogen sources, nitrate transportation factors, and 

attenuation factors using nonlinear regressions. They estimated 

nitrate concentrations separately for shallow and deep wells, 

as deeper wells are likely to have better groundwater quality 

and different driving factors related to that better quality. For 

the shallow wells, nitrogen source variables are farm fertilizer, 

manure from confined animal feeding operations, orchards/

vineyards, population density, croplands/pasture/fallow; 

transport is explained by water input, carbonate rocks, basalt 

and volcanic rocks, drainage ditch, slope, glacial till, and clay; 

and attenuation is explained by fresh surface water withdrawal, 

irrigation tailwater recovery, histosol soil type, and wetlands. 

For the deep wells, nitrogen source variables are farm fertilizer, 

manure from confined animal feeding operations, orchards/vineyards, population density; variables explaining transport to aquifer are water input, glacial till, semiconsolidated sand aquifers, 

sandstone and carbonate rocks, drainage ditch, and Hortonian 

overland flow; and attenuation is explained by fresh surfacewater withdrawal, irrigation tailwater recovery, dunne overland 

flow and well depth. These models do not explicitly consider 

the dynamic, temporal nature of the groundwater pollution 

problem associated with shifts in land-use patterns and repeated 

application of fertilizers; however, they do provide insights for 

modeling groundwater nitrate dynamics to reflect the changes in 

nitrate over time associated with changing land use.

Figure 4. Diagram showing conceptual schematic for models and biophysical drivers of the empirical application of the integrated 

assessment approach.
10 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

y = 27546x −5E+07 

Rϸ = 0.6686 

 750,000 

 950,000 

 1,150,000 

 1,350,000 

 1,550,000 

 1,750,000 

 1,950,000 

 2,150,000 

2000 2005 2010

Soybeans (ha) 

Corn (ha) 

Linear (Corn (ha))

Year 

Area in, hectares

Year

y =27546×−5E+07

 R 2

=0.6686

Soybeans (ha)

Corn (ha)

Linear (Corn (ha))

2000 2005 2010

2,150,000

1,950,000

1,750,000

1,550,000

1,350,000

1,150,000

950,000

750,000

Figure 5. Graph showing trends in corn and soybeans in the 35-county northeastern Iowa study region, 2001–2010. Linear trend 

line of corn over time suggests an increase in area of production with more than 66 percent of the dependent variable’s variation 

explained by the regression. Source data is from the National Agricultural Statistics Service’s Cropland Data Layer. 

Another important factor to consider in the mechanism 

of groundwater vulnerability is the degree to which an aquifer 

at depth is connected to land-surface processes. One factor 

can be described as confined versus unconfined groundwater systems (Prior and others, 2003). Confined groundwater 

systems are more protected by aquitards that have minimal 

pore space, high presence of sedimentary rocks and colloids, and minimal presence of preferential flow paths that 

can result in lower levels of nitrate contamination (Rodvang 

and Simpkins, 2001; Tesoriero and Voss, 1997; Nolan and 

others, 1997; Mueller and others, 1995; and Kross and others, 

1990). Unconfined aquifers can have highly fractured bedrock 

and conduits for preferential flows of rapid groundwater 

infiltration and solute mobility via advection (Rodvang and 

Simpkins, 2001; McKay and others, 1993), which are often 

present in limestone and dolomite bedrock formations.

Dynamic Nitrate Pollution

In this research, the accumulated nitrate pollution that 

has moved through the hydrogeologic system and, observed 

over time in a given well, is modeled as a difference equation 

(Yadav, 1997; Kim and others, 1993), referred to as the cumulative nitrate indicator (CNI). The CNI uses the measured nitrate 

level in the current and previous time periods and the addition 

of nitrate to the existing pool or amount in the given year from 

the nitrogenous fertilizer applied over the defined period of 

time. The difference equation estimates the annual change or the 

dynamics of nitrate concentration over the given time period.
Organic N 

Ammonification 

Nitrification 

NO2-

De

Soil layers

Adsorption Leaching 

Plant / microbialsynthesis

Wastewater and wastewater effluent Mineral fertilizers

Plant residue andanimal waste Atmosphere 

Organic N, NH2 N2, N2O

NH3 / NH4+

NO3-

NH3 / NH4+, NO3-

Organic N 

NH3 / NH4+, NO3-

Organic N 

NH3 / NH4+ NO3-

Fixation 

NH3 NO3-

Precipitation and dustfall 

Groundwater level

Mineralization 

NH3

olatization 

Crops 

v

Figure 6. Diagram showing the nitrogen cycle. NH3, ammonia; NH4+, ammonium; NO3-, nitrate; N, nitrogen; NH2, ammonium; N2, nitrogen gas; N2

NO, nitric oxide gas; NO2-, nitrous dioxide. Modified from U.S. Environmental Protection Agency (1994), Canter (1997), and Brady and Weil (2002
12 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

 CNIt = CNIt–1

+Σi =1 (ΔNO3

-

) t−i

T

 . (9)

The simplified equations for estimating concentration of 

nitrate in a well are:

i=1

CNIt =℘0+℘1 CNIt−1+ Σbi ΑCt−i +θqth+ εt

T

, (10)

i=1

CNIt =℘0+℘1 CNIt−1+ Σbi ΑC×LF t−i+ θqth+ εt

T

, (11)

i=1

CNIt =℘0+℘1 CNIt−1+ Σbi FΑCt−i + θqth+ εt

T

, (12)

where℘is a regression coefficient, β is the estimate of the 

variables’ coefficient, AC is the area under corn production 

in capture zones t−i as discussed in the capture zone (CZ) 

delineation section, FAC is the fraction of corn production area 

out of total area in CZs t−i. AC and FAC were calculated using 

CDL for the years 2001 through 2010, LFis the leachate factor 

for the time period t−i and is estimated using ArcSWAT as 

discussed in nitrogen leaching section, θqth is the thickness of 

geologic Quaternary material, and e is the regression residual. 

The simplified model accounts for time-variant variables, such 

as AC and FAC , and time-invariant variables, such as θqth, 

across two spatial dimensions. 

The CNI was initially estimated with the ordinary least 

squares (OLS) method. Using OLS to estimate CNI can lead to 

the possibility of having a problem with omitted variables. In 

this study, fixed effect models were used to account for omitted variables specific to wells. Therefore, the CNI equations 

were estimated as:

 i=1 CNIj,t

=℘0+℘1 CNIj,t−1+ Σ bi ΑC LFi,t−i +

θqth+ εj,t

T

,

i,t−i (13)

 

i=1

CNIj,t=℘0+℘1 CNIj,t−1+ Σ bi FΑCi,t−i +

θqth+ εj,t

T

.

 

 (14)

where j is a given well. Although a fixed effect model can 

correct the omitted variables problem, using a lagged variable 

as one of the regressors can cause an autocorrelation problem 

within the residuals. A standard approach in such a case is 

to estimate the regression equation with the Arrelano-Bond 

(1991) method, which assumes that residuals are not correlated across time. Arrelano and Bond proposed estimating 

first-differences of both sides of the equations and use the 

lagged level as an instrument to obtain generalized method of 

moments (GMM) estimates. GMM is often robust in dealing 

with the violation of the assumptions of normality and homoskedasticity. The first-differences of equation 13 and 14 for the 

Arellano-Bond difference GMM estimation (one-step) method 

yields: 

i=1

∆CNIj,t=℘1∆CNIj,t –1+ Σ bi ΑCt−i LFt−i + θqth+ εj,t

T

, (15)

 

i=1

∆CNIj,t=℘1∆CNIj,t−1+ Σ bi FΑCt−i +θqth+ εj,t

T 

 . (16)

The Arellano-Bond difference equation was also estimated 

using interaction term between AC and a Quaternary thickness categorical variable, where thickness categories were 0 

to 15 meters (m), 15 to 30 m, 30 to 60 m, 60 to 92 m, and 92 

m and greater.

 Capture-Zone Delineation

In this study, we delineate individual CZs around specific 

wells that contribute to the fate and transport dynamics of 

nitrate observed in wells. Using the delineated CZs and the 

MRLI observations, the area under various land uses in the 

CZs of wells are estimated, which in turn is used to quantify 

nitrate contributed to groundwater pollution. Earlier research 

on groundwater pollution defined the CZ for water percolating 

from the land surface simply as a circular area around a well. 

For example Nolan and others (2002) used a circular area with 

diameter 500 m to model nitrate contamination in groundwater in given period, and Kolpin (1997) used diameters of 200, 

500, 1,000, 1,500, and 2,000 m to assess the sensitivity of buffer size to land uses and groundwater contamination. The Iowa 

Department of Natural Resources (IDNR) made improvements 

on the circular-area delineation method for their wellhead 

protection program with the adoption of an ArcGIS version of 

an analytic elemental model (AEM) developed by the University of Buffalo (Silavisesrith and Matott, 2005). Given the 

difficulties of modeling flow paths in limestone and dolomite, 

both in karst and at depth, and in areas with inadequate data 

in nonkarst regions, the IDNR adopted conservative buffering rules of 1.60 kilometers (km) and 0.76 km in those cases. 

Otherwise, the IDNR used the AEM on a well-by-well basis, 

which included expert opinion on a case-by-case basis of flow 

directions, interactions among neighboring wells, and thickness of lithology (Chad Fields, IDNR, oral commun., 2011). 

In this analysis, we specify the surface CZs for each time 

period t, using AEM (Strack and Haitjema, 1981) at a regional 

level. The AEM output assists with adequately characterizing 

the CZs in a given region, and constructs delineation areas 

essential for transferring this method to another geographic 
Empirical Application and Methods 13

study region. The AEM software models regional groundwater 

flow and is used to delineate time-of-travel from annual CZs 

around individual wells (Rabideau and others, 2007). Also 

known as a particle tracking method, this simplified model 

attempts to account for complex hydrogeologic conditions 

(following Dupuit-Forchheimer approximation (Dupuit, 1863; 

Forchheimer, 1886)) to characterize three-dimensional (3D) 

groundwater flow in 2D models. CZs for wells in aquifers with 

negligible ambient flow are circular, whereas those for the 

wells with significant ambient-flow conditions can be comet or 

irregularly shaped as the horizontal influx of water through the 

hydrogeologic system creates the nonuniformity in geometric 

form of the CZ delineation. The radius of a circular capture 

zone for a well is calculated using a standard water balance 

equation (Haitjema, 2006):

 3.14159( )

QT r nb = , (17)

where r is the radius of capture zone, Q the pumping rate of 

well, T is the desired time of travel, n is the porosity in aquifer, 

and b is the thickness of aquifer. In the case of wells in an 

ambient-flow region, a dimensionless time parameter T is calculated to distinguish whether the ambient flow is significant 

using the following equation (Ceric and Haitjema, 2005):

 

 T=

2(3.14159)Q2

T

0

nbQ

~ , (18)

where Q0 is the uniform flow rate. If T<0.1, ambient flow is 

considered negligible, and thus the capture zone is estimated 

as circular and stays as such up to 0.1<T<1. If the ambient 

flow is large, in other words T >1, the capture zone is irregularly shaped. 

The annual CZs are delineated using the software, Split© 

(a software program using the analytic element to model 

single-layer groundwater flow in heterogeneous aquifers). The 

software was originally designed by Jankovic (2001) to delineate CZs for one time period for an individual well. Because 

of the regional focus of this work, the USGS automated Split 

to make an ArcGIS tool capable of efficiently delineating CZs 

for a number of wells (in other words, 300 wells were run in 

one batch) for a number of time periods across a large area 

(J. Jones and others, USGS, written commun., 2011). CZs 

were delineated using the USGS modification of the ArcAEM/

Split model for the wells with known or derived hydrogeologic properties. Derivation was required because some of 

the hydrogeologic properties, such as thickness of aquifer 

and hydraulic conductivity, were not available for the wells. 

The thickness of two aquifers was calculated, and ordinary 

and regression kriging was applied to interpolate a hydraulic 

conductivity surface (see appendix 3 for details). 

Crop-Area Calculation Using MRLI 

For the purpose of modeling the nitrate contribution 

to individual wells of particular fields across the 35-county 

region, a geospatial method was developed to calculate the 

corn/soy area in CZs over a 10-year period. The MRLI derived 

CDL from 2001 through 2010 was used to estimate the area 

of corn contributing nitrate to wells from their respective CZs. 

The CDLs for Iowa were clipped using GIS for the study 

region and were reclassified into four LULC categories—corn, 

soybean, other agricultural, and nonagricultural land use. The 

rasters were then converted into polygons. The annual CZs 

polygons for wells from the ArcAEM/Split model were then 

used to intersect the CDL polygons for each year to calculate 

the corn area in each CZ and marginal zones for each year. 

An example illustrating the area calculation is provided in 

figure 7 and area of corn for each CZ for a particular well 

(Jeanne Jones and others, USGS, written commun., 2011). 

Using the CDL data from 2001 through 2010, areas of 

specific land use—corn, soybean, other agricultural crops, 

and nonagriculture—were calculated for all the HRUs in the 

study region on an annual basis using ArcGIS 9.3 and ArcGIS 

10 tools developed by the USGS Western Geographic Science Center (WGSC). This information was used to estimate 

dynamic nitrate pollution (or the CNI) to explain groundwater 

nitrate accumulation over time from changes in crop-rotation 

patterns. 

Nitrogen-Leaching Estimation

Inorganic fertilizer and manure applied to agricultural 

croplands are known to be the most common sources of 

aboveground nitrogen loading to groundwater (Hallberg, 1989; 

Keeney, 1986). Studies of the amount contributed from different nitrogen sources vary widely (Ruddy, 2006; Libra and 

others, 2004). According to the county-level nutrient estimates 

by Ruddy (2006), farm fertilizer contributes 69 percent of 

the total nitrogen inputs to land as compared to 0.16 percent 

from confined livestock and 0.06 percent from atmospheric 

deposition. Another study by the Iowa Geologic Survey (Libra 

and others, 2004) of the State’s nitrogen budget for 68 watersheds, which accounts for approximately 80 percent of the 

State, found that fertilizer accounted for 25 percent, manure 

accounted for 13 percent, atmospheric (wet and dry) deposition accounted for 16 percent, and disturbed (in other words, 

tilled or plowed) soils accounted for 26 percent. Although it 

represents a fraction of the nitrogen applied to a landscape, 

livestock wastes are used as nutrients applied to crops (Taraba 

and others, 1985). Because of the limitation of transportation 

costs, manure is often applied in the greatest rates in areas with 

high livestock densities (Greatz and Nair, 1995), which would 

include confined feed lots operations that help to concentrate 

manure and its collection. Hallberg (1989) states that the nitrate 

∼

∼

∼

∼
14 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

pollution associated with livestock feedlots is a localized 

problem. A study of nitrogen isotopes found that the primary 

sources of nitrate in sampled wells in Iowa were inorganic 

nitrogenous fertilizer (Schaap, 1999). Another study in Big 

Spring groundwater basin in Iowa shows a close correlation of 

increased nitrogenous fertilizer application in the region with 

the nitrate pollution in water (Libra and others, 1992). Therefore, we assume that the fertilizer-intensive agricultural sector 

is the primary driver of groundwater nitrate pollution in Iowa.

If a prior year’s crop was corn and the current year’s 

crop is corn, the recommended application rates for nitrogenous fertilizer before crop emergence is 170 to 225 kg/ha. 

If a prior year’s crop was soybeans and the current year’s 

crop is corn, the recommended application rate drops to 

110 to 170 kg/ha (Iowa State University Extension, 1997). 

Over the period 2000 to 2010 in the Corn Belt, the Food and 

Agricultural Policy Research Institute (FAPRI) estimated 

an annual application rate of 163.1 kg/ha to corn production 

with a standard deviation of 6.0 kg/ha (Food and Agricultural 

Policy Research Institute, 2012). For soybeans, nitrogenous 

fertilizer is not required for growth because the plant is a 

nitrogen fixer; however, manure management plans have 

been shown to apply manure on soybean fields on the order 

of 100 to 200 kg/ha to facilitate volatilization and disposal 

of wastes (Jackson and others, 2000). Over the period 2000 

to 2010 in the Corn Belt, FAPRI estimates an annual application rate of 23.3 kg/ha of manure to soybean fields with a 

standard deviation of 8.3 kg/ha.

After a crop has been fertilized and taken up essential 

nutrients for growth, an important consideration for this 

research is the amount of unused fertilizer that mobilizes as 

nitrate. As compared to the cation ammonium, which has a 

positive charge, the leaching of the anion nitrate out of the 

soil profile and into the subsurface hydrogeologic system is 

more likely to occur as nitrate ions are not adsorbed by the 

negatively charged colloids present in most soils (Brady and 9

For more information on STATSGO see http://soils.usda.gov/survey/

geography/ssurgo/description_statsgo2.html.

EXPLANATION

Corn

Fallow / idle cropland

Grass / pasture / non-ag.

Soybeans

Urban / developed

Catchment zones

q

Well

12345678910

0 15 30 45 60 METERS

0 50 100 150 200 FEET

Figure 7. Map showing 

ArcAEM/Split model 

output and capture zone 

result on the National 

Agricultural Statistics 

Service’s 2007 Cropland 

Data Layer (see http://

datagateway.nrcs.

usda.gov/). Non-ag., 

nonagricultural.

Weil, 2002). However, in the lower soil horizons where pH is 

low, the anion exchange capacity is higher and can therefore 

adsorb nitrates. Given certain land-use types, management 

practices, soil characteristics, and geologic conditions (such 

as karst), the dominant factor determining the rate of leaching of nitrates from soils and into the subsurface is often 

the amount of water applied to the land surface—either as 

precipitation or irrigation—that infiltrates and percolates 

through the vadose zone (Canter, 1997). Another driving 

factor of leaching is the presence of minor topographic 

depressions under which leaching is more extensive (Fortin 

and others, 1991; Keller and others, 1988). Without accounting for tile drainage systems, in which a drainage network 

is installed under fields to dewater them for production 

purposes, this analysis is particularly focused on nitrate that 

migrates out of lower soil profiles and the bottom of the root 

zone as leachate.

To estimate leachate in this research, we employ 

ArcSWAT. It requires the inputs of topography, watersheds, 

and rivers (fig. 8), as well as soils (from the NRCS State Soil 

Geographic (STATSGO) database9

) and LULC (from the 

CDL) to derive HRUs. ArcSWAT inputs climate (temperature 

and precipitation) from regional weather stations and, with 

crop rotation patterns and farm management practices such as 

fertilizer application established by the user, the model can run 

monthly to multiyear simulations to estimate leachate. From 

external sources of nitrogen and the remaining nitrogen not 

taken up by plants and crops, the estimated concentration of 

nitrate in solution is related to anion exclusion, soil porosity 

and water saturation, and the water balance among surface 

runoff, lateral flow, and (or) percolation. The amount of nitrate 

measured as nitrogen in percolation is estimated from the 

water volume and average concentration in the soil layer as:

−
Empirical Application and Methods 15

EXPLANATION

IDNR-based major rivers

NHD-based watersheds r

IOWA

Study region

0 60 KILOMETERS

20 40

20 40

0 60 MILES

p

Figure 8. Map showing watersheds, rivers, and digital elevation model for the study region in northeastern Iowa. These geospatial 

datasets are also used as ArcSWAT inputs. IDNR, Iowa Department of Natural Resources; NHD, National Hydrography Dataset.

