The Influences of Land Use and 

Land Cover on Climate; An Analysis of the 

Washington-Baltimore Area That Couples 

Remote Sensing with Numerical Simulation

GEOLOGICAL SURVEY PROFESSIONAL PAPER 1099-A
The Influences of Land Use and 

Land Cover on Climate: An Analysis of the 

Washington-Baltimore Area That Couples 

Remote Sensing with Numerical Simulation

By ROBERT W. PEASE, CAROL B. JENNER, and JOHN E. LEWIS, JR

THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

GEOLOGICAL SURVEY PROFESSIONAL PAPER 1099-A

An example of environmental analysis using 

land use and land cover information

UNITED STATES GOVERNMENT PRINTING OFFICE, WASHINGTON : 1980
UNITED STATES DEPARTMENT OF THE INTERIOR 

CECIL D. ANDRUS, Secretary

GEOLOGICAL SURVEY

H. William Menard, Director

The use of manufacturer and brand names is for identification purposes 

only and does not constitute endorsement by the U.S. Geological Survey.

Library of Congress Cataloging in Publication Data

Pease, Robert W

The influences of land use and land cover on climate.

(Geological Survey professional paper ; 1099-A

Bibliography: p.

Supt. of Docs, no.: I 19.16:1099-A

1. Washington, D. C., region Climate.

2. Baltimore region, Md. Climate. 3. Land use Washington, D. C., region. 4. Land use Maryland Baltimore region. I. Jenner, Carol B., joint author. II. Lewis, John E., 1937- joint author. III. Title. IV. 

Series: United States Geological Survey. Professional paper ; 1099-A.

QC984.D6P4 551.6'9'752 79-607068

For sale by the Superintendent of Documents, U.S. Government Printing Office

Washington, D.C. 20402
FOREWORD

The publication of the 1099-series of USGS Professional Papers represents the foundation of a new research area in climatology. This new 

field of investigation, termed "Land Use Climatology," promises to extend 

our understanding of earth-atmosphere interactions, especially as influenced by the surface energy budget. The papers in this series are an outgrowth of the Geological Survey's Central Atlantic Regional Ecological 

Test Site (CARETS) project, undertaken in coordination with, and with 

funding support from, the National Aeronautics and Space Administration.

The thrust behind the climate research in the CARETS project was 

threefold: (1) to define the relations between land use and land cover 

and several important climatic factors relations that must be understood 

in order to evaluate accurately the climate effects of land use and land 

cover changes; (2) to demonstrate that the surface distributions of energy 

balance phenomena, as influenced by land use and land cover, could be 

mapped and analyzed from remotely sensed data; and (3) to demonstrate 

and verify the validity of numerical simulation modeling as a method for 

analyzing how various land use and climatic parameters are related.

This first paper, 1099-A, by Robert W. Pease, Carol B. Jenner, and 

John E. Lewis, Jr., encompasses all three objectives. It examines the 

nature and causes of relations between land use and land cover patterns 

and energy exchange phenomena using both remotely sensed data and 

simulation modeling. The second, 1099-B, by Wallace E. Reed and John E. 

Lewis, Jr., looks more closely at the first objective and describes how land 

use and land cover data were successfully used, in combination with air 

quality modeling, to estimate sulfur dioxide (S02 ) and particulate emissions, diffusion, and impact patterns. The third, 1099-C, by Francis W. 

Nicholas and John E. Lewis, Jr., concentrates on the relationship between 

land use and land cover and aerodynamic roughness, an essential input 

to climate simulation modeling. This study carefully describes the influence of various land use types on the turbulent transfer of energy to the 

atmosphere. The fourth study, 1099-D, by Carol B. Jenner and Robert W. 

Pease, compares two techniques for analyzing relationships between land 

use and surface radiation as measured by remotely sensed data. One is 

based on computer analysis by grid cells, the other is based on manual interpretation by polygons. Finally, the fifth, 1099-E, by Gordon M. Greene, 

examines the influences of several urban surface parameters, as determined by land use, on the urban thermal regime. This analysis is based 

on a test of the sensitivity of model-predicted surface temperatures to 

simulated changes in the urban surface parameters.

Although these studies are contributions to basic research, they also 

offer great potential in applications-oriented research. The techniques developed and tested in this professional paper series should prove to be an 

asset in the National Climate Program's analysis of relations between climate variability and land and water resources.

f\-

JAMES R. ANDERSON 

Chief Geographer

in
CONTENTS

Abstract ____________________________

Introduction ______________________

Purpose and scope _________________

Acknowledgments ____________________

Antecedent Studies ___________________

Radiometric measurement by remote means __

Numerical simulation in land use climatology

Land use climatology ________________________

Effect of urban areas ____________________

Methods of observation _______________

Mapping of surface-energy balance phenomena from 

satellite-acquired data ___________________

The S-192 multispectral scanner as a data source 

Constructing the map of surface radiation 

temperatures ___________________

The gray-window atmospheric effect ___ 

Calibration using characteristics of the

atmosphere _________________

The need for a data-correction factor _____

Constructing the maps of surface albedos and net 

radiation _______________________

Page

Al 

1 

1 

3 

3 

3

5

6

6

6

11

12

14

20

21

Page

Mapping by numerical simulation ———————————— A23

Surface energy budget models — —— ——————— 23

The Outcalt clear-sky model ____———————— 23

Net radiation _______——— —— __————— 24

Sensible heat and latent energy fluxes ———— 25

Soil heat flux ________——————————— 27

General flow of the model calculations ————— 27

Two-dimensional simulation and mapping ———— 27

Land use and land cover classification and

sources of information ________________ 28

Land use and land cover data collection —— 29 

Surface characteristics associated with land

use and land cover types __________ — __ 31

Simulation of surface temperature distribution 31 

A comparison of simulated and observed

surface temperature patterns ___________ 31

Appraisal _________________________—— 32

Energy balance maps from remotely sensed

imagery ________________—_ — _ — ___ — _ 35

Energy budget simulation ________—————— 36

The coupled experiment _____________ — _ — ____ 37

Summary ______________ —— ____—————— 37

References __________________________ 38

ILLUSTRATIONS

FIGURE 1. Map showing the parts of the

Washington-Baltimore area imaged 

and simulated __________________

2. Map showing distribution of surface 

radiation temperatures in the 

Washington-Baltimore area, 10:05 

a.m. EDT, August 5, 1973 __________

3. Map showing distribution of surface

albedos, Washington-Baltimore area, 

10:05 a.m. EDT, August 5, 1973 ____

4. Map showing the distribution of surface 

net radiation, Washington-Baltimore 

area, 10:05 a.m. EDT, August 5, 1973

5. Graph showing the frequency distribution 

of pulse code modulation (PCM) counts 

from scientific data output channel 21 

of the S-192 scanner.________

6. Graph showing Rawinsonde atmospheric 

profile, 7:15 a.m. EDT, August 5, 1973, 

Dulles International Airport, 

Washington, D.C. ___________

7. S-192 scanner image of the mapped area _

Page

A2

10

14

15

16

Page

FIGURE 8. Graph showing extinction coefficients (7) 

at a wavelength of 10.591 /*m, for 

various visibility ranges _________ A18

9. Diagram of absorption spectrum of

peroxyacetyl nitrate ____________ 19

10. Graph showing diurnal variation in

substrate temperatures produced by

the Outcalt clear-sky model _______ 27

11. Flow chart for the energy budget simulation model ___________-___-__ 28

12. Map showing land use and land cover in

the Baltimore area _______—____ 30

13. Map of simulated surface radiation temperatures in the Baltimore area, 

10:05 a.m. EDT, August 5, 1973 __ 33

14. Map of observed surface radiation temperatures in the Baltimore study 

area, constructed as a line-printed 

gray map for comparison with 

simulated temperature map _____ 34

15. Graph showing the dust factor as a

function of meteorological visibility _ 36
VI CONTENTS

TABLES

Page

TABLE 1. S-192 detector band coverage _______ All

2. Gray-window analysis of the atmospheric 

profile, including effects of water 

vapor only ____________________' 17

3. Gray-window analysis of the atmospheric 

profile, including effects of water 

vapor, aerosols, and gaseous pollutants 19

4. Procedure for converting pulse code

modulation (PCM) counts to temperature in degrees Celsius (T° C) __ 20

5. Procedure for constructing an albedo map

of the Washington-Baltimore area ___ 22

Page

TABLE 6. Input boundary conditions for the Outcalt

clear-sky model ______________ — _ A28

7. Land use and land cover classification

system for use with remotely sensed

data ____________________ 29

8. Values of surface characteristics used as

inputs for each land use and land cover 

category in the simulation model _ — _ 31

9. Meteorological conditions over Baltimore,

Md., 10:05 a.m. EDT, August 5, 1973__ 32 

10. Simulated surface temperatures of each

land use and land cover type, Baltimore 

area, 10:05 a.m. EDT, August 5, 1973 32

SYMBOLS USED IN THIS TEXT

Symbol

a

Cti

b

C

Ci

C2

d

Definition

k 

m

n 

P

Pa— Pi

r' 

r 

t 

u

Albedo.

Empirical constant of 0.55.

Empirical constant of 0.056.

Mass absorption coefficient.

First radiation constant in the Planck

equation. 

Second radiation constant in the Planck

equation. 

Dust and haze particle concentration, in

particles per cubic centimeter. 

Bulk diffusivity. 

Thermal diffusivity. 

Natural log base. 

Frequency of land use type k within the

cell i, j. 

Acceleration of gravity, in centimeters per

second squared. 

Hour angle. 

Obstacle height. 

Slope of surface, in degrees above the

horizon.

Von Karman constant. 

Optical air mass. 

Mean mixing ratio of atmosphere layer, in

grams of water per kilogram of dry air. 

Number of sample points in the grid cell. 

Atmospheric pressure, in millibars. 

Pressure depth of atmosphere layer, in

millibars.

Diffuse incoming solar radiation. 

Diffuse radiation received by a shadowed

wall.

Specific humidity. 

Diffuse solar radiation downscattered as

part of beam attenuation. 

Flux of momentum near surface. 

Instantaneous Earth-Sun distance. 

Earth-Sun distance when r=l. 

Mean Earth-Sun distance. 

Time, in seconds. 

Windspeed.

Symbol 

U*

w

Definition

W m

z

Zd

z,

ZG

Z0 

Zi

Z2 

Z3 

A0

A,

B

C

CP

C,

D_

DP°C

Ebb (T°K)

El

E^

F

F'

G

Go

H

HQ

I

/o

L 

K 

K*

Friction velocity.

Water vapor, in precipitable centimeters

(grams per square centimeter). 

Precipitable water in the atmospheric

column, in millimeters. 

Height above surface. 

Atmospheric damping depth. 

Depth in the soil. 

Soil damping depth. 

Roughness length. 

Soil depth 1. 

Soil depth 2. 

Soil depth 3.

A constant equal to 1.3114 ex-4. 

A constant equal to 4.7650 ex-6. 

Backscattered diffuse solar radiation. 

Pulse code modulation (PCM) count. 

Specific heat of air at constant pressure. 

Soil volumetric heat capacity. 

Spacing of obstacles. 

Mean dew point of air layer, in degrees

Celsius. 

Blackbody equivalent of air column_radiance

for mean column temperature T in

degrees Kelvin.

Energy emitted by the surface. 

Energy absorbed by the surface. 

View factor (Outcalt, 1971). 

View factor (Riefsnyder and Lull, 1965). 

Flux of heat entering the soil. 

Downward longwave radiation, in watts per

square meter. 

Kinetic or sensible heat fhix into the

atmosphere.

Mean height of objects above the surface. 

Time increment. 

Radiant signal emitted by the Earth's

surface.

Radiant energy received aloft. 

Thermal diffusivity value. 

Turbulent diffusivity for heat.
CONTENTS VII

Symbol Definition

Km Eddy diffusivity for momentum.

Kv> Turbulent diffusivity for water vapor.

L Latent heat of vaporization.

LE Latent energy used in evapotranspiration.

Pv Vapor pressure, in millibars.

Q Direct solar radiation received at the

surface, in watts per square meter. 

Q+<7 Incoming solar radiation, in watts per

square meter. 

Q, Solar radiation received at any moment

upon an imaginary plane parallel with

the Earth's surface and at the outer

edge of the atmosphere, per unit area. 

Qta Solar constant, in watts per square meter. 

Quprt Beam radiation received by an illuminated

wall. 

R Radiance, in watts per square centimeter

per' steradian per micrometer. 

Rlm Longwave component of net radiation. 

72i w t Longwave radiation emitted by the Earth's

surface. 

Riwl Longwave radiation emitted by the

atmosphere. 

Rn Radiation balance at the terrestrial surface

(net radiation).

Rsw Shortwave component of net radiation. 

72,tot Solar energy reflected by the Earth's

surface. 

72, w | Solar radiation incident upon the Earth's

surface. 

Ruprt Total solar radiation received by a vertical

wall.

72* Radiance received aloft. 

R,B Radiance received aloft from a highradiance calibration target. 

72«i, Radiance received aloft from a lowradiance calibration target.

72o Radiance emitted at the Earth's surface. 

72;// Radiance emitted by a high-radiance

calibration target. 

Roi Radiance emitted by a low-radiance

calibration target. 

72 I Upward radiance of air layer, or upwelling

radiation.

Ri ' Upward radiance of air layer 1. 

722 I Upward radiance of air layer 2. 

72s t Upward radiance of air layer 3. 

72i t Upward radiance of air layer 4. 

72s t Upward radiance of air layer 5. 

72a t Upward radiance of air layer 6. 

72ta Upward radiance of air column. 

R \f Downwelling radiation. 

Ri Bulk Richardson number. 

Sk Silhouette area of the average building. 

gfa Specific area, or the average lot area of

each building.

SF Shadow fraction. 

T Transmissivity of air. 

T2 Transmissivity of air layer 2. 

T3 Transmissivity of air layer 3. 

Tt, Transmissivity of air layer 4. 

TB Transmissivity of air layer 5.

Symbol Definition

To Transmissivity of air layer 6.

To, Temperature at the atmospheric damping

depth. 

TI,J Average surface temperature in a single

square-kilometer grid cell. 

Tk Simulated temperature associated with

land use and land cover type k. 

Tj Soil temperature.

TskV Mean temperature of atmospheric downradiation, in degrees Kelvin. 

Tt Transmissivity of turbid air. 

Ttir Thermal infrared transmissivity of air. 

Ti-n Visual transmissivity of turbid air. 

Twv Transmissivity of air layer containing

water vapor. 

Twv.t Transmissivity of air layer containing

water vapor, aerosols, and gaseous

pollutants. 

Tz Temperature at screen height, in degrees

Kelvin.

To Dewpoint, in degrees Celsius. 

T°C Temperature, in degrees Celsius. 

Ti°G Soil temperature at depth 1, in degrees

Celsius. 

2VC Soil temperature at depth 2, in degrees

Celsius.

7VC Soil temperature at depth 3, in degrees 

__ Celsius. 

T°C Mean temperature of air layer, in degrees

Celsius. 

To Temperature of the Earth's surface, in

degrees Kelvin.

T°J$. Temperature, in degrees Kelvin. 

7TC K Mean temperature of air column, in

degrees Kelvin.

Vu Meteorological visibility. 

W\ Radiant energy, in watts per square

centimeter per micrometer (Planck

equation).

WF Wet fraction. 

Z Solar zenith angle. 

Z' Solar zenith angle with respect to a sloping

surface, 

a Solar azimuth angle, in degrees with

respect to due south, 

a' Aspect of surface slope, in degrees with

respect to due south. 

8 Solar declination. 

e Emissivity. 

7 Extinction coefficient. 

7(tr Thermal infrared extinction coefficient. 

yvis Visual extinction coefficient. 

K Thermal diffusivity of soil. 

X Wavelength interval in the sensing window. 

P Air density. 

ff Stefan-Boltzmann constant. 

<rs w Shortwave scattering coefficient. 

<f> Latitude. 

F Adiabatic lapse rate. 

0 Potential temperature. 

corr. Slope correction factor.
THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

THE INFLUENCES OF LAND USE AND LAND COVER ON CLIMATE:

AN ANALYSIS OF THE WASHINGTON-BALTIMORE AREA THAT COUPLES

REMOTE SENSING WITH NUMERICAL SIMULATION 1

By ROBERT W. PEASE,* CAROL B. JENNER,S and JOHN E. LEWIS, JR.4

ABSTRACT

The Sun drives the atmospheric heat engine by warming 

the terrestrial surface which in turn warms the atmosphere 

above. Climate, therefore, is significantly controlled by complex interaction of energy flows near and at the terrestrial 

surface. When man alters this delicate energy balance by 

his use of the land, he may alter his climatic environment 

as well. Land use climatology has emerged as a discipline 

in which these energy interactions are studied; first, 

by viewing the spatial distributions of their surface 

manifestations, and second, by analyzing1 the energy exchange 

processes involved. Two new tools for accomplishing this 

study are presented: one that can interpret surface energy 

exchange processes from space, and another that can simulate the complex of energy transfers by a numerical simulation model. Use of a satellite-borne multispectral scanner as 

an imaging radiometer was made feasible by devising a graywindow model that corrects measurements made in space for 

the effects of the atmosphere in the optical path. The simulation model is a combination of mathematical models of 

energy transfer processes at or near the surface. Integration 

of these two analytical approaches was applied to the Washington-Baltimore area to coincide with the August 5, 1973, 

Skylab 3 overpass which provided data for constructing 

maps of the energy characteristics of the Earth's surface. 

The use of the two techniques provides insights into the relationship of climate to land use and land cover and in predicting alterations of climate that may result from alterations of the land surface.

INTRODUCTION

The impact of man's use of the land on both his 

local and worldwide climatic environment has become a significant concern. In its simplest terms, 

climate involves the characteristics and ongoing

1 The research reported herein was funded in part by the National 

Aeronautics and Space Adminstration and U.S. Geological Survey as one 

phase of the Central Atlantic Regional Ecological Site (CARETS) Project.

