US NATIONAL CROPLAND SOIL MOISTURE MONITORING USING SMAP

Zhengwei Yang1, Rick Mueller1, Wade Crow2

1National Agricultural Statistics Service, USDA, Fairfax, VA 22030, USA

2Agricultural Research Service, Hydrology and Remote Sensing Lab, USDA, Beltsville, MD 20705

ABSTRACT

This paper investigates at the pre-launch stage the feasibility 

of using NASA SMAP mission results for US national 

operational crop soil moisture monitoring. The purpose of 

using remote sensed SMAP data for crop soil moisture 

monitoring is to eliminate data collection subjectivity, 

reduce cost, increase cropland soil moisture monitoring data 

consistency, and operational efficiency. In this paper, the 

SMAP simulated data product time series, such as 

L2_SM_A, L3_SM_A/P, L4_SM, L1C-S0_HiRes are first 

evaluated for their suitability for NASS operational cropland 

soil moisture monitoring by comparing SMAP results with 

the NASS’ survey based weekly soil moisture observation 

data for their consistency and robustness. The preliminary 

results illustrate that SMAP products have the potential for 

NASS operational use at least for county level soil moisture 

statistics. This paper also explores a technical route to build 

a Web-service based interactive soil moisture monitoring 

system for map visualization, dissemination, and analysis

based on SMAP results.

Index Terms— SMAP, soil moisture monitoring, crop 

condition monitoring, VegScape

1. INTRODUCTION

Crop condition information is critical to decision making in 

both public and private sectors that concern agricultural 

policy, production, food security, and food prices. Crop 

conditions change quickly due to various growing condition 

changes, such as temperature, soil moisture, fertilization, or 

disease, etc. Therefore, timely, frequent, fully geospatial 

covered and sufficiently high resolution observation 

throughout the season is necessary to monitor crop 

conditions. The National Agriculture Statistics Services 

(NASS) of the United States Department of Agricultural 

(USDA) publishes weekly crop progress and condition 

report; soil moisture condition is part of crop condition 

report. NASS currently monitors crop soil moisture 

condition by weekly field observations for counties in 45 

states. State-level estimates are based on subjective field 

observations not on objective measurements. The surveyed 

topsoil and subsoil moisture are published weekly during the 

growing season. It is useful in reflecting some major events 

in US crop production such as massive flood, drought and 

long last dry cold winter, which result in drops in corn and 

soybean acreages and in yields. However, the soil moisture 

reports are subjective and qualitative assessment. Field 

observations are from volunteers. They are not precise, not 

consistent, unreliable and inefficient, and not fully 

geospatially covered as shown in Fig. 1. The survey 

operation is a burden to farmers and NASS field officers. Its 

operational cost is very expensive. To improve NASS 

cropland soil moisture monitoring, this paper proposes to 

use the remote sensed soil moisture data for US national 

cropland soil moisture monitoring. Specifically, the remote 

sensing results from NASA Soil Moisture Active and 

Passive (SMAP) mission [1] is proposed to be adapted. This 

paper will (1) study at the pre-launch stage the feasibility of 

using SMAP mission results to support US national crop 

condition monitoring and other NASS operational data 

needs, such as crop yield modeling needs; (2) explore a 

technical route to build a remote sensing based soil moisture 

monitoring system prototype based on the feasibility of 

application of SMAP data products.

2. DATA AND STUDY AREA

The SMAP mission is currently planned for launch in late 

2014. This paper study early adapting of SMAP products at 

pre-launch phase during which only simulated SMAP data 

products (no actual SMAP observational data) are available. 

The SMAP applications to be prototyped are to be 

completed based on the pre-launch simulated data. For 

better crop condition monitoring, the soil moisture data 

resolution should match the field size. Therefore, this 

application system utilizes high resolution SMAP data 

products, such as L3SM_A/P, L4_SM, or L1C-S0_HiRes. 

To derive weekly high spatial resolution soil moisture data 

products, other remote sensing data such as MODIS 

products are to be fused or assimilated with SMAP data. For 

cropland-specific monitoring, a crop mask derived from 

NASS Cropland Data Layer [2][3][4]. It is idea for USDA 

NASS to monitor agriculture of every county in all 48 

conterminous states in the U.S. Therefore the study area will 

cover all conterminous states in the U.S.
Table 1. Sample NASS survey based soil moisture report

Figure 1. NASS Survey Based Soil Moisture (Week of May 4, 

2003).

3. METHODOLOGY

The goal for the US cropland soil moisture monitoring 

application system based on SMAP data is to provide 

objective and quantitative soil moisture measurement and 

assessment. It requires US national geospatial coverage with 

high resolution of at least sub-county resolution monitoring. 

To down-scale the SMAP data, data fusion and assimilation 

are performed. The monitoring system will have the 

capabilities of automatic data collection, processing and 

publishing, and online visualization, analysis and 

dissemination. The calibrated and validated SMAP soil 

moisture products as well as derived products will be 

published and disseminated to end users via web service 

based application system for NASS operations.