 NO3 p

– = [ NO3 ]

– ×Qp

 , (19)

where NO3 p

– is the nitrate moved to the underlying soil layer 

by percolation in kilograms of nitrogen per hectare (kg N/ha)

NO3

−     is the concentration of nitrate in the mobile water that 

is moving through the soil layer in kilograms of nitrogen per 

millimeters of water (kg N/mm H2O), and Qp is the amount of 

water percolating to the underlying soil layers and aquifers in 

mm H2O. 

Leachate is estimated by ArcSWAT at the HRU level, of 

which there are a number for any given watershed or HUC. 

The user can define thresholds to define the number of HRUs 

within a HUC or watershed, which depends on the combination of LULC, soils, slopes, and the thresholds. In this work, 

the thresholds are set conservatively as 10-percent areal coverage for a given LULC (although all locations of corn and soybeans are included), 15-percent areal coverage for a given soil 

class, and 20-percent areal coverage for a given slope class. 

Hazard Function and Groundwater Survival

Estimation of the benefits of MRLI involves estimating the cumulative groundwater nitrate pollution and fitting 

it into a groundwater survival model. For the purposes of 

this study, groundwater survival means that the groundwater 

remains potable with nitrate contamination below the MCL of 

10 mg/L. The true state of contamination in space and time at 

the regional scale is unknown and can be only estimated with 

uncertainty. Thus, there is a need for a probabilistic model of 

groundwater vulnerability. Furthermore, modeling the survivability of an aquifer as perturbed by LULC changes provides 

an estimate of the vulnerability of particular land uses in 

specific regions.

Three major approaches to analyzing groundwater 

vulnerability exist—(1) overlaying of physical, mapped 
16 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

10For more information on NAWQA see http://water.usgs.gov/nawqa/.

11See, for example, page 24 of Nocedal and Wright (2006) for a description 

of this optimization algorithm, which is based on the independent work of the 

four eponymous mathematicians.

characteristics to create an index of vulnerability; (2) processoriented methods using mathematical models to approximate subsurface flows, reactions, and dynamics; and (3) 

statistical methods where known levels of contamination 

are used to make inferences (Canter, 1997). In terms of the 

first approach, three problems are that the method (1) is not 

related to concentration, and thus the 10 MCL for nitrate; (2) 

does not account for significant heterogeneity in the subsurface environment, which would result in changing observed 

nitrate concentration levels at any given well; and (3) does not 

account for differential nitrogen loading at the land surface 

as a result of cropping patterns and management techniques 

(Canter, 1997). In the analysis we provide, the method relies 

on the second two approaches to create a series of models.

We apply a proportional hazards model to quantify the 

temporal risk of contaminating groundwater resources. The 

constraining risk we analyze is the risk of exceeding the MCL 

of 10 mg/L for nitrate contamination. The failure of a well is 

the event when the MCL is exceeded, and survival of a well 

is the condition where failure never occurs. In a proportional 

hazards model the probability of well survival, G (eq. 20), 

depends on the number of years, j, that elapse from any point 

in time a well is found to be in the surviving condition and the 

attributes of the well xi

:

 

 G( j|xi

)=exp –exp (β xi+γj) , (20)

where β is the vector of parameters corresponding to the effect 

of well characteristics, xi, on survival and γj is the parameter 

quantifying the effect of elapsed time on survival.

We only observe the condition of a well, but the well 

data represent a sample of all of the aquifers analyzed, so this 

model can apply to any location in an aquifer. The attributes 

of the well include depth, CNI value, date, and location. 

The proportional hazards model provides a data field, which 

is applied in our study region to watersheds delineated at 

roughly the level of hydrologic unit code (HUC) 12 (table 

1). HUCs are also termed watersheds and subbasins in this 

manuscript. Nitrate concentration is included among the well 

attributes through the CNI, so that crop types and plot characteristics are included as well. To assess the revealed social 

preference for groundwater risk that does not include explicit 

linkage to changing land uses and takes the current state of the 

landscape as a given, the nitrate concentration is part of that 

social preference choice described in equation 3 and therefore 

not included as an explanatory variable for survival. 

We use all available nitrate groundwater concentration 

measurement data in the region that had the necessary attribution, of which we found almost 20,000 wells from the USGS 

National Water-Quality Assessment Program (NAWQA)10

and IDNR sources (table 1). This data in a well failure context is both right censored (failure occurred after a specific 

time but not how long after) and interval censored (failure 

occurred between two dates but not exactly which date in the 

interval), and because the data were not collected systematically for our purposes, the censor intervals are arbitrary (fig. 

9). For example, a well i might be tested once and found 

in a surviving state and never tested again, so we conclude 

that it potentially fails in the future. Another well might be 

known to be in a failed state from every sample collected. 

Some wells are tested and found surviving and found in a 

failed state when next tested, so we know failure occurred 

between the time of these two tests. Some wells are found to 

survive multiple time periods before a test shows that it has 

failed, so we know both the surviving interval and the failure 

interval. Some wells are found to survive multiple times and 

never found failing, so we conclude that it might fail after the 

last test. Sometimes the failure is identified if it occurs in the 

subsequent time period when testing occurred in both time 

periods. The remainder of figure 9 illustrates variations of 

these categorical possibilities. 

If we begin with a nitrate concentration measurement in 

a nonfailed well, we always have a lower bound on the failure 

time, so none of the data is left censored after eliminating 

failed wells. Some of the wells never fail, however, so if it 

is right censored we only know that the failure time is past 

the last well sample date. The wells that do fail constrain the 

failure time between the sample date when the nitrate concentration was first found to exceed the MCL and the prior 

sample date, so the data is interval censored. For some wells 

with high sampling rates, this interval consists of a single 

month, so we actually have an exact failure month, but for 

most wells, the interval consists of several possible failure 

months. The censoring occurs because of a nonsystematic 

sampling pattern, so it is classified as noninformative censoring. The proportional hazard estimation method described by 

Finkelstein (1986) applies to the type of data we have. The log 

likelihood function for this method is:

 L= log αij exp –exp(β xi

+γj–1) i=1 j=1

N m

–exp –exp(β xi+γj) 

Σ Σ

,

 (21)

 

where αij=1 if j is in the failure interval for well i and 0 

otherwise for N wells that might fail within m years. We 

used the method of Broyden, Fletcher, Goldfarb and Shanno 

(BFGS)11 to estimate values of γj and β that maximize L for 

the well data. 
Empirical Application and Methods 17

Table 1. Description of data used for estimation of economic value of remote-sensing information in the northeastern Iowa study region.

 [USGS, U.S. Geological Survey; NRCS, Natural Resources Conservation Service; n.a., not applicable; m, meters; CNI, cumulative nitrate 

indicator, LULC. Land use/land cover]

Name Source Scale/accuracy Purpose Comments 

Elevation at the base of 

the aquifer and 

elevation at the top of 

the aquifer

Iowa Department of 

Natural Resources

n.a. To derive the thickness of 

the aquifer; use in capture 

zone delineation and CNI

n.a.

Source water assessment 

and protection wells

Iowa Department of 

Natural Resources

Horizontal varies 

from 35 to 1,135 

m

Used in depth interpolation, 

CNI and groundwater 

survivability

n.a.

Private well tracking 

system wells

Iowa Department of 

Natural Resources

Horizontal = +/- 

25 m

Used in CNI and 

groundwater survivability

Includes information on 

construction, lithology, 

pumping and capacity, and 

water quality

NAQWA monitoring 

wells

USGS National 

Water-Quality 

Assessment 

Program

n.a. Used in CNI and 

groundwater survivability

Includes measurements of 

nitrate

Cropland Data Layer National Agricultural 

Statistics Service

30- to 56-m pixels LULC, agricultural 

production, CNI input, 

ArcSWAT input

n.a.

Digital elevation model 

(DEM)

USGS 10-m pixels ArcSWAT input n.a.

STATSGO soils NRCS 1:250,000 scale, 

250-m pixels

ArcSWAT input n.a.

Watershed boundary 

dataset

National Hydrography 

Dataset , USGS

Hydrologic Unit 

Code 12, scale 

1:24,000

ArcSWAT input, ground

water survivability 

reporting unit

Required editing of linework 

due to State boundaries and 

ArcSWAT input requirements

Major river network Iowa Department of 

Natural Resources

Positional accuracy 

≈ 570 m

ArcSWAT input Required editing of linework 

due to apparent errors and 

ArcSWAT input requirements

Aquifer properties 

(porosity, hydraulic 

conductivity, and 

others) 

Iowa Department of 

Natural Resources

n.a. Used in capture zone delineationn.a.
18 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

Value of Information Estimation

Effective policy formation requires knowledge about the 

behavioral, production, and technological responses to alternative strategies (Bernardo and others, 1993a). A full policy 

analysis was beyond the scope of the analysis in this report, 

rather we develop tools to calculate the most value possible 

using the MRLI information. This includes an abstraction 

from precise policies that could be used to achieve that possible increase in value. With these tools, the value provided 

by MRLI can be estimated by the incremental benefit of 

increased revenue from land with the use of MRLI without 

further deteriorating groundwater quality. The VOI is estimated as (1) the economic benefit stream of a net increase 

in agricultural production across a region without sacrificing 

groundwater resources and (2) how agricultural production 

and its environmental impacts may change with or without the availability of MRLI. This includes focus on using 

improved information from new technology (in other words, 

MRLI obtained from satellites), which allows for estimating 

the joint production of agricultural goods and environmental 

impacts with and without the inclusion of MRLI, is discussed 

further below.

Without MRLI Case

This study is a retrospective analysis of the 2001 to 2010 

time period, which relies on observed conditions for production of corn and soybeans and observed groundwater quality 

in the study region as the baseline alternative. Because this 

baseline alternative did not use MRLI in the IAA applied in 

this research, we label this alternative the “Without MRLI” 

case. Effectively conducted at a county scale from sampled 

data, the reported crop patterns are the consequence of the 

policies, R*(ω(0),α), prevailing during the study period as are 

the observed crop values, PQ*R(ω(0),α). The VOI of the “without 

MRLI” case is compared to the VOI of the “with MRLI” case. 

The regulatory constraint, α, is the observed outcome for aquifers in the study region assessed using NAWQA and IDNR 

well sample data to estimate the proportional hazard model as 

a measure of the risk to groundwater resources.

With MRLI Case: Enhanced Landscape 

Configuration

The with MRLI case, which leverages a technological 

advance, informs what portions of the landscape could (or 

Sample well, i

Failed well (NO3 >MCL)

Surviving well (NO3

<MCL)

Failure interval

Time, j –

–

Figure 9. Diagram illustrating arbitrarily interval-censored data used for the proportional hazards model. The proportional hazards 

model was used to quantify the groundwater failure process. Failure is defined as the groundwater nitrate concentration exceeding the 

Maximum Contamination Level (NO3 >MCL) and survival is within the limit (NO3 <MCL). Groundwater samples were collected from wells 

in a pattern that was neither systematic nor randomized for the purposes of this analysis. Testing of the samples indicated either the 

well was a surviving well or a failed well, thus isolating time intervals in which the failure occurred as shown by the solid lines.

− −
Materials and Data 19

12Using this technique Asada (2002), for example, analyzes the first best 

economic growth determined by a central planner and next finds a decentralized solution to compare real world mechanisms to the true optimum. Under 

free market conditions the solution is often shown to be equivalent to the 

central planner solution, but the central planner solution is more tractable.

could not) support increased agricultural production, and leverages the improved information structure of MRLI. The social 

tolerance for risk levels can include scenarios of under- or 

over-regulation pertaining to groundwater contamination. This 

evaluation needs the spatiotemporal resolution of MRLI, and 

requires spatially explicit tradeoffs, optimization of land use, 

and (or) consideration of wall-to-wall regulatory restrictions. 

In the empirical demonstration of the IAA, a useful 

exercise is estimating a nonpolicy specific solution to equation (1) or, alternatively, estimating a solution in which the 

policy instrument is the direct assignment of cropping patterns. This exercise is analogous to “central planner” analyses 

in economic growth theory12—the presumption is not that a 

central planner will actually make the choices but rather that 

we can establish a criterion to which we can compare the 

market based solution to solutions with alternative regulatory incentives and mechanisms. Thus, we are establishing a 

better possible outcome that we would seek to duplicate with 

a basket or collection of policies that decisionmakers could 

draft and (or) pass as legislation. Bernado and others (1993a) 

outlined various groundwater protection strategies available 

to policymakers, such as management-oriented, incentivebased, and regulatory alternatives. Regardless of the policy 

vehicle used to incentivize the particular changes in individual 

(microeconomic) and aggregate (macroeconomic) behavior, 

Benardo and others (1993b), took the approach of evaluating three activities—(1) the restriction of total farm nitrogen 

applications by one-third, (2) the restriction of unit-area 

nitrogen application by one-third, and (3) entirely eliminating 

the use of selected pesticides. Although the MRLI data were 

not used in the IAA to inform particular policies that would 

influence crop patterns, we outline how the method developed 

here can be used in considering economic incentive programs, 

policy rules, and location-specific changes in land management that could be brought to bear on developing a reasonable, enhanced configuration of the landscape. These methods 

can be used to additionally constrain the optimal allocation 

of land uses derived in the results of this paper to refine the 

current estimates of VOI, which would more comprehensively 

reflect the policymakers’ decision space. 

As an example of an IAA used as a DSS, Feng and 

Babock (2008) used an analytical framework that linked 

acreage allocation decisions with responses in total cropland 

area, which included adjustments in input usage, land-share 

allocations and total cropland area. Similar to the method 

used in our report, the authors also used characteristics of the 

land and its quality for production. They address the direct 

land-use changes that come from two sources—(1) cropland 

put into corn production from another crop and (2) other lands 

taken from another use and put into crop production. 

For our application, our enhanced landscape consists of 

identifying potential acreage changes to a baseline area of corn 

grown on each land type in each year of the analysis. From the 

enhanced landscape with MRLI data available, the case of the 

information structure not including MRLI data is subtracted 

(eq. 4). For the enhanced landscape configuration, the increase 

or decrease in corn area results in complementary increases 

or decreases in soybean area and implies changes towards an 

optimal pattern of LULC. The RATS (Regression Analysis 

of Time Series) linear programming algorithm (see below) 

was used to identify the optimal change in corn area for each 

subbasin land category. Practically, this direct assignment of 

crops in an HRU consists of choosing a corn/soybean cropland 

defined by satellite imagery and ArcSWAT and assigning an 

alternate crop choice. Given the history of crops for a watershed and other characteristics of the land in the HRU, such 

as soils and slope, both environmental risk and crop production are calculated. The solution is to assign HRUs within a 

given watershed with alternative crop choices each year until 

economic value is maximized subject to the environmental 

constraint. The HRUs are then aggregated within a given 

watershed. The binding environmental constraint is nitrate 

contamination of groundwater resources. We approximate 

the constraint by limiting the nitrate leaching to a level that 

does not worsen the risk that the groundwater resources will 

no longer be potable. Given what is known about locations 

sensitivity to nitrate leachate and groundwater pollution, the 

enhancement calculation is the choice of crop area assigned to 

corn or soybean for each land category in each HRU for each 

year of the analysis. The change in crop area combined with 

the estimated yield of that crop and its market price enables 

the calculation of crop value with and without MRLI that is 

used to calculate a VOI for each year of the study period (eq. 

5). These yearly VOI values imply an EAI for the full study 

period and an NPV that would be possible if this same EAI is 

achieved in the indefinite future (eq. 6).

Materials and Data

In this section, we present details on the northeastern 

study region in Iowa, the MRLI-based, NASS-classified CDL, 

crop price data, well datasets, hydrogeologic characteristics, 

soil characteristics, slope and landscape topographic features, 

and rivers and watershed datasets. Groundwater vulnerability 

datasets were discussed previously. Data were collected (table 

1), analyzed, modeled, and derived using various statistical 

and geospatial analyses tools in ArcGIS 9.3 and 10, Microsoft® Excel and Access, RATS (Regression Analysis of Time 

Series, version 7, ©2007 Estima, Inc., Evanston, Illinois) and 

STATA statistical software (release 11, ©2009 StataCorp LP, 

College Station, Texas).
20 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

KANSAS

IOWA

TEXAS

MINNESOTA

MISSOURI

ILLINOIS

NEBRASKA

OHIO

OKLAHOMA

ARKANSAS

WISCONSIN

NORTH DAKOTA

INDIANA

MICHIGAN

KENTUCKY

TENNESSEE

ALABAMA GEORGIA

MISSISSIPPI

SOUTH DAKOTA

EXPLANATION

Study region

Corn

Cotton

Rice

Sorghum

Soybeans

Sunflowers

Peanuts

Tobacco

Barley

Drum wheat

Spring wheat

Winter wheat

Other small grains

Winter wheat / soybeans

Rye

Oats

Millet

Speltz

Canola

Flaxseed

Safflower

Alfalfa

Other hays

Sugarbeats

Dry beans

Potatoes

Other crops

Vegetables and fruits

Watermellon

Lentils

Peas

Clover/wildflowers

Fallow / idle Cropland

Grass / pasture / non-ag

Woodland

Peaches

Apples

Grapes

Christmas trees

Other tree nuts and fruit

Urban / developed

Water

Wetlands

Aquaculture

NLCD-open water

NLCD-developed/open space

NLCD-developed/low intensity

NLCD-developed/medium intensity

NLCD-developed/high intesity

NLCD-barren

NLCD-deciduous forest

NLCD-evergreen forest

NLCD-mixed forest

NLCD-shrubland

NLCD-grassland / herbaceous

NLCD-pasture/hay

NLCD-woody wetlands

NLCD-herbaceous wetlands

p

0

0 200 KILOMETERS

100 200 MILES

100

Figure 10. Map showing case study location in reference to the full extent of National Agricultural Statistics 

Service’s 2007 Cropland Data Layer (see http://datagateway.nrcs.usda.gov/) for the Midwest. Red outline 

shows 35-county case study area in northeastern Iowa. NLCD, National Land Cover Dataset.
Materials and Data 21

Northeastern Iowa Study Region 

Shifting land-use patterns and vulnerable groundwater 

conditions in northeastern Iowa (fig. 10) makes this area 

an appropriate study site for the application of the IAA. 

The study region contains 5.4 million ha across 35 counties in northeastern Iowa overlain on aquifers of Silurian and 

Devonian age (fig. 11). The Silurian and Devonian aquifers 

are composed mainly of porous dolomite and limestone and 

shale, and are the most common aquifers in the study region. 