- Department of Earth Science, University of California, Riverside, Calif.

3 Department of Geography, University of Wisconsin, Madison, Wis.

4 Department of Geography, McGill University, Montreal, Canada.

phenomena of the lower atmosphere that are controlled by the terrestrial surface over which the atmosphere lies or from which it has moved. The 

ultimate controls of the surface are its absorption of 

shortwave solar and downward longwave atmospheric radiation; the manipulation of the absorbed 

energy by the surface and the substrate; and the 

energy transfer back across the Earth-air interface, 

mostly to the overlying air but partly to outer space. 

The terrestrial surface and substrate thus act as intermediate agents in the climatic process, and the 

nature of the surface-air interactions must be understood because these agents are the driving forces 

that control the climatic environment.

The nature of the energy flow into and out of the 

surface and its substrate is complex and depends 

upon such factors as the amount of solar and atmospheric radiation reaching the surface, the portion of this energy that the surface will absorb, the 

heat capacity and diffusivity of the substrate, and 

the characteristics of the surface that control the 

movement of energy back into the atmosphere by 

radiant emission or turbulent transfer. As man 

alters the terrestrial surface, he changes this complex array of processes and alters his climatic environment, sometimes beneficially, but often in ways 

detrimental to his existence.

PURPOSE AND SCOPE

It is the purpose of this study to develop and 

examine ways to understand better the terrestrial 

surface and its interaction with the air above it. 

Techniques were developed for rapid measurement 

by satellite-borne electro-optical scanners of phenomena related to energy exchange across the surface-to-air interface. Computer techniques also were 

developed to replicate the same phenomena by means

Al
A2 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

SKYLAB 

FLIGHT PATH

60 MILES

10 20 30 40 50 60 70 80 90 100 KILOMETERS

FIGURE 1.—The parts of the Washington-Baltimore area imaged and simulated. The dashed 

outline delineates the maps of energy-exchange phenomena in this report. The dots outline the area mapped by numerical simulation.

of numerical simulation, using mathematical models 

of the processes involved. An appropriate name for 

this avenue of study is "land use climatology."

Currently, there is controversy regarding changes 

in global climates that result from man's addition of 

gases and particulates to the atmosphere, but relatively little thought has been given to the goal of 

this study—the effect on the energy balance of man's 

manipulation of the Earth's surface. Our concern

is not with changes in climate on a global scale, but 

rather changes at a regional or mesoscale induced 

by land use. The area of study, which is approximately 6,300 km2 , includes the cities of Washington, 

D.C., and Baltimore, Md. (fig. 1). Within this area 

are the two major urban centers, their suburbs, agricultural land, woodland, and the water surface of 

the upper Chesapeake Bay. The mesoscale of the 

study does not imply that disruption of energy ex-
INTRODUCTION A3

change phenomena on a global scale would have no 

impact on world climates, but rather that this problem is still to be assessed. The appropriate scale 

of application at this time is regional. At this scale, 

the impact of land use and land cover on local 

climates can be delineated and a concerted response 

to potential changes can be made.

This paper combines the results of two separate 

investigations, each with the common goal of energy 

balance mapping, in order to compare the feasibility 

and accuracy of the two approaches. One of the investigations uses data gathered by the Earth Resources Experimental Package (EREP) of the National Aeronautics and Space Administration 

(NASA) Skylab program. The S-192 scanner in the 

Skylab 3 spacecraft provided information on the 

thermal state of the Earth's surface in the Washington-Baltimore area at 10:05 a.m. EDT on August 5, 

1973. This information was used to construct maps 

that show the synoptic distribution of surface radiation temperatures, albedos, and net radiation. The 

other investigation produced maps showing the distribution of energy budget phenomena by use of 

a general numerical computer model. Linking the 

results of the two experiments thus demonstrates 

the potential utility of a combined analysis of the 

relationship of land use and land cover to local and 

regional climates.

The two experiments are coupled by place and 

time. The mapped numerical simulation of surface 

energy budget data in Baltimore and its environs 

coincides spatially with a portion of the area 

mapped with remotely acquired data (fig. 1). Input 

parameters for the simulated maps match those that 

existed at the time of the imaging overpass of the 

Skylab spacecraft, but only to an extent compatible 

with the independent testing of a general simulation 

model. Where the model depends upon internal 

means to determine certain parameters, specific data 

from the remote mapping experiment were not substituted, in order not to compromise the independence of the general approach.

The initial independence of the two parts of the 

inquiry is responsible for differences in investigative 

formats. Most conspicuous is the difference in the 

size of the data averaging cells—that for the mapping from remotely sensed data is about half the 

.area of that used for simulation mapping. This difference in size affects the comparative resolution of 

surface detail slightly but does not affect general 

patterns of distribution, hierarchical orders of 

values, and the like. In brief, it was felt best to keep

each investigation initially independent and then to 

compare results.

ACKNOWLEDGMENTS

The basic material of this study is derived from 

chapter 3 of the final report to the Johnson Spacecraft Center for experiment 469 performed by the 

U.S. Geological Survey (Alexander and others, 

1976).

The authors thank Dr. Samuel I. Outcalt of the 

University of Michigan, author of the Outcalt simulation model, for his counsel in the application of 

the model for the Baltimore simulation, and thank 

all others who have contributed to the successful 

completion of this inquiry.

Appreciation is also extended to Dr. David H. Miller of the University of Wisconsin, Milwaukee, for 

his critical reading of the manuscript and valuable 

suggestions that have been incorporated in this 

paper.

ANTECEDENT STUDIES

Several investigations preceded the inquiries by 

the U.S. Geological Survey into the remote sensing 

of surface energy phenomena and the numerical 

modeling of the energy balance. The following descriptions summarize previous work which led to the 

present study.

RADIO-METRIC MEASUREMENT BY REMOTE MEANS

The first remote measuring of surface radiation 

temperatures with an electro-optical scanner were 

carried out across the island of Barbados, just north 

of Bridgetown, as part of the Barbados Oceanographic and Meteorological Experiment (BOMEX). 

The Texas Industries RS-14, one of the first scanners with thermal-infrared capacity and capable of 

calibration, was used in the experiment as an 

imaging radiometer (Pease, 1971).

The success of this thermal mapping experiment 

gave impetus for further experimentation with scanners for measuring surface energy exchange phenomena remotely. The second endeavor was to map 

a mid-latitude city in the United States (1) to 

study the heat island effect of cities, (2) to make a 

series of maps of the surface thermal state to 

demonstrate diurnal changes, and (3) to map other 

surface energy exchange phenomena such as surface 

albedos and the distribution of net radiation. The 

second mapping experiment, with Baltimore, Md., as 

the test site city, was designed specifically as a precursor to the Skylab climatology investigation on 

which this study is based. Three flight lines over
A4 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

the city of Baltimore were imaged at sunrise, midmorning, and early afternoon on May 11, 1972. From 

the data gathered, maps of the heat island buildup 

were constructed. Maps showing the distribution of 

energy emitted by the surface, surface albedos, 

energy absorbed by the surface, and the distribution 

of net radiation were constructed for the flightpath 

across the heart of the city in the early afternoon 

(12:45 p.m. EST).

The BOMEX and Baltimore missions employed 

scanners in low-flying aircraft. Spatial perspectives 

were therefore limited to flightpath swaths approximately 2 km wide. The opportunity to employ a 

broader perspective by using a satellite came with 

the NASA Skylab program. Earlier scanners also 

could not be calibrated to the surface radiance because their electronic circuitry employed automatic 

gain control (AGC). The gain of these instruments 

changed with each scanline to keep the dynamic 

range of values within the sensitometric range of 

the film on which the image was initially recorded 

by a cathode ray tube. Three subsequent improvements permitted the RS-14 used in the BOMEX experiment to be calibrated to the surface radiance: 

(1) the replacement of automatic instrument gain 

with constant gain which made image elements from 

various parts of the scanpath comparable; (2) internal calibration sources, the radiances of which 

were recorded along with the scan image, provided 

a means for converting the diverse voltage pattern 

of the image to a radiance pattern with absolute 

values; and (3) the image pixel information was 

recorded directly onto magnetic tape which could 

handle the wide range of values produced when the 

instrument gain was held constant.

The BOMEX experiment gave the first insight 

into the problems of calibrating the radiant signals 

received at the scanner with the signals actually 

emitted by the terrestrial surface. It was discovered 

that an airport runway, which measured 52°C radiometrically at the surface, measured only 40°C from 

an altitude of 300 m. Even though the thermalinfrared sensing was in the so-called "water vapor 

window," the atmosphere in the optical path to the 

instrument severely dampened the surface-emitted 

signal. To circumvent this poorly-understood problem, a target-type calibration of the scanner was employed. Several contrasting targets, of sufficient size 

to be measured densitometrically on a photographic 

film image made from the magnetic tape, were measured radiometrically at the surface and plotted 

against the corresponding film transmittance. The 

curve produced in this manner matched the sensitometric curve of the film used for photo image printout. From this curve, image transmittances were 

converted to corresponding values of radiant emission and radiation temperature (Pease, 1971, p. 

504).

At the same time it was perceived that the alteration of surface radiant emission followed a graywindow model wherein the air in the optical column 

attenuated the surface signal as a function of air 

transmissivity in the sensing spectral band, and at 

the same time added its own emitted radiation to 

the weakened surface signal. An apparent reduction 

in signal strength occurs when the air, cooler than 

the surface, attenuates more than it emits. The following relationship was established theoretically:

Iy = t[Ebb (T°K)] + (l-t)I0 , (1) 

where L is the radiant energy received aloft, e is the 

emissivity of the air column, [Eb1> (T°K)~\ is the 

blackbody equivalent of the radiance of the air column for the mean column temperature T in degrees 

Kelvin, and 70 is the radiant signal emitted by the 

Earth's surface. With an accurate knowledge of the 

radiance values received at the scanner and of the 

meteorological characteristics of the optical path between surface and sensor, a calibration of the scanner with the thermal-infrared emission of the surface could be made without calibration targets. 

Although known to be theoretically possible, this 

type of calibration was not made until the Skylab 

inquiry described in this paper.

To construct maps for other than the simple 

thermal state, it was necessary to record simultaneously both Earth-emitted thermal-infrared imaged 

information and shortwave or reflected solar radiation. This need dictated the use of a calibrated multispectral scanner, and the M-7 scanner of the Environmental Research Institute of Michigan 

(ERIM) was chosen for the Baltimore experiment. 

Calibration of the three spectral bands of the multispectral M-7 scanner was done using target values 

and was performed in the same manner as for the 

single channel in the BOMEX equipment.

Imaged data from the BOMEX and Baltimore 

missions were retrieved densitometrically from film 

transparencies made from magnetic tapes. The size 

of the data-averaging cell on the image transparency 

was the size of the aperture of a custom-fabricated 

densitometer. For the initial BOMEX experiment, a 

simple densitometer was fabricated by using an adjustable light source, a silicon cell behind a relatively large aperture, and a measuring meter. To 

preserve the integrity of the matrix of cells, trans-
INTRODUCTION A5

parent acetate was ruled in a grid with a line 

spacing that produced squares of the same size as 

the densitometer aperture. This grid was placed 

over the film image transparency to give an x-y location to values as film transmittances were measured.

For the images from the Baltimore mission, a 

more sophisticated wide-aperture densitometer was 

fabricated and placed in the cursor position on an 

x-y digitizer, with the integrity of the grid-cell 

matrix maintained by the quantizer display of the 

digitizer. Accurate registration of matrix cells was 

particularly important because two or more image 

matrices were necessary to construct maps of surface albedos, energy absorbed by the surface, and 

net radiation (Pease and Nichols, 1976).

In working with the Baltimore data, ERIM showed 

that the mapping of surface energy exchange phenomena could also be accomplished directly from the 

scanner magnetic tapes, bypassing the densitometric 

analysis of film transparencies.

Except for the maps of radiation temperatures, 

maps constructed for the Baltimore mission required 

the combination of data from two or three multispectral channels, and experience was gained in 

ways to accomplish this. For the albedo maps, two 

shortwave or solar channels were chosen, one on 

either side of the rapid rise in chlorophyll reflectance that centers on the 0.725-/xm wavelength. The 

wavelengths chosen were 0.58-0.64 /^m in the visual 

band and 1.0-1.4 /^m in the near infrared. In the 

calculation of albedos, satisfactory weights for the 

reflectances proved to be 60 percent in the visual 

band and 40 percent in the near infrared (Pease and 

Pease, 1972, p. 10). A knowledge of the values of 

surface albedos plus the values of downwelling longwave radiation from the atmosphere permitted calculation of energy absorbed by the surface which, 

combined with energy emitted, gave net radiation. 

To obtain the distribution of net radiation, inputs 

of data from three channels were combined (Pease 

and Nichols, 1976, p. 1369). The combination of information from separate multispectral channels 

later became an integral part of the Skylab inquiry.

NUMERICAL SIMULATION IN LAND USE CLIMATOLOGY

More recently, considerable success has been 

achieved in the simulation of the surface energy 

balance by computer. Simulation models have been 

developed for both urban and nonurban conditions. 

The general approach is based upon the surface 

energy conservation equation:

(2)

where Rn is the radiation balance at the terrestrial 

surface, H the kinetic or sensible heat flux into the 

atmosphere, LE the latent energy used in evapotranspiration, and G the flux of heat entering the soil. 

All four terms are described in energy flux density 

units. With additional terms, such as those for the 

phase change in snowmelt or the energy used in 

photosynthesis, the energy conservation equation is 

considered to be valid over time scales that vary 

from 1 second on up (Sellers, 1965, p. 101). It is 

this equilibrium property of the surface energy expenditure system upon which the concept of surface 

energy balance simulation is based.

The various surface energy balance models, discussed in context in a later section, differ only in 

parameterization of particular terms, in input requirements, and in assumptions made. The general 

approach to each, however, is identical. For each of 

the four components of the energy balance equation, 

a combination of input boundary conditions and 

derived quantities expresses the component as a 

function of surface temperatures. For any given set 

of conditions, there is only one equilibrium surface 

temperature which balances the equation.

Outcalt (1971) developed a digital version of an 

analog model of the energy balance of the Earth's 

surface created by Myrup (1969). Although Myrup 

had developed his model primarily for agricultural 

surfaces, Outcalt revised it to include specific processes associated with urban surfaces. The Outcalt 

version simplified simulation by substituting terrain 

parameters related to land use and land cover for 

the meteorological parameters originally employed. 

This substitution permitted data to be input by areas 

as large as several city blocks to simulate conditions 

on intricate urban surfaces. Outcalt (1972c) demonstrated that his model was reasonably accurate 

in predicting surface temperatures associated with 

various land uses by testing it against actual temperatures at two times on August 5, 1970, in Ann 

Arbor, Mich., and its environs. In all cases, the ranking of thermal values was correctly simulated, and 

differences between simulated and observed maximum temperatures were within 15 percent of the 

total temperature range. These results not only contradicted the concept of a monolithic urban heat island but also indicated that (1) surface wetness is 

largely responsible for thermal contrasts within the 

heat island in summer, and that (2) the absorption 

of beam radiation by vertical walls is the most important environmental factor in winter.
A6 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

LAND USE CLIMATOLOGY

EFFECT OF URBAN AREAS

Cities are commonly called "heat islands" because 

a combination of architectural forms, extensive 

paved surfaces, scarcity of growing plants, and 

combustion of fossil fuels interact to produce higher 

temperatures than those in surrounding nonurban 

lands. In a central business district (CBD), most 

surfaces have low albedos, or reflectivities, to sunlight and thus are good absorbers of solar energy.

If there were no processes by which this energy 

could enter and be stored by the substrate, temperatures in the daytime would be significantly higher 

than they are, and the heat island effect would disappear at sundown. But urban substrates have high 

heat capacities and movement of absorbed energy 

into the substrate by conduction both lowers heat 

island temperatures by day and returns the stored 

heat to the surface during the night, often maintaining the heat long after sundown. Thus, over a 

24-hour or diurnal period, the temperatures of urban 

surfaces and the air immediately above them are 

the products of both the incoming solar radiation 

and the physical nature of urban surfaces. In brief, 

local climates are products of the types of land use 

and land cover as well as of the solar input.

When land use and land cover is mapped from 

remotely sensed data, several levels of discrimination of spatial detail are employed (Anderson and 

others, 1972 and 1976). A Level I discrimination 

separates urban and rural categories, and the city, 

due to land use and land cover thermal infractions, 

may indeed appear as a monolithic hot spot. A Level 

II discrimination separates business districts, with 

their omnipresent hard surfaces, from residential 

areas in which planted areas act as energy sinks to 

produce cooler temperatures. A Level III discrimination separates high-density residential areas from 

low-density areas, the latter with a greater percentage of planted lands in the form of lawns, gardens, and trees, all conducive to lower temperatures. 

Large parks within a city perform the same function. At this level, a city ceases to be a monolithic 

heat island and becomes, instead, a mosaic of land 

use and land cover pieces (polygons), each with its 

own effect on the air immediately above. Indeed, the 

concept of a city as a monolithic heat island may be 

an artifact of obsolete observation methods, like the 

traditional automobile transecs through urban 

thermal canyons that do not detect the many surfaces, such as roofs, which are not observable along 

the route.

, The foregoing description suggests that the effect 

of land use and land cover upon the air above, and 

thereby on climate, can also be studied at various 

levels of resolution. These studies may range from 

the microscale level of a single land use or land 

cover type, or specific polygon, to the mesoscale level 

of a large area that may include several cities and 

any intervening agricultural and wild lands. The 

multispectral scanner has lifted the level of inquiry 

from the microscale perspective possible with lowflying aircraft, to the mesoscale perspective possible 

with a satellite-borne scanner. The scale for studying the dynamics of the urban heat island falls somewhere between these two extremes but is a scale at 

which the mosaic of differing land use types can be 

discriminated.

