goes surface insolation to estimate wetlands evapotranspiration

GOES surface insolation to estimate wetlands evapotranspiration

Jennifer M. Jacobsa,*, David A. Myersa, Martha C. Andersonb, George R. Diakc

aDepartment of Civil Engineering and Coastal Engineering, University of Florida, Gainesville, FL 32611-6580, USAbDepartment of Soil Science, University of Wisconsin, Madison, WI 53706, USA

cCooperative Institute for Meteorological Satellite Studies (CIMSS/SSEC), Space Science and Engineering Center, University of Wisconsin,

Madison, WI 53706, USA

Received 1 October 2001; revised 8 May 2002; accepted 14 May 2002

Abstract

Incoming solar radiation derived from GOES-8 satellite observations, in combination with local meteorological

measurements, were used to model evapotranspiration from a wetland. The wetland experiment was conducted in the Paynes

Prairie Preserve, North Central Florida during a growing season characterized by significant convective activity. The satellite

solar radiation measurements generally agreed with pyranometer data gathered at the site. The satellite net radiation estimates

were in good agreement with the 30-min averages of measured net radiometer data. Satellite derived net radiation estimates

were used in the Penman–Monteith and Priestley–Taylor models to calculate evapotranspiration. The calculated instantaneous

evaporative fluxes were in good agreement with 30-min average ground-based eddy correlation system measurements. The

daily averages of modeled evapotranspiration were in very good agreement ðr2 ¼ 0:90Þ with reference eddy flux

measurements. q 2002 Elsevier Science B.V. All rights reserved.

Keywords: Evapotranspiration; Geostationary operational environmental satellite; Solar radiation; Wetlands

1. Introduction

Distributed climate parameters available from

satellites can offer significantly enhanced information

for improving the understanding of climate, water

supply, agricultural production, and ecosystems over

lumped or point indices. The application of remote

sensing methods to estimate evapotranspiration has

the advantage of good spatial resolution and excellent

spatial coverage, but may have the disadvantage of

infrequent sampling and considerable expense (Kite

and Drooger, 2000). The Geostationary Operational

Environmental Satellite (GOES) provides enhanced

temporal resolution with hourly estimates of solar

radiation that are critical to evapotranspiration

calculations and has a spatial resolution, that is,

significantly better than that available from most

ground-based pyranometer networks.

The earliest satellite insolation studies were

conducted in the Western Hemisphere using the first

generation GOES systems (GOES 1–7). Pinker et al.

(1995), Schmetz (1989) reviewed the observations

made using the visible band from the GOES and other

satellites. The results show that the instantaneous

solar insolation estimation errors are typically less

than 10% on a daily basis and hourly values range

from 15 to 20%. In addition, as a geostationary

satellite, the GOES systems’ temporal resolution

provides numerous measurements throughout a day

0022-1694/02/$ - see front matter q 2002 Elsevier Science B.V. All rights reserved.

PII: S0 02 2 -1 69 4 (0 2) 00 1 17 -8

Journal of Hydrology 266 (2000) 53–65

www.elsevier.com/locate/jhydrol

* Corresponding author. Tel.: þ1-352-392-9237.

E-mail address: [email protected] (J.M. Jacobs).

http://www.elsevier.com/locate/jhydrol

that may be adequate to estimate daily shortwave

fluxes. The second generation of GOES, GOES-8 and

GOES-10, provides significant improvements over

the previous GOES system with respect to the visible

sensor.

The GOES solar radiation product may be applied

directly to estimate evapotranspiration from well-

watered regions. Diak et al. (1998) routinely apply

GOES derived solar radiation to estimate daily crop

evapotranspiration for Wisconsin using ground-

truthed surface insolation measures (Diak et al.,

1996). Stewart et al. (1999) used GOES incoming

solar radiation estimates in a comparison among three

evapotranspiration formulations for an irrigated

agricultural setting in northwest Mexico. Garatuza--

Payan et al. (2001) successfully validated the GOES

derived evapotranspiration estimates using measured

values at the same northwest Mexico location.

The general objective of this research was to

evaluate the applicability of satellite derived solar

insolation to evapotranspiration estimation in a

Florida wetland during the growing season. Atmos-

pheric conditions during the growing season are

characterized by active convective systems with

extremely dynamic cloud systems. These conditions

represent a significant challenge for remote sensing.

This paper first compares the GOES estimates of solar

radiation and net radiation to measured values. Then,

the wetland evapotranspiration is modeled using the

Penman–Monteith and Priestley–Taylor approaches

and compared to evapotranspiration measured by an

eddy flux system.

2. Experimental data

This study was conducted for 18 days in July (day

of year 183–201) during the 2001 growing season in

Paynes Prairie State Preserve, a large highland marsh

system in North Central Florida, USA. The exper-

imental period occurred 3 years into a drought that

commenced April 1998, where the rainfall deficit was

approximately 1.1 m from April 1998 to June 2001.

The period immediately preceding the experiment

coincided with a return to a wetter rainfall pattern that

maintained the water table near the surface during the

experiment. The energy flux and meteorological

observations shown in Fig. 1 illustrate the daily

Fig. 1. The daily total measured values of rainfall (vertical bars), evapotranspiration (A) and net radiation (D) during the experiment period.