Typically in carbonate-rock dominated systems, porosity and 

permeability depend on fractures, bedding planes, and solution 

caverns, which can increase the relative rate of infiltration and 

percolation and the mobility contaminants. Such aquifers are 

often described as a single, large unit because the rocks making up individual units are similar in hydrogeologic properties 

and are hydraulically connected. Locally, shale and dolomites 

laden with clay act as aquicludes (Iowa Department of Natural 

Resources, 2003). The largest percentage of Iowa’s farmland 

(76 percent) is devoted to croplands, with 92 percent of these 

croplands dedicated to growing corn and soybeans (Causarano 

and others, 2008). The State of Iowa produces 30 percent of 

the Nation’s ethanol, and there are 42 ethanol plants in the 

State (Iowa Corn Growers Association, 2012). The CDL data 

for 2001 through 2010 shows a shifting land-use pattern from 

corn-soy crop rotation to corn monocropping in many parts 

of the State (fig. 12). In Iowa, 80 percent of drinking water is 

derived from groundwater, and the possibility of nitrate contamination in wells in the northeastern part of the State makes 

this area an even more compelling candidate for applying the 

methods of this report. 

MRLI: Cropland Data Layer

For the 35-county study region, the CDL provided 

estimates of corn and soybean production from 2001 to 2010, 

which shows a general trend toward increasing corn production and decreasing soybean production (fig. 5). Because of 

the large ethanol production and typical crop-rotation patterns, 

the crops of interest are corn and soybeans. In the conceptual 

framework that was a precursor to this empirical application 

(Bernknopf and others, 2012), the authors characterized sensors 

(in particular Landsat, Advanced Wide Field Sensor (AWiFS), 

and MODIS) and proposed to compare the moderate-resolution 

land image (MRLI) information structure among them and 

traditional nonremote sensing techniques. If that approach 

were applied here, the length of record between Landsat (38 

years) and AWiFS (5 years) and the distinctions between sensor 

characteristics would have been highlighted further. However, 

because of a number of reasons, the analysis undertaken in 

this study precluded the ability to classify raw imagery into 

a consistent, accurate LULC dataset. The most important 

reason was the lack of availability of historical ground-truth 

information. We approached the NASS about accessing the 

June Agricultural Survey (JAS) and the Common Land Unit 

(CLU), but the USDA agency does not preserve their historical records. Consequently, we chose the operational, standard 

data product of the highest known quality, length, and with 

the most relevant thematic LULC system, NASS’s CDL. We 

also contracted with the University of Pennsylvania in an 

attempt to classify additional Landsat-based LULC products, 

the success of which is described below.

In Iowa, the NASS’s CDL has been produced since 2000, 

yet the standardized nature of their product has fluctuated 

(fig. 12, table 2). From 2000 to 2005, the sole data source was 

Landsat. Because of the problems with the Scan Line Corrector of Landsat 7, NASS switched data sources to AWiFS and 

Moderate-resolution Imaging Spectroradiometer (MODIS) 

for the years 2006 to 2008. Johnson (2008) found that AWiFS 

was a valid alternative to Landsat-5 for agricultural regions 

with larger field sizes, and AWiFS offered the added benefits 

of larger swath widths and shorter revisit frequencies. For 

the 2009 and 2010 years, NASS switched again to include 

all three data sources (Landsat, AWiFS, and MODIS). Thus, 

there is no standardized product for analysis of the problem. 

Additional fluctuations include ground-truth data (JAS until 

2006, then CLU to present), changing classification algorithms to derive the product (maximum classifier until 2006, 

then decision-tree classifier to present), software platforms 

(in-house NASS software techniques with Peditor until 2006 

and then ERDAS, Inc., Imagine), annual error accuracies and 

metrics (table 2, for crops of interest), and output cell size (30 

m until 2006, 56 m from 2006 to 2008, and then 30 m again 

in 2009 and 2010). 

Depending on the year, the resolution or pixel size of the 

data is 30 or 56 m (Johnson and Mueller, 2010). The larger 

swath widths of AWiFS resulted in fewer scenes needing 

to be collected (State analysis districts in table 2). Table 2 

characterizes the accuracies of the datasets for 2000 through 

2010, which are important to keep in mind when they are 

used in further analysis. In comparing sensor characteristics 

and Landsat-based and AWiFS-based classification results, 

the change in pixel size has been shown to have the highest sensitivity to changes in accuracies (Johnson, 2008). In 

general, the accuracies from 2000 to 2006 for corn land-cover 

types were 87–97 percent and for soybeans were 86–98 

percent. From 2007 to 2010, producer and user accuracies for 

corn land-cover types were 96–98 percent and 97–98 percent, respectively. During the same period, producer and user 

accuracies for soybean land-cover types were 95–97 percent 

and 95–98 percent, respectively. The errors include misclassification; for example, spot-checking certain pixels classified 

as corn within an urban setting with aerial photography suggested that the land cover was actually residential landscaping, and spot checking pixels classified as pasture showed that 

they were actually pavement. 

As a result of the gap in Landsat imagery as source data 

for the CDL, we contracted the Wharton Geospatial Initiative 
22 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

Capture zones of wells

Devonian aquifer

Silurian aquifer

Other areas of the study region

Capture-zone number

1

2

3

4

5

6

7

8

9

10

Well

0 30 60 120 KILOMETERS

0 30 60 90 120 MILES

90

0 200 400 METERS

0 500 1,000 1,500 FEET

p

Figure 11. Map showing the distribution of capture zones (CZs) delineated using ArcAEM/Split for the northeastern Iowa study 

region, which includes 35 counties. The CZs are used for calculating annual nitrogen loading in the cumulative nitrate indicator. The 

inset map shows the CZs for a particular well and their annual location during a 10-year period.
EXPLANATION

CornSoybeansSmall grains / hayAlfalfaFallow / idle croplandGrass / pasture / non-ag.WoodlandUrban / developedNLCD-developed/low intensityNLCD-developed-open spaceNLCD-developed/medium intensityNLCD-developed/high intensityNLCD-barrenNLCD- grassland, herbaceousNLCD-deciduous forestNLCD-pasture/hayNLCD-woody wetlands

0 1 2 4 Kilometers0 0.5 1 2 Miles

Waterloo

q

2000 2001 2002

2003 2004 2005

2006 2007 2008

2009 2010

0 1 2 KILOMETERS

0 1 2 MILES

Figure 12. Map showing crop rotation patterns with the National Agricultural Statistics Service’s 2007 Cropland Data Layer (see http://datusda.gov/) in a small part of the northeastern Iowa study region, which includes the City of Waterloo, 2000–2010. Note area highlighted in bchange in crop rotations after 2005. NLCD, National Land Cover Database. non-ag. , nonagricultural.
24 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

Table 2. Shifting imaging sensors, accuracies, and metrics of the National Agricultural Statistics Service’s Cropland Data Layer, 

2000–2010.

[By convention, producer’s accuracy and omission error always sum to 1.0, as do the User’s Accuracy and Commission Error. Source: CDL metadata 

2000 to 2010 (available at http://datagateway.nrcs.usda.gov). Imaging sensors—Landsat, Advanced Wide Field Sensor (AWiFS), Moderate-resolution Imaging 

Spectroradiometer (MODIS). avg., average; pct., percent; n.a., not applicable]

Accuracies and metrics Landsat 

(2000–2005)

AWiFS/MODIS 

(2006–2008)

Landsat/AWiFS/ 

MODIS 

(2009–2010)

Year 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

State analysis districts 4 8 9 8 6 5 2 1 1 1 1

Corn

Percent correct (avg.) 96.59 87.47 95.26 93.35 96.10 93.82 87.53 n.a. n.a. n.a. n.a.

Commission error (pct. avg.) 6.90 9.38 5.74 4.64 1.58 3.27 7.19 n.a. n.a. n.a. n.a.

Kappa coefficient (avg.) 94.16 80.61 92.55 88.43 93.03 88.59 77.53 n.a. n.a. n.a. n.a.

Producer’s accuracy (pct.) n.a. n.a. n.a. n.a. n.a. n.a. n.a. 97.53 96.58 97.85 96.62

Omission error (pct.) n.a. n.a. n.a. n.a. n.a. n.a. n.a. 2.47 3.42 2.15 3.38

Kappa coefficient n.a. n.a. n.a. n.a. n.a. n.a. n.a. 0.95 0.92 0.95 0.93

User’s accuracy (pct.) n.a. n.a. n.a. n.a. n.a. n.a. n.a. 97.57 97.86 98.13 97.55

Commission error (pct.) n.a. n.a. n.a. n.a. n.a. n.a. n.a. 2.43 2.14 1.87 2.45

Conditional kappa n.a. n.a. n.a. n.a. n.a. n.a. n.a. 0.95 0.95 0.96 0.95

Soy

Percent correct (avg.) 92.11 88.69 94.26 93.21 98.32 94.42 86.90 n.a. n.a. n.a. n.a.

Commission error (pct. avg.) 5.69 9.60 3.76 7.03 3.84 6.34 10.61 n.a. n.a. n.a. n.a.

Kappa coefficient (avg.) 89.73 84.57 88.52 89.11 96.69 93.60 79.61 n.a. n.a. n.a. n.a.

Producer’s accuracy (pct.) n.a. n.a. n.a. n.a. n.a. n.a. n.a. 97.02 96.24 96.95 95.75

Omission error (pct.) n.a. n.a. n.a. n.a. n.a. n.a. n.a. 2.98 3.76 3.05 4.25

Kappa coefficient n.a. n.a. n.a. n.a. n.a. n.a. n.a. 0.96 0.94 0.95 0.93

User’s accuracy (pct.) n.a. n.a. n.a. n.a. n.a. n.a. n.a. 96.74 95.78 97.74 97.32

Commission error (pct.) n.a. n.a. n.a. n.a. n.a. n.a. n.a. 3.26 4.22 2.26 2.68

Conditional kappa n.a. n.a. n.a. n.a. n.a. n.a. n.a. 0.96 0.93 0.96 0.96

at the University of Pennsylvania in an attempt to classify the 

period of 2006 to 2008 with Landsat data. Given the lack of 

ground-truth data, the effort was marginally successful and 

had an error rate of about 15 to 22 percent (depending on 

the year) distinguishing between corn and soybeans (Amos 

and others, 2010). As a result—and in spite of data processing changes and accuracies that have been noted above—we 

decided to continue to use the CDL for the temporal and 

spatial characterization of LULC in the empirical application 

of the research project as it was the best available dataset. In 

working with the CDL, we also discovered that a part of Iowa 

within our study area in the 2000 CDL had not been classified, 

producing a data gap in our analysis that required our time series 

to begin in 2001. Most importantly, the CDL was able to provide 

LULC on an annual basis. For the purpose of our analysis and 

its focus on corn and soybeans, we simplified the extensive 

thematic CDL LULC classes into four classes—corn, soybeans, 

other agricultural, and nonagriculture. Although our models 

and analyses use these mapped corn and soybean classification 

results in multiple ways, it should be noted that they are not 

absolute, deterministic cover types, and a level of uncertainty 

must be associated with them. 

Crop Price Data

The analysis is restricted to marginal changes in production of two commodities; corn grain and soybeans. Thus, 

the price of these commodities during the period of analysis 

is crucial to the calculation of market values in this study. 

Data from the monthly Agricultural Prices reports (National 

Agricultural Statistics Service, 2012) are the basis for the 

commodity price data. Primary price survey data for these 

reports are collected each month as a randomized sampling 

of prices received by producers from virtually all mills and 

elevators that purchase corn grain and soybeans in the United 

States. Grain for seed is excluded from this data. The sales 
Materials and Data 25

0

100

200

300

400

500

600

700

800

900

1,000

1975 1980 1985 1990 1995 2000 2005 2010

Ye ar

Cor n

Soybeans

Annual commodity price, in 2010 dollars per metric ton

Figure 13. Graph showing average annual real prices received for corn grain and soybeans by U.S. producers. The data are 

derived from the monthly Agricultural Prices reports of the National Agricultural Statistics Service, Agricultural Statistics Board, 

U.S. Department of Agriculture. Prices deflated to the 2010 real level using the U.S. Department of Labor Bureau of Labor Statistics 

consumer price index for all urban consumers U.S. city average of all items.

volumes are used to estimate a national average and a yearly 

average that correspond to the value of the cumulative crop. 

We used the annual average price received as the value of the 

commodities, which assumes (1) any modeled difference in 

sales of grain produced will be sold following the same pattern 

as the observed sales, (2) shifts in the market supply curve are 

small enough that we can ignore the change in equilibrium 

price, and (3) the difference between local and national prices 

are negligible. We deflated the prices to a real level (2010 

prices) using the consumer price index for all urban consumers 

(CPI-U) U.S. city average of all items (Bureau of Labor Statistics, 2012). All values in this report are expressed in real terms 

at the 2010 price level. The real prices received for corn and 

soybeans (fig. 13) exhibit a declining trend until about the end 

of the past century and have continued to increase since then.

Wells

Data on physical properties and water quality of wells 

(table 1, fig. 14) in Silurian, Devonian, and Ordovician 

aquifers in the study region were collected from IDNR and 

NAWQA. The observations from these wells are used in both 

the application of the CNI, as well as the aquifer vulnerability and time to failure models. Our comprehensive database 

of more than 35,000 wells integrates numerous disparate 

datasets and sources of wells and their characteristics. 

This included reprojecting geospatial datasets to consistent 

projection; mapping tabular data with latitude and longitude 

coordinates; calculating nearest neighbor distances as a 

spatial threshold for possible duplicate observations; joining 

(tabular and spatial), merging, and unioning multiple datasets 

in logical, sequential steps into a single geodatabase; clipping 

datasets to the 35-county study region; and deleting unnecessary fields and duplicate records. 

The wells’ depths range from just below the surface to 

1,220 m, with an average depth of more than 50 m and a standard deviation of approximately 40 m (see Iowa Department 

of Natural Resources, table 1). This is in contrast to southern 

and western Iowa where approximately 52 percent of domestic 

wells are less than 15 m deep and are contained within sandy 
26 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

and

p

Bedrock types and nitrate levels (mg/L)

Iowa cities

Silurian

Ordovician

Devonian

0.00–460

4.61–10.00

10.01–39.30

Well Depth

8.00–35.00

35.01–300.00

300.01–2715.00

0 180 KILOMETERS

0 45 90 180 MILES

45 90

135

135

EXPLANATION

Figure 14. Map of Iowa showing a sample of National Water quality Assessment Program (NAWQA) wells in Silurian, Devonian, 

and Ordovician aquifers and cities in the northeastern Iowa study region. The sample includes nitrate levels found in the wells

and their depths. mg/L, milligrams per liter. 

drift zones of glacial-till deposits (Kross and others, 1990). An 

interpolated surface of more than 32,000 well depths shows 

a trend of (1) deeper wells in the northeast and (2) shallower 

wells in the north-south band in the middle of the study area 

(fig. 15). From more than 7,500 wells tested at various times, 

water yields average more than 139 liters per minute (L/min.) 

with a standard deviation of 359 L/min. (see Iowa Department 

of Natural Resources, table 1). For more than 2,900 wells, the 

measured values for hydraulic conductivity averaged 11.6 m 

per day (mpd) with a standard deviation of 52.5 mpd (see Iowa 

Department of Natural Resources, table 1). 

Hydrogeologic Characteristics 

Heterogeneity in the hydrogeologic properties of the 

northeastern Iowa study region that govern the nitrate dynamics in the aquifers include the thickness of the Quaternary 

materials, aquifer characteristics, and well characteristics. The 

thicknesses of Quaternary materials range from 0 to 198 m in 

the study region (IDNR database). Aquifers in these Quaternary 

materials below 92 m are less likely to be impacted by leached 

nitrate (Robert Libra, IDNR, oral commun.). Therefore, a spatial-data layer for the thicknesses of Quaternary deposits in the 

study area was created for following thickness ranges: 0 to 15 

m, 15 to 30 m, 30 to 60 m, 60 to 92 m, and 92 m and greater. 

Data on aquifer characteristics such as type of aquifer, 

thickness of aquifer, flow direction, gradient, and hydraulic conductivity, as well as well characteristics such as well 

production and radius, were collected and derived from the 

IDNR database. For the wells without information on aquifer 

properties, values for specific properties were derived using 

data layers and ArcGIS software. For example, thickness and 

hydraulic conductivity layers were derived from available data 

from IDNR. Thickness of the aquifer (Th) was derived as:

 
Tama Linn

Iowa

Fayette Clayton

Benton

Clinton

Cedar

Butler

Jones

Floyd

Scott

Hardin

Jackson

Franklin

Johnson

Dubuque

Grundy

Worth

Allamakee

Delaware

Howard

Louisa

Mitchell

Bremer

Winneshiek

Hancock

Buchanan

Poweshiek

Black Hawk

Washington

Cerro Gordo Chickasaw

Muscatine

Winnebago

EXPLANATION

 Interpolated well de

0–2829–4546–6566–9394–238

0 15 30 0 10 20 

Study region

IOWA

0

0 15

10 20 

30

4

Figure 15. Map showing interpolated well depths for the 35-county northeastern Iowa study region. 
28 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

p

Thickness (feet)

0

100

200

300

400

500

600

IOWA

Study 

region

0

0 100 KILOMETERS

25 100 MILES

25 50

50 75

75

Figure 16. Map showing derived Silurian and Devonian aquifer thicknesses in the northeastern Iowa study region. Source is Iowa 

Department of Natural Resources. 

 Th=Elt

−Elb , (22)

where Elt is the elevation at the top of the aquifer and Elb is 

the base elevation of the aquifer. The thickness layer was 

derived using GIS data and the technique of map algebra 

(ESRI, 2012) (fig. 16), and aquifer thickness for each well was 

calculated using spatial join in ArcGIS. A hydraulic-conductivity (HC) surface was created using the spatial interpolation 

method kriging (refer to appendix 3 for details). 

Soil Characteristics

For the study area, the soils included in the analysis were 

related to natural boundaries, such as watersheds, rivers, and 

ridgelines. The NRCS STATSGO dataset was used (table 

1). The soils database used in the analysis had a variety of 

important characteristics including hydrologic drainage group, 

texture, clay, silt, sand and rock content, number of layers in 

the soil, depth of specific soil layers, maximum rooting depth, 

moist bulk density, available water capacity, organic carbon 

content, saturated HC, and electrical conductivity.