The following is a practical example of the use of 

this type of information. According to the laws of 

atmospheric thermodynamics, a city functioning as 

a heat island would be under an air layer slightly 

lower in mass atmospheric pressure than the surrounding rural air, causing inward air flow toward 

the hot core on calm days. Aerial effluents from 

heavy industries, sewage treatment plants, and the 

like, placed according to tradition on the east side of 

the city in our westerly wind belt, may engulf the 

city under conditions of a stagnant boundary layer. 

This situation could occur regardless of the location 

of industry with respect to the city center, when 

regional wind velocities are low. If cooler types of 

land use and land cover were distributed throughout 

the heat island core, these thermally induced pollution diffusion patterns could be changed.

Understanding the factors which control local 

winds during pollutant-concentrating meteorological 

conditions may help man manipulate spatial variables that affect local climate. For example, informed 

placement of heavy industries or activities with potential disaster risks could benefit environmental 

quality without sacrificing economic use of the land. 

The ability to observe and simulate the magnitudes 

and directions of local and regional surface thermal 

gradients will help achieve these land use planning 

goals. The ability to observe and map energy phenomena more sophisticated than simple thermal contrasts will also provide insight into the nature of 

inputs into terrestrial surface energy systems.

METHODS OF OBSERVATION

Earlier efforts to analyze the climatic effect of 

land use and land cover types, especially urban, were 

hampered by inadequate methods of observation. 

Too few observational sites were employed and data
MAPPING OF SURFACE-ENERGY BALANCE WITH SATELLITE DATA A7

were averaged over unnecessarily long times. Synoptic observations, from which an understanding of 

the dynamics of energy exchange phenomena of the 

Earth's surface can be developed, have been almost 

impossible to obtain except by inadequate automobile transects. New methods are needed to study the 

dynamics of the complex surface energy relationships.

A calibrated electro-optical scanner, whether airborne or spaceborne, acts as an imaging radiometer 

which can measure the distribution of energy exchange phenomena over a large area in a few moments. If the scanner is multispectral, the information it supplies can be used not only to measure and 

map synoptic patterns of surface thermal state but 

also surface albedos, the energy absorbed by the 

surface, and the distribution of net radiation at the 

surface. Net radiation is the balance between the 

energy absorbed by the surface and the energy 

emitted. If positive in value, it represents energy 

entering the terrestrial thermal system; if negative 

a net loss.

One of the problems that has plagued the traditional observer with his array of radiometers on the 

ground has been the choice for logistic reasons, of 

sites that represent various surface elements. No 

single observation site can represent a heterogeneous terrain. Instead, in our method, the scanner substitutes the average value of a matrix cell for the 

representative site, with the size of the cell appropriate to the ground resolution desired. The system 

of using a matrix of cells is unequaled in flexibility 

and accuracy by any ground measurement program because the number of measured points can 

be as many as the number of image elements 

(pixels) supplied by the scanner, or fewer when 

blocks of pixel values are averaged. Ground resolution changes accordingly. The data can be recorded 

on magnetic tape and transferred directly to digital 

computers for automated processing without laborious manual preprocessing, or keypunching, into a 

computer system.

Although calibrated scanline data provide a way 

to study the diversity of surface energy exchange 

phenomena as they relate to land use and land cover, 

these data give no direct insight into the nature of 

the processes that create the diversity. An analytical 

tool that can give such insight is a numerical computer model that simulates the processes above and 

below the Earth's surface and the interaction of 

these processes across the surface-to-air interface. 

A model is especially informative about processes if 

it consists of integrated submodels. These make possible the greatest single value of the energy simulation model, its capability for sensitivity testing. By 

changing inputs to the model subroutines so as to 

simulate changes wrought by either man or nature, 

an investigator can predict effects on the larger integrated energy exchange system.

The model, with its inherent flexibility for the 

examination of processes by numerical simulation, 

and the multispectral scanner, with its capability for 

examining the effects of processes, are two efficient 

research tools for studying land use climatology. At 

this stage of experimental development, one acts as 

a check on the accuracy of the other.

MAPPING OF SURFACE ENERGY BALANCE

PHENOMENA FROM SATELLITE-ACQUIRED

DATA

Three maps, showing the distribution of phenomena related to the energy balance at the terrestrial surface, were made from data acquired by 

the S-192 scanner during the Skylab 3 mission. 

These maps show the distribution of surface radiation temperatures (fig. 2), surface albedos (fig. 3), 

and surface net radiation (fig. 4). The maps are 

printed by the GRIDPLOT method where data cells 

are printed as small squares with pattern densities 

that indicate relative values. Each cell represented 

the average value of the radiances of 100 pixels of 

the scanner imagery and has 10 pixels on each side. 

We chose this level of averaging or block filtering of 

information to produce a degree of generalization 

compatible with the scale of the maps. Each pixel is 

about 72 m on a side, and each data cell represents 

a square, 720 m on a side.

Averaging or smoothing data is preferable to 

attempting to find representative sites. All values of 

radiance are averaged within each pixel. The mean 

value of radiance for a 100-pixel block is the average 

of all individual pixel values in that block. The degree of generalization used for map construction, 

however, affects the utility of the mapped data as 

information sources for numerical simulation. 

Although the simulation model to be described is 

two-dimensional, it uses point values whereas the 

data cells mapped from remotely sensed data are 

0.72 km on a side. It would appear at first glance 

that a single pixel (only 72 m on a side) would be 

a better approximation to a data point than a data 

cell. Problems arise in using pixels as point data for 

simulation, however, because the radiance value of 

a pixel on a magnetic tape is difficult to relate to a 

surface feature unless the feature is conspicuous.
A8 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

EXPLANATION 

Temperatures,in degrees Celsius

D 25"-28° 3T-3T 3T-370 ABOVE 40°

FIGURE 2.—Distribution of surface radiation temperatures in the Washington-Baltimore area, 10:05 EDT, 

August 5, 1973. Note prominent heat islands of the two large metropolitan areas. Temperatures less 

than 25°C are those of clouds. Map is computer-drafted with GRIDPLOT program using 0.52-km2 data 

cells.

Scanner-acquired data that have been smoothed or 

averaged in data cells, on the other hand, can be 

spatially related to two-dimensional simulation cells 

with difficulty.

THE S-192 MULTISPECTRAL SCANNER AS A 

DATA SOURCE

Only the August 5, 1973, imaging pass of the 

Skylab S-192 scanner provided thermal-infrared
MAPPING OF SURFACE-ENERGY BALANCE WITH SATELLITE DATA A9

EXPLANATION 

Albedo,in percent

15 KILOMETERS

<05 05-10 10-15 15-20 20-25 D >25

FIGURE 3.—Distribution of surface albedos, Washington-Baltimore area, 10:05 a.m. EDT, August 5, 1973.

Albedos greater than 0.25 (white) are those of clouds.

data over the central Atlantic seaboard of sufficient 

quality to be of use in constructing the energy balance maps. Although this flight path extended southeastward from the Shenandoah Valley across the 

Delmarva Peninsula, only about one-fourth of the 

total path was used for mapping. The area mapped 

(fig. 1) extended from the Potomac estuary to the 

Chesapeake Bay and included the urban areas of

Washington and Baltimore plus a large expanse of 

semi-urban and nonurban lands to the east and 

south of these two cities (see larger dashed rectangle 

on base map). Much of the water surface of the 

contiguous upper Bay was also included.

The S-192 scanner system has 13 bands, or spectral sensitivities, ranging from 0.41 to 12.5 /mi. 

Thirteen detectors are simultaneously irradiated by
A10 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

\

EXPLANATION o is KILOMETERS 

Surface net radiation, in watts per square meter

ED <490 E3 490-625 ^ 525-580 dUB 560-595 ^ 595-630 TM 630-665 i§ 665-700 W >700 

FIGURE 4.—Distribution of surface net radiation, Washington-Baltimore area, 10:05 a.m. EDT, August 5. 1973.

energy from the terrestrial surface, each detector 

producing an electronic output signal corresponding 

to the average energy received from the spot on the 

Earth's surface shown in the scanner system's instantaneous field of view. In the longer wavelengths, 

the energy consists of the product of the combined 

radiances of the surface and intervening atmosphere, while in the shorter wavelengths the energy 

is sunlight reflected by the surface or reflected and 

scattered upward by the atmosphere. The detector 

bands and their respective spectral intervals are 

shown in table 1. Two scan rates were used in the 

data collection by spacecraft. Subsequent processing 

of magnetic tapes reduced those data to pixel values.
MAPPING OF SURFACE-ENERGY BALANCE WITH SATELLITE DATA All

TABLE 1.—S-192 detector band coverage

[Source: NASA/Skylab (1973, p. 3-5); IR is infrared ]

Band

3

4

5

6

7

11

12

13

8

9

10

1

2

Description

Blue-green

Green-yellow

Orange-red

Red

Near IR

Near IR

Near IR

Thermal IR

Near IR

Near IR

Near IR

Violet

Violet-blue

Spectral 

range (/J.m)

0.52-0.56

0.56-0.61

0.62-0.67

0.68-0.76

0.78-0.88

1.55-1.75

2.10-2.35

10.2-12.5

0.98-1.08

1.09-1.19

1.20-1.30

0.41-0.46

0.46-0.51

SDO

channel

1,2

3,4

5,6

7,8

9,10

11,12

13,14

15,16,21

19

20

17

22

18

The S-192 system makes a conical scan which 

produces a circular scan track of the terrestrial surface and a corresponding scanline on the imagery, 

skewed moreover by both the simultaneous forward 

motion of the spacecraft and rotation of the Earth. 

The angle of the side of the scan cone to the cone 

axis is 15°. Only the forward 110° segment of the 

cone is recorded. The scanline can be located with 

respect to the scanner position, therefore, only by 

correcting for scan cone and skewness geometry and 

knowing the altitude, velocity, and track azimuth of 

the satellite. The imaged path on August 5, 1973, 

was 69 km wide.

Data from the 13 spectral bands were recorded 

on 21 of the 28 tracks of the magnetic recording 

tape as scientific data output (SDO) channels. The 

relation of the SDO channels to the spectral bands 

is indicated in table 1. SDO channel 21 (10.2-12.5 

^m) was used for the map of radiation temperatures 

and SDO channel 7 (one of the reflective bands at 

0.68-0.76 fi.m) for the map of surface albedos. The 

map of the distribution of net radiation was made 

by combining the information from the two bands 

with ground and atmospheric characteristics. This 

project used data from only these two SDO channels.

Data recorded in the spacecraft were Millerencoded in a pulse code modulation (PCM) form at 

a density of 16,177.86 bits per inch at a tape speed 

of 60 in./s. Processing of the scanner tapes after 

they were returned to Earth in the command module 

included skew removal, decommutation, conversion 

to Greenwich Mean Time (GMT), and the production of straight scanlines. The lines were straightened by using pixels from adjacent lines to form a 

straight sequence across, and at right angles to, the 

velocity vector of the spacecraft. Since the chord of 

a circle is shorter than the arc it subtends, the number of pixels in a straightened path is fewer (approximately 1,033) than on a curved scanline 

(1,240). However, some detail is thereby lost, and 

individual pixels are not perfectly oriented with 

respect to the scanpath.

The reformatted tapes were sent to the users by 

NASA and are the data sources for this project. 

Digital values are in PCM counts in a hexadecimal 

code which limits the number of values on. each 

channel of a digitized tape to 255. This number of 

values for any channel was more than adequate, 

however, since it considerably exceeded the data 

spread requirements of the phenomena measured.

In the reformatted NASA tapes, each scanline 

had its own data block that included pixel values for 

all channels, plus ancillary information such as 

latitude and longitude of the first pixel of each 

straightened line, the location of the spacecraft nadir 

point at the moment of imaging, and the direction 

and velocity of the spacecraft. Of the locational information, only the nadir point and first pixel were 

useful for mapping.

The first step in using the NASA-supplied data 

was to separate data values for the pixels of discrete 

scanlines into channel files. In this manner, all pixel 

data for SDO channel 21, for example, appeared in 

a single file rather than mixed with other channels 

in line-by-line blocks. This step consumed the greatest amount of computer time because it required 

reading all data, much of it useless to the project. 

The magnitude of the task was reduced by extracting data only from the scanlines needed (1,000 

from about 4,000 in the entire scanpath).

CONSTRUCTING THE MAP OF SURFACE RADIATION 

TEMPERATURES

The method for constructing the map of surface 

temperatures was the same as that used for all three 

energy balance phenomena maps. First, data cells 

were created from the channel pixel files by averaging 10 adjacent pixel values on each of 10 adjacent 

lines. This produced a smoothing block of 100-pixel 

values. The data cells were then placed in matrices 

which yielded the machine-drafted maps (figs. 2 and 

4) and the line-printed graymap (fig. 14). Whereas 

the machine-drafted maps have the planimetry of 

the scanner-acquired image, the graymaps, on which 

one line-printed symbol corresponds to one data cell, 

do not. The graymaps do not have the planimetry 

of the original image because 10 symbols are typed 

per inch per line across-track, but there only 8 lines 

to an inch along-track. This distortion can be corrected by using a 4 x 5 symbol data cell, but the 

map will be large and unwieldy. A compromise is
A12 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

achieved by using a 2 x 2i/2 symbol block with two 

symbols in one line of the cell and 3 in the other.

Although portrayed in hierarchical intervals, each 

data cell has its own specific value, which is stored 

for further use. When plotted with a vector lineplotter, contiguous cells within a given interval are 

joined to form a polygon of given rank, but cell 

patterns still show conspicuously. One possibility 

would be to smooth the edges manually to form 

isarithmic maps. Another possibility would be to 

apply cell values to cell centroids as .control points 

for automated isarithmic mapping. The number of 

cells in the maps, however, would demand an excessively large computer memory unless the number 

were reduced by further smoothing.

The NASA tapes supply data as PCM counts. The 

equation for converting the PCM counts of SDO 

channel 21 (thermal infrared) to radiances received 

at the scanner is:

Re = A0 + CAlt (3)

where Rs is the radiance received aloft in watts per 

square centimeter per steradian per micrometer 

(W cm- 2 ster- 1 um- 1 ), A0 is a constant equal to 

1.3114 ex -4 (1.3114 x 10~4 ), C is the PCM count 

(NASA/Skylab, 1973, p. 5-19) and A^ is a constant 

of 4.7650 ex -6. When Chesapeake Bay was used as 

a substitute calibration target, it was discovered 

that a correction factor of 1.0513, or approximately 

5 percent, was needed to give rational values to 

temperatures derived by the NASA-supplied equation. For this reason, the conversion equation used 

was

Rs = 1.0513 (A0 + CA1 ). (4)

The rationale for the use of this correction factor 

will be discussed subsequently.

The form of the radiance (R) used in these equations has a strict definition. Not only is the energy 

measured in steradians as a measure of radiant 

intensity, but it is confined to a wavelength interval 

of 1 fjjn. This definition arises from the fact that 

the energy intensity values are for the spectrally 

narrow sensing window of channel 21 (10.2-12.5 

/x,m) and not for a spectral ban covering all wavelengths. For this reason, the normal (!T°K) 4 relationship of temperature to the flux of radiation represented by the blackbody curve of the StefanBoltzmann equation cannot be used to convert S-192 

radiances to radiation temperatures. Because of the 

manner in which the blackbody curve passes through 

the sensing window, a temperature-dependent coefficient would be needed that varies from (T°K) 4C to 

(T°K) 52 . The problem is avoided by using the basic

Planck equation by which the radiance in any given 

micrometer wavelength interval can be converted to 

a radiation temperature and vice versa.

The form of the Planck equation used is based 

upon wavelength rather than frequency (Hudson, 

1969, p. 35). Converted from radiant energy (TFX ) 

to a radiance (R) by adding •* to the denominator, 

the equation becomes

R =

ec2/\T°K

(5)

where R is the radiance in W cm- 2 ster- 1 /^m- 1 

for a 1-^m wavelength interval in the sensing window (A) ; G! and c2 are the first and second radiation 

constants with values of 3.7415 ex +4 (W cm- 2 

pm- 4 ) and 1.43879 ex +4 ( Mm°K), respectively; 

T°K is the temperature in degrees Kelvin; and e 

the natural log base. When all constants are combined, equation (5) becomes

R . Q;^ . (6 )

If a temperature is desired from a value of R, the 

following transposition of equation 6 is used:

1251 T°K =

In 0.05921

(7)

+ 1

R

Since radiation temperatures provide a convenient common denominator for converting one 

blackbody expression to another, the conversion of 

the measured R to this form is an initial step in the 

data analysis of the thermal-infrared band. The first 

two steps of this conversion process, then, involve 

the use of equation (4) followed by equation (7).

Equation (3) converts PCM counts to cgs (centimeter-gram-second) energy units, or W cm- 2 . The 

American Meteorological Society stipulates, however, the use of Systeme International (SI) units 

for climatological work, which expresses energy 

flows in W m- 2 , or mks (meter-kilogram-second) 

units. The current paper accepts NASA data in its 

cgs form but expresses results in SI units. A certain 

amount of cross-translation between the two energy 

expression systems is therefore present which frequently finds common ground in radiometric or 

radiation temperatures. Although derived from cgs 

units, the millibar (mb) is retained to describe 

atmospheric pressure rather than the relatively unfamiliar kilopascal.

THE GRAY-WINDOW ATMOSPHERIC EFFECT

If the R, received aloft were converted to radiation temperatures, they would portray the surface
MAPPING OF SURFACE-ENERGY BALANCE WITH SATELLITE DATA A13

as being colder than it is. During the August 5 

imaging pass, Chesapeake Bay would appear as 

having had a surface radiometeric temperature of 

21 °C, whereas its temperature had been measured 

radiometrically on August 3 as ranging from 26°C 

to 29°C. This discrepancy arises from the effect of 

the air in the optical path of the scanner, previously 

noted as the gray-window model or effect.

The purpose of the following is to restate equation (1) in terms of the R recorded on the NASA 

tapes. The air absorbs a portion of the surfaceemitted radiance (R0 ) according to the air transmissivity (T). To the attenuated surface signal is 

added the radiance of the air column (R | a). Thus, 

in gray-window theory, there would be no effect by 

the optical path if the surface temperature and the 

mean temperature of the air column were the same. 