3.1. Validation and Calibration

The SMAP data products and their derived down-scale 

products have to be validated and calibrated before being 

used for cropland soil moisture monitoring. This proposed 

two validation methods. The first method is to validate the 

SMAP data and their derivative products using existing 

NASS soil moisture survey data. The sample NASS soil 

moisture survey data are shown as in Table 1. The data 

categorized in 4 qualitative levels. For a given area, the 

surveys provide percentages for each level. As shown in Fig. 

1, the CONUS county level average soil moisture map was

derived by weighting the percentages of every soil moisture 

level. The purpose of this manipulation is to simplify the 

comparison between the survey data and SMAP results. As 

shown in Fig. 2, SMAP L3-AP soil moisture data are 

presented in numeric number while NASS soil moisture 

survey data are presented in qualitative categories. 

Therefore, it is necessary to quantify the NASS soil moisture 

survey data categories in SMAP soil moisture data scale. 

This quantification will be performed using Delphi method 

[5][6]. The quantification will result in a quantitative range 

of each NASS surveyed soil moisture level. The cropland 

soil moisture ground truth data is critical for SMAP data to 

be validated and calibrated for cropland soil moisture 

monitoring. The second method is based on the controlled 

ground truth measurement with soil moisture sensors. A 

large scale sensor network is proposed to continuously 

collect ground truth data crop land. With SMAP data and 

ground truth measurement data at hand, an empirical look-up

table or mapping equation will be derived.

Figure 2. SMAP L3-AP soil moisture(left) vs. NASS Surveyed 

topsoil moisture(right) – different metrics.
Figure 3. SMAP soil moisture assessment using NDVI Ratio to 

Historical Median by their correlation.

3.2. SMAP Data Downscaling

The spatial resolutions of SMAP products usually lower than

the most crop field sizes. It is necessary to downscale the 

SMAP data to the field level size so that crop specific soil 

moisture monitoring using SMAP became possible. Piles, et 

al. proposed an approach to improve the spatial resolution of 

Soil Moisture and Ocean Salinity (SMOS) soil moisture 

estimates with the use of higher resolution visible/infrared

(VIS/IR) satellite data [7]. The algorithm is based on the socalled “universal triangle” concept that relates VIS/IR

parameters, such as the Normalized Difference Vegetation 

Index (NDVI), and Land Surface Temperature (Ts), to the 

soil moisture status. This method combines the accuracy of 

SMOS observations with the high spatial resolution of 

VIS/IR satellite data into accurate soil moisture estimates at 

high spatial resolution. They reported that results were fairly 

agreed with ground-based soil moisture measurements, and 

that the soil moisture variability was effectively captured at 

10 and 1 km spatial scales without a significant degradation 

of the root mean square error. The specification of the 

SMAP is similar to that of SMOS. Therefore, it is proposed 

to apply this downscaling method to SMAP data to derive 

high resolution SMAP derivative products. 

3.3. SMAP Data Assessment

The calibrated SMAP soil moisture products as well as 

derived products to be used for crop soil moisture 

monitoring need to be to be evaluated to see if these data are 

consistent with the existing soil moisture data from SMOS

or evapotranspiration data products derived from other 

sensors, such as LanSat [8]. Moreover, the correlation 

between the SMAP soil moisture data and crop vegetation 

condition data derived from MODIS [9] also needs to be 

examined since the crop condition is strongly correlated with 

water supply, which is indicated by soil moisture.

4. SYSTEM DESIGN

The calibrated and validated SMAP soil moisture products 

as well as derived products need to be published, visualized, 

accessed, analyzed, and disseminated to end users via online 

application. The online geospatial application system will 

reuse the existing crop vegetation condition monitoring 

system-VegScape [10]. The application system has a 

service-oriented architecture (SOA) with which the 

MapServer [11] is configured as the server of WCS, WFS, 

and WMS to support retrieval and rendering of soil moisture 

data. The W3C Web service is adopted in implementing the 

Web geoprocessing service and Web processing services for 

SMAP data reformatting, reprojection, downscaling and 

quantification while web map services is implemented for 

data visualization, publishing and dissemination. In the 

implementation, open source JavaScript libraries and 

OpenLayers [12], are used to develop a rich Internet 

application which can be accessed through most common 

browsers, such as Internet Explorer, Firefox, Safari, Opera, 

and Chrome, without installing any additional software or 

plug-in. The VegScape SOA architecture is scalable. A 

SMOP component can be added to the VegScape framework 

directly as shown in Fig. 4. Moreover, most of the VegScape 

browser based client user interface components including

data layers, legends, product selection, overview window, 

map window, and a toolbar can be reused. Only data layers, 

legends, product selection components need to be modified 

to include SMAP data products. The new additions will

include SMAP data layers, map legends, and product

selection options. 

5. CONCLUSION

This paper proposes to use SMAP data products for US 

national cropland soil moisture monitoring. It is expected 

that using SMAP data in cropland soil moisture monitoring 

will be consistent, reliable, efficient and low cost. It will 

substantially reduce the survey burden. The SMAP data 

products provide objective and quantitative assessment and 

fully geospatial coverage. The preliminary study 

demonstrates that it is possible to use SMAP L3 AP soil 

moisture product for sub-county topsoil survey monitoring. 