Slope, Depressions, and Topographic Position

The USGS digital elevation model (DEM) of northeastern Iowa (table 1) and its shaded relief provides an insight 

to the topography and relief of the study region. We used the 

USGS 10-m DEM (table 1), and the 35 case-study counties 

had elevations that varied from 138.9 to 443.2 m above sea 

level (fig. 17). As mentioned previously, topographic characteristics play an important role in the fate and transport of 

nitrogen. For example, high slopes tend to have more overland runoff and lateral movement of nitrogen, depressions 

and topographic convergences tend to have higher levels 

of percolation and infiltration, topographic position along 

a hillslope and across a landscape can be related to water 

table depths, baseflow provided to rivers, and nutrient uptake 

dynamics along the flowpath to a river, canal, drainage ditch, 

or waterway. The ArcGIS version of ArcSWAT requires 

DEM inputs to calculate such watershed and subbasin 

characteristics. For the analysis, slopes were classified into 5 

ranges—0–2 percent, 2–5 percent, 5–8 percent, 8–12 percent, 

and greater than 12 percent.
Tama Linn

Iowa

Fayette Clayton

Benton

Clinton

Cedar

Butler

Jones

Floyd

Scott

Hardin

Jackson

Franklin

Johnson

Dubuque

Grundy

Worth

Hancock

Allamakee

Delaware

Louisa

Mitchell Howard

Bremer

Winneshiek

Buchanan

Poweshiek

Black Hawk

Washington

Cerro Gordo Chickasaw

Muscatine

Winnebago EXPLANAT

Elevation, in 

500110 r

0 15 30 0 12.5 25 

IOWA

Study re

0 20 60 KILO

0 20 40 

40

p

Figure 17. Map showing shaded relief of topography for the 35-county northeastern Iowa study region. Source is U.S. Geological Survey Nationa
30 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

Rivers, Watersheds, and Hydrologic Response Units

Although datasets are available and analyses are conducted 

at coarser and finer scales, this research is mostly organized 

into the spatial units of watersheds for reporting. The National 

Hydrography Dataset (NHD) Watershed Boundary Dataset 

HUC level 12 (table 1) is used as the starting point for delineating watersheds. Alterations to the watershed boundaries were 

made for the sake of conforming to the State of Iowa’s boundaries, as well as for merging of HUCs that were not intersected by 

the river network. The case study has 603 watersheds and went 

beyond the extent of the anthropogenic-boundaries of the 35 

counties so as to reflect more of the natural ecosystem processes. The river network was provided by IDNR and required 

routing and delineation improvements on the basis of the DEM. 

Also, because of the requirements of ArcSWAT, any given HUC 

could only have one river segment contained within it. For 

example, a watershed that had both a headwater stream originating in it as well as a higher-order stream flowing through it was 

simplified to preserve the inter-watershed connectivity of the 

higher-order stream network (fig. 8).

Results and Discussion

This section presents the results for the research on agricultural production, aquifer contamination, and VOI estimates. 

An overall discussion follows at the end of the section, which 

also considers avenues for future work and research efforts.

Agricultural Production

Annual simulations were run to estimate yield and area of 

corn and soybeans from 2001 to 2010. Results are reported at 

the subbasin level even though multiple HRUs were analyzed 

within a given subbasin. For the 603 subbasins, there was 

a median of 7,910 HRUs over the 10 years, with a standard 

deviation of 316. The total number of HRUs for all HUCs in 

the study region varies by year, with a range in HRUs per year 

of 7,758 to 8,813. The number of HRUs per HUC per year 

is in the range of 2 to 27. Yield estimates and area results are 

summarized by LULC and year (table 3). The area-weighted 

average of each subbasin’s aggregated yield (corn, soybeans) 

from 2001 to 2010 is provided as well (fig. 18). The values are 

classified with the Jenks natural breaks classification method 

(in other words, reducing the variance within classes, while 

maximizing the variance between classes) and provide an 

indication of the overall agricultural productivity of certain 

subbasins over time.

Nitrate Contamination of Aquifers

In this subsection, we discuss the results of the dynamic 

nitrate-pollution models and analysis, the estimation of leachate, and groundwater vulnerability.

Dynamic Nitrate Pollution 

Given certain hydrogeologic and well conditions, two 

shapes for annual CZs are expected to occur in the study 

region—(1) no subsurface flow will create concentric circles, 

and (2) the presence of subsurface flow will create irregular 

shapes (figs. 7 and 11). The CNI equations were estimated 

using nitrate levels (mg/L) observed in the wells over the 

period 2001 through 2010 and regressed on the previous year’s 

nitrate levels (mg/L) by year leached quantity of nitrate (kg/

ha) from each CZ and thickness of Quaternary material (m). 

The leached quantity of nitrate in each CZ was estimated as 

the product of area under crop (corn), and the nitrate leach factor (kg /ha) was obtained from ArcSWAT results for the well 

location. Area under corn in the CZ and the marginal zones13

of respective wells were calculated for each well using MRLI 

as discussed earlier. The CNI equations were also estimated 

by substituting leached quantity by (1) the leached fraction, 

(2) area of corn in each CZs (ha), (3) interaction of corn acres 

in CZs, and (4) Quaternary deposit thickness (m) in the well 

location (table 4). The leached fraction is the quantity leached 

from the area that produced corn in the CZ divided by the total 

area in the CZ. Fraction of corn was estimated by dividing 

the area under corn by total area in the CZ. All of these area 

calculations depended on the MRLI-derived CDL. 

Wells with two or more nitrate observations over the 

period of 2000 through 2010 were used in the CNI analysis, 

and 80 out of 350 observations showed decrease in nitrate 

level. The ordinary least square (OLS) estimation of CNI

equations 10 and 11 are shown in table 5. The lags (previous 

year’s nitrate measurement) for area and leached quantity have 

positive signs and are significantly different from zero suggesting that the CNI levels in wells are significantly affected 

by the input factors specified in the independent variables. 

The area under corn and nitrate leached from capture zones 

(1, 2, 3, and 4–10) have expected signs, however, they are not 

significant. Theoretically, the greater the thickness of the Quaternary materials, the lower the values of CNI in wells should 

be; however, the OLS results do not conform to it. The CNI

equations specified with the OLS regression equation were 

estimated using the fraction of corn area in CZs and fraction 

of leachate in CZs. Most of the independent variables were 

statically insignificant, except for certain CZs (2 and 4–10) 

that in the fraction of leachate equation were significant at the 

10 percent level, which may not have sufficient explanatory 

power as variables may have been omitted. 

The Arellano-Bond estimation results shown in table 6 

suggest that the annual change in nitrate level in wells is not 

significantly affected by change in nitrate level in the previous year for CNI equations 15 and 16. The positive signs and 

13Marginal zones refer to the area in CZ of a well, from which left over pollutants from year t−1 are added to the well in year t.
Results and Discussion 31

Year LULC type Mean yield (t/ha) Total yield for study 

region (t)

Mean total yield 

per HRU (t)

Mean area of HRU 

(km2

)

2001 Other Ag. 5.91 14,195,362.5 5,639.8 9.4

Corn 7.46 11,067,551.9 6,369.5 8.5

Non Ag. -- -- -- 8.8

Soybeans 3.32 4,319,402.9 2,372.3 7.4

 Total 29,582,317.3 

2002 Other Ag. 5.92 15,104,376.3 5,914.0 10.0

Corn 9.75 14,302,940.1 8,059.1 8.6

Non Ag. -- -- -- 4.9

Soybeans 2.27 2,527,774.5 1,421.7 6.6

 Total 31,935,090.9 

2003 Other Ag. 5.91 13,025,402.5 5,864.7 9.6

Corn 9.74 16,255,344.1 8,461.4 9.3

Non Ag. -- -- -- 4.9

Soybeans 2.26 2,779,423.8 1,503.3 7.0

 Total 32,060,170.4

2004 Other Ag. 5.94 12,014,125.7 5,387.5 8.8

Corn 9.73 16,387,295.2 8,649.5 9.4

Non Ag. -- -- -- 4.2

Soybeans 2.27 2,921,613.2 1,580.3 7.4

 Total 31,323,034.2 

2005 Other Ag. 5.91 13,249,121.5 5,441.1 9.1

Corn 9.75 14,983,288.1 7,974.7 8.6

Non Ag. -- -- -- 4.7

Soybeans 2.27 2,728,292.2 1,499.3 6.8

 Total 30,960,701.8 

2006 Other Ag. 5.88 5,637,308.6 3,316.1 4.9

Corn 9.75 16,370,874.0 8,519.7 9.4

Non Ag. -- -- -- 7.0

Soybeans 2.27 2,790,736.7 1,569.0 7.3

 Total 24,798,919.3 

2007 Corn 9.74 16,540,934.1 8,817.3 9.7

Non Ag. -- -- -- 10.8

Soybeans 2.28 1,923,920.9 1,127.7 5.2

 Total 18,464,854.9 

Table 3. Crop yield estimates and area calculations for four land-use/land-cover (LULC) types of the northeastern 

Iowa study region, 2001–2010.

[ag.,agricultural; non-ag., nonagricultural;HRU, hydrologic response unit; --, no data; t, metric tons; ha, hectares; km2, kilometers]
32 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

 Year LULC type Mean yield (t/ha) Total yield for study 

region (t)

Mean total yield 

per HRU (t)

Mean area of HRU 

(km2

)

Table 3. Crop yield estimates and area calculations for four land-use/land-cover (LULC) types of the northeastern 

Iowa study region, 2001–2010.—Continued

[ag.,agricultural; non-ag., nonagricultural;HRU, hydrologic response unit; --, no data; t, metric tons; ha, hectares; km2, kilometers]

significance level of the coefficients on quantity of nitrate 

leached from CZs 2, 3, and 4–10 suggest that an increase in 

corn area or the amount of nitrate leached increases the nitrate 

added to wells each year. The insignificance of nitrate leached 

from the first year CZ may be due to less or no corn produced 

in the area nearby wells, or it may be due to the lack of greater 

than 1-year-old water in the pumped well. The nitrate leached 

in 2nd-year CZ infiltrated water increases nitrate in wells more 

than by the nitrate leached in 3rd-year CZ infiltrated water. 

Results suggest that the CNI equation including the independent variable of leached quantity (eq. 15) performed better than 

the one using only corn area (eq. 16). The results show that the 

thicker the Quaternary material, the lower the increase in the 

nitrate level, as expected. 

The results of the Arellano-Bond estimation of the CNI

include using corn area and Quaternar thickness interaction 

terms (table 7). The estimations show that corn area overlain 

on as much as 15 m of Quaternary materials positively and 

significantly increases the nitrate levels in the wells, whereas 

corn area overlain on more than 15 m of Quaternary materials 

are not significant. Given this result of groundwater survivability improving in areas with thicker Quaternary materials, 

the allocation of agricultural production was better optimized 

to derived an estimate of VOI.

Leachate Estimation

Turning to the other product of the IAA’s joint output, 

namely leachate, it was estimated using ArcSWAT. Similar to the 

yield estimates, multiple HRUs were derived for the estimates of 

leachate rate. Given that HRUs are not explicitly mapped, leachate estimates are reported at the subbasin or watershed level. 

Total leachate and average (mean) leachate rate are presented 

by year for corn and soybeans (table 8). The location and values 

of nitrate leaching rates are area-weight averaged to the subbasin level for the corn and soybean LULC classes (fig. 19). The 

values are classified with the natural Jenks method. 

The agricultural production of corn and soybeans can 

have the ancillary impact of increased nitrogen loading, 

leaching, and percolation. Some studies have addressed 

leaching and percolation rates that can be directly compared 

to our results. Using four irrigation levels and three nitrogen application levels for maize crops, Gheysari and others 

(2009) observed at a 2-m scale nitrogen leaching rates of 0.0 

to 8.4 kg/ha, with higher values related to land-use treatments of high fertilization and over irrigation. Using 2.4-m 

deep lysimeters, Owens and others (1994) measured mean 

annual nitrogen leaching rates of 31.6 to 47.1 kg/ha during 

a 6-year study in eastern Iowa of corn and soybean rotation. 

2008 Other Ag. 5.96 81,694.2 1,633.9 2.9

Corn 9.75 16,040,976.8 8,439.2 9.7

Non Ag. -- -- -- 10.1

Soybeans 2.28 2,363,372.7 1,330.2 6.1

 Total 18,486,043.8 

2009 Other Ag. 6.06 89,295.4 1,594.6 2.4

Corn 9.74 16,076,559.9 8,502.1 9.3

Non Ag. -- -- -- 9.9

Soybeans 2.27 2,448,455.4 1,404.9 6.5

 Total 18,614,310.7 

2010 Other Ag. 6.00 537,593.7 1,576.5 2.4

Corn 9.75 16,595,206.6 8,973.0 7.3

Non Ag. -- -- -- 9.3

Soybeans 2.26 2,409,897.8 1,368.7 6.3

 Total 19,542,698.1 

 Grand total 255,768,141.5 
Results and Discussion 33

EXPLANATION

Subbasins, 2001

Average yield (t)

25,410–46,369

14,891–25,409

8,154–14,890

3,615–8,153

4–3,616

EXPLANATION

Subbasins, 2002

Average yield (t)

25,329–41,432

13,324–25,328

6,808–13,323

2,273–6,807

0–2,272

EXPLANATION

Subbasins, 2003

Average yield (t)

24,612–47,590

13,730–24,611

6,831–13,730

2,282–6,830

0–2,281

EXPLANATION

Subbasins, 2004

Average yield (t)

26,845–48,706

14,199–26,844

7,008–14,198

2,244–7,007

0–2,243

EXPLANATION

Subbasins, 2005

Average yield (t)

23,641–41,472

12,887–23,640

6,171–12,886

2,034–6,170

0–2,033

EXPLANATION

Subbasins, 2006

Average yield (t)

28,324–49,348

15,032–28,323

7,563–15,031

2,537–7,562

0–2,536

EXPLANATION

Subbasins, 2007

Average yield (t)

28,117–50,096

14,472–28,116

6,956–14,471

2,228–6,955

0–2,227

EXPLANATION

Subbasins, 2008

Average yield (t)

24,455–41,911

13,008–24,454

6,573–13,007

2,227–6,572

0–2,226

EXPLANATION

Subbasins, 2009

Average yield (t)

25,369–51,558

13,936–25,368

7,062–13,935

2,368–7,061

0–2,367

EXPLANATION

Subbasins, 2010

Average yield (t)

23,795–43,384

13,512–23,794

6,858–13,511

2,434–6,857

0–2,433

p

0 100 200 KILOMETERS

0 50 100 200 MILES

50

Figure 18. Subbasin-level yield maps for combined corn and soybean production estimated from ArcSWAT for the northeastern 

Iowa study region, 2001 to 2010. t, metric ton.
34 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

Variables Mean Std. dev. Min Max

Well nitrate (mg/L) 2.02 3.26 0.00 20.00

Change in well nitrate (mg/L) 0.21 0.70 -2.00 4.10

Area under corn in CZ1 (ha) 0.41 0.97 0.00 9.49

Area under corn in CZ2 (ha) 0.62 1.11 0.00 8.75

Area under corn in CZ3 (ha) 0.76 1.12 0.00 7.89

Area under corn in CZ4 (ha) 0.77 1.06 0.00 6.27

Area under corn in CZ5 (ha) 0.94 1.21 0.00 5.5

Area under corn in CZ6 (ha) 0.87 1.18 0.00 5.95

Area under corn in CZ7 (ha) 0.76 1.00 0.00 5.10

Area under corn in CZ8 (ha) 0.6 0.81 0.00 3.70

Area under corn in CZ9 (ha) 0.62 0.96 0.00 3.86

Area under corn in CZ10 (ha) 0.10 0.23 0.00 0.52

Quaternary thickness (m) 23.61 16.55 7.62 76.20

Well depth (m) 82.78 65.08 20.42 464.80

Leached quantity in CZ1 (kg/ha) 0.26 1.51 0.00 14.91

Leached quantity in CZ2 (kg/ha) 0.00 0.00 0.00 0.00

Leached quantity in CZ3 (kg/ha) 0.53 1.98 0.00 15.85

Leached quantity in CZ4 (kg/ha) 0.58 2.13 0.00 15.95

Leached quantity in CZ5 (kg/ha) 0.66 2.34 0.00 14.72

Leached quantity in CZ6 (kg/ha) 0.55 1.93 0.00 11.93

Leached quantity in CZ7 (kg/ha) 0.34 1.47 0.00 9.89

Leached quantity in CZ8 (kg/ha) 0.01 0.06 0.00 0.35

Leached quantity in CZ9 (kg/ha) 0.00 0.00 0.00 0.00

Leached quantity in CZ10 (kg/ha) 0.00 0.00 0.00 0.00

Fraction of corn area in CZ1 0.11 0.21 0.00 1.00

Fraction of corn area in CZ2 0.17 0.24 0.00 1.00

Fraction of corn area in CZ3 0.20 0.26 0.00 1.00

Fraction of corn area in CZ4 0.19 0.23 0.00 1.00

Fraction of corn area in CZ5 0.22 0.24 0.00 0.94

Fraction of corn area in CZ6 0.22 0.23 0.00 0.90

Fraction of corn area in CZ7 0.25 0.24 0.00 0.85

Fraction of corn area in CZ8 0.30 0.24 0.00 0.86

Fraction of corn area in CZ9 0.30 0.20 0.07 0.73

Fraction of corn area in CZ10 0.09 0.09 0.09 0.09

Table 4. Summary statistics for cumulative nitrate indicator (CNI) data.

[Std. dev., standard deviation; Min, minimum; Max, maximum; CZ, capture zone; mg/L, milligrams per liter; ha, hectares; kg/ha, kilograms per hectare; 

m, meters]
Results and Discussion 35

Variables Area

coefficients

Leached-quantity

coefficients

Lag (measured NO3

-

 in previous year) 0.93

(0.000)***

0.94

(0.000)***

Capture zone 1 0.07

(0.177)

0.03

(0.44)

Capture zone 2 0.03

(0.67)

0.04

(0.085)*

Capture zone 3 0.002

(0.951)

−0.02

(0.701)

Capture zones 4 to 10 0.01

(0.919)

−0.02

(0.062)*

Quaternary thickness 0.01

(0.381)

0.01

(0.452)

Constant −0.38 −0.23

Table 5. Ordinary-least-square estimates for cumulative nitrate indicator equations.

[Values in parentheses are p-values—***, 99.9-percent level of confidence; **, 97.5-percent level 

of confidence; *, 95-percent level of confidence. NO3

-

, nitrate]

Variables Area

coefficients

Leached-quantity

coefficients

Lag (change in measured NO3

-

 from 

previous year) 0.08

(0.736)

0.24

(0.200)

Capture zone 1 2.28

(0.046)**

0.77

(0.000)***

Capture zone 2 5.88

(0.014)**

3.59

(0.000)***

Capture zone 3 1.71

(0.018)**

1.88

(0.000)***

Capture zone 4 to 10 0.10

(0.164)

0.04

(0.000)***

Quaternary thickness −0.47

(0.007)***

−0.74

(0.000)***

Table 6. Arrelano-bond dynamic panel-data estimation results for cumulative nitrate 

indicator equations 

[Values in parentheses are p-values—***, 99.9-percent level of confidence; **, 97.5-percent level of 

confidence; *, 95-percent level of confidence. NO3

-

, nitrate]
36 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

Variables Coefficients

Lag (change in measured NO3

-

 from previous year) 0.22

(0.438)

Area under corn in CZ1 in Quaternary thickness 1 3.30**

(0.019)

Area under corn in CZ2 in Quaternary thickness 1 8.80***

(0.006)

Area under corn in CZ3 in Quaternary thickness 1 2.61***

(0.008)

Area under corn in CZ4 to 10 in Quaternary thickness 1 0.11

(0.194)

Area under corn in CZ1 in Quaternary thickness 2 531.28

(0.89)

Area under corn in CZ2 in Quaternary thickness 2 244.96

(0.89)

Area under corn in CZ3 in Quaternary thickness 2 192.51

(0.89)

Area under corn in CZ4to10 in Quaternary thickness 2 0.90

(0.889)

Area under corn in CZ1 in Quaternary thickness 3 omitted

Area under corn in CZ2 in Quaternary thickness 3 −0.65

(0.943)

Area under corn in CZ3 in Quaternary thickness 3 −7.12

(0.926)

Area under corn in CZ4 to 10 in Quaternary thickness 3 −25.49

(0.921)

Table 7. Arrelano-Bond dynamic panel-data estimations using three 

categories of thickness for Quaternary deposits.