The R0 signal will be damped if the surface is the 

warmer of the two, and strengthened if it is the 

colder. This is true because the atmosphere absorbs 

the same amount of surface-emitted energy as it 

emits (absorptivity equals emissivity). When 

warmer than the air, however, the surface emits 

more energy and thus the amount of energy absorbed 

by the air will be greater than the amount of energy 

emitted by the air. Curves for correcting Rx to R0 

approach a line of equal value where Rg = R0 at the 

mean temperature (or equivalent radiance) of the 

optical path.

The concept and calculations for the gray-window 

effect can be simplified for a scanner carried aboard 

a spacecraft outside the atmosphere. The Rx signal 

received at the scanner is then composed of the R0 

signal attenuated according to the transmissivity of 

the intervening atmosphere. To this is added R^a 

in the same wavelengths. The simplified relation 

then becomes

Rs = TR0 + R^a. (8)

By transposition of this relation, an equation that 

uses characteristics of the optical path for calibration of the surface radiance to that received by the 

sensor can be stated as

RO —

Ry - R a

(9)

By insertion of this equation into the data analysis 

procedure, the radiance measured by a satellite can 

be used to derive a surface radiation temperature. 

The sequence of equations for conversion of SDO 

channel 21 data to surface radiation temperatures 

is equation (4), equation (9), and equation (7).

Although the sequence of mathematical manipulation of data appears simple, the derivation of correct

values for equation (9) is not. T in the sensing 

wavelength interval is the product of water vapor 

absorption residuals in the water vapor window; 

absorption by aerosols in the air, indicated as atmospheric turbidity; and absorption by gaseous atmospheric pollutants that are nearly transparent. R t a 

is the emission of all of these atmospheric components controlled by the emissivity and temperature 

of the air column.

Complexities in the meteorological analysis of the 

air column make the use of calibration targets appealing. No targets were prepared for the August 5, 

1973 imaging pass. The only water surface, for 

which temperatures measured radiometrically at the 

appropriate time were available, was Loch Raven 

Reservoir north of Baltimore which, unfortunately, 

was just outside of the imaging path.

Lack of an adequate calibration target on August 5 suggested the desirability of using the surface 

of Chesapeake Bay as a substitute. This necessitated 

obtaining temperatures of the bay surface, preferably radiometric, for the appropriate time. The most 

useful temperatures were those measured radiometrically by the Johns Hopkins University oceanographic vessel, the Ridgely Warfield, on August 3, 

2 days prior to the Skylab passage. Because of the 

size of the upper bay, its temperature is considered 

not to have changed significantly in the 2-day interval. Furthermore, the upper bay is less subject 

to tidal exchange with ocean water than the lower 

bay. Because there was a spatial variation in surface 

readings of from 26° to 29°C, it was necessary to 

determine an exact relationship between temperatures and PCM counts. To accomplish this, temperatures of the bay surface were mapped with alphanumeric symbols representing PCM counts so that 

each 100-pixel block was represented by a letter symbol, and a histogram was made of the frequency 

distribution of the PCM counts (fig. 5). From the 

map it was ascertained that a PCM count of 149 is 

equivalent to a surface radiometric temperature of 

27°C, or 300°K. It should be noted that the surface 

of Loch Raven Reservoir, the large body of fresh 

water north of Baltimore, had a surface temperature 

of 26.5°C at 10:05 a.m. EDT on August 5. In this 

manner, Chesapeake Bay provided a calibration 

target against which the signal modification by the 

air in the optical path could be checked.

One target, however, cannot provide enough data 

for converting R~ to R0 . Had two targets been available, the conversion could simply have been accomplished by applying the relation
A14 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

D 

Q 

O

LU

Q 

O 

O

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

A PCM count of 149 is equivalent 

to a surface radiometric 

temperature of 300°K

20 30 40 50 60

PIXELS, IN THOUSANDS

70 80 90

FIGURE 5.—Frequency distribution of pulse code modulation (PCM) counts from scientific data 

output channel 21 of the S-192 scanner. Data are for the Washington-Baltimore area.

T = (10)

where H and L are subscripts that designate highand low-radiance calibration targets, respectively. 

When T, R£, and R0 are known, R^a can be found 

by a transposition of equation (8). The upradiance 

of the atmosphere is a constant for the calibration

values, and the atmospheric correction model provides all of the other values.

CALIBRATION USING CHARACTERISTICS OF THE 

ATMOSPHERE

The existence of only one valid calibration target 

for the mapping of the thermal state of the surface, 

however, forced the use of atmospheric characteris-
MAPPING OF SURFACE-ENERGY BALANCE WITH SATELLITE DATA A15

300

400

cc. 

<

CO

500

£ 600

D 

(/) 

(/)

LU

£ 700 

O

cctu

X

Si 800

900

1000

TEMPERATURE

-30 -20 -10 o 10 20

TEMPERATURE, IN DEGREES CELSIUS

FIGURE 6.—Rawinsonde atmospheric profile, 7:15 a.m. EDT, 

August 5, 1973, Dulles International Airport, Washington, 

D.C. Data are from the National Weather Service, Asheville, N. C.

tics to calibrate the sensor to the surface. To accomplish the calibration, it was necessary to determine 

T in the sensing wavelength interval and R f a in 

the same interval. A Rawinsonde profile, made 3 

hours before the satellite passage, was then used to 

determine the water vapor content of the optical 

path (fig. 6). Although the atmospheric sounding 

was made at only one point in the imaged area 

(Dulles International Airport) and represented conditions 3 hours before imaging, the stagnation and 

apparent homogeneity of the airmass made the 

record quite usable. Synoptic weather conditions, 

indicated by both the profile and the daily weather 

map, consisted of a moist and turbid surface or 

boundary layer separated from a thick capping layer 

of dry subsiding air above by a distinct temperature 

inversion at about 740 mb. Stagnation in the boundary layer is indicated on the weather map by low 

wind velocities and a high pressure cell over the 

central Atlantic States. The inversion apparently 

inhibited, but did not eliminate altogether, the buildup of small and widely scattered cumulus humilis

clouds. Bands of high cirrus clouds extended across 

Chesapeake Bay in the southern corner of the 

imaged area (fig. 7).

Water vapor in the optical path acts both as an 

absorber and emitter of radiant energy between 10.2 

and 12.5 ^m, even though this spectral band is in the 

least absorptive part of the 8- to 14-/xm water vapor 

window. Absorption is a function of transmissivity, 

which is defined in terms of water vapor by the 

Bouguer-Beers Law as e~cw, where c is a mass absorption coefficient with a value of 0.1 in the 

10.2-12.5-/xm sensing band (Kondratyev, 1969, p. 

119) and w is the water vapor expressed as precipitable centimeters (gem- 2 ). The value of w can 

be determined by the following equation:

w =

(p2 -

9

(ID

where m is the mean mixing ratio of the layer of 

atmosphere in grams of water per kilogram of dry 

air, p2 — pt is the pressure depth of the atmosphere 

layer in millibars, and g is the acceleration of 

gravity, 980 cm s~2 . The Rawinsonde sounding lists 

temperatures and dew points as depressions from 

kinetic (sensible) temperatures for various pressure heights. A pseudoadiabatic chart is the simplest 

way to obtain mixing ratios, although a form of 

Teton's equation can be used:

m

3798(107 - 5 */rc + 237 - 3 ) 

P

(12)

where TD is the dew point in °C and p is the atmospheric pressure in millibars. The value of m is very 

close to that of qw, the specific humidity, and for our 

purposes can be considered the same.

Since upward emission from each small but finite 

layer of the atmosphere is modified by the layer 

above through attenuation and additional emission, 

a high-resolution numerical integration of values 

upward will yield the most accurate value of Rt a 

into space. Analysis is more easily accomplished, 

however, by treating the atmosphere as consisting of 

a smaller number of layers, particularly when more 

than one modifying element must be combined at 

each level (table 2). The water vapor and temperature profile data are tabulated as if the atmosphere 

were in five layers below the inversion but in a 

single layer above, because the subsiding air above 

has low water vapor content and turbidity and thus 

is relatively inactive as a modifying element. The 

data for layer 1, that closest to the surface, are at 

the top of the table 2.

The overall thermal infrared (IR) transmissivity 

of the profile is calculated by multiplying all discrete
A16 THE INFLUENCES OF LAND USE AND LOWER COVER IN CLIMATE ANALYSIS

FIGURE 7.—S-192 scanner image of the mapped area. The image is a film print of data from scientific 

data output channel 7 (band 6), the channel (0.68-0.76 /on) used for mapping surface albedos. The 

image, a mosaic of information from two sequent tapes, retains the conical linescan format.

layer transmissivities together. For the simple water 

vapor model described by table 2, the value of T is 

0.823. R | a is calculated by multiplying the upward 

radiance of layer 1 (R^ t) by the transmissivity of 

layer 2 (T2 ) to which is added the upward radiance 

of layer 2 (R2 f ) . This accumulated value is processed 

through layer 3 in the same manner, and this method 

is repeated for all layers and into space. The process 

is shown by the following relationship:

a=

= 1.3534 ex -4 Wcm~2 ster (13)

The subscripts indicate the layer numbers. A calibration model which considers only water vapor 

modifications is then

- 1.3534 ex-4 (14)

0.823

Using this model and making the assumption that a 

PCM count of 149 is the equivalent of 27°C (300°K), 

the radiation temperature of the hottest data cell 

in the Baltimore CBD was 39.18°C.

It is apparent from the synoptic meteorological 

conditions that prevailed during the Skylab 3 pas-
MAPPING OF SURFACE-ENERGY BALANCE WITH SATELLITE DATA A17

TABLE 2.—Gray-window analysis of the atmospheric profile, including effects of water vapor only

[Data source: Rawinsonde profile for Washington, D.C. (Dulles International Airport)]

Range: Pressures at top and bottom of air layer, in millibars. 

Depth: Pressure depth of air layer, in millibars.

r°C: Mean temperature of air layer, in degrees Celsius. 

DP°C: Mean dew point of air layer, in degrees Celsius.

•in: Mean mixing ratio of air la>er, in grams per kilogram.

w: Water vapor in air layer as precipitable centimeters (grams per

square centimeter). 

Tict-: Transmissivity of air layer containing water vapor.

e: Emissivity of air layer.

R^ : Radiance emitted upward from air layer, in watts per square 

centimeter per steradian per micrometer.

Note: Values for 2V n, e, and R'V apply only to spectral band 13, scientific data output channel 21, of the 10.2-12.5 nm. sensing window.

Layer

1000-950

950-900

900-850

850-800

800-740

Inversion — 740

740-365

Depth

50

50

50

50

60

mb (2.45

375

T°C

21.64

19.28

15.56

11.90

7.15

km)

-12.0

DP°c

12.6

9.6

5.0

1.3

1.8

9

8

6

5

5

-36.0

ra

.3

.2

.2

.1

.7

.35

w

0.474

.418

.316

.026

.349

.134

rp

0.954

.959

.969

.974

.966

.987

€

0.046

.041

.031

.026

.034

.013

3,

3

2.

1.

2

Bt

,9563 ex —5

.4057 ex -5

.4350 ex —5

.9302 ex —5

.3409 ex -5

.6429 ex —5

sage on August 5 that factors other than water 

vapor were modifying the surface signal. A meteorological visibility of only 16 km reported at Washington, D.C., on the daily weather map indicated 

significant atmospheric aerosol density (turbidity), 

probably associated with urban pollution that accompanies stagnant air conditions. It has been noted in 

similar synoptic situations that the turbidity becomes fairly evenly dispersed when the stagnant 

airmass persists for some time. The aerosols remain 

in the boundary layer because the temperature inversion inhibits vertical motion of lower air into the 

air aloft. This was observed on two occasions in the 

summer of 1975 during aircraft departures from 

Dulles Airport west of Washington. Turbidity disperses upward in the boundary layer, possibly with 

a slight increase just below the inversion. A homogeneity of atmospheric components in the surface 

or boundary layer is assumed for this study, however.

Previous work in atmospheric optics by McClatchey and others (1972) as well as by Pease in j 

the eastern end of the Los Angeles Basin in the 

summer of 1975 indicates that aerosols and gaseous 

pollutants in a scanner optical path both absorb and 

radiate between the wavelengths of 10.2 and 12.5 ^m. 

Therefore, during turbid pollution conditions, as 

apparently existed over the Los Angeles test site at 

the time of imaging, the effect of aerosols and pollutants must be included in the gray-window calibration model.

Turbid air transmissivity (Tf ) in thermalinfrared wavelengths is not the same as in visual 

wavelengths for the same airmass conditions. In the 

short wavelengths, the aerosols primarily scatter 

light. McClatchey and others (1972) indicate that

at a wavelength of 0.514 /*m and a meteorological 

visibility (7,,,) of 5 km, scattering of light exceeds 

absorption by a ratio of more than 1 : 100, 000, but at 

a wavelength of 10.59 ^m in the same airmass, absorption will be almost triple the scattering. Several 

methods exist for ascertaining Tt in the visual wavelength realm, but conversion of this value to a 

thermal-infrared equivalent is still a problem.

Meteorological visibility is routinely reported with 

weather observations and can be converted to the 

visual transmissivity of turbid air (TV,- S ) by either 

of two methods. One involves the following two 

steps :

(1) o-^km- 1 = 3.912/7,,,, and (2) Tvt ,

(15)

where o-.sl(. is the shortwave scattering coefficient. A 

second and more direct method is

r, i. s km- 1 =(0.02) 1 /%. (16)

The second method depends upon three facts: (1) 

that by definition, the value 0.02 is the minimum 

contrast the eye can detect when a dark object is 

outlined against the skyline, (2) that contrast under 

these conditions is interchangeable with transmissivity, and (3) that sequential transmissivities are 

multiplicative and therefore bear an exponential relation to each other. A meteorological visibility of 

16 km becomes a T1( , of 0.783 km- 1 by either 

method.

A more difficult and less definitive step is the 

conversion of a visual transmissivity to an equivalent transmissivity in the thermal infrared (Tfir), 

which is an expression needed in a complete atmospheric correction model. There is little in the litera-
A18 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

ture to use as a guide for this process. On the basis 

of Mie theory, as expressed by McClatchey and 

others (1972), the ratio of extinction coefficients 

between the 0.51-/mi and 10.5-<mi wavelengths is 

0.082. Tnr can then be expressed as (Tn,) 0082 . The 

term "extinction coefficient (y)" is used here to 

include the sum of both absorption and scattering 

coefficients. In figure 8, extinction coefficients for a 

thermal-infrared wavelength of 10.591 /xm are 

plotted against meteorological visibilities ranging 

from 5 to 23 km. Use of this translation of visual 

to longwave turbidity transmissivities for the August 5 imaging gives a Ttlr of 0.980 km- 1 : 0.952 for 

the entire boundary layer below the inversion, or 

about 0.990 for each of the 50-mb layers in the profile calculations. Although Tilr modifies the surface 

signal less than water vapor (TWH. = 0.823 km- 1 ), 

Ttir nevertheless is a factor to be considered in the 

calibration.

A calibration model which incorporates both 

water vapor and turbidity effects, based upon the 

above value of Tti, is

R0

#,-1.712 ex -4 (17)

0.7884

The ratios of thermal-infrared (10.59 /x,m) to visual extinction coefficients (y«r/y«to), obtained by observations within the Los Angeles Basin in summer, 

are on the same order as the values obtained by the 

Mie theory at times when turbidity consists only of 

moisture haze or evaporating fog. When these conditions prevail, ratios that range from 0.05 to 0.10 

have been empirically ascertained. These ratios compare favorably with the ratio of 0.082 derived from 

the work of McClatchey and others (1972). Under 

conditions where smog pollutants are prevalent, 

however, the ratio rises rapidly and approaches 1.0.

UJ 

O 

<-> .01

Z

o

o

.001

0 5 10 15 20 25 

METEOROLOGICAL VISIBILITY (VM) IN KILOMETERS

FIGURE 8.—Extinction coefficients (7) at a wavelength of 

10.591 pm, for various visibility ranges (McClatchey and 

others, 1972).

This high value is taken as evidence that gaseous 

pollution can be a significant factor in the graywindow effect. When mixtures of aerosol haze and 

gaseous pollution exist, ratios of yfir to yvis commonly 

range from 0.20 to 0.50 with 0.30 being a typical 

value for moisture haze that contains a moderate 

amount of gaseous pollution. This finding agrees 

with Yates and Taylor (Wolfe, 1965, p. 206-210) 

for summer measurements along horizontal paths 

over Chesapeake Bay, for which a typical ratio was 

0.30.

Spectrometric analysis of common smog gases 

supports the assumption of pollution effects. 

Although the strong absorption band of ozone 

(9.6 //m) is outside the sensing waveband, several 

minor constituents (for example, propylene and 

ethylene) have minor absorption-emission bands 

within the sensing band. Peroxyacetyl nitrate 

(PAN), a persistent smog gas and one of the most 

prevalent where pollution is derived from automobile exhausts, has strong absorption-emission 

characteristics between 10 and 12 ^m (fig. 9). For 

the gas concentrations for which the curve was prepared, transmission in the thermal-infrared sensing 

band is only 74 percent, which suggests a significant 

gray-window effect.

Use of the 0.30 extinction coefficient ratio converts the 0.78 km- 1 visual transmissivity of August 5, derived from the meteorological visibility at 

Washington, B.C., to an equivalent Tnr transmissivity of 0.93 km- 1 or (0.78)°' 3 . This value translates to approximately 0.965 per 50-mb layer. An 

analysis of the optical path over the imaging site, 

which includes the turbidity-pollution factor, is presented in table 3. For each layer, the value of Tv,, 

from table 2 is multiplied by the Tt derived from the 

depth of the layer in fractions of a kilometer and the 

0.3 coefficient, to obtain the overall transmissivity 

(TKl.jf ) of the layer. Multiplication of all layer transmissivities gives a value of 0.6925 for the entire 

atmospheric column in the sensing waveband.