This paper also presents the application system design and 

technical route to build a soil moisture monitoring system 

prototype. The future research includes (i) find the 

vegetation impact on SMAP data results since; (ii) assess 

errors caused by low 9km resolution; (iii) establish the 

mapping relation between different SMAP’s soil moisture 

measurement and the qualitative assessment of NASS soil 

moisture survey result once the real SMAP data are 

available.
NDVI: calculating, mosaicking, & clipping

NDVI2010.04.30

MODIS Surface Reflectance (MOD09GQ) Resolution: 250m Bands: Band 1(620-670nm) and 2(841-876nm)

WCS WMS

WCS Administrative boundaries (Geographic coordinates, shapefile format) WFS

WPS

NDVI daily

(Band2-Band1)/ (Band2+Band1) (Band2-Band1)/ (Band2+Band1) NDVI NDVI

NDVI (daily, weekly, or biweekly) NDVI (daily, weekly & biweekly)

NDVI 2010.05.10 NDV 2010.04.29

+...+

...... NDVI weekly & biweekly: maximum value composite (MVC)

Mosaicking & clipping

MVC

NDVI weekly composite NDVI weekly composite NDVI weekly composite

NDVI biweekly composite NDVI biweekly composite NDVI biweekly composite

MVCI weekly & biweekly (current-mean)/mean

(current-mean)/ mean

MVCI weekly MVCI weekly MVCI weekly

MVCI biweekly MVCI biweekly MVCI biweekly (current-mean)/ mean NDVI weekly NDVI biweekly MVCI weekly MVCI biweekly

MVCI (weekly or biweekly) MVCI (weekly or biweekly)

Legend

Process Data store Interface Data

Figure 4. SMAP data processing component added to scalable VegScape framework

6. REFERENCES

[1] Entekhabi, D. et al. “The Soil Moisture Active Passive (SMAP) 

Mission.” Proceedings of the IEEE 98.5: pp.704-716, 2010. 

©Copyright 2010 IEEE.

[2] Boryan, C., Yang, Z., Mueller, R., and Craig, M., “Monitoring 

US Agriculture: The US Department of Agriculture, National 

Agricultural Statistics Service Cropland Data Layer Program,” 

Geocarto International, 26, (5): 341-358. 

[3] Han, W., Yang, Z., Di, L., Mueller, R., “CropScape: A Web

service based application for exploring and disseminating US 

conterminous geospatial cropland data products for decision 

support.” Computer and Electronics in Agriculture, Vol. 84, June, 

2012, pp. 111–123.

[4] C. Boryan, Z. Yang, L. Di, “Deriving 2011 Cultivated Land 

Cover Data Sets Using USDA National Agricultural Statistics 

Service Historic Cropland Data Layers”, Proc. of 2012 IEEE 

International Geoscience & Remote Sensing Symposium, Munich, 

Germany, July 2012.

[5] Normal C. Dalkey, “The Delphi Method: An Experimental 

Study of Groug Opinion,” Report RM-5888-PR, June 1969, 

Rand Corp.

[6]Harold A. Linstone, Murray Turoff, The Delphi Method: 

Techniques and Applications, Reading, Mass.: Addison-Wesley, 

1975, ISBN 978-0-201-04294-8.

[7] Piles, M. et al., “Downscaling SMOS-Derived Soil Moisture 

Using MODIS Visible/Infrared Data,” IEEE Transactions on

Geoscience and Remote Sensing, Vol. 49 , Issue: 9, 

[8] Martha C. Anderson, Richard G. Allen, Anthony Morse, 

William P. Kustas, “Use of Landsat thermal imagery in monitoring 

evapotranspiration and managing water resources,” Remote 

Sensing of Environment, Volume 122, July 2012, pp. 50–6.

[9] ] Yang, Z., Di, L., Yu, G., Chen, Z., “Vegetation Condition 

Indices for Crop Vegetation Condition Monitoring,” Proc. of IEEE 

IGARSS 2011, Vancouver, Canada, July 2011.

[10] Z. Yang, G. Yu, L. Di, B. Zhang, W. Han, R. Mueller, “Web 

Service-based Vegetation Condition Monitoring System –

VegScape”, Proc. of IEEE IGARSS 2013, Melbourne, Australia, 

August 2013.

[11] Kropla, B., 2005. Beginning MapServer: Open Source GIS 

Development. Apress, Berkley, CA, 448pp.

[12] OpenLayers, 2011: Free maps for the Web, OpenLayers. 

http://www.openlayers.org/, [accessed 8 Januray, 2013].

SMAP Data Products

L3SM_A/P, L4_SM, 

or L1C-S0_HiRes

WCS

Weekly Soil Moisture

WCS, WMS

End weekly soil moisture data

SMAP Converted

Preprocessing,

reformatting,

reprojecting,

mosaicking,

filtering,

downscaling,

.....