[Quaternary thickness: 1, as much as 15.24 meters (m); 2, 15.25–30.48 m; 3,greater than 

or equal to 30.5 m; area under corn in hectares. Values in parentheses are p-values—***, 

99.9-percent level of confidence; **, 97.5-percent level of confidence; *, 95-percent level of 

confidence. NO3

-

, nitrate; CZ, capture zone]

For a 40-ha experimental field in southwestern Iowa under 

annual corn production with regular nitrogen application 

of 168 kg/ha, Steinheimer and others (1998) found annual 

nitrogen leaching rates in wells for the period 1969–90 to be 

in the range of 0.45 to 81.0 (kg/ha) with a mean of 22.76 (kg/

ha). For more than 80 percent of the State of Iowa, Libra and 

others (2004) found that individual watershed outputs range 

from 3.36 to 38.11 kg/ha. Over the 2001 to 2010 period, our 

estimates of average nitrate leached (kg/ha) are relatively 

constant, with the exception of 2001 (table 8). It is important 

to note that for our results (table 8), the regional, annual averages are compiled from individual HRUs in discrete subbasins, and the range of values for our 2001 to 2010 estimates is 

0.0 to 307.6 (kg/ha) with a standard deviation from the mean 

of 6.76 (kg/ha). This observation would help to explain what 

would appear to be relatively low estimates of nitrate leaching rates (table 8) in comparison to the literature cited above. 

Furthermore, the average annual nitrate leaching rate over 

the entire region could decrease because of the preponderance of low values across the region with the exception of 

just a few areas with high leachate values (as suggested in the 

spatial distribution of fig. 19). Finally, the residual nitrogen 

left over from previous years’ activities is not accounted for in 

these estimates, as the estimates were produced from simulations conducted on an annual basis. Other studies have focused 

on nitrate leaching measurements obtained at the outfalls of 

tile-drainage systems (Weed and Kanwar, 1995; Malone and 

others, 2010); values from such measurements are not the same 

as the values in our study, which address the mechanistic model 

of percolation of water out of the bottom of soil horizons and 

recharge into the aquifer system, which then relate to nitrate 

measurements in wells. 

The balance of nitrate loading is heavily skewed towards 

corn production versus soybeans, as would be expected as the 

result of nitrogen fertilizer being applied to corn crops. The 

contribution of soybeans to the nitrogen load is a bit more difficult to understand; however, it is a documented phenomenon 

(Angle, 1990). Possible explanations include the fact that 

legumes (and their nodules), such as soybeans, are fixers of 

atmospheric nitrogen and produce ammonia (NH3

) in the soils 

in which they grow. Consequently, soybeans add nitrogen to 

the soil, which could already contain a fraction of nitrogen, 

and the infiltration and percolation of precipitation could 

mobilize nitrogen down through the soil layers.
Results and Discussion 37

YEAR LULC Average nitrate 

leaching 

(kg/ha)1

Total nitrate 

leached (kg)

2001 Corn 0.60 722,944

Soybeans 0.27 319,125

 Total 0.43 1,042,069

2002 Corn 1.42 2,155,602

Soybeans 0.34 355,331

 Total 0.88 2,510,933

2003 Corn 1.58 2,434,198

Soybeans 0.31 328,587

 Total 0.94 2,762,785

2004 Corn 1.66 2,160,913

Soybeans 0.34 444,825

 Total 1.00 2,605,738

2005 Corn 1.54 2,070,425

Soybeans 0.33 385,803

 Total 0.93 2,456,228

2006 Corn 1.48 2,231,498

Soybeans 0.31 392,205

 Total 0.89 2,623,703

2007 Corn 1.63 2,327,863

Soybeans 0.30 245,782

 Total 0.96 2,573,645

2008 Corn 1.40 2,068,243

Soybeans 0.33 409,423

 Total 0.86 2,477,666

2009 Corn 1.72 2,474,250

Soybeans 0.30 340,655

 Total 1.01 2,814,904

2010 Corn 1.54 2,803,963

Soybeans 0.28 321,375

 Total 0.91 3,125,337

Grand total 0.88 24,993,008

1

Note: Average nitrate leach rates include hydrologic response units (HRUs) that 

do not contain any leaching, which drags down the value for the whole year.

Table 8. Average nitrate leaching rates and total leached amount 

estimated from ArcSWAT for the northeastern Iowa study region, 

2001–2010.

[LULC, land use/land cover; kg, kilograms; ha hectares]

Fertilizer nitrate leaching in the study region is mostly in 

a northwest-southeast trending band (fig. 19). It appears that 

this band is following the topography and surface drainage 

pattern in the study region, suggesting that particular riverine 

systems and watersheds could more susceptible to loading 

nitrogen into the groundwater system. This spatial distribution is relatively consistent over time, except in 2001 when 

there was more leachate in the northern subbasins of the study 

region. It is worth noting that in many HRUs no leaching 

occurred. This could be related to a variety of factors such as 

uptake of nitrogen by plants, limited percolation or precipitation, soil characteristics that limited nitrate mobility or caused 

denitrification, or other factors. Because of the area-weighted 

average method for portraying the results of our nitrate leaching estimates, the influence of some of the individual HRUs 

could have been reduced.

Groundwater Vulnerability

In table 9, we provide select variables, parameters, 

estimates, and standard error results for equation 20 (the 

probability of well failure; that is that a well doesn’t survive 

as a result of groundwater contamination). For one of the 

subbasins, table 9 shows that well survivability was observed 

to be higher for deeper wells, as depth has a negative coefficient and also was improving over time as the time trend 

and years have negative coefficients, which decrease the 

probability of well failure. Deep wells are both expected to 

be inherently less vulnerable to pollution from the surface 

and also typically valued as important sources for municipal 

Table 9. Proportional hazards model estimated 

effects of explanatory factors on probablitly of well 

survival (equation 20).

[m, meters]

Variable1 Parameter Estimate3

Depth (m) β1 −0.0087 (0.0001)

Time trend 

(days)2

β2 −0.00022 (0.00002)

Year 1 γ1 −3.601 (0.056)

Year 2 γ2 −3.264 (0.035)

Year 3 γ3 −3.108 (0.018)

Year 4 γ4 −3.033 (0.013)

Year 5 γ5 −2.988 (0.010)

Year 6 γ6 −2.968 (0.007)

Year 7 γ7 −2.958 (0.005)

1

Number of subbasin indicator variables=18,615.

2

Subbasin indicator variables omitted for clarity=603.

3

Standard error in parenthesis.
38 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

p

EXPLANATION

Subbasins, 2001

Avg. leachate (kg)

0–142

143–498

499–985

986–2,101

2,102–4,016

EXPLANATION

Subbasins, 2002

Avg. leachate (kg)

0–671

672–2,014

2,015–4,009

4,010–6,594

6,595–11,353

EXPLANATION

Subbasins, 2003

Avg. leachate (kg)

0–739

740–2,433

2,434–4,850

4,851–7,825

7,826–16,993

EXPLANATION

Subbasins, 2004

Avg. leachate (kg)

0–654

655–2,197

2,198–4,462

4,463–8,161

8,162–19,305

EXPLANATION

Subbasins, 2005

Avg. leachate (kg)

0–527

528–1,684

1,685–3,129

3,130–5,483

5,484–9,497

EXPLANATION

Subbasins, 2006

Avg. leachate (kg)

0–739

740–2,219

2,220–4,030

4,031–6,568

6,569–13,981

EXPLANATION

Subbasins, 2007

Avg. leachate (kg)

0–739

740–2,430

2,431–4,806

4,807–9,292

9,293–16,348

EXPLANATION

Subbasins, 2008

Avg. leachate (kg)

0–627

628–2,091

2,092–5,296

5,297–11,685

11,686–21,254

EXPLANATION

Subbasins, 2009

Avg. leachate (kg)

0–699

700–2,219

2,220–3,892

3,893–6,296

6,297–18,592

EXPLANATION

Subbasins, 2010

Avg. leachate (kg)

0–709

710–2,351

2,352–5,479

5,480–11,358

11,358–18,328

0 100 200 KILOMETERS

0 50 100 200 MILES

50 150

150

Figure 19. Maps showing subbasin-level nitrate leaching associated with corn and soybean production estimated from 

ArcSWAT for the northeastern Iowa study region, 2001 to 2010. Avg., average; kg, kilograms.
Results and Discussion 39

water supplies, so finding a higher survival rate is reasonable. The survivability of deeper wells is consistent with 

other studies focused on comparable physiographic areas 

elsewhere (Nolan and Hitt, 2006; Warner and Arnold, 2010). 

The increase in well survivability over time is a significant 

result because—using well survivability as an indicator for 

groundwater quality—it provides evidence that policies 

brought to bear in protecting groundwater have been effective. Holding depth constant at 30 m and the date constant 

at the median date of the data (January 18, 2005), we can 

plot part of the family of well or groundwater survivability 

curves for the example of three of the HUC level 12 subbasins in the study region (fig. 20). These curves flatten out 

after 7 years, so we can effectively summarize the survivability characteristics of a subbasin by the 10-year survival 

probability (fig. 21). These results quantify the environmental constraint we adhere to in equation 2 when approximating the regulator’s problem by maximizing the value of crop 

production. It is interesting to note that the results from the 

nitrate yield and leaching estimates come from different data 

and models than the groundwater vulnerability estimates, so 

the higher agricultural production and lower leaching rates 

for the western part of our study region (figs. 18 and 19) corroborate the higher groundwater survival probabilities (fig. 

21) in the same part of the study region.

Value of Information Results

The MRLI VOI is derived from both (1) informing decisions better by applying the statistical population rather than a 

ground-based sampling approach to estimate the joint output of 

a regional land-use portfolio (in other words, reducing uncertainty) and (2) constructing an optimal scenario of the landscape 

using MRLI-derived scientific information (in other words, 

applying the data) and other geospatial data and models where 

net crop production can be increased without sacrificing groundwater quality (Wu and Segerson, 1995). The observed value of 

corn and soybean production in the 35-county northeastern Iowa 

1 2 3 4 5 6 7 8 9 10

 

 

Survival probablity

1

0.998

0.996

0.994

0.992

0.99

Subbasin 1, figure 21

Subbasin 2, figure 21

Subbasin 3, figure 21

Elapsed years after well sampling

Figure 20. Graph showing groundwater-survival probability curves of three subbasins in the 35-county northeastern Iowa 

study region. The proportional hazards model was used to quantify the probability that groundwater will “survive” by remaining 

within the 10 milligrams per liter (mg/L) standard for nitrate contamination. This model was calibrated using data from wells 

sampled and tested for nitrate concentration by the Iowa Department of Natural Resources and the National Water Quality 

Assessment Program from wells throughout the study region between 1940 and 2010. 
EXPLANATION Subbasin survival probability, in percent 99.3–100.0 98.3–99.2 97.1–98.2 94.6–97.0 90.3–94.5 84.9–90.2 77.4–84.8 62.6–77.3 28.9–62.5 7.1–28.8 p 3 2 1 IOWAStudy region 0 10 20 30 40 50 KILOMETERS 0 10 20 30 40 50 MILES Figure 21. Map showing groundwater failure and subbasin probability of survival for the northeastern Iowa study region. Groundwater-surviv

curves for numbered subbasins (1, 2, and 3) are show in figure 20 and are a result of equation 21. 
Results and Discussion 41

with MRLI

without MRLI

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Year

0

1

2

3

4

5

6

7

Corn and soybean crop value, in billions of 2010 dollars

Figure 22. Bar graph showing annual flow of benefits with and without moderate-resolution land imagery (MRLI) for the 35-county 

northeastern Iowa study region. The value of corn and soybean production using the observed land-use pattern is shown by the blue 

bars. The higher value illustrated by the red bars would be possible without increasing the risk of groundwater contamination if corn 

production were moved to lands identified to be less prone to leach nitrate and additionally to lands with fate and transport properties 

that render aquifers less vulnerable to leached nitrate. 

study region (without MRLI, fig. 22) varied from $2.5 billion to 

$5.7 billion with year to year productivity variability superimposed on the increasing real-price trends. By moving corn 

production to lands identified to be less prone to leach nitrate 

and additionally to lands with fate and transport properties that 

render aquifers less vulnerable to leached nitrate, the value of 

the crop can be increased substantially, while holding level the 

risk of groundwater contamination. The EAI of the increased 

production during the study period is $858 (±197) million (fig. 

22) using the Office of Management and Budget discount rate 

guideline for public investment of 2.3 percent in Circular A-94 

Appendix C (Office of Management and Budget, 2011). This 

amounts to a NPV of $38.1 (±8.8) billion if a similar stream 

of benefits were to accrue from the MRLI information into the 

indefinite future. Because the land unit for optimization was at 

the subbasin level we can map the patterns of increased crop 

value across the study region (fig. 23).

We have provided the optimal benefit window that policymakers must work within, and the actual window will certainly 

be less than the optimal, but how much less than the maximum 

benefit will depend on the policy tools considered acceptable 

and how well these tools are implemented. In concert with the 

spatially explicit application in this analysis, a number of other 

economic incentive programs and management policies could 

be assessed with the IAA to alter the behavior of individuals 

across the study region. This assessment of potential behavioral 

change could assess different outcomes for the optimally allocated LULC on the landscape. These include:

• Mandated requirements of RFS,

• Farm Bill direct subsidies and changing commodity 

program payments that would alter relative prices and 

likely lead to changes and cropping patterns (Wu and 

Segerson, 1995),

• USDA/NRCS programs that offer long-term conservation program contracts that pay to maintain fallow 

lands in vulnerable areas,
EXPLANATION Subbasins Net present value, in percent 48–64 34–47 28–33 25–27 22–24 18–21 14–17 10–13 7–9 1–6 p Study region IOWA 0 12.5 25 50 KILOMETERS 0 12.5 25 50 MILES Figure 23. Map showing the net present value estimates for the value of information (VOI) calculations at the subbasin-level that overlap the 35

northeastern Iowa study region.
Results and Discussion 43

• Limit fertilizer application in vulnerable well CZs 

(Hall, 1992; Fricker, 1983; Andersson and others, 

1984), 

• Land-use planning in vulnerable areas that includes 

zoning restrictions for certain land uses, easements on 

particular lands, and (or) recharge protection zones 

(Adams and Foster, 1992).

The inclusion of such policies in the analysis would likely 

decrease the VOI estimates and represent a more realistic 

allocation of land uses.

Additional Discussion

Using the CDL, and MRLI such as that provided by Landsat as a key input, the agricultural production is well distributed 

and varied across the time of the analysis and the space of the 

study region. The reason that the HRUs varied over time was 

that the LULC classification of the CDL varied over time, as did 

the source imagery’s pixel size. The range in HRUs is important 

because it provides an indication of the potential heterogeneity of 

yield, resolution of the analysis (area) and nitrate leachate values, 

as well as the potential for certain HUCs to have their LULCs 

reallocated in the optimization routine. It is important to note that 

although the reporting unit is a HUC or subbasin, finer resolution and spatially explicit HRUs that are not explicitly delineated 

reflect the site conditions (e of eq. 1) that influence yield (q of eq. 

1). It is also important to note that MRLI VOI is inherently related 

to behaviors observable using the MRLI information, thus because 

we cannot observe inputs or farm management practices (v and 

z of eq. 1) directly with MRLI information, we assumed these 

stayed constant given the crop choice, which we could observe. In 

future work, a richer model of these practices should be incorporated in a detailed policy analysis to account for predicted changes 

in unobserved practices that would occur along with the desired 

changes induced in crop planting patterns.

The spatiotemporal resolution of the yield results are 

well beyond estimates that are traditionally accomplished with 

county-level estimates. In comparison to the 35 counties that 

cover the study region, we provided estimates from 603 subbasins each containing multiple HRUs. The area of the HRUs 

is generally smaller than 10 km2 (table 3). The range of county 

level yields from NASS sampling results in our region in 2002 

for corn was 7.62 to 10.97 tons/ha, whereas the range among 

subbasins from the ArcSWAT calculations in 2002 were 6.40 to 

11.55 tons/ha. Because counties aggregate multiple subbasins, 

the range for counties should be within the range for subbasins, 

because any county containing an extreme subbasin will also 

contain subbasins not at that extreme. From 2001–2010, the corn 

and soybean mean yields (table 3) are comparable to those provided by FAPRI (table 10). On an annual basis, the corn yields 

from ArcSWAT are both greater and lower than those from 

FAPRI, and soybean yields from ArcSWAT are generally lower 

than those from FAPRI, which could be for a variety of reasons 

related to the parameters, inputs, and management factors of ArcSWAT or how FAPRI collects their estimates. In addition, FAPRI 

is reporting national estimates that include a wide range of biophysical characteristics and management practices, whereas our 

results are provided for just the northeastern part of Iowa. In our 

study region, estimates suggest that corn crops regularly provide 

more total yield and mean total yield per HRU than do soybean 

crops, which is consistent with expectations.

Although this analysis does not include a specific set of 

policies and (or) economic incentives as presented earlier, 

the result of such policies is likely to induce changes to land 

allocation. Agricultural growing areas are more prominent in 

the northwest of our study region (fig. 18), but our results are 

highly dependent on the spatial accuracy of the CDL classification as corn and soybeans. Other subbasins in the central 

and southern parts of our study region are also productive; 

however, very little production occurs in the northeast of our 

study region. Available county enrollment in NRCS conservation programs data (National Resources Conservation Service, 

2010) was used to determine the past trends of lands unavailable for production (table 11). The highest year of enrollment 

in the programs was fiscal year 2010, when 2.9 percent of the 

total lands in the study region were out of production. Overall, 

the NRCS conservation programs are likely to have little influence on the overall amounts of land in production. 

Similar to Bernardo and others (1993b), FAPRI estimates 

of production can be important to consider for induced land 

allocation changes. National estimates from 2005 and projections to 2025 of total ethanol production and the feedstock 

from corn for ethanol production (table 12) suggest increasing 

production. Furthermore, additional FAPRI national estimates 

from 2005 and projections to 2025 of soybean production, 

as well as of soybean oil available for biodiesel production, 

also suggest increasing production (table 12). If agricultural 

production in the study region continues to be sensitive to the 

production of ethanol and biodiesel, the increasing production 

projected by FAPRI, until a plateau in about 2016 or 2017, is 

likely to continue to perturb land allocation. 