As shown in table 2, the R t from each layer is 

attenuated by the transmittance of the layer above, 

and so forth, to the top of the atmosphere. The 

values of R | in the extreme right-hand column of 

table 3, however, are based on the sum of the effects 

of water vapor, aerosols, and gaseous pollutants 

emission, and the total emissivity of the layer 

(e = l-!T) with respect to these three elements 

and its Kelvin temperature. The R t a recorded 

by a satellite-borne scanner is 2.4097 ex —4 

W cm- 2 ster- 1 ^m- 1 . From this value, a conversion
MAPPING OF SURFACE-ENERGY BALANCE WITH SATELLITE DATA A19

100

90

80

70

60

50

40

30

20

10

23456789 10 11 12 13 14

WAVELENGTH, IN MICROMETERS

FIGURE 9.—Absorption spectrum of peroxyacetyl nitrate. The non-shaded area is the 10.2- to 12.5-^ni sensing window 

of thermal-infrared scientific data output channel 21 of the S-192 scanner (Scott and others, 1957).

15

TABLE 3.— Gray-window analysis of the atmospheric profile, including effects of water vapor, aerosols, and gaseous pollutants

[Data source: Rawinsonde profile for Washington, D.C. (Dulles International Airport) 1115 GMT (7:15 a.m. EDT) August 5, 1973]

Air layer bottom to top: Pressure in millibars and elevation in kilometers. 

Air layer depth: Pressure in millibars and thickness in kilometers. 

Twv: Transmissivity of air layer containing water vapor. 

Tt: Transmissivity of air layer containing aerosols and gaseous pollutants.

Twr.t: Transmissivity of air layer containing water vapor, aerosols, and

gaseous pollutants. 

e: Emissivity of air layer.

R f : Radiance emitted upward from air layer, in watts per square 

centimeter per steradian per micrometer.

Air layer

No.

1

2

3

4

5

Inversion — 740

6

Bottom-top

mb

1000-950

950-900

900-850

850-800

800-740

mb, 2.45 km

km

0-0.42

0.43-0.85

0.86-1.32

1.33-1.82

1.83-2.45

2.46-8.21

Depth

mb

50

50

50

50

60

375

km

0.42

0.42

0.46

0.49

0.62

Water vapor

TV u

0.954

0.959

0.969

0.974

0.966

£

0.046

0.041

0.031

0.026

0.034

Bf

3.9563 ex

3.4057 ex

2.4350 ex

1.9302 ex

2.3409 ex

-5

— 5

— 5

— 5

— 5

— 5

Aerosols and 

gaseous pollutants

Tt

0.969

0.966

0.966

0.963

0.963

e

0.031

0.034

0.034

0.036

0.037

Total for

fit

2.6659 ex —5

2.8242 ex —5

2.6706 ex —5

2.6726 ex —5

2.5474 ex —5

entire atmosphere

Water vapor, 

aerosols, and 

gaseous pollutants

Twv.t

0.924

0.928

0.936

0.939

0.931

0.987

0.6925

R/\

6.6221 ex —5

6.2299 ex —5

5.1056 ex —5

4.6028 ex —5

4.8883 ex —5

0.6429 ex —5

2.4097 ex —4

equation for surface radiation taking a vertical path 

through the atmosphere to the sensor is

tf.-2.4097 ex -4 Ro = ——-————————. (18)

0.6925

The equation used to make the map of surface 

radiation temperatures (fig. 2) was corrected for 

the 15° slant path of the conical scan pattern of the 

S-192 scanner and is:

c -2.4947 ex -4 (19)

0.6835

but values from either equation are similar. The 

procedure for converting PCM counts of SDO channel 21 to radiation temperatures in degrees Celsius 

is given in table 4.

To corroborate the values of T and R | a used to 

compute equation (19), a day was chosen in which 

a mixture of moisture haze and pollution in the 

eastern end of the Los Angeles Basin represented 

the mixture in the boundary layer through which the 

Skylab scanner imaged on August 5 over the Washington-Baltimore area. The day was July 24, 1975, 

and the site was near the city of San Bernardino 

where atmospheric soundings were being made by 

the Forest Fire Laboratory of the U.S. Forest Service. Downward longwave radiation between 10 and 

12 /*m was measured with a Barnes PRT-5 radiation theromometer. After corrections were made to 

the San Bernardino data for the difference in depth, 

temperature, and atmosphere profile between the
A20 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

TABLE 4.—Procedure for converting pulse code modulation (PCM) counts to temperature in degrees Celsius (T°C)

[If a model other than the urban model described is desired, substitute different values based on R\ a and T for the constants in step 2 and alter

conversion factor appropriately]

Step 1. Convert PCM count to R, :

JB,=1.0513 (Ao+CAi).

where: R,=.radiance received at scanner in

W cm~2 ster'1 /unr1, 

1.0513=:correction/niatching factor,

C=PCM count, and 

4a=4.7650ex-6.

Step 2. Convert R* to R0 : where: S0=radiance at surface in W cm~2 ster~

jRs -2.4947 ex -4 

0.6835 '

StepS. Convert R0 to T°K: where: y/f^teniperature, in degrees Kelvin.

T°K=- 1251

In

0.05921

+ 1

Step 4. Convert T°K to T°G:

turbid layers at the two sites, the down-radiance at 

San Bernardino was computed to be 2.4397 

ex-4 Wcm- 2 ster-Vm" 1 . This compared well 

with the value of R^a used in equation (19). Calculations using atmospheric profile data show that 

downward longwave radiation measured at the surface is essentially the same as upward longwave 

radiation at the top of the atmosphere.

Because of the uncertainty in the accuracy of the 

corrections made for gaseous pollution and turbidity, 

the differences between the three conversion models 

were determined by using each to calculate a surface 

radiation temperature for a high PCM count 

(PCM = 176). It was assumed that a PCM count of 

149 is equivalent to a surface radiation temperature 

of 27°C. For each of the following equations, a PCM 

count of 176 is equivalent to the following temperatures :

Equation 14:

water vapor only = 39.18°C 

Equation 17:

water vapor + aerosols = 40.14°C 

Equation 19:

water vapor + aerosols 

+ gaseous pollutants = 41.30°C

The difference is not great, but logic demands the 

most complete and accurate answer.

THE NEED FOR A DATA-CORRECTION FACTOR

As previously stated, a correction of about 5 percent was made in the conversion of PCM counts to

radiances. When the PCM count of 149 matches a 

radiation temperature of 27 °C, as occurred on 

August 5 on the surface of Chesapeake Bay, no 

choice of transmissivity will yield an upradiance 

sufficiently large to match the radiance calculated 

from the atmospheric elements in the optical path. 

This problem is not simply due to an incorrect choice 

of model values, because the upradiance is considered 

a function of the transmissivity of the active atmospheric layers in the column and because emissivity 

is the unity complement of the transmissivity 

(e=l — T). To satisfy the emissivity-transmissivity 

relationship without the correction factor, the temperature of the bay would have to be near 21 °C, far 

colder than was measured radiometrically from the 

Ridgely Warfield. The upradiance is also a function 

of air temperature, but, again, a much colder air 

than was present would be necessary to permit a 

true transmissivity-emissivity relationship without 

the correction factor.

The correction factor used was obtained by calculating a value of R0 that matched 27°C (300°K), 

using equation (6). A corresponding value for Rz 

was calculated with equation (8). The factor is then

Correction factor =

Rg (calculated) (20)

Rz (NASA supplied) 

A physical basis for this apparent discrepancy 

between spacecraft-acquired radiance values and the 

gray-window calibration may be due to the scattering of the sensed surface signals by aerosols in the
MAPPING OF SURFACE-ENERGY BALANCE WITH SATELLITE DATA A21

optical path. Whereas signals absorbed by these 

particles will be replaced by particle emission, photons scattered out of the optical path may have no 

replacements. Loss of radiant energy without replacement would cause the transmissivity of the air 

column to be less than the unity complement of the 

emissivity.

There has been too little investigation of the 

nature of scattering of longwave (10-/mi) radiation 

by typical aerosol populations. Workers in the field 

have been prone to ignore scattering when clouds 

are not present (Kondratyev, 1969, p. 558). Water 

vapor has been assumed to absorb but not scatter 

radiation of these wavelengths. McClatchey and 

others (1972, p. 24) indicate, however, that radiation at a wavelength of 10.591 /mi is about equally 

absorbed and scattered by typical aerosol populations, a fact that gives scattering some significance 

when the gray-window model is used to calibrate 

scanners sensing through turbid paths.

One can make a case for a condition in which any 

solid or liquid particle in the air of the optical path 

can deflect off-axis photons toward the sensor and 

in this way replace on-axis radiation lost by scattering. This radiation would not have originated at the 

point being imaged, however, nor would the radiation strength be tied to the emissivity and temperature of the air column as is the case in aerosol 

emission.

The directional nature of aerosol scatter of 10-/mi 

radiation is also poorly understood. More than 95 

percent of most aerosol populations, other than 

clouds and fog, are particles with radii of 1.0 /an or 

less (Junge, 1963, p. 117). Since this dimension is 

less than one-tenth the wavelength of the sensing 

radiation, moderate Rayleigh scattering should exist 

and considerable omnidirectional scatter should take 

place. Rayleigh scatter by a particle with a l-^m 

radius bears about the same relation to lO-^m radiation as scatter by a particle in cigarette smoke does 

to visible light. For particles in the population with 

still smaller radii, Rayleigh-type scatter would be 

even stronger. In the case of omnidirectional scatter, surface-emitted on-axis radiation should have 

about the same chance of reaching the sensor, after 

scattering, as aerosol-emitted radiation, a condition 

favoring the unity complement relationship of the 

emissivity and transmissivity of the optical path.

Many of the particles in aerosol populations over 

urban centers are opaque and, considering the ratios 

of their sizes to the sensing wavelengths, should predominantly backscatter by reflection (Kondratyev, 

1969, p. 182). The degree of backscatter, however,

depends upon the reflectivity of the particle and 

probably would be less than 10 percent of the incident radiation with a 10-/j.m wavelength. The remainder of the radiation would be absorbed. Diffraction scatter of the surface signal being imaged, 

although strongly forward, would still be deflected 

away from the sensor.

It is clear that scatter of the longwave radiation 

by aerosol populations in the scanning optical axis 

may create uncertainty in the gray-window calibration. The correction factor described may be a pragmatic and empirical solution to the problem of uncertainty, but a solution which necessitates a surface 

calibration target, such as Chesapeake Bay provided 

for this investigation.

CONSTRUCTING THE MAPS OF SURFACE ALBEDOS AND 

NET RADIATION

When the map of surface albedos (fig. 3) was 

made, the gray-window calibration of the scanner 

sensor to the surface was not used because no adequate model for handling the effects of the scatter 

of shortwave or solar light had been developed. A 

calibration approach was used in which natural surfaces, the albedos of which are well known, acted 

as surrogates for targets measured specifically for 

the inquiry.

Because albedo values are fairly conservative 

when measured for specific conditions, such as the 

seasonal state of vegetation, contrasting targets of 

known albedo were used. For the low end of the 

reflectance range, the water surface of Chesapeake 

Bay was chosen. Deep water with low turbidity and 

few waves has an albedo of about 2 or 3 percent. 

A value of 2.5 percent was given to the darkest 

water surfaces of the bay under cloud-free conditions. For the high end of the albedo range, fields 

with bright herbaceous or grass cover were chosen, 

and these were given an albedo value of 22 percent, 

which is typical for bright vegetated surfaces in the 

summer. The 22-percent albedo value matches measurements made in Baltimore in May 1972 by the 

authors, and is supported by the work of S.R. Pease 

and R.W. Pease (1972, p. 21), and Sellers (1965, 

p. 21). As an intermediate target value, an albedo 

of 10 percent was assigned to the CBD of Baltimore 

(Pease and Nichols, 1976, p. 1369).

A spectral sampling technique was employed to 

measure surface albedos with the S-192 scanner, 

rather than a technique that attempted to measure 

all reflected sunlight. Integration of information 

from all shortwave scanner channels would require 

extensive computer time and be prone to errors that
A22 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

arise from the weighting of a number of channels 

with overlapping wavebands. Dirmhirn (1968) determined that spectrally selective sensors are feasible for measuring albedos if they sample on either 

side of the rapid rise in plant reflectance that centers at a wavelength of 0.725 ^m. Two bands are 

used, one sensing at wavelengths shorter than and 

the other at wavelengths longer than this critical 

point. Weighting of the results was based on the 

wavelength distribution of sunlight, 60 percent in 

the shorter wavelengths and 40 percent in the longer 

(Pease and Pease, 1972, p. 7). One band can be 

used if it straddles the midpoint with a sensitivity 

that matches the 60/40 weighting. Although spectrally narrow, the sensitivity of the S-192 band 6 

(SDO channel 7) of 0.68 to 0.76 ^m has this weighting and has been used singly to determine albedos. 

Vegetation was the critical surface sampled. Other 

surfaces, such as blacktop, water, and a generalized 

CBD surface, have monotonic reflectance curves that 

change gradually and uniformly and, for them, the 

narrow sample suffices well.

Because the relationship of PCM counts to the 

radiances they represent is linear, because a targettype calibration was used, and because the final 

product does not represent an energy value, no conversion was made from PCM counts to radiances. 

Rather, the count numbers for the calibration targets were plotted directly against the albedos they 

represented to create a conversion curve.

Several cells with albedos higher than 25 percent 

are present on the map (fig. 3). These light spots 

are clouds and land classified as disturbed or transitional, which typically may reflect more than 50 

percent of the sunlight. One cloud shadow west of 

Baltimore appears as an area of low albedo. On a 

map of surface albedos, made from information 

acquired above the cloud level, clouds produced unwanted spectral noise, but no attempt was made to 

remove it.

Procedures for constructing the albedo map are 

listed in table 5. The construction of maps of surface albedos and net radiation is not the major interest of this inquiry, which is to compare the thermal state of the surface measured by remote means 

with that determined by simulation. These maps 

were made, however, to demonstrate the feasibility 

«f measuring spatial patterns of energy exchange 

phenomena requiring the combination of information from two or more bands of a multispectral 

scanner in a spacecraft.

Each 100-pixel cell of the net radiation map not 

only includes a radiation temperature and albedo

TABLE 5.—Procedure for constructing an albedo map of the 

Washington-Baltimore area

1. Create a file of SDO channel 7 PCM counts.

2. Block filter this file into 100-pixel cells (10 adjacent 

pixels on 10 adjacent lines) and average the pixel values 

within each cell.

3. Make a histogram of the values of the block-filtered cells. 

This histogram will show a trimodal distribution: low 

values for Chesapeake Bay, an intermediate grouping 

for land reflectances, and a small high-value group for 

cloud reflectances.

4. Assign the lowest PCM counts of the lowest count group 

an albedo value of 0.025 (2.5 percent).

5. Assign the highest PCM counts of the intermediate 

group an albedo value of 0.22 (22 percent).

6. Create a curve based on PCM counts plotted against 

albedos. This curve will be a straight line between the 

two plotted points.

7. Write a linear equation for the curve.

8. Create a plot tape of albedo values that correspond to 

the cells of the matrix.

9. Enter the hierarchy of value intervals into the plotting

program: for example, 0.05 (5-percent) intervals. 

10. Plot map by:

a. Line-printed cells for graymap (2- x 2.5-symbol

spacing),

b. Machine-drafted grid-cell plot as in figure 3, or by, 

c. Contouring data, if memory capacity of computer 

permits it. Additional block filtering is suggested if 

isarithmic maps are desired.

information but also solar input and atmospheric 

downradiation data.

To calculate the radiation balance for each data 

cell, the radiation temperature must first be converted to energy emitted by the surface (£"T) by 

the use of an appropriate form of the StefanBoltzmann equation, (5.678 ex -8) (T°K)\ with 

energy in watts per square meter. Determination 

of energy absorbed by the surface (E-l ) is more 

complex. Incoming solar radiation, both beam (Q) 

and diffuse (q), is multiplied by (1 —a), where a 

is the albedo, to find the solar energy absorbed. The 

value for incoming solar radiation, measured with 

a pyranometer at Loch Raven Reservoir at the time 

of the satellite overpass, was 859 W m~2 . Based on 

the assumption of a homogeneous boundary airmass, 

this value was used for the entire area mapped. 

Because no measurement was made of the downward longwave atmospheric radiation at the same 

time, a value was estimated by use of an empirical 

equation by Brunt, as restated by Kondratyev (1969, 

p. 572) :

(21)

where G0 is the downward longwave radiation in 

W m-2 , a the Stefan-Boltzmann constant of 5.678 

ex —8, Te the temperature at screen height (approximately 1.8 meters above the ground) in degrees
MAPPING BY NUMERICAL SIMULATION A23

Kelvin, Pv the vapor pressure in millibars, and «i 

and b are empirical constants of 0.55 and 0.056, 

respectively. When all constants and a vapor pressure of 16.71 mb are entered, the equation becomes

G^O.74^. (22) 

The net radiation (Rn ) for each data cell becomes

(23)

where e is an emissivity of 0.95, T0 the temperature 

of the Earth's surface in degrees Kelvin, Q + q the 

incoming solar radiation in watts m~2 and a the 

albedo derived from figure 3.

The accuracy of the net radiation map (fig. 4) is 

only as good as the accuracy of the albedo map. 

Values computed, however, appear reasonable considering the humid conditions of the boundary layer 

which inhibited evaporation from Chesapeake Bay 

and evapotranspiration of vegetated areas. Loss of 

absorbed energy by evaporation during daytime 

hours is the prime cause of a positive net radiation 

balance.

MAPPING BY NUMERICAL SIMULATION

Numerical modeling of the planetary boundary 

layer and interface has progressed significantly 

since the early studies by Halstead and others 

(1957). Models have been developed that display 

differing degrees of complexity and sophistication. 