J.M. Jacobs et al. / Journal of Hydrology 266 (2000) 53–6554

rainfall, net radiation and evapotranspiration values

observed during the experiment. Six days had rainfall

events with 7.8 cm of total rainfall during the

experiment. Considerable variability in evapotran-

spiration occurred during the observation period. This

is primarily attributed to the variability in the net

radiation, as soil water was not limited during the

experiment.

The Paynes Prairie State Preserve study area is

described in detail by Jacobs et al. (2002). The study

was conducted in a wet prairie community located in

the Preserve (2983401400N, 8281604600W). The wet

prairie is a relatively flat, treeless plain with a

moderately dense ground cover. In the wet prairies,

the emergent herbaceous perennials between 0.4 and

1.2 m tall were predominant and dense. The common

species were Panicum hemitomon Schultes (maiden

cane), Polygonum hydropiperoides Michx. (mild

water-pepper), and Ptilimnium capillaceum Michx.

(mock bishop’s weed) (Patton and Judd, 1986; Tobe

et al., 1998). Eupatorium capillifolium Lam. (dog

fennel) was prevalent and Sesbania sp. was scattered

throughout; both species were up to 1.5 m tall. The

mean canopy height was 1.0 m. Field observations

showed that the majority of the root zone was

contained in the upper 10 cm soil layer with

approximately 95% of the root zone contained in the

upper 25 cm soil layer. The site’s soils include

Emeralda fine sandy loam, Wauberg sand, and

Ledwith Muck.

2.1. Surface insolation from GOES visible satellite

data

NASA and National Oceanic and Atmospheric

Administration (NOAA) cooperatively designed, built

and deployed the GOES system. The GOES-8 system

provides hourly observations of emitted radiation in

18 thermal infrared bands over the eastern US, from

which atmospheric temperature, winds, moisture, and

cloud cover can be derived. The visible band of the

GOES can be used to detect cloud cover and

subsequently to provide estimates of incoming solar

radiation or insolation.

The approach used to estimate insolation from the

visible Earth images is described in detail by Gautier

et al. (1980) with modifications by Diak and Gautier

(1983). The algorithm is described here only briefly.

A simple physical model of radiative transfer is used.

Images are compared against a reference clear sky

image of the surface albedo. The surface albedo

comparison indicates if a point is clear or cloudy. If

the point is clear, a clear model of bulk radiative

transfer is used to adjust the insolation for effects of

ozone adsorption, Rayleigh scattering and water

vapor absorption. If the point is cloudy, the cloudy

radiation model is applied to determine the cloud

albedo. The cloudy model, assuming plane-parallel

clouds, calculates the atmospheric effects above and

below the cloud separately.

The solar radiation data were developed using the

methods of Diak et al. (1996). The data collection and

processing has been automated by the Man-computer

Interactive Data Processing System (McIDAS) at the

University of Wisconsin Space Science and Engin-

eering Center (SSEC). The GOES data are archived

and processed to yield hourly fields of insolation

values. For this experiment, hourly data were

extracted from a 20 km grid cell centered at 29.68N

and 82.28W, but resolution to approximately 1 km

grid size is possible. Up to 12 hourly images were

available daily during the study period.

2.2. Reference flux measurements

Energy flux measurements were made within an

energy balance framework. All instruments were

mounted on a 6.1 m tower. The net radiation was

measured with a net radiometer (Radiation Energy

Balance Systems Q7.1). The temperature and relative

humidity were measured using a shielded Vaisala

model HMP 45C sensor (Vaisala, Inc.). Wind speed

and direction were measured with an CS 800-L

anemometer (RM Young, Inc.). These instruments

were mounted approximately 4.0 m above the ground

surface. Measurements were made every minute and

averaged over 30 min intervals. Ground heat flux was

measured by RFT 3.1 heat fluxes plates installed

approximately 2 cm below the surface (Radiation

Energy Balance Systems). Evapotranspiration was

measured directly using the eddy covariance

approach. The sensible and latent heat flux measure-

ments were made using CSI CSAT3 3-D Sonic

Anemometer (Campbell Scientific Instruments, Inc.

(CSI)) that measures the three wind components and

the virtual temperature, and a CSI KH20 Krypton

J.M. Jacobs et al. / Journal of Hydrology 266 (2000) 53–65 55

hygrometer. The flux instruments were installed at

4.0 m above the ground surface. Fluctuations in wind

speed, virtual air temperature and vapor density were

sampled at 6 Hz and 30-min average co-variances

were calculated to estimate the fluxes. The latent heat

fluxes were corrected for temperature-induced fluctu-

ations in air density (Webb et al., 1980) and for the

hygrometer sensitivity to oxygen (Tanner and Greene,

1989). Sensible heat fluxes were corrected for

differences between the sonic temperature and the

actual air temperature (Schotanus et al., 1983). Both

the sensible and latent heat fluxes were corrected for

misalignment with respect to the natural wind

coordinate system (Baldocchi et al., 1988). The

Bowen-ratio method was used to close the surface

energy balance relationship (Twine et al., 2000).

3. Theory and models

Jacobs et al. (2002) found that the Penman–

Monteith equation coupled with an empirical soil

water parameterization method provided the best

estimates of actual evapotranspiration for the study

site among the methods tested. During the experiment

period, the vegetation was not water-limited, thus the

evapotranspiration estimates were made directly from