Because corn/soybean production is the dominant use of 

land in our study region, its value is the dominant component of 

VOI for the region. If similar results hold for other corn/soybean 

production regions, then further VOI proportional to the size of 

the crop is available, because Landsat imagery is collected globally and therefore available for management of any other corn/

soybean growing region. Note that a general equilibrium analysis would be necessary for application of this technology in an 

area large enough to substantially affect the world price for corn 

or soybeans. The primary general equilibrium result is a shift 

in the world grain supply curve, so the value of the baseline 

production is the same, but the unit value of the new production 

is less than the baseline world price vector.

Future Research 

The IAA provides a foundation to pursue a variety of 

research directions. By varying the MCL, the IAA can be used 

to assess the survivability of aquifers under a more or less 

strict nitrate health standard relative to the current standard. 
Table 10. United States corn and soybean production estimates and projections, 2000 to 2026, from Food and Agricultural Policy Research Institue (2012)

[Growing season format: number/two-digit year; ha, hectares; t, metric tons] Growing season 00/01 01/02 02/03 03/04 04/05 05/06 06/07 07/08 08/09 09/10 10

Corn Area harvested (thousand ha) 29,315 27,829 28,057 28,710 29,797 30,399 28,586 35,013 31,796 32,173 32,9

Yield (t/ha) 8.59 8.67 8.12 8.92 10.06 9.29 9.36 9.46 9.66 10.34 

Production (thousand t) 251,854 241,377 227,767 256,229 299,876 282,263 267,503 331,177 307,142 332,552 316,1

Soybeans Area harvested (thousand ha) 29,303 29,533 29,339 29,331 29,930 28,835 30,191 25,960 30,231 30,919 31,0

Yield (t/ha) 2.56 2.66 2.56 2.28 2.84 2.90 2.88 2.81 2.67 2.96 

Production (thousand t) 75,057 78,673 75,011 66,784 85,017 83,508 87,002 72,860 80,750 91,419 90,6

Growing season 13/14 14/15 15/16 16/17 17/18 18/19 19/20 20/21 21/22 22/23 23

Corn Area harvested (thousand ha) 35,013 35,375 35,723 35,638 35,759 35,900 35,932 35,843 35,953 36,079 36,1

Yield (t/ha) 10.32 10.45 10.59 10.74 10.87 11.00 11.14 11.27 11.40 11.53 

Production (thousand t) 361,207 369,712 378,393 382,764 388,867 395,047 400,335 404,023 409,788 415,853 421,0

Soybeans Area harvested (thousand ha) 29,880 29,842 29,721 29,868 29,929 29,965 30,010 30,088 30,045 30,012 30,0

Yield (t/ha) 3.00 3.02 3.05 3.07 3.10 3.12 3.15 3.17 3.20 3.22 Production (thousand t) 89,521 90,135 90,545 91,833 92,830 93,610 94,484 95,446 96,008 96,638 97,3
Results and Discussion 45

Program 2003 (ha) 2004 (ha) 2005 (ha) 2006 (ha) 2007 (ha) 2008 (ha) 2009 (ha) 2010 (ha) 2011 (ha)

Cumulative 

program 

totals (ha)

Wetland Reserve 

Program 1,081 1,291 -- 851 713 -- 499 633 1,386 6,454

Grassland Reserve 

Program 57 135 335 -- -- -- 203 -- -- 730

Environmental 

Quality 

Improvement 

Program

-- -- -- 25,924 14,867 -- 12,202 9,428 13,489 75,909

Wildlife Habitat 

Improvement 

Program

-- -- -- 323 40 -- 295 143 84 884

Conservation 

Stewardship 

Program

-- -- -- -- -- -- -- 143,932 65,615 209,547

Emergency 

Watershed 

Protection Program

-- -- -- -- -- -- -- 2,867 4,690 7,557

Emergency Wetland 

Reserve Program -- -- -- -- -- -- -- -- 10,583 10,583

Annual totals 1,137 1,426 335 27,097 15,619 -- 13,198 157,004 95,847 --

Percentage of case 

study region 0.02 0.03 0.01 0.50 0.29 -- 0.24 2.91 1.77 --

Table 11. Natural Resources Conservation Service (NRCS) land conservation programs summary for the 35-county northeastern Iowa study region, 

2003 to 2011.

[Numbers are tallied from county-level figures in the study region and provided by the NRCS (2010). --, no data/not applicable; ha, hectares]

More agrochemicals could be added to the model to obtain 

a comprehensive analysis of the impacts of the chemicals on 

groundwater. The range of ecosystem services in the model 

could be expanded to include soil retention and increased productivity, other drinking water quality improvements related to 

surface hydrology, eutrophication and importance of riparian 

buffers, habitat preservation and wildlife corridors for biodiversity, wetland conservation/restoration for water purification, and other crops for consumption and biofuels. Analysis 

of other policy issues is possible, including (1) issues such 

as tax incentives and user fees to reduce fertilizer use and to 

reduce the planting of crops with high fertilizer demands (de 

Haen, 1982; Wu and Segerson, 1995) and (2) economic incentives to adopt best management practices, precision agriculture 

(a concept that relies on new technologies, such as satellite 

imagery, to observe and respond to variations within fields), 

and (or) technological advances in crop strains.

The IAA could be used to assess alternative land management practices. For example, by using a high-resolution, 

lidar-based bare-earth model, as well as Soil Survey Geographic (SSURGO)14 soil databases and additional LULC 

types, certain known hotspots that are susceptible to groundwater pollution could be studied at finer-resolution to focus on 

14For more information on the NRCS SSURGO database see http://soils.

usda.gov/survey/geography/ssurgo/.

management-based scenarios and analysis of nutrient dynamics. 

Examples include (1) testing the efficacy of shallow-rooted and 

deep-rooted plants to remove residual nitrogen not taken up 

by primary crops, (2) analysis of the effectiveness of riparian 

buffer strips that are widely used across the Midwest to control nutrient loading, (3) using data provided by the IDNR on 

locations of livestock operations, estimates of their types and 

numbers animals, and characteristics of their associated waste 

streams (as suggested by Loeher, 1977; Barth, 1985; Westerman and others, 1985; and Jackson and others, 2000, to address 

manure as a compounding factor of aquifer contamination), (4) 

stratifying the landscape into fields, corrals for livestock, liquid 

manure holding ponds, and manure-treated forage fields for 

more regulatory relevance to management units and to consider 

the dynamics of groundwater recharge rates, volatilization, and 

hillslope gradients on groundwater quality (Harter and others, 

2002; van der Schans and others, 2009), and (5) incorporating 

the hydrologic flow paths and alteration of nitrogen dynamics 

associated with presence and absence of berms and tile drains.

Modeling the groundwater in our research required 

certain simplifying assumptions. Future work with the IAA to 

address groundwater flows and fate and transport of chemical 

constituents could include improving travel-time calculations 

and regional representation of the hydrogeologic system with 

USGS’s MODFLOW (modular finite-difference flow model) 

and ArcSWAT. This could include such simulation and DSS 

experiments as (1) drilling new wells into locations known to 
46 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

Table 12. United States biofuels production and feedstock source estimates and projections, 2005 to 2025, from Food and 

Agricultural Policy Research Institue (2012).

[L, liters; t, metric tons]

have lower degrees of groundwater vulnerability; (2) reducing the amount of pumping and groundwater extraction to 

increase the mean residence time of groundwater in the subsurface, thereby increasing the chance of such processes as 

dilution, dispersion, and denitrification; and (3) more explicit 

inclusion of denitrification processes at depth, the mixture of 

waters and their ages, recharge compositions, and the spatial 

variability and heterogeneity of the controlling factors of biophysical processes associated with groundwater (Green and 

others, 2010). In addition, extending the length of Landsat 

land-classification archive data included in modeling from 

2000 back to 1972 would improve estimation of the survival 

probabilities of the groundwater.

The IAA can be adapted to conduct analyses at the State 

level for Iowa, while retaining the spatial explicitness of the 

geography. Furthermore, the IAA could be used in a greater 

context to analyze eutrophication of waterways in the Mississippi River watershed and associated hypoxia in the Gulf of 

Mexico. Nutrient loading from agricultural lands and other 

nonpoint sources can lead to impacts on commercial and recreational fisheries in the Gulf of Mexico. The control of such 

nutrient loading in the watershed poses technical and administrative challenges (Dzombak, 2011). The primary technical 

challenge is addressing the results of nitrogen and phosphorous 

loads increasing algal biomass in waterways, which when 

decomposed by bacteria leads to a reduction in dissolved 

oxygen that then leads to hypoxia and deleterious impacts on 

aquatic life. The hypoxic zone associated with the Mississippi River watershed is the largest in the western hemisphere 

(Brady and Weil, 2002). Because the Clean Water Act’s main 

focus is on point sources and the study of mitigation strategies for nonpoint sources and their implementation, a primary 

administrative challenge is the lack of regulation and enforcement on nonpoint source control (Dzombak, 2011). Overall, 

90 percent of the nitrogen load from the Mississippi River that 

drains into the Gulf of Mexico has been shown to come from 

nonpoint sources (National Research Council, 2008). During 

the period 2000 to 2002, Iowa has been shown to contribute 

20 percent of the nitrogen load annually delivered to the Gulf 

of Mexico by the Mississippi River (Libra and others, 2004). 

The groundwater orientation of our work and the IAA could 

be expanded to include surface-water nitrogen, phosphorous, 

and sediment loading as additional constraints to the economic 

maximization problem (eg. 2). The regulatory context of the 

Clean Water Act’s primary nonpoint source reduction mechanism, namely the characterization of and opportunities for 

source reduction through total maximum daily loads, would 

make this effort compelling. Also, results could be compared 

to results from the USGS’s SPARROW15

 model. The set of 

decision support tools in the IAA could be formalized to allow 

for improved modeling and simulation of land-use reallocation, 

crop rotation, and land management changes that influence 

nonpoint source runoff and nutrient loading. 

15SPARROW (spatially referenced regressions on watershed attribute) 

relates in-stream water-quality measurements to spatially referenced characteristics of watersheds, including contaminant sources and factors influencing 

terrestrial and aquatic transport (http://water.usgs.gov/nawqa/sparrow/).

Feedstock source 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Ethanol

Production (million L) 14,780 18,489 24,685 35,237 41,404 49,528 46,818 46,890 50,675 53,907 56,899

Feedstock in ethanol production

Corn (thousand t) 35,983 45,097 61,710 83,040 101,486 118,844 119,424 112,454 120,786 127,645 133,816

Corn stover (thousand t) 0 0 0 0 5 16 114 408 713 959 1,208

Biodiesel

Production (million L) 404 970 1,801 2,282 1,817 2,095 3,402 3,666 3,378 3,406 3,454

Feedstock in biodiesel production

Soybean oil (thousand t) 369 832 1,308 1,333 876 941 1,491 1,413 1,033 1,006 1,019

Feedstock source 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Ethanol

Production (million L) 57,945 58,037 58,169 58,318 58,500 58,675 58,829 58,964 59,084 59,193

Feedstock in ethanol production

Corn (thousand t) 135,306 134,553 133,882 133,200 132,509 131,802 131,094 130,371 129,644 128,922

Corn stover (thousand t) 1,454 1,723 2,003 2,317 2,691 3,037 3,310 3,536 3,717 3,858

Biodiesel

Production (million L) 3,500 3,556 3,602 3,637 3,672 3,705 3,736 3,769 3,796 3,824

Feedstock in biodiesel production

Soybean oil (thousand t) 1,041 1,079 1,108 1,129 1,151 1,169 1,176 1,177 1,159 1,131
Acknowledgments 47

The IAA could be adapted to larger extents in the Midwest and other locations in the United States. Our study region 

in Iowa is only a small part of the larger region of the Midwest 

that produces corn and soybeans (fig. 10). With similar input 

datasets16, we could increase the extent of the IAA. Since 2008 

at a minimum, the CDL has been produced for the Southeast, 

New England and the rest of the Eastern States, the Pacific 

Northwest, and California. Consequently, applying this IAA 

in those regions would likely increase the VOI for MRLI. The 

CDL also covers a variety of other agricultural products that 

have additional value and alternate production practices. These 

alternate production practices and regulatory frameworks 

would likely lead to the inclusion of other nonpoint source 

pollutants and expanded DSSs in the IAA. 

Summary and Conclusions

The MRLI provided by Landsat is used widely in several 

economic sectors in the United States, providing multiple 

benefits to society (Miller and others, 2011). Quantification of 

societal benefits from Landsat is of urgent importance in the 

Landsat Data Continuity Mission (LCDM) and the continued operation of Landsat missions; the next Landsat satellite 

launch is planned for February 2013. We adapted an IAA to 

estimate the value of MRLI information in the application 

nexus of three major sectors, namely environmental science 

and management application, LULC, and agricultural sectors, which were identified as some of the largest application 

sectors by Miller and others (2011). The use of the MRLI, 

other datasets, models, and scientific information provides an 

improved estimate of the VOI in our study region, because 

it increased the potential economic value without sacrificing groundwater quality. In the agricultural example we used 

for the new application of the MRLI archive, the EAI of the 

enhanced landscape configuration is $858 (±197) million, 

which results in an NPV for the remotely sensed data of $38.1 

(±8.8) billion (in 2010 dollars) over an indefinite future. 

In this research, we developed and applied models for estimating agricultural production, nitrate leaching, and groundwater 

nitrate dynamics that were coupled with groundwater protection 

and economic optimization models in an IAA framework to estimate VOI. The models in the IAA incorporated economic, environmental, geological, and hydrological sciences in the land-use 

management problem and were conducted in northeastern Iowa, 

which is underlain by Silurian and Devonian aquifers. Also, the 

IAA is a soft-coupled DSS with the objective of maximizing 

agricultural production (an ecosystem service) by reallocating 

LULC, while maintaining the risk of reduced future groundwater 

quality for potability (another ecosystem service) in an aquifer to 

a regulated level. The constrained optimization model included 

two constraints—(1) the risk of exceeding the 10 mg/L MCL for 

nitrate in groundwater is not increased, and (2) only land used 

between 2001 and 2010 for corn or soybean production can be 

reallocated. The application of the IAA demonstrated how the 

characterization of agricultural production and its environmental 

impacts may change with or without the availability of MRLI.

 In this analysis, we have demonstrated that MRLI can 

be used operationally in a regulatory application. Although the 

example is an abstraction from an actual implementation of an 

environmental regulation, more constraints can be added to the 

DSS for regulating the impacts of agriculture on a wider range 

of ecosystem services. The modeling tools developed here were 

designed to analyze the ramifications of agricultural production 

on groundwater vulnerability. The case study demonstrated the 

VOI of MRLI by providing more accurate agricultural production and nitrate leaching estimates and additional benefits 

to society by more efficiently allocating production without 

sacrificing groundwater quality in an agricultural region. Thus, 

this use of MRLI—in conjunction with other scientific datasets 

and process models—provides an increase in potential economic 

value and, hence there is a positive VOI attributed to archived 

Earth observation data. Our analysis shows that the benefits of 

the Landsat archive and MRLI could be large over space and 

time as an operational Earth observation tool for managing land 

and natural resources.

Acknowledgments

The authors would like to thank the Land Remote Sensing Program for funding and supporting this research, especially Bruce Quirk and Barron Bradford. We would also like 

to acknowledge the contributions of the USGS WGSC’s 

senior management, namely Mara Tongue, Susan Benjamin, 

and Larry Gaffney in supporting, reviewing, and overseeing 

the work. Also, many colleagues and collaborators assisted 

with the research, laid its groundwork, and provided valuable 

knowledge, data, and insights without which we would not 

have been successful in our endeavors. They include Paul Amos 

(University of Pennsylvania), Larry Beard (USGS), Ron Beck 

(USGS), Barbara Bekins (USGS), James Calzia (USGS emeritus), Chris Ensminger (IDNR), Mike Gannon (IDNR), Dario 

Garcia (USGS), Michael Gould (USGS), Christopher Green 

(USGS), David Halsing (URS Corporation), James Hendley 

(USGS), Caroline Hermans (City University of New York), 

Mary Howes (IDNR), Jeanne Jones (USGS), Stephen Kalkhoff 

(USGS), Leonard Konikow (USGS), Richard Leopold (formerly 

of IDNR), Lixia Liao (USGS), Robert Libra (IDNR), Molly 

Macauley (Resources for the Future), Rick Mueller (NASS), 

Vivian Nguyen (USGS), Alan Rabideu (University of Buffalo), 

Benjamin Sleeter (USGS), Raghavan Srinivasan (Texas A&M 

University), Kenneth Steif (University of Pennsylvania), Robert 

Swanson (USGS), Prasad Thenkabail (USGS), Ate Visser (Lawrence Berkeley National Laboratory), Judy Weathers (USGS), 

and Tamara Wilson (USGS). Finally, we would like to thank our 

peer reviewers, Bernard T. Nolan (USGS), Leslie Richardson 

(USGS), Lynne Koontz (USGS), Holly Miller (USGS), and 

Benjamin Simon (Department of the Interior) for their thoughtful 

and insightful comments.

16The IDNR datasets are available for all of Iowa, as are the rest of the input 

data (table 1). In other States, if a data gap existed, adequate well data would 

most likely be the most significant one.
48 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

References Cited

Adams, B., and Foster, S.D.D., 1992, Land-surface zoning for 

groundwater protection: Journal of the Institution of Water 

and Environmental Management, v. 6, no. 3, p. 312–320.

Amos, P., Steif, K., Wachter, S.M., 2010, Remote sensing classification procedure for identifying corn and soybean crops 

in Iowa with Landsat imagery: University of Pennsylvania, 

Spatial Integration Laboratory for Urban Systems, Working Paper no.12, accessed October 2011, at http://gislab.

wharton.upenn.edu/silus-research.htm.

Andersson, R., Kindt, T., Johansson, P-O., and Maxe, L., 

1984, An attempt to reduce nitrate content in ground water 

used for municipal water supply by changing agricultural 

practices: Nordic Hydrology, v. 15, no. 4–5, p. 185–194.

Angle, J.S., 1990, Nitrate leaching losses from soybeans (Glycine max L. Merr.): Agricultural, Ecosystems & Environment, v. 31, no. 2, p. 91–97.

Antle, J., and Just, R.J. 1991, Effects of commodity program 

structure on resource use and the environment, in Just R., 

and Bockstael, N., eds., Commodity and resource policies in 

agricultural systems: Berlin, Springer-Verlag. p. 97–128. 

Antle, J., and McGuckin, T., 1993, Technological innovation, 

agricultural productivity, and environmental quality, in

Carlson, G., Zilberman, D., and Miranowski, J., eds., Agricultural and environmental resource economics: New York, 

Oxford University Press. p.175–220. 