The type of models range from those primarily concerned with the terrestrial surface and a shallow 

boundary layer above it (10 to 30 m) , to those which 

are dynamic characterizations of the entire planetary boundary of the atmosphere and include the 

traditional Ekman spiral (surface to 1,000- or 

1,500-m altitude) .

Because there are a variety of models for use in 

surface energy budget studies, and because many 

of the workers in the field are less than completely 

familiar with numerical simulation of surface energy budget phenomena, this section first describes 

in general terms the model that has been used and 

then its application to the two-dimensional mapping of conditions that match the maps derived 

from remotely sensed data.

SURFACE ENERGY BUDGET MODELS

Models developed around the surface energy 

budget approach have been employed in a wide 

variety of applications that include agriculture 

(Halstead and others, 1957; Myrup, 1969), permafrost, and pack ice behavior (Outcalt, 1972a, b. and 

c). In general, the surface models can be subdivided 

into two groups: the first is based upon the dynamic 

equations of motion, with mass and energy related 

to wind and temperature in a series of discrete sublayers of the boundary layer; the second is based 

on the assumption of steady-state conditions with a 

constant downward longwave radiation from the 

atmosphere, lack of advective effects, and a clear 

or open sky. Both groups of models use data collected at a single point and therefore are spatially 

one-dimensional.

This study utilizes the second, the simpler steadystate model, but extends it spatially to two dimensions. Steady state was employed by Watson and 

others (1971) in a radiative-conductive surface 

model which, however, does not consider turbulent 

transfer of energy in the atmosphere, to describe 

geologic patterns according to differences in thermal characteristics of surface materials. Constant 

atmospheric inputs also have been used by Myrup 

(1969), Outcalt (1972a), Goddard (1974), and 

Kahle and others (1975).

THE OUTCALT CLEAR-SKY MODEL

Myrup (1969) pointed out possible applications of 

the energy-balance approach to the modeling of 

urban-rural temperature differences. Outcalt's 

(1971) digital version added several specific urbanoriented input parameters, which make the model 

both applicable to the study of the interactions 

of land use and land cover and climate and more 

easily extrapolated into a two-dimensional system 

which is based upon land use and land cover polygons. Because the current work is an exploratory 

inquiry, a clear-sky model which considered only 

low-velocity air movement was chosen to reduce the 

number of factors to be considered. The model is 

summarized in the following section.

The model is based upon the surface energy conservation equation (eq. 2), which can be rewritten 

as

Rn -H-LE-G = Q (24)

In this form, each of the four terms, or flux densities, are functions of an equilibrium, which is an 

inherent property of the surface energy balance 

for times that range from 1 second on up. If all 

other inputs to the model are known, either observed 

or derived, there will be only one surface temperature that will drive equation (24) to a zero equilibrium. Temperature is a controlling factor in each of 

the energy flows related to the four terms but is
A24 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

most obvious in the role it plays in Rn . An iterative 

algorithm is used to determine the one temperature 

that satisfies the equilibrium of the equation.

NET RADIATION

To expand the basic simulation model into a usable 

form, each of the flux densities of the energy flows 

must be examined separately. Flux densities are the 

intensities of energy flows and, for radiant energy, 

are values of R. Balance at the surface between 

downwelling radiation (Rl) and upwelling radiation (R |) is separated into solar or shortwave 

(Rsw ) and longwave (Riw ) components in the following equation:

Rn = (Rsw I - Rsw t) + (Rto I - Riv, t) • (25) 

Here, Rsw I represents solar radiation incident upon 

the terrestrial surface and R8W f solar energy reflected by the surface. The difference in the pair of 

values is the solar radiation absorbed by the surface. Downwelling longwave radiation (R^l ), is 

that emitted by the atmosphere, a small part of 

which (usually less than 10 percent) is reflected 

and is included with the surface emission in the 

term (Rlw ^).

To calculate Rslo l the following procedure is 

used. First, the solar beam received at any moment 

upon an imaginary plane parallel with the Earth's 

surface and at the outer edge of the atmosphere, per 

unit area is

I ———— V r

2 cos Z (26)

where Q8e is the solar constant in W m~2 ; r and rt 

are the mean and instantaneous Earth-Sun distances^ respectively ; r' is the Earth-Sun distance 

when f=l and Z is the solar zenith angle. At any 

particular moment, Z is given by

<j>-cos S-cos h), (27) 

where 0 is the latitude, 8 is the solar declination 

(north positive), and h is the hour angle (morning 

negative) .

Empirical expressions ascribed to Brooks by Gates 

(1962) are used to calculate the depletion of Q,c by 

atmospheric scattering and, to a small extent, by 

absorption. If the sum of the Brooks expression is 

considered an extinction coefficient, combination 

with equation (26) yields :

- )«exp - 0.089

/*•*' / I

Tt

1013

0.75 -0.174

(28)

20

°-°-0.083 cos Z,

where wm is the precipitable water in the atmospheric column in millimeters, d is the dust and haze 

particle concentration in particles per cubic centimeter, and m is the optical air mass and is equal to

In the case of a sloping surface, the term cos Z' 

replaces cos Z in equations 26 and 28 according to 

the relationship:

cos Z' = cos i- cos Z + sm Z-sin i- cos (« + «'), (29)

where i is the slope in degrees above the horizon, 

a is the solar azimuth angle and a the aspect of the 

slope, both in degrees with respect to due south 

(east negative). The solar azimuth angle can be 

found from :

a = sin- 1 (cos8-&infe/sinZ). (30)

The complete expression for direct solar radiation 

at the surface is equation (28) with cos Z' substituted for cos Z.

In addition to the direct but attenuated solar 

beam, the total downwelling solar radiation reaching the surface includes diffuse radiation downscattered by air molecules and atmospheric aerosols. 

The diffuse radiation has two components. The larger 

is the solar radiation downscattered as part of the 

beam attenuation ((?'), and the smaller is half of 

the scattering of surface reflected light (B) . This 

back scattering is about 6 percent of the reflection 

because it is Rayleigh scattering. Mie scattering by 

aerosols is toward space and does not significantly 

add to the value of B. The combined term (Q + q) 

is equivalent to Rsw I •

In the Outcalt model, the assumption is made that 

diffuse radiation received by the surface is equal 

to half that scattered by the atmosphere. This, of 

course, is a simplification that does not take into account the fact that most of the light scattered by 

particles as large as haze or dust reaches the surface as a part of the diffuse radiation because the 

light is forward scattered. The amount of forward 

scattering, however, depends upon the size distribution of the aerosol population and is therefore difficult to model without sophisticated input data. Lacking these data, the experimental inquiry in this 

paper used the simple assumption in the Outcalt 

model that all scattering is half upward and half 

downward. This assumption keeps the model intact 

but may underestimate the solar input slightly, especially if considerable atmospheric turbidity is also 

assumed. The calculation of diffuse downradiation 

is then:
MAPPING BY NUMEEICAL SIMULATION A25

2r' 2

jcosZ (1-exp [- 0.083 (d corr.

(31)

where corr. is a slope correction factor given by

corr. = cos (32)

B is treated as a separate term and is:

B = —— (Q + q') 1-exp - 0. 083 (d m)°- 9 corr.

2 (33)

The total downwelling solar radiation is, then,

(34)

and the net shortwave radiation at the surface is, 

therefore,

R»A - R,u^ =(!-«) (Q + q). (35) 

In most modern cities, multi-storied buildings 

create large areas of shadow. The downward solar 

flux is intercepted and only diffuse and backscattered radiation may reach the shadowed areas. The 

percentage of surface in shadow varies spatially, 

depending on the height and density of structures 

and vegetation, and also temporally, depending upon 

Sun angle. In Outcalt' s urban model, calculations of 

the "shadow fraction" (SF) are based on an arbitrarily chosen function,

SF=(l-cosZ)\ (36)

Accompanying the shadow effect, which causes 

a decrease in solar radiation at the surface, is the 

effect of the vertical faces of buildings that intercept the solar beam and increase the reflected load 

at the surface. Again, this reflected radiation component varies with Sun angle as well as with building configuration. To deal with this component, 

Outcalt (1971) considered the total solar radiation 

receipt for a vertical wall, rotated through the day 

so that the angle of its exposure at all times is 

equal to Z. The geometrical calculations involved 

here are identical to those for the surface. The total 

"upright" solar radiation receipt (Ruprt) is given by

^liprt == Qiiprt + 2qi,p,-t, (37)

where Quprt is the beam radiation received by an 

illuminated wall and quprt the diffuse radiation received by a shadowed wall.

Combination of the additional "urban radiation" 

terms with the previously developed net solar radiation equation (35) produces

+ R

(38)

uprt

where q = q' + B, Si is the silhouette area of the 

average building1 , and S2 is the specific area or the 

averag-e lot area of each building. The terms Si and 

S2 are those defined by Lettau (1969) and will be 

used again in connection with the roughness length 

determination, the use for which he defined them.

Thermal radiation is calculated as a function of 

surface temperature and "effective sky temperature." The latter quantity is defined by Reifsnyder 

and Lull (1965) as the temperature "appropriate 

to" the total amount of longwave-emitted sky radiation; that is, the atmosphere is treated as a blackbody radiating1 at the effective sky temperature. 

Outcalt (1971) uses a constant daily effective sky 

temperature equal to the daily shelter-level mean 

temperature depressed by 22°C. The basic equation 

is then

Rnc = f(o-T f. kv — a TO), (39)

where RI W is the longwave component of net radiation, TMV is the mean temperature of atmospheric 

downradiation in °K, and e is the emissivity of both 

surface and sky and is assumed to be unity. In an 

urban area, however, building walls are obstacles 

to downward longwave global radiation and the 

enhancement of ground radiation. Outcalt (1972b) 

takes the approach of Reifsnyder and Lull (1965, 

p. 34), who define the view factor for thermal radiation as "the fraction of the radiation leaving a surface in all directions that is intercepted by another 

surface." The view factor (F) is related to obstacle 

height (h0 ) and the spacing of the obstacles (D) 

expressed by

D

= sin2 [ arctan

\ 2h0

(40)

Outcalt (1971) considers that the view factor (Ff ) 

is equivalent to 2 times the silhouette ratio (Si/S2 ), 

previously defined:

F' = 2(Si/S2 ). (41) 

Thus,

Rllc = t[(l-F')aT*slcy /(F-l)vT*0 -\. (42)

SENSIBLE HEAT AND LATENT ENERGY FLUXES

The fluxes of sensible heat and latent energy are 

represented by the traditional equations:

•do

and

H = — p CpKji

LE = — pLKw

(43)

(44)
A26 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

where p is air density, Cp is air specific heat at constant pressure, L is the latent heat of vaporization, 

Kh and Kw are turbulent diffusivities for heat and 

water vapor, respectively, d is potential temperature, qw is specific humidity, and z is the height 

above the surface.

In the Myrup and Outcalt models the diffusivities 

for heat, water vapor, and momentum (Km ) are 

assumed to be equal. By utilizing the logarithmic 

wind relationship and solving for (Km ), Kh and Kw 

may also be found.

Under near neutral conditions, wind speed varies 

exponentially with height and may be expressed by 

the logarithmic wind profile equation (Sellers, 

1965),

u =

k

\n (45)

where u is the windspeed; u* the friction velocity, 

k the Von Karman constant (0.04) ; and z0 the 

roughness length, the height above the surface at 

which the wind speed is zero. Roughness length is 

calculated by the formula

He

(Si/S2 ), (46)

where He is the mean height of objects above the 

surface. Differentiating equation (45) with respect 

to windspeed and height gives

du u

(47) 

dz kz

The flux of momentum near the surface (r) is described by

(48)

Substituting equation (48) in equation (47) gives

„ u*

Thus,

kz 

rkz

and since, by definition, 

then

w\ •— *7/ *** IP*? **• />t — W> t\jLt .

The substitution of equation (51) for u* yields

In z/z0

(49)

(50)

(51)

(52)

(53)

Since it is assumed that Kn = Kw = Km , the flux density equations become

-pCPk2 u fipw

and

£1 — -

In z/z0 3ln z

'

T 77t _

-o L kzu 7) aTM

In z/z0

.

3ln z

(54)

(55)

To improve the model, Outcalt (1972b) added a 

stability correction term to equations (54) and 

(55) and obtained this sensible heat equation

(56)

where Ri is the bulk Richardson number according 

to Fleagle and Businger (1963, p. 193) .

In finite difference form, equations 55 and 56 

become

H =

[In z/z0

and

LE = • — pLk'2 u

(57)

(58)

where r is the adiabatic lapse rate and Td denotes 

the temperature at the atmospheric "damping" 

depth («d ). Outcalt (1972b) defines this mixedlayer depth as that at which diurnal surface effects 

upon temperature and wind are almost completely 

damped out, that is, the depth of the mixed layer. 

Myrup uses 300 m as a constant damping depth for 

urban applications. Outcalt modifies this approach 

and calculates zd as the level in the atmosphere at 

which two particular expressions for diffusivity 

converge. 

These functions are

(59)

a bulk diffusivity value, and

5.184 ex 5

(60)

a thermal diffusivity term defined by Terzaghi 

(1952) as the damping depth for a 12-hour temperature wave in a homogeneous medium. In the iterative procedure used to find the convergent value of 

z(J , values of z« are incremented in 1-cm steps from 

the roughness length, and the final value for zd is 

obtained as the point at which the two functions 

converge. This admittedly simple approach results 

in values which vary spatially with surface roughness and from day to day depending on wind speed. 

The damping depth remains constant throughout 

the day, however.
MAPPING BY NUMERICAL SIMULATION A27

SOIL HEAT FLUX

The soil heat flux G is described by the equation

= k- (61)

where k is the thermal soil diffusivity of the soil 

Cs represents the soil volumetric heat capacity 

(Sellers, 1965), T8 is the soil temperature and za is 

depth in the soil. The integrated form of equation 

(61) for the one-dimensional case is

G = — p Cs (62)

dz.

where T2 and 2\ denote soil temperature at depths 

z-2 and zlt respectively. Diffusion here is considered 

to be purely a process of molecular conduction so 

that the thermal diffusivity can be considered independent of depth (Monteith, 1973). 

Thus,

} dzs

Substitution into equation (62) gives

G = — p Ca K —————,

(63)

(64)

representing the soil heat flux term in a one-layer 

system. The Outcalt model treats a four4ayer system using this same relationship, making possible 

the introduction of a realistic thermal lag into the 

diurnal soil temperature variations at different 

levels. The penetration depth of the diurnal temperature wave into the soil is calculated by the Terzaofhi 

(1952) relationship used previously (eq. 61). The 

soil damping depth (za ) is given by

zG =(12tK y\ (65) 

where t = 4.32x10* sec (12 hrs).

The additional depths, zG/2, 2 G /4, and z0/8 in the 

soil are used as nodes for the calculation of the soil 

heat flux term. At any particular level the rate and 

direction of temperature change is expressed by

^Tg ffifil 

• ——-. .(ob)

at ^

In finite difference form then, the temperature at 

depth z-2 in the soil at time increment / is given by

T0 (I -1)- -1)

where T3 is the temperature at some depth 2 3

, (67)

Q

TIME, IN HOURS

FIGURE 10.—Diurnal variation in substrate temperatures 

produced by the Outcalt clear-sky model.

The use of a set of similar equations, each corresponding to one of the five nodal depths, effectively 

introduces a lag in soil temperature change with 

depth. Figure 10 illustrates the nature of this lag 

for the case of a soil with thermal diffusivity of 

0.005 cm2 sec- 1 for winter solstice, clear-sky conditions. Substrate thermal diffusivity is generally 

expressed in cgs units.

GENERAL FLOW OF THE MODEL CALCULATIONS

Input boundary conditions required for the Outcalt model were divided into three categories. These 

categories of inputs and their primary function in 

the simulation procedures are shown in table 6.

Computed boundary conditions include the penetration depths of both the atmospheric and substrate diurnal temperature waves (eqs. 60, 61, 65, 

and fig. 10), the atmospheric exchange coefficient 

(eq. 53), and the roughness length (eq. 46). The 

general flow of calculations is shown in figure 11.

TWO-DIMENSIONAL SIMULATION AND MAPPING

Land use and land cover information was coupled 

with a surface energy budget model in an attempt 

to predict the distribution of urban temperatures in
A28 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

TABLE 6.—Input boundary conditions for the Outcalt clearsky model

I. Temporal data

Solar declination 1

II. Meteorological data

Atmospheric dust content 2 

Air relative humidity -•"'•*•* 

Precipitable water ~

III. Surface characteristics 

Albedo 5 

Obstacle height"

Radius vector of the Sun

Station pressure 2 8 

Air temperature 4 ' 8 

Wind velocity 1>b

Silhouette ratio c 

Surface wet fraction 7

1 Required for the generation of diurnal values of solar radiation.

- Used in the calculation of the attenuation of the solar beam by atmospheric scattering and absorption.

3 Used in the determination of the latent energy flux.

4 Serves as boundary condition at the level of the atmospheric mixing 

height.

•"' Used in the calculation of solar radiation terms.

6 Used in the calculation of the roughness length. The silhouette ratio is 

important in both solar and longwave radiation computations.

7 Used in the calculation of specific humidity at the surface. 

s At screen height.

Baltimore and its environs. Input parameters simulated the conditions that prevailed at the time of 

the imaging overpass of Skylab 3 at 10:05 a.m. EDT 

on August 5, 1973. With this timing, the map made 

from spacecraft-acquired data could be used to check

READ INPUT

BOUNDARY

CONDITIONS

J_

CALCULATE COMPUTED 

BOUNDARY CONDITIONS

GENERATE HOURLY 

SOLAR RADIATION VALUES

EXPRESS:

fln=f (7"o)

/.£=f (T0 )

H=1 (T0)

G=f(T0)

WRITE VALUES OF 

FLUX TERMS

FIGURE 11.—Flow chart for the energy budget simulation

model.

the results of the simulation experiment. No attempt 

was made to simulate the budget of the entire 

imaged area, only of the smaller area shown in figure 1, in order to keep the simulation experiment 

within the limits of feasibility.