Arellano, M., and Bond, S., 1991, Some tests of specification for panel data—Monte Carlo evidence and 

an application to employment equations: Review of 

Economic Studies, v. 58, p. 277–297.

Asada, T., 2002, Growth versus environment in dynamic models of capital accumulation: Discrete Dynamics in Nature 

and Society, v. 7, no. 2, p. 101–109.

Barth, C.L., 1985, Livestock waste characterization—A new 

approach, in Agricultural waste utilization and management: Proceedings of the Fifth International Symposium of 

Agricultural Wastes, American Society of Agricultural Engineers, p. 286–295.

Bernardo, D.J., Mapp, H. P., Sabbagh, G.J., Geleta, S., Watkins, K. B., Elliott, R. L., and Stone, J.F., 1993a, Economic 

and environmental impacts of water quality protection policies—Framework for regional analysis: Water Resources 

Research, v. 29, no. 9, p. 3069–3079.

Bernardo, D.J., Mapp, H. P., Sabbagh, G.J., Geleta, S., Watkins, K. B., Elliott, R. L., and Stone, J.F., 1993b, Economic 

and environmental impacts of water quality protection 

policies—Application to the central High Plains: Water 

Resources Research, v. 29, no. 9, p. 3081–3091.

Bernknopf, R.L., Brookshire, D.S., McKee, M., and Soller, 

D.R., 1997, Estimating the societal value of geologic map 

information—A regulatory application: Journal of Environmental Economics and Management, v. 32, p. 204–218.

Bernknopf, R., Forney, W., Raunikar, R., and Mishra, S., 2012, 

A general framework for estimating the benefits of Moderate Resolution Land Imagery in environmental applications, 

in Macauley, M., and Laxminarayan, R., eds., Value of 

information—Methodological frontiers and new applications: Washington, D.C., Springer Dordrecht, p. 257–300.

Bock, B.R., and Hergert, G.W., 1991, Fertilizer nitrogen management, in Follet, R.F., Keeney, D.R., and Cruse, R.M., 

eds., Managing nitrogen for groundwater quality and farm 

profitability: Madison, Wis., Soil Science of America, Inc., 

p. 139–164.

Bourg, C.W., 1984, Producers’ guide to nitrogen management; nitrogen and irrigation management— Hall County 

water quality special project: Lincoln, Neb., University 

of Nebraska, Cooperative Extension Service, Lincoln, p. 

c1–c10.

Brady, N.C., and Weil, R.R., 2002, The nature and properties 

of soils, 13th ed.: Upper Saddle River, N.J., Prentice Hall, 

960 p. 

Buckwell, A., 1989, Economic signals, farmers’ response and 

environmental change: Journal of Rural Studies, v. 5, no. 2, 

p. 149–160.

Bureau of Labor Statistics, 2012, Consumer Price Index all 

urban consumers (CPI-U)—U.S. city average all items: 

Bureau of Labor Statistics database, accessed March 16, 

2012, at ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt.

Canter, L.W., 1997, Nitrates in groundwater: Boca Raton, Fla., 

CRC Press, 263 p.

Causarano, H.J., Doraiswamy, P.C., McCarty, G. W., Hatfield, 

J.L., Malik, S., and Stern, A.J., 2008, EPIC modeling of soil 

organic carbon sequestration in croplands of Iowa: Journal 

of Environmental Quality, v. 37, p. 1345–1353.

Ceric, A., and Haitjema, H., 2005, On using simple time-oftravel capture zone delineation methods: Ground Water, v. 

43, no. 3, p. 408–412.
References Cited 49

Committee on Techniques for Assessing Ground Water Vulnerability, 1993, Ground water vulnerability assessment—

Predicting contamination potential under conditions of 

uncertainty: Washington, D.C., National Research Council, 

National Academy Press, p. 1–11 and p. 42–86.

Delhomme, J.P., 1978. Kriging in the hydrosciences: Advances 

in Water Resources. v. 1, no. 5, p. 251–266.

Dupuit, J., 1863, Etudes Theorique et Practiques sur le Mouvement des Eaux dans les Canaux Decouverts et a Travers 

les Terrains Permeables, 2nd ed.: Paris, France, Dunod.

Dzombak, D.A., 2011, Nutrient control in large-scale U.S. 

watersheds—The Chesapeake Bay and Northern Gulf of 

Mexico: National Academy of Engineering, The Bridge, 

p. 13–22.

Eckhardt, D.A.V., and Stackelberg, P.E., 1995, Relation of 

ground-water quality to land use on Long Island, New York: 

Ground Water v. 33, p. 1019–1033.

ESRI, 2012, An overview of the map algebra toolset: ESRI 

Web site, accessed on August 24, 2012, at http://resources.

esri.com/help/9.3/arcgisengine/java/gp_toolref/spatial_

analyst_toolset_overview/an_overview_of_the_map_

algebra_toolset.htm.

Farajalla, N.S., Deyle, R.E., Vieux, B. E., and Canter, L.W., 

1993, Correlating nitrate levels in ground water with 

agricultural land use in Oklahoma, in Proceedings of 1993 

Joint CSCE-ASCE National Conference on Environmental 

Engineering: New York, N.Y., American Society of Civil 

Engineers, p. 469–476.

Feng, H., and Babcock, B.A., 2008, Impacts of ethanol on 

planted acreage in market equilibrium: Iowa State University, Center for Agricultural and Rural Development, Working Paper 08-WP 472, 32 p.

Finkelstein, D.M., 1986, A proportional hazards model for 

interval-censored failure time data: Biometrics, v. 42, no. 4, 

p. 845–854.

Forchheimer, P., 1886, Ueber die Ergiebigkeit vonBrunnenAnlagen und Sickerschlitzen. Hannover, Germany, Zeit 

schrift des Architekted und Ingenieurs Vereins, v. 32, 

p. 539–564.

Food and Agricultural Policy Research Institute, 2012, FAPRI 

U.S. and world agricultural outlook: Food and Agricultural 

Policy Research Institute Web site, accessed January 20, 

2012, at http://www.fapri.iastate.edu/outlook/.

Fortin, G., van der Kamp, G., Cherry, J.A., 1991, Hydrogeology and hydrochemistry of an aquifer-aquitard system 

within glacial deposits, Saskatchewan, Canada: Journal of 

Hydrology, v. 126, p. 265–292.

Frank, M.D., Beattie, B.R., and Embleton, M.E., 1990, A 

comparison of alternative crop response models: American 

Journal of Agricultural Economics, v. 70, p. 597–603.

Fricker, W., 1983, Origins of nitrates in groundwater of the 

Bunz Valley: Wasser, Energie, Luft, v. 75, no. 3, p. 75–77.

Gheysari, M., Mirlatifi, S.M., Homaee, M., Asadi, M.E., and 

Hoogenboom, G., 2009, Nitrate leaching in a silage maize 

field under different irrigation and nitrogen fertilizer rates: 

Agricultural Water Management, v. 96, p. 946–954. 

Greatz, D.A., and Nair, V.D., 1995, Fate of phosphorous in 

Florida spodisols contaminated with cattle manure: Ecological Engineering, v. 5, p. 163–181.

Green, C.T., Böhlke, J. K., Bekins, B.A., and Phillips, S.P., 

2010, Mixing effects on apparent reaction rates and isotope 

fractionation during denitrification in a heterogeneous aquifer: Water Resources Research, v. 46, W08525, 19 p.

Groffman, P.M., and Tiedje, J.M., 1989, Denitrification in 

north temperate forest soils—Spatial and temporal patterns 

at the landscape and seasonal scale: Soil Biology and Biogeochemistry, v. 21, p. 613–620.

Hanson, G.C., Groffman, P.M., and Gold, A.J., 1994, Denitirfication in a riparian wetlands receiving high and low 

groundwater nitrate inputs: Journal of Environmental Quality, v. 23, p. 917–922.

Hall, D.W., 1992, Effects of nutrient management on nitrate 

levels in ground water near Ephrata, Pennsylvania: Ground 

Water, v. 30, no. 5, p. 720–730.

Hallberg, G.R., 1989, Pesticide pollution of groundwater in 

the humid United States: Agriculture, Ecosystems and Environment, v. 26, p. 299-367.

Haitjema, H., 2006, The role of hand calculations in groundwater flow modeling: Ground Water, v. 44, no. 6, p. 786–791.

Harter, T., Davis, H., Mathews, M.C., and Meyer, R.D., 2002, 

Shallow groundwater quality on dairy farms with irrigated 

forage crops: Journal of Contaminant Hydrology, v. 55, 

p. 287–315.

Iowa Corn Growers Association, 2012, Ethanol quick facts: 

Iowa Corn Growers Association Web site, accessed Feb-
50 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

ruary 21, 2012, at http://www.iowacorn.org/documents/

filelibrary/ethanol/Iowa_Ethanol_talking_points_

August__53FE1540A203B.pdf.

Iowa Department of Natural Resources, 2003, Iowa’s groundwater basics: Iowa City, Iowa, Iowa Department of Natural 

Resources, Iowa Geological Survey, Educational Series 6, 

92 p.

Iowa State University Extension, 1997, Nitrogen fertilizer 

recommendations for corn in Iowa: Ames, Iowa, Iowa 

State University Extension, publication Pm-1714, 4 p. 

accessed July 19, 2011, at http://www.extension.iastate.edu/

publications/pm1714.pdf.

Jankovic, I., 2001, Slit—Win32 computer program for 

analytic-based modeling of single-layer groundwater flow 

in heterogeneous aquifers with particle tracking, capturezone delineation, and parameter estimation, ver. 2.3: 32 p., 

accessed October 15, 2010, at http://www.orser.psu.edu/

GISSupport/SPLIT_Manual.pdf.

Jackson, L.L., Keeney, D.R., and Gilbert, E.M., 2000, Swine 

manure management plans in north-central Iowa—Nutrient 

loading and policy implications: Journal of Soil and Water 

Conservation, v. 55, no. 2, p. 205–212.

Johnson, D.M., 2008, A comparison of coincident Landsat-5 

TM and Resourcesat-1 AWiFS imagery for classifying croplands: Photogrammetric Engineering and Remote Sensing, 

v. 74, no. 11, p. 1413–1423.

Johnson, D.M., and Mueller, R., 2010, The 2009 Cropland 

Data Layer: Photogrammetric Engineering and Remote 

Sensing, v. 76, no. 11, p. 1201–1205.

Kapp, J.D., 1986, Implementing best management practices to 

reduce nitrogen levels in northeast Iowa ground water: Dublin, Ohio, Proceedings of the Conference on Agricultural 

Impacts on Ground Water, National Water Well Association, 

p. 412–427. 

Keeney, D.R., 1986, Sources of nitrate to ground water: Critical Review of Environmental Control, v. 16, p. 257–304.

Keller, C.K., van der Kamp, G., Cherry, J.A., 1988, Hydrogeology of two Saskatchewan tills, I—Fractions, bulk permeability, and spatial variability of downward flow: Journal of 

Hydrology, v. 101, p. 97–121.

Keller, C.K., van der Kamp, G., Cherry, J.A., 1991, Hydrogeochemistry of clayey till, 1—Spatial variability: Water 

Resources Research, v. 27, no. 10, p. 2543–2554.

Kim, C.S., Hostetler, J.E., and Amacher, G., 1993, The regulation of groundwater quality with delayed responses: Water 

Resources Research, v. 29, p. 1369–1377.

Kolpin, D.W., 1997, Agricultural chemicals in groundwater 

of the Midwestern United States—Relations to land use: 

Journal of Environmental Quality, v. 26, p. 1025–1037.

Kross, B.C., 2002, Nitrate toxicity and drinking water standards—A review: The Journal of Preventive Medicine, v. 

10, no. 1, p. 3–10.

Kross, B.C., Hallberg, G.R., Bruner, D.R., Libra, R.D., Rex, 

K.D., Weih, M.B., Vermace, M.E., Burmeister, L.F., Hall, 

N.H., Cerryholmes, K.L., Johnson, J.K., Selim, M.I., 

Nations, B.K., Seigley, L.S., Quade, D.J., Dudler, A.G., Sesker, M.A., Lynch, C.F., Nicholson, H.F., Hughes, J.P., 1991, 

Iowa statewide rural well-water survey data report—Initial 

analysis: Iowa Department of Natural Resources, Technical 

Information Series 19, 142 p.

Landsat Science Team, 2008, Free access to Landsat imagery: 

Science, v. 320, no. 5879, p. 1011

Liao, L., Green, C.T., Bekins, B.A., and Bohlke, J.K., 2012, 

Factors controlling nitrate fluxes in groundwater in agricultural areas: Water Resources Research, v. 48, 18 p., 

W00L09, accesed March 16, 2012, at http://www.agu.org/

journals/wr/wr1202/2011WR011008/2011WR011008.pdf.

Libra, R.D., Hallberg, G.R., Rowden, R.D., Bettis, E.A., III, 

Kalkoff, S.J., Baker, D.G., 1992, Environmental geology of 

Big Spring groundwater basin northeast Iowa: Iowa Department of Natural Resources, Geological Survey Bureau, 

Guidebook Series 15, 51 p. 

Libra, R.D., Wolter, C.F., and Langel, R.J., 2004, Nitrogen 

and phosphorous budgets for Iowa and Iowa watersheds: 

Iowa Geological Survey, Technical Information Series 47, 

accessed July 26, 2011, at http://www.iowadnr.gov/portals/

idnr/uploads/water/standards/nbfull.pdf.

Lichtenberg, E., and Shapiro, L.K., 1997, Agriculture and 

nitrate concentrations in Maryland Community Water System wells: Journal of Environmental Quality, v. 26,

 p. 145–153.

Loeher, R.C., 1977, Pollution control for agriculture: New 

York, N.Y., Academic Press, Inc., p. 79–89.

Macauley, M.K., 2005, The value of information: a background paper on measuring the contribution of spacederived earth science data to national resource management: 

Washington, D.C., Resources for the Future, Discussion 

Paper 05–26, 30 p.

Malone, R.W., Jaynes, D.B., Ma, L., Nolan, B.T., Meek, D.W., 

and Karlen, D.L., 2010, Soil-Test N recommendations augmented with PEST-optimized RZWQM simulations: Journal 

of Environmental Quality, v. 39, p. 1711–1723.
References Cited 51

Mas-Colell, A., Whinston, M., Green, J., 1995, Microeconomic 

theory: Oxford, U.K., Oxford University Press, 981 p.

Matheron, G., 1963, Principles of geostatistics: Economic 

Geology, v. 58, p. 1246–1266.

Matheron, G., 1967, Kriging, or polynomial interpolation 

procedures: Canadian Institute of Mining Bulletin, v. 60, no. 

665, p. 1041–1045.

McFadden, D., 1975, The revealed preferences of a government bureaucracy—Theory: The Bell Journal of Economics, v. 6, no. 2, p. 401–416.

McKay, L.D., Cherry, J.A., and Gillham, R.W., 1993, Field 

experiments in a fractured clay till—Solute and colloid 

transport: Water Resources Research, v. 29, no. 12, p. 

3879–3890.

Miller, H.M., Sexton, N.R., Koontz, L., Loomis, J., Koontz, 

S.R., and Hermans, C., 2011, The users, uses, and value of 

Landsat and other moderate-resolution satellite imagery 

in the United States—Executive report: U.S. Geological 

Survey Open-File Report 2011–1031, 48 p., July 26, 2011, 

at http://pubs.usgs.gov/of/2011/1031/.

Mirvish, S.S., 1995, Role of N-nitroso compounds (NOC) and 

N-nitrosation in etiology of gastric, esophageal, nasopharyngeal and bladder cancer and contribution to cancer of known 

exposures to NOC: Cancer Letters, v. 97, no. 2, p. 271.

Mueller, D.K., Hamilton, P.A., Helsel, D.R., Hitt, K.J., Ruddy, 

B.C., 1995, Nutrients in ground water and surface water of 

the United States—An analysis of data through 1992: U.S. 

Geological Survey Water Resources Investigations Report 

95–4031, 74 p. (Also available at http://pubs.er.usgs.gov/

publication/wri954031/.)

National Agricultural Statistics Service, 2012, Agricultural 

prices: National Agricultural Statistics Service Web site, 

accessed multiple times September 2010 through March 

2011 at http://usda.mannlib.cornell.edu/MannUsda/

viewDocumentInfo.do?documentID=1002.

National Research Council, 2008, Mississippi River water 

quality and the Clean Water Act—Progress, challenges, and 

opportunities: Washington, D.C., National Academies Press, 

252 p.

National Resources Conservation Service, 2010, Iowa NRCS 

programs: National Resources Conservation Service Web 

site, accessed on January 19, 2010, at http://www.ia.nrcs.

usda.gov/programs/.

Neitsch, S.L., Arnold, J.G., Kiniry, J.R., and Williams, J.R., 

2011, Soil and water assessment tool— Theoretical documentation, version 2009: Texas Water Resources Institute 

Technical Report 406, accessed January 1, 2012, at http://

swatmodel.tamu.edu/documentation/.

Nocedal, J., and Wright, S.J., 2006. Numerical Optimization, 

2nd ed.: Berlin, Germany, New York, N.Y., Springer-Verlag, 

686 p.

Nolan, B.T., and Hitt, K.J., 2006, Vulnerability of shallow 

groundwater and drinking-water wells to nitrate in the 

United States: Environmental Science and Technology, v. 

40, no. 24, p. 7834–7840.

Nolan, B.T., Hitt, K.J., and Ruddy, B.C., 2002, Probability of 

nitrate contamination of recently recharged ground waters 

in the conterminous United States: Environmental Science 

and Technology, v. 36, no. 10, p. 2138–2145.

Nolan, B.T., Ruddy, B.C., Hitt, K.J., Helsel, D.R., 1997, Risk 

of nitrate in groundwaters of the United States—A national 

perspective: Environmental Science and Technology, v. 31, 

p. 2229–2236.

Odeh, I., McBratney, A., and Chittleborough, D., 1994, Spatial prediction of soil properties from landform attributes 

derived from a digital elevation model: Geoderma, v. 63, 

no. 3–4, p. 197–214.

Odeh, I., McBratney, A., and Chittleborough, D., 1995, 

Further results on prediction of soil properties from terrain 

attributes—Heterotopic cokriging and regression-kriging: 

Geoderma, v. 67, no. 3–4, p. 215–226.

Office of Management and Budget, 2011, Discount rates for 

cost-effectiveness, lease purchase, and related analyses: 

Office of Management and Budget, Circular A094 Appendix 

C, accessed July 23, 2012, at http://www.whitehouse.gov/

omb/circulars_a094/a94_appx-c.

Owens, L.B., Edwards, W.M., and Shipitalo, M.J., 1994, Nitrate 

leaching through lysimeters in a corn-soybean rotation: Soil 

Science Society of America Journal, v. 59, no. 3, p. 902–907.