Land uses are superimposed on types of land 

cover. It is the interpretation of land use and land 

cover that supplies input values and sets input 

parameters for simulation. The degree to which this 

is true is indicated by comparison of the observed 

and the predicted patterns of radiation temperatures. From the historical standpoint, the relationship of land use and land cover to temperature was 

considered by Hammon and Duenckel (1902) and 

Duckworth and Sandberg (1954), who pointed out 

that the highest temperatures within a city were 

usually recorded at an urban core comprised of the 

CBD and industrial districts. Later, Chandler 

(1967) pointed out that the density and height of 

buildings, rather than their area, determined the 

effect of urbanization upon temperatures. Lewis and 

others (1971) conducted temperature traverses 

through Washington, B.C., and noted that temperatures were linked to land use and land cover. Subsequently, Nicholas (1974), in a detailed study, 

compared different land use and land cover classification schemes as indicators of surface roughness.

LAND USE AND LAND COVER CLASSIFICATION AND 

SOURCES OF INFORMATION

The choice of a land use and land cover classification system which can be used to effectively distinguish differences in surface characteristics that are 

significant in the creation of local climates is essential for accurate model input data. Frequently, land 

use classifications are based upon strictly economic 

appraisals. For example, distinction might be made 

between banking and retail sales activities, but not 

between high- and low-density residential uses. This 

type of classification does not provide suitable differentiation of surface characteristics for climatic 

simulation. Zoning classifications that include density specifications should be ideal for providing data 

on pavement fraction and building spacing, but they 

often indicate only potential use and may not represent the actual land cover conditions of urban surfaces at a given time. Finally, the classification 

scheme selected must provide information for all 

parts of an urban area, not just the city center; and 

the scheme should be applicable to other urban areas 

as well.

To facilitate State and national land use planning, 

the U.S. Geological Survey developed a land use and
MAPPING BY NUMERICAL SIMULATION A29

land cover classification system for use with remotely sensed data (Anderson and others, 1972 and 

1976). This classification is particularly well suited 

for specifying surface characteristics that can be 

used as simulation inputs because land cover is included together with land use designations. A modification of the original Anderson system has been 

used for the energy budget mapping by numerical 

simulation described in this study. The classification 

is hierarchical, with two levels specified (table 7). 

The authors of this system realized that no single 

system of land use and land cover classification is 

suitable for the needs of all potential users and suggested that further breakdowns into third and 

fourth levels be developed by the user. Nicholas

TABLE 7.—Land use and land cover classification system for 

use with remotely sensed data

[Level I classification in boldface type; Level II in lightface]

1. Urban and Built-up Land

01. Residential

02. Commercial and Services

03. Industrial

04. Extractive

05. Transportation, Communications, and Utilities

06. Institutional

07. Strip and Cluster Settlement

08. Mixed

09. Open and Other

2. Agricultural Land

01. Cropland and Pasture

02. Orchards, Groves, Bush Fruits, Vineyards, and 

Horticultural Areas

03. Feeding Operations

04. Other

3. Rangeland

01. Grass

02. Savannas (Palmetto Prairies)

03. Chaparral

04. Desert Shrub

4. Forest Land

01. Deciduous

02. Evergreen (Coniferous and Other)

03. Mixed

5. Water

01. Streams and Waterways

02. Lakes

03. Reservoirs

04. Bays and Estuaries

05. Other

6. Nonforested Wetland

01. Vegetated

02. Bare

7. Barren Land

01. Salt Flats

02. Beaches

03. Sand other than Beaches

04. Bare Exposed Rock

05. Other

8. Tundra 

01. Tundra

9. Permanent Snow and Icefields 

01. Permanent Snow and Icefields

Source: Anderson and others, 1972.

(1974) has suggested a third level suitable for the 

purposes of urban climate simulation.

LAND USE AND LAND COVER DATA COLLECTION

A land use and land cover map that employs the 

two-level classification of table 7 is available at a 

scale of 1:100,000 for the Baltimore metropolitan 

area (U.S. Geological Survey, 1973). The photomosaic base for the map is gridded in square-kilometer cells of the Universal Transverse Mercator 

grid system. The portion of the map that includes 

the simulation site is shown at a reduced scale in 

figure 12.

The sizes of land use and land cover polygons vary 

considerably; the smallest mapped units are about 

0.04 km2 . Ideally, the area of each polygon within 

a given sampling cell (1 km2 ) should be accurately 

measured with a planimeter, or tabulations from 

digitized maps should be used to obtain the exact 

percentages of each land use and land cover type. 

Each of these methods, however, requires many 

man-hours of effort. As an alternative, a sampling 

scheme to collect and aggregate land use and land 

cover information from the map was employed in 

this study.

Urban climates can be examined at a variety of 

scales. Interest can focus on a single urban "thermal 

canyon," or the city can be viewed as a unit made 

up of a mosaic of Level II or more detailed uses. A 

knowledge of the dimensions of individual buildings 

that make up a street corridor in the CBD and the 

reflectivity of each building surface is necessary to 

model the radiation climate at a microscale. A mesoscale analysis may be based upon typical microstudies but also may be based upon analyses of general conditions that prevail in each land use and 

land cover type. A good example of the latter 

approach is to be found in the work of Nicholas 

(1974).

The present simulation study is on a mesoscale 

and covers, therefore, a rather large area. Treatment 

of block-by-block variations in input parameters to 

the model is not feasible. Furthermore, land use 

and land cover maps are not available for a microscale examination. Following procedures outlined by 

Jenner (1975), an aligned sample of eight points 

was taken to create an average value for a squarekilometer sampling cell. The values of the land use 

and land cover types upon which each of the points 

fell were averaged to obtain the value for the entire 

map data cell. The disadvantages frequently associated with this approach (Berry and Baker, 1968) 

do not apply here because the scale used is too coarse
A30 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

7«'JJ' ISO

0 1 2 MILES I——i—H——H—i

4 KILOMETERS

FIGURE 12.—Land use and land cover in the Baltimore area. Category designations 

appear in table 7 (U.S. Geological Survey, 1973).
MAPPING BY NUMERICAL SIMULATION A31

to pick up block-by-block periodicities in land use 

and land cover characteristics.

Land use and land cover information was extracted directly from the CARETS land use and 

land cover maps of the U.S. Geological Survey 

(1973). Adjustments were made in the classification 

system to more greatly differentiate characteristics 

useful in the simulation. With the aid of highaltitude aerial photographs, a subdivision was made 

between high- and low-density residential use, and 

"institutional lands" (schools and hospitals, for example) were treated as a combination of commercial 

and open land. Completely open lands were modeled 

as parks, and "strip " and mixed uses were classed 

as commercial.

SURFACE CHARACTERISTICS ASSOCIATED WITH 

LAND USE AND LAND COVER TYPES

The energy balance simulation model requires the 

following input data related to surface characteristics: (1) roughness length, (2) silhouette ratio, (3) 

wet fraction, (4) substrate thermal diffusivity and 

heat capacity, (5) surface albedo, and (6) surface 

emissivity. These terms are discussed in the following paragraphs.

For Baltimore, Nicholas (1974) calculated typical 

values for roughness length by the Leittau (1969) 

formula and developed a third level for the U.S. 

Geological Survey land use classification system 

based upon variations in z0 . In the present study, 

the values of z0 assigned to specific land use and land 

cover types are based upon the Nicholas results, as 

are typical silhouette ratios (table 8).

Values of the surface wet fraction (WF) were 

determined from high-altitude aerial color-infrared 

photographs. Because the WF at this season was 

closely related to the extent of vegetated areas, its 

value was readily determined from red plant signatures on the photographs. Association of these values 

with land use and land cover is also included in table 

8.

For the simulation, soil heat capacity was held at 

a constant 0.5 calories cm- 3 °C~1 . Typical thermal 

diffusivity (K) values for soil and artificial surface, 

are 0.005 cm2 sec- 1 and 0.020 cm2 sec" 1 , respectively. 

Values of (K) associated with land use types were 

made a function of WF by the relationship:

tf=[0.005TFF+0.020(l-W)]. (68) 

Variations in albedo over a city are caused by 

both the diversity of surface materials and by building geometry, together with the effects of multiple 

reflections. Albedos of urban surfaces, determined

TABLE 8.—Values of surface characteristics used as inputs 

for each land use and land cover category in the simulation 

model *

Category (percent) 2 (cm)

WF 

(percent)

Sl/S2

(ratio) 3

Residential:

High-density 14 151 5 0.19

Low-density 18 73 60 0.13

Commercial and services 15 78 5 0.02

Industrial 15 90 10 0.10

Extractive 12 3 10 0.01

Transportation 15 6 15 0.02

Open 22 80 90 0.71

Cropland 20 25 95 0.01

Orchard 20 115 90 0.25 

Forest:

Heavy* 22 127 95 0.25

Light 5 22 25 90 0.25

Nonforested wetland 18 10 95 0.25

Beaches 10 2 15 0.00

1 Data from Nicholas, 1974.

2 Data from Rung and others, 1964; Barry and Chambers, 1966; Pease 

and Nichols, 1976.

3 Derived from equation (38).

4 Designates 40 percent or more crown density. 

3 Designates 10 to 40 percent crown density.

from aerial studies, generally range from 0.11 to 

0.25 (table 8).

The e value of 0.90 applied to all surfaces was 

based upon values cited by Sellers (1965) for almost 

all natural and manmade surfaces. In the simulation, 

the value applies to the whole spectrum of the thermal infrared and therefore does not contradict the 

value of 0.95 used in the remote mapping experiment 

for the scanner sensing bands. Between wavelengths 

of 8 fj.m and 13 ^m, virtually all values of e are 

greater than 0.90 (Wolfe, 1965, p. 75).

SIMULATION OF SURFACE TEMPERATURE 

DISTRIBUTION

The approach for modeling the spatial distribution of surface temperatures follows that developed 

by Jenner (1975). For each land use and land cover 

type, a set of surface characteristics is input into 

the model, along with prevailing synoptic weather 

parameters. The output includes the surface radiant 

temperatures associated with that land use and land 

cover type. The average surface temperature for a 

single square kilometer grid cell is given by

(

m v 

^fi'i-Jc'Tk ) 

k=l /

/n, (69)

where k denotes the land use and land cover type 

and a total of m land use and land cover types is 

considered; Tk is the simulated temperature associated with land use and land cover type k; fi>jjk is 

the frequency of land use and land cover type k
A32 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

TABLE 9.—Meteorological conditions over Baltimore, Md. 

10:05 a.m. EDT, August 5, 1973

[Source: National Weather Service]

Meteorological parameter

Station pressure 

Precipitable water 

Dust content 

Mean air temperature 

Mean air relative humidity 

Mean wind velocity

Value

1015.0 mb 

40 mm 

1 3 particles cnr3 

24.0°C 

61 percent 

270 cm sec"1

1 According to the Brooks solar generator model as restated by Gates 

(1962). The value should be considered only a relative indicator of 

turbidity and has no real relationship to the actual aerosol population.

within the cell i, j ; and n is equal to the number of 

sample points in the grid cell.

The rationale is that each land use and land cover 

type contains a mix of surface properties that contribute to a characteristic temperature regime. The 

average temperature of a grid cell was the spatial 

average of the temperatures of 8 sample points representing different land use and land cover types.

Meteorological data for the Baltimore area, at 

10:05 a.m. EDT, on August 5, 1973, are listed in 

table 9. Surface temperatures associated with specific land uses and land covers are listed in table 10, 

as generated for 10:05 EDT of the test day. The 

spatially averaged temperature distribution for the 

entire simulated area is mapped in figure 13.

A COMPARISON OF SIMULATED AND OBSERVED 

SURFACE TEMPERATURE PATTERNS

Both the map made by simulation (fig. 13) and 

the map made from remotely sensed data (fig. 14) 

show the general heat island structure of the city

TABLE 10.—Simulated surface temperatures of each land use 

and land cover type, Baltimore area, 10:05 a.m. EDT, 

August 5, 1973

Land use and land cover type T°C

Residential:

High-density

Low-density 

Commercial 

Industrial 

Extractive

Transportation and Utilities 

Institutional 

Strip 

Mixed 

Open 

Cropland 

Orchard 

Forest by crown density:

Heavy (40 percent or more)

Light (10 to 40 percent) 

Water

Nonforested Wetlands 

Beaches and Sand

30.9

24.9

30.8

30.1

35.0

36.9

27.2

30.8

30.8

25.4

24.2

22.7

22.3

26.6

24.0

26.3

34.9

of Baltimore and the satellite heat islands of outlying commercial centers. Patterns of temperature 

distribution on the two maps have a general similarity but differ with respect to ground resolution and 

specific temperature values.

Patterns on the map made from spacecraftacquired information, the "observed" map, are more 

intricate than those shown on the map of the simulated temperature field. This difference indicates 

a greater spatial smoothing of data in the simulation 

process, where the 1-km2 cell was nearly four times as 

large as the 0.52-km2 cell used in making the observed map. The difference in resolution also arises 

from the fact that the simulation data cell included 

an average of only eight points taken from randomsized land use and land cover polygons, while the 

smaller remotely observed cell included an average 

of 100 pixel values derived from a more comprehensive surface analysis.

There is a noticeable difference between the two 

maps in the orientation of the axis of the Baltimore 

heat island: the horizontal axis of the observed 

map has a more northwesterly trend than the eastwest axis of the simulation map. This discrepancy 

points out the need for Level III land use and land 

cover mapping. The northernmost city boundary, 

an east-west line, was used to separate high- and 

low-density residential areas in the simulation because of the lack of a mapped boundary between 

these two residential types on the land use and land 

cover map.

A comparison of radiation temperatures of specific target areas on the simulated-data and observeddata maps indicates that simulated values are conservative. This is most obvious in the Baltimore 

CBD, where temperatures are highest, and the discrepancy is on the order of 6° to 8°C. On the simulated-data map, these temperatures fall in a hierarchical interval of 34° to 37°C, whereas on the 

observed-data map they slightly exceed 41 °C. Jenner 

(1975) found similar discrepancies when she applied the same modeling approach to Washington, 

D.C., and its environs and further ascertained that 

daily maximum temperatures were most prone to 

to this error. Possible causes for the discrepancy 

are given below.

APPRAISAL

The completion of this inquiry has indicated avenues for improvement and further study. Appraisal 

of results and suggestions for improvements and 

further investigation are discussed in the following 

sections.
APPRAISAL A33

01 2345 KILOMETERS

X»M«X< X yuJOO«-.«8

TEMPERATURES < C1

FIGURE 13.—Simulated surface radiation temperatures in the Baltimore area, 

10:05 a.m. EDT, August 5, 1973.
A34 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

•V".'\:«,;ViS'VVVV".-'.-".'\-y'.-A

FIGURE 14.—Observed surface radiaion temperatures, in degrees Celsius, 

in the Baltimore study area, constructed as a line-printed graymap 

for comparison with the map of 

simulated temperatures. Remotely 

sensed information is from the Skylab S-192 multispectral scanner, 

band 13, scientific data output 

channel 21.

40"

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xxxxxxxxx MPacgePP gaggggBSC IIIIIIIII

XXXX3XXXX 966646666 888858888 IIIIHIIII

xxxxxxxxx eaeeGeeee iaaaasgga aiiiinii xxJJxJJxx lleeeleee glgaaaaag iiiiiini

RADIATION TEMPERATURES C)
APPRAISAL A35

ENERGY BALANCE MAPS FROM REMOTELY 

SENSED IMAGERY

An understanding of the relationships between 

turbid air transmissivity in the visual wavelengths 

and the corresponding transmissivity in the thermal 

infrared is based upon the theoretical calculations 

of McClatchey and others (1972) at the Air Force 

Cambridge Research Laboratories. Experimental 

verification of this theory is still tenuous and is 

handicapped by the existence of gaseous pollution 

absorption and emission bands in the scanner sensing windows. Inquiry into the effects of gaseous 

pollution is still in an embryonic state, as the study 

of analogous conditions at San Bernardino indicates 

(see section on "Calibration Using Characteristics 

of the Atmosphere"). The nature of the scattering 

of radiation with wavelengths near 10 ^m by aerosols makes necessary a correction factor for remotely sensed absolute radiation values, which in 

turn necessitates the use of at least one calibration 

target. Because of this necessity, the study could 

meet the goal of no calibration target only with a 

possible 5-percent error.

This is a phase of atmospheric optics that needs 

more study. With present knowledge, perhaps the 

"safest" values of downward atmospheric radiation 

would be those measured with a broadband longwave radiometer on the ground at the time of the 

scanning overpass.

Although the primary effort of this inquiry has 

been to map surface radiation temperatures derived 

from the thermal-infrared spectral sensing band 

of the S-192 multispectral scanner, a lesser effort 

has been expended to develop ways to map surface 

albedos from shortwave sensing channels and to 

map the distribution of surface net radiation from 

the combination of data from thermal and shortwave channels. The use of a single channel to sample 

albedos is a shortcut. Results appear rational despite the narrow spectral window used (0.68-0.75 

/mi) because the band straddles the sharp peak in 

chlorophyll absorption. Significantly prominent absorption bands outside of the sampling band, however, can cause errors of some magnitude. A case 

in point is the albedo of snow, which is too high 

because the water absorption band at 1.4 /^m is not 

measured (Dirmhirn, 1968). Broadening the sample 

by using two channels, one on either side of the 

peak in chlorophyll reflectance, should give consistently better results.

A test of the accuracy of albedo mapping is an 

evaluation of the accuracy of derived values of net 

radiation, because these values combine information from the albedo sampling band with that from 

the thermal-infrared band. Any great error in ascertaining surface albedos would result in irrational 

net radiation values. The values of surface net radiation appear reasonable, however, considering the 

warm and humid surface boundary layer of the 

atmosphere that prevailed during the test. A net 

radiation value of 732 Wm~ 2 for the water surface 

of Chesapeake Bay may seem a little low, but the 

low value may be due to the slowing of the evaporation process by humid air.

The maps of the surface energy budget phenomena show the energy balance effects of different land 

use and land cover types and add to our understanding of the impact on climate of the diversity of manmade and natural surface elements. The fact, the 

shape, and the intensity of the urban heat islands 

in midsummer are shown. Forested lands, together 

with Chesapeake Bay, form cool annuli around the 

urban areas. These annuli conceivably could resist 

the movement of air in the lower boundary layer 

toward the cores of the cities. It is of interest to 

note that wooded lands have radiation temperatures 

similar to that of the air over the water surface of 

Chesapeake Bay. This may well reflect the fact that 

leaf temperatures are similar to that of the air that 

surrounds them, and the air temperature in the 

lower boundary layer is closely related to that of 

Chesapeake Bay under conditions of regional air 

stagnation.

Although the absorption of solar energy by city 

cores is relatively high, it is apparently offset by 

the high surface energy emission that is responsible 

for the heat island effect. Net radiation, the absorption of radiative energy into the terrestrial substrate system, is about the same as for surrounding 

nonurban lands. The maps demonstrate, then, that 

cities are not islands of high intake of radiative 

energy despite their existence as islands with temperatures higher than the surrounding environs.

Clouds comprise unwanted mapping noise. A 

cloud appears on the maps as a spot of comparatively low radiation temperature, high albedo, and 

slightly low net radiation when the temperature 

and albedo anomalies are combined. Cloud shadows 

are spots of slightly low temperature and false low 

albedo, a combination that produces areas of false 

high net radiation when compared with surrounding areas with the same land use and land cover. 

When the percentage of cloud cover is more than 

minimal, mapping by remotely sensed data becomes 

impossible. Cloudiness is a constraint upon the utility of the remote information gathering procedure.
A36 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

These are but a few observations and applications of the information that can be gleaned from 

synoptic maps of surface energy balance phenomena 

made from remotely gathered data. Substantial success has been achieved in using multispectral scanners as imaging radiometers to acquire the necessary data and construct these maps, but the problems mentioned in the appraisal still need to be 

investigated and, with knowledge so gained, the 

methods improved.

ENERGY BUDGET SIMULATION

The noteworthy achievement of the simulation 

mapping experiment has been the development of 

ways to extend the Outcalt simulation model into 

a spatially two-dimensional form suitable for the 

construction of maps. Temperature distribution 

patterns so derived agree in form with those on 

maps made from multispectral scanner data but 

have poorer resolution of pattern features. Temperature ranges and hierarchies are similar to those 

on maps of remotely sensed data, but absolute temperatures are lower. There is the possibility, of 

course, that either or both of the mapping methods 

are in error, but the remotely sensed map data 

correlate better with ground-measured values. The 

following is an examination of possible sources of 

error in the simulation.

The conservative nature of the model, as regards 

temperature, may have several possible causes. In 

equation (31), half of the diffuse solar radiation 

is assumed to be scattered toward space, whereas, 

in reality, this probably does not occur. Only Rayleigh-scattered radiation fits this specification, and 

its value amounts to less than 6 percent of the solar 

constant. Pure Rayleigh scatter is seldom experienced at the surface; more commonly it is a mixture 

of diffuse radiation produced both by air molecules 

and much larger aerosols. Since scattering by aerosols is predominantly forward, only a small amount 

of solar radiation is lost to the surface by atmospheric turbidity. Pyranometric readings on smoggy 

days are almost as high as on clear, nonturbid days, 

perhaps being deficient by only a few percent. If 

half of the solar radiation is scattered by aerosols 

back into space, the value of solar input to the surface is reduced and simulated temperatures may be 

lower-than-real. A restructuring of equation (31) 

would result in a more realistic solar generator for 

the model.

Another need in the model is a valid statement 

of turbidity (eq. 28). In the Outcalt model, dust

and haze concentrations of one to three particles 

per cm3 have been used. The definition in equation 

(28) of the dust and haze particle concentration 

as particles per cubic centimeter is meaningless 

because the definition does not specify the size distribution of the aerosol population. According to 

McClatchey and others (1972), small haze particles, 

with radii of 0.10 /xm, average about 19,000 per cm3 

in the first 10 km of the atmosphere when the meteorological visibility is 5 km at the surface. Under 

the same conditions of visibility, particles with radii 

of 1.0 fjim average only 1.5 per cm3 . A definition that 

approximates values used in the current form of 

the solar generator model is the number of counts 

per cubic centimeter of particles with radii of 1.0 

pm, averaged over the first 4 km of an atmospheric 

column as follows: five samples in all, with counts 

taken at the surface and 1-, 2-, 3-, and 4-km levels. 

The definition of symbol (d) in equation (28) is 

changed from that of a dust and haze particle concentration to a "dust factor," on the basis of the 

above description. A graph which relates that dust 

factor to meteorological visibility is shown in figure 

15. The graph is based on the assumption of a typical 

distribution of aerosols upward from the surface and 

may need modification if a pronounced inversion 

traps particulates.

0 5 10 15 20 25 30 

METEOROLOGICAL VISIBILITY (VW),IN KILOMETERS

FIGURE 15.—The dust factor as a function of meteorological 

visibility. (Data from McClatchey and others, 1972, p. 8-9).
SUMMARY A37

Other possible causes for the conservative nature 

of the model are the assumption of a constant diurnal damping depth, the assumption that the longwave radiant temperature of the sky is a constant 

22°C below the sensible air temperature at screen 

height, and the use of general constants for soil 

heat flux diffusivity and heat capacity.

In addition to needed refinements of the model, 

attention should be focused upon a land use and 

land cover classification with more detail relevant 

to surface energy balances and related temperatures. 

Land use and land cover classes, as mapped, generalize small terrain features such as houses, lawns, 

and fields, and small areas of forest. This generalization was particularly bothersome in the current 

study because no demarcation was provided between high- and low-density residential areas, 

whereas the two have markedly different energy 

balance characteristics. This suggests that a third 

level should be devised with classification definitions 

not only applicable to a wide variety of sites but 

pertinent to energy transformations. In brief, within 

basic land use and land cover categories, there 

should be more detailed sampling of the heterogene-1 

ous features now classified within a single use.

Further inquiry should be carried out regarding 

methods for obtaining average values of simulation 

sampling cells. Perhaps actual area averages obtained by automated means might well be better 

than the eight-point sampling approach used in the 

current study. Much might be learned from a comparison of the simulated-data and observed-data 

maps by using such statistical analyses as twodimensional spectral analysis (Rayner, 1971).

THE COUPLED EXPERIMENT

The coupling of the experiments has tested the 

two approaches for making synoptic maps of surface energy exchange phenomena against each 

other. This method of testing has been expedient 

for the current experiment, but the question may 

well be asked regarding the utility of doing so in 

the future.

Mapping with remotely sensed data permits maps 

to be constructed both of moderately large areas 

and with good resolution. Maps made with 20-pixel 

averaging cells have a resolution quite comparable 

to land use and land cover polygons in the Washington-Baltimore area. The degree of averaging is sufficient to reduce the noise created by anomalous pixel 

values and give a degree of consistency that makes

comparison with land use and land cover polygons 

valuable. Relation between energy exchange and 

land use and land cover patterns can be readily 

observed, but there is no insight into causes of existing relationships without analysis of the effect of 

inputs into numerical simulations of the patterns. 

The observed-data maps show land use and land 

cover/energy budget relations, whereas the numerical simulation shows the causes of the relations.

The maps made in this study are for close-to-aninstant of time. But we know that no energy balance relations are static. Constantly changing energy transfer balances react to diurnal and seasonal 

changes. Increasing turbidity or cloudiness also 

alters inputs to these intricate surface energy transformation systems. Continual changes can be recorded on a series of maps, but there are at present 

severe restrictions on gathering sufficient data to 

do so. Periodic changes can be simulated, however, 

and, because numerical models are closely tied to 

land surface parameters, the changes can be simulated. In so doing, the ways that changing surface 

inputs affect the energy balances can be ascertained.

The coupling of the techniques in the current 

inquiry is intended to give insight into the potential utility of mating the remote acquisition of 

energy balance data with numerical simulation.

SUMMARY

The purposes of this study have been threefold: 

(1) the development of techniques for mapping the 

surface distributions of basic energy balance phenomena from an orbiting spacecraft, (2) the development of techniques for mapping surface radiation 

temperatures by numerical simulations, and (3) the 

combination of the two techniques to achieve an 

incisive understanding of the relations of man's 

use of the land to local and regional climates. Both 

1 and 2 are valid lines of inquiry in their own rights 

but find common ground in the energy conservation 

equation (eq. 2).

Each of the terms of the equation—net radiation, 

soil heat flux, the sensible heat flux across the surface-to-air interface, and the heat energy that becomes latent when water evaporates or a plant 

transpires—is a complex function of a set of variables. A number of these variables can be assessed 

by remote sensing methods. The wet fraction of 

soil, as represented by vegetation cover, and surface features that produce aerodynamic roughness 

are amenable to traditional photointerpretation.
A38 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

Several of the variables, however, require fairly 

sophisticated measurement, difficult to achieve 

on a two-dimensional surface. For this reason, an 

electro-optical scanner was used as an imaging radiometer to map the surface distributions of radiation 

temperatures, albedos, and net radiation.

The energy conservation equation is the common 

base that permits remotely observed measurements 

of energy balance phenomena to provide both inputs 

for the simulation model and as checks as to its 

accuracy. The current study, however, has been 

more concerned with the development of the two 

sets of techniques than in the use of one technique 

to gather information for the other.

The surface characteristic chosen for comparison 

of the two techniques, or approaches, is the thermal 

state expressed by the distribution of radiation 

temperatures. Using the approaches described in 

the current study, however, it is possible to run 

similar experiments with other data sets by changing land use and land cover parameters to match 

both urban and rural land uses and land covers 

and their climatic impact.

The use of a numerical simulation model in conjunction with analyses of maps derived from multispectral scanner data is a promising approach to 

the study of land use climatology. Measurements 

can be made by the scanner that are difficult to 

impossible to make from the ground, and these 

measurements can be augmented by simulation. It 

is entirely possible that, by reversing the research 

approach, a more useful land use and land cover 

classification, suitable to a new subdiscipline, can 

be devised using a satellite multispectral scanner 

system. Such inquiries will be aided by research 

agencies that are aware of the benefits of understanding man's impact on climate.

REFERENCES

Alexander, R. H., Lewis, J. E., Lins, H. F., Jenner, C. B., 

Outcalt, S. I., and Pease, R. W., 1976, Applications of 

Skylab data to land use and climatological analysis: 

National Aeronautics and Space Administration, Type 

III Final Report under Skylab/EREP Investigation No. 

469, Pt. A (CARETS), NTIS E77-10123.

Anderson, J. R., Hardy, E. E., and Roach, J. T., 1972, A landuse classification system for use with remote-sensor data: 

U.S. Geological Survey Circular 671, 16 p.

Anderson, J. R., Hardy, E. E., Roach, J. T., and Witmer, 

R. E., 1976, A land use and land cover classification 

system for use with remote sensor data: U.S. Geological 

Survey Professional Paper 964, 28 p.

Barry, R. G., and Chambers, R. E., 1966, A preliminary 

map of summer albsdo over England and Wales: Quarterly Journal of Royal Meteorological Society, v. 92, p. 

543-548.

Berry, B. J. L., and Baker, A. M., 1968, Geographic sampling, 

in Berry, B. J. L., and Marble, D. F., eds., Spatial Analysis: Englewood Cliffs, N. J., Prentice-Hall, p. 91-100.

Chandler, T. J., 1967, Night-time temperatures in relation to 

Leicester's urban form: Meteorology Magazine, v. 96, 

p. 244-250.

Dirmhirn, Inge, 1968, On the use of silicon cells in meteorological radiation studies: Journal of Applied Meteorology, v. 7, no. 4, p. 702-707.

Duckworth, F. S., and Sandberg, J. S., 1954, The effects of 

cities upon horizontal and vertical temperature gradients: Bulletin of American Meteorology Society, v. 35, 

p. 198-207.

Fleagle, R. G., and Businger, J. A., 1963, An introduction to 

atmospheric physics: New York, Academic Press, 346 p.

Gates, D. M., 1962, Energy exchange in the biosphere: New 

York, Harper and Row Biological Monographs, 151 p.

Goddard, W. B., 1974, A climate simulation model applied 

to arctic pack ice: Aidjex Bulletin, no. 25, p. 55-73.

Halstead, M. H., Richman, R. L., and Merryman, J. D., 1957, 

A preliminary report on the design of a computer for 

micrometeorology: Journal of Meteorology, v. 14, no. 4, 

p. 308-325.

Hammon, W. H., and Duenckel, F. W., 1902, Abstract of a 

comparison of the minimum temperatures recorded at 

the U.S. Weather Bureau and the Forest Park Meteorological Observatories, St. Louis, Mo., for the year 1891: 

Monthly Weather Review, v. 30, no. 1, p. 12-13.

Hudson, R. D., 1969, Infrared system engineering: New 

York, John Wiley, 642 p.

Jenner, C. B., 1975, Simulation of land-use effects on the 

urban temperature field: Unpublished Ph.D. dissertation 

on microfilm at the University of Maryland, College 

Park.

Junge, C., 1963, Atmospheric chemistry and radioactivity: 

New York, Academic Press.

Kahle, A. B., Gillespie, A. R., Goetz, A. F. H., and Addington, J. D., 1975 Thermal inertial mapping: 10th International Symposium on Remote Sensing of Environment, Ann Arbor, Michigan, 1975, Proceedings, p. 985- 

994.

Kondratyev. K. Ya., 1969, Radiation in the atmosphere: 

New York, Academic Press, 912 p.

Kung, E. C., Bryson, R. A., and Lenschow, D. H., 1964, 

Study of a continental surface albedo on the basis of 

flight measurements and structure of the Earth's surface 

cover over North America: Monthly Weather Review, 

v. 92, no. 12, p. 543-564.

Lettau, H. H., 1969, Note on aerodynamic roughness parameter estimation on the basis of roughness element description: Journal of Applied Meteorology, v. 8, no. 5, 

p. 828-832.

Lewis, J. E., Nicholas, F. W., Scales, S. M., and Woolum, 

C. A., 1971, Some effects of urban morphology on streetlevel temperatures at Washington, D.C.: Washington 

Academy Sciences Journal, v. 61, p. 258-265.
REFERENCES A39

McClatchey, R. A., Fenn, R. W., Selby, J. E. A., Volz, F. E., 

and Garing, J. S., 1972, Optical properties of the atmosphere (3d ed.) : Air Force Cambridge Research Laboratories, Bedford, Mass., 108 p., NTIS AD-753 075.

Monteith, J. L., 1973, Principles of environmental physics: 

London, Arnold, 241 p.

Myrup, L. 0., 1969, A numerical model of the urban heat 

island: Journal of Applied Meteorology, v. 8, no. 6, 

p. 908-918.

NASA/Skylab, 1973, Earth resources production processing 

requirements for electronic sensors Rev. A, Ch. 2, Sec. 5 

(PHO-TR524) : Johnson Spacecraft Center, Houston, 

34 p.

Nicholas, F. W., 1974, Parameterization of the urban fabric: a study of surface roughness with application to 

Baltimore, Maryland: Unpublished Ph.D. dissertation 

available on microfilm at the University of Maryland, 

College Park.

Outcalt, S. I., 1971, A numerical surface climate simulator: 

Geographical Analysis, v. 3, p. 379-393.

———— 1972a, A reconnaissance experiment in mapping and 

modeling the effect of land use on urban thermal regimes: Journal of Applied Meteorology, v. 11, no. 8, 

p. 1369-1373.

———— 1972b, The development and application of a simple 

digital surface climate simulator: Journal of Applied 

Meteorology, v. 11, no. 4, p. 629-636.

1972c, A synthetic analysis of seasonal influences in

the effects of land use on the urban thermal regime: 

Archiv fur Meteorologie, Geophysik, und Bioklimatologie, 

ser. B, v. 20, p. 253-260.

Pease, R. W., 1971, Mapping terrestrial radiation emission 

with a scanning radiometer: 7th International Symposium on Remote Sensing of the Environment, Ann 

Arbor, Michigan, 1971, Proceedings, v. 1, p. 501-510.

Pease, R. W., and Nichols, D. A., 1976, Energy balance 

maps from remotely sensed imagery: Photogrammetric 

Engineering and Remote Sensing, v. 42, no. 11, p. 1367- 

1374.

Pease, S. R., and Pease, R. W., 1972, Photographic films as 

remote sensors for measuring albedos of terrestrial 

surfaces: U.S. Geological Survey Interagency Report 

USGS-225, 43 p.

Rayner, J. N., 1971, An introduction to spectral analysis: 

London, Pion Ltd., 174 p.

Reifsnyder, W. E., and Lull, H. W., 1965, Radiant energy 

in relation forests: U.S. Department of Agriculture, 

Forest Service Technical Bulletin 1344, 111 p.

Scott, W. E., Stephens, E. R., Hanst, P. L., and Doerr, R. C., 

1957, Further developments in the chemistry of the atmosphere : Proceedings of the American Petroleum 

Institute, v. 37, p. 171-183.

Sellers, W. D., 1965, Physical climatology: Chicago, University of Chicago Press, 272 p.

Terzaghi, Karl, 1952, Permafrost: Journal of Boston Society 

of Civil Engineering, v. 39, p. 319-368.

U.S. Geological Survey, 1973, Central Atlantic Region Ecological Test Site (CARETS) map, Baltimore sheet, 

1:100,000: U.S. Geological Survey open file report 74-148.

Watson, Ken, Rowan, L. C., and Offield, T. W., 1971, Application of thermal modeling in the geologic interpretation of IR images: International Symposium on Remote 

Sensing of the Environment, 7th Ann Arbor, Michigan, 

1971, Proceedings, v. Ill, p. 2017-2042.

Wolfe, W. L., ed., 1965, Handbook of military infrared technology: Office of Naval Research, Washington, D.C., 

906 p.

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