Peoples, M.B., Freney, J.R., Mosier, A.R., 1995, Minimizing 

gaseous losses of nitrogen, in Bacon, P.E., ed., Nitrogen 

Fertilization in the Environment: New York, N.Y., Marcel 

Dekker, p. 565–602.

Peterjohn, W.T., and Correll, D.L., 1984, Nutrient dynamics in 

and agricultural watershed—Observations on the role of a 

riparian forest: Ecology, v. 65, p. 1466–1475.

Prior, J.C., Boekhoff, J.L., Howes, M.R., Libra, R.D., VanDorpe, P.E., 2003, Iowa’s groundwater basics—A geologic 

guide to the occurrence, use and vulnerability of Iowa’s 

aquifers: Iowa Department of Natural Resources, Iowa 
52 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

Geological Survey Educational Series 6, 92 p.

Rabideau, A.J., Craig, J.R., Silavisesrith, W., Fredrick, K., 

Flewelling, D.M., Jankovic, I., Becker, M., W., Bandilla, 

K., Matott, L.S., 2007, Analytic-element modelling of 

supraregional groundwater flow—Concepts and tools for 

automated model configuration: Journal of Hydrologic 

Engineering, v. 12, no. 1, p. 83–96.

Ross, T.W., 1984, Uncovering regulators’ social welfare 

weights: The RAND Journal of Economics, v. 15, p. 

152–155.

Ruddy, B., Lorenz, D., and Mueller, D.K., 2006, County-level 

estimates of nutrient inputs to the land surface of the conterminous United States, 1982–2001: U.S. Geological Survey 

Scientific Investigations Report 2006–5012, September 24, 

2010, at http://pubs.usgs.gov/sir/2006/5012/.

Rupert, M.G., 1998, Probability of detecting atrazine/desethylatrazine and elevated concentrations of nitrate (NO2

+NO3-

N) in ground water in the Idaho part of the Upper Snake 

River Basin: U.S. Geological Survey Water-Resources 

Investigations Report 98–4203, 32 p.

Rodvang, S.J., and Simpkins, W.W., 2001, Agricultural contaminants in Quaternary aquitards—A review of occurrence 

and fate in North America: Hydrogeology Journal, v. 9,

 p. 44–59.

Schaap, B.D., 1999, Concentrations and possible sources of 

nitrate in water from the Silurian-Devonian aquifer, Cedar 

Falls, Iowa: U.S. Geological Survey Water-Resources 

Investigations Report 99–4106, 19 p., October 28, 2010, at 

http://pubs.usgs.gov/wri/1999/4106/.

Schlesinger, W.H., 1997, Biogeochemistry—An Analysis 

of Global Change, 2nd ed.: San Diego, Calif., Academic 

Press,. 588 p.

Schuh, W.M., Klinkebiel, D.L., Gardner, J.C., and Meyer, 

R.F., 1997, Tracer and nitrate movement to groundwater in 

the Northern Great Plains: Journal of Environmental Quality, v. 26, p. 1335–1347.

Silavisesrith, W., and Matott, L.S., 2005, ArcAEM—GISbased application for analytic element groundwater modeling; documentation and user’s guide, draft version 2.13: 

accessed August 17, 2011, at http://www.groundwater.

buffalo.edu/software/ArcAEM/ArcAEM.pdf.

Steinheimer, T.R., Scoggin, K.D., and Kramer, L.A., 1998, 

Agricultural chemical movement through a field-size watershed in Iowa—Subsurface hydrology and distribution of 

nitrate in groundwater: Environmental Science and Technology, v. 32, p. 1039–1047.

Strack, O.D.L., and Haitjema, H.M., 1981, Modeling double 

aquifer flow using a comprehensive potential and distributed singularities 1—Solution for homogeneous permeabilities: Water Resources Research, v. 17, no. 5, p. 1535–1549.

Taraba, J.L., Newton, L., Safley, L.M., Jr., Westerman, P.W., 

Hill, D.T., Ramsey, D.S., Nordstedt, R.A., and Hegg, R.O., 

1985, Research in animal water management in the Southeastern U. S., in Agricultural Waste Utilization and Management: Proceedings of the Fifth International Symposium 

of Agricultural Wastes, American Society of Agricultural 

Engineers, p. 257–275.

Tesoriero, A.J., and Voss, F.D., 1997, Predicting the probability of elevated nitrate concentrations in the Puget Sound 

Basin—Implication for aquifer susceptibility and vulnerability: Ground Water, v. 35, no. 6, p. 1029–1039.

U.S. Environmental Protection Agency, 1994, Nitrogen control: 

Lancaster, Penn.,Technomic Publishing Company, Inc., , 22 p.

van der Schans, M.L., Harter, T., Leijnse, A., Mathews, M.C., 

and Meyer, R.D., 2009, Characterizing sources of nitrate 

leaching from an irrigated dairy farm in Merced County, 

California: Journal of Contaminant Hydrology, v. 110, 

p. 9–21.

Varian, H., 1999, Intermediate microeconomics—A modern 

approach, 5th ed.: New York, N.Y., W.W. Norton & Company, 550 p.

Ward, M.H., deKok, T., Levallois, P., Brender, J., Gulis, G., 

Nolan, B.T., and VanDerslice, J., 2005, Drinking water 

nitrate and health- Recent findings and research needs: 

Environmental Health Perspectives, v. 115, p. 1607–1614.

Warner, K.L., and Arnold, T.L., 2010, Relations that affect 

the probability and prediction of nitrate concentration in 

private wells in the glacial aquifer system in the United 

States: U.S. Geological Survey Scientific Investigations 

Report 2010–5100, 73 p., available at http://pubs.usgs.gov/

sir/2010/5100/.

Webster, R., Oliver, M.A., 2007, Geostatistics for environmental scientists, 2nd ed.: Chichester, U.K., John Wiley & Sons, 

Ltd., 309 p., doi:10.1002/9780470517277.index.

Weed, D.A.J., and Kanwar, R.S., 1995, Nitrate and water present in and flowing from root-zone soil: Journal of Environmental Quality, v. 25, no. 4, p. 709–719.

Westerman, P.W., Safley L.M., Jr., Barker, J.C., and Chescheir, 

G.M., 1985, Available nutrients in livestock wastes, in Agri-
References Cited 53

cultural Waste Utilization and Management: Proceedings of 

the Fifth International Symposium of Agricultural Wastes, 

American Society of Agricultural Engineers, p. 295–308.

Weyer, P.J., Cerhan, J.R., Kross, B.C., Hallberg, G.R., Kantamneni, J., Breuer, G., Jones, M.P., Zheng, W., Lynch, C.F., 

2001, Municipal drinking water nitrate level and cancer 

risks in older women—The Iowa Women’s Health Study: 

Epidemiology, v. 11, no. 3, p. 327–338.

Wu, J., and Segerson, K., 1995, The impacts of policies and 

land characteristics on potential groundwater pollution in 

Wisconsin: American Journal of Agricultural Economics, v. 

77, no. 4, p. 1033–1047.

Yadav, S.N., 1997, Dynamic optimization of nitrogen use 

when groundwater contamination is internalized at the 

standard in the long run: American Journal of Agricultural 

Economics, v. 79, no. 3 p. 931–945.
54 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

Appendixes 1–3
Appendix 1—Integrated Assessment Approach’s Assumptions and Their Types 55

Assumption type Assumption

Economic for the producer Planting decisions are based on current and last season’s market prices. 

Profit maximization.

Decisions are made on an annual basis.

Marginal changes in production have no impact on market prices.

The difference between local and national prices is negligible.

Any modeled difference in sales of grain produced will be sold following the same pattern as the 

observed sales.

Economic for the region Partial equilibrium approximation of regulations, R, does not affect crop prices, P.

Market is competitive and operates efficiently.

Shifts in the market supply curve are small enough that we can ignore the change in equilibrium price.

Rational expectations are used, and thus similar flow of benefits from MRLI, value from crops, and 

MCL thresholds into the indefinite future are expected.

Risk of groundwater contamination is established by scientific analyses and is a given standard to be 

implemented for a region.

Yield for a HUC is estimated as an aerial percentage of a HUC and related to the particular HRUs in 

the HUC.

Optimization occurs within a given HUC between HRUs.

Nitrogen loading and leaching Does not include considerations of population and developed areas that could contribute sources of 

nitrogen from the use of leach fields, lawns, and sewer outfalls.

Does not account for atmospheric deposition of nitrogen sources, nor differences in geologic parent 

materials. In terms of the latter, geologic nitrate is known to persist in organic-rich shales, weather 

till, and fine-textured lacustrine sediments and is often associated with older groundwater and high 

chloride and other salts (Rodvang and Simpkins, 2001). 

Does not include many farm management activities that could influence the amount of nitrogen that 

could enter the hydrogeologic system, such as seed selection, weed and insect management, tillage, 

tile drainage, reduction of overland flow, and others.

Assumes the difference in nitrogenous fertilizer application between different corn crops’ end use (in 

other words, grain, silage, food) is negligible. 

Assumes that the agriculture sector is the primary driver of groundwater nitrate pollution.

Does not consider irrigation as it is minimally employed in this region (less than 1 percent).

In a given year, leachate is a weighted average of the HRUs in a particular HUC. 

Groundwater modeling Assumes two-dimensional, uniform ambient flow field of groundwater, and not three-dimensional flow.

ArcAEM and Split computer programs characterize the hydrodynamics at the water table, not at the 

ground surface.

ArcAEM and Split computer programs assume no interaction among wells, their site characteristics, 

and individual operation as may occur in a dense well field network (Ceric and Haitjema, 2005).

Assumes that the regional approach will not change the net volume (in other words, balance of extraction and recharge) of water in the aquifer systems.

Does not address post-extraction, technological treatments, such as ion exchange, reverse osmosis, or 

electrodialysis, as they are remediation techniques for nitrate.

Appendix 1—Integrated Assessment Approach’s Assumptions and Their Types

[MRLI, moderate-resolution land imagery; MCL, maximum contamination levels; HUC, hydrologic unit code; HRU, hydrologic response unit]
56 An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality

Appendix 2—Nitrogen Cycle

The nitrogen cycle is the cycle in which organic nitrogen is mineralized, plants uptake the mineralized nitrogen, and 

eventually the nitrogen is returned to the soil in residues. Nitrate is highly mobile in the soil and subsurface. Note that there are 

a number of other sources and processes at work, where soil organisms are the driving force for reactions in the cycle. The five 

primary paths of nitrate in the nitrogen cycle are (1) synthesis or immobilization by microorganisms, (2) removal through plant 

uptake by way of roots, (3) leaching into the groundwater system, (4) volatilization into the atmosphere from the land surface, 

and (5) re-entry into the atmosphere from the subsurface through denitrification. 

Without considering the usage of nitrification inhibitors, soil conditions, timing of application, and other surface and 

residue management techniques, commercial fertilizers have been shown to lose from ~0 percent to as much as 50 percent of 

their nitrogen through volatilization of ammonia (Peoples and others, 1995), and—depending on air flow rate, temperature, 

soil cation exchange capacity, pH and method of application—applied manure can volatilize 21 to 27 percent (Westerman and 

others, 1985). 

Denitrification is often considered the last step of the nitrogen cycle, as it is the major process that returns nitrogen gas (N2) 

to the atmosphere (Schlesinger, 1997). The general, four distinct steps of facultative anaerobes completing denitrification are:

 NOˉ

→NOˉ →NOˉ →N2O→N2 3 2 . (23)

Depending on the electron donors, degree of oxygen present, substrates, and other site conditions, more specific characterization of biological denitrification can be described by either heterotrophs that gain energy from carbon oxidation, autotrophs 

that gain energy from sulfide oxidation, or iron oxidation; all result in the reduction of nitrate (NO3

-

) and the associated byproducts of N2

 and others such as sulfate, iron in two valance states, water, and bicarbonate. In addition to the presence of electron 

donors, factors known to influence denitrification rates include low topographic positions, shallow water tables and high moisture content, acidity17, more anaerobic sites, unfractured geologic zones, and clay-rich sediments (Rovang and Simpkins, 2001). 

Under conditions of snowmelt and rainfall runoff, wetlands have been shown to allow the infiltration of water and transport it 

laterally outside of the reducing, anoxic environment more often than transporting it vertically to the degree of only 1 percent of 

precipitation recharged to zones of anoxic till (Keller and others, 1991; Schuh and others, 1997).

Because of solubility and mobility of nitrates in water, once nitrates enter the saturated zone below the water table, the ions are 

relatively persistent and dispersive. However, if highly anaerobic conditions and an electron donor exist, nitrate concentrations can 

be reduced by denitrification in groundwater before drinking water is extracted from wells (Canter, 1997). Measuring denitrification 

is difficult because of its high spatial variability; however, soil texture has been shown to be correlated with denitrification and is 

considered one of the more reasonable proxies for regional estimates of denitrification (Groffmand and Tiedje, 1989). Considering 

the importance of landscape position, riparian buffers between agricultural fields and stream courses have been shown to be productive denitrifying zones (Hanson and others, 1994; Peterjohn and Correll, 1984). Although all of these factors and drivers of the 

nitrogen cycle are not necessarily included in our model, it is important to note their potential influence in our results.

17Note that high acidity—which can be increased by fertilizers or manures laden with ammonia (NH3

)—will result in reduced denitrification and the production of nitrous oxide (N2

O), which can deleterious influence the atmosphere as a powerful greenhouse gas, a stratosphere ozone depletor, and contribute to the 

formation of acid rain (Brady and Weil, 2002; Schlesinger, 1997).
Appendix 3—Kriging of Hydraulic Conductivity 57

Appendix 3—Kriging of Hydraulic Conductivity

Kriging is a geospatial technique for interpolating values for a variable in an unobserved location using neighboring point 

observations (Webster and Oliver, 2007). The kriging method was developed by Matheron (1963, 1967). This method was 

introduced to study complex hydroscience systems by Delhomme (1978). Regression kriging (RK) was introduced by Odeh and 

others (1994, 1995). In addition to the spatial autocorrelation used in the ordinary kriging method, RK includes both the 

environmental autocorrelation and spatial autocorrelation to interpolate variables for unobserved locations. The RK involves 

kriging the variable using a regression estimator with environmental (hydrogeological) variables that have a neighborhood trend. 

The prediction uncertainties are then included by incorporating the regression errors into the kriged variables. 

Hydraulic conductivity (HC) depends on hydrogeological properties of an aquifer18, suggesting environmental and spatial autocorrelation could matter. Therefore, an attempt was made to krige HC surfaces using regression kriging. The HC was 

regressed using combinations of transformed variables and the best estimation of a log-log regression equation for reducing wide 

ranging quantities to smaller ranges. For the RK technique, the regression equation for the all aquifers in the study region is:

 InHC =β1 + β2 InEB+ β3 InTh + β4 InAQ + ε , (24)

where β is the estimate of the coefficient, EB is the base elevation of the aquifer, Th is the thickness of the aquifer, AQ the type 

of aquifer, and ε is the regression remainder. Table 13 provides the variables and their parameter estimates. The HC regression 

layer was created using the ArcGIS spatial analyst tool (fig. 24). The residuals are kriged (fig. 25) and added to the regressionestimation-kriged layer to obtain the final kriged layer (fig. 26) for the variable HC. Thus, a spatially interpolated HC surface 

was used to derive HC values for the wells without HC information for the purposes of delineating capture zones (CZs) for a 

larger number of wells.

18 During the course of the analysis, it was noted that geologic faults bisected the Silurian aquifer. Their presence influenced the approach and results of ordinary kriging techniques.

Table 13. Log-log regression equation for kriging of 

hydraulic conductivity in Devonian and Silurian aquifers in 

the northeastern Iowa study region.

Variables Estimated parameters1

Base elevation 1.93 (0.12)***

Thickness -0.317 (0.06)***

Aquifer type (dummy) 1.92

Constant -9.74

R2 0.8088

1

Numbers in parentheses are standard errors—***, significant at 

99.5-percent confidence level.
00 25 100 KILOMETERS 25 50 

50 75 75 Hydraulic conductivity (ft/day) High: 76.994 Low: 0.0184956 IOWA Study region Figure 24. Image of regression kriging without residuals, using hydraulic conductivity for the 35-county northeastern Iowa study region a

day, feet per day.
p

Hydraulic conductivity (ft/day)

High: 16.6156Low: 0.0459815

IOWA

Study region

0

0 25 100 KILOMETERS

25 50 

50 75

75

Figure 25. Image of final regression kriged layer, using hydraulic conductivity for the 35-county northeastern Iowa study region as an exaper day.
p IOWA Study region Hydraulic conductivity (ft/day) High: 387.969 Low: 0.118334 00 25 100 KILOMETERS 25 50 

50 75 75 Figure 26. Image of final regression kriged layer, using hydraulic conductivity for the 35-county northeastern Iowa study region as an exa

feet per day.
Menlo Park Publishing Service Center, California

Manuscript approved for publication October 1, 2012

Edited by James W. Hendley II

Layout and design by Judy Weathers and Vivian Nguyen 
Forney and others—An Economic Value of Remote-Sensing Information—Application to Agricultural Production and Maintaining Groundwater Quality—Professional Paper 1796

In this study, the U.S. Geological Survey demonstrated the value of information (VOI) 

from remote sensing by using improved agricultural production and nitrate-leaching 

estimates enabled by Earth-observing satellites. As compared to the situation without 

satellite information, these estimates can provide additional benefits to society through 

more optimal allocation of production without sacrificing public health and groundwater quality in an agricultural region. In Iowa, groundwater pumped from aquifers, and 

often stored in water towers, is used as the source for drinking water by as many as 80 

percent of residents. For this application in northeastern Iowa, the marginal benefit of 

the VOI (in 2010 dollars) is estimated to be $858 million ± $197 million per year, which corresponds to a net present value of $38.1 billion ± $8.8 billion for that flow of benefits into 

the foreseeable future.

Using cropland information derived from Landsat and similar Earthobserving satellites, U.S. Geological Survey scientists modeled the relations among land uses, agricultural production, and dynamic nitrate (NO3

-

) 

contamination of aquifers in the northeastern Iowa study region. Corn is a 

primary crop of Iowa and is used in biofuels such as ethanol. Iowa produces about 30 percent of the Nation’s ethanol. Agricultural production often 

includes heavy application of nitrogenous fertilizer that can affect groundwater quality.

Landsat image within a 35-county region in Iowa studied by the U.S. Geological Survey. 

Acquired August 31, 2007, at the peak of the midwestern growing season in the United 

States, this image shows healthy cropland (corn, soybeans, and other crops) in greens, 

cleared and developed land in pinks, and water bodies (ponds and the Iowa River) in 

dark tones. Each pixel in the image covers an area of 30 by 30 meters of the ground.

Does remote-sensing information such as that from Landsat provide economic 

benefits to society, and can this value be estimated? Landsats 5 and 7 currently 

orbit the Earth at an altitude of 705 kilometers and return to observe the same 

point above the Earth every 16 days. The Landsat Data Continuity Mission (see 

front cover), the next satellite in the Landsat program, is scheduled to launch in 

February 2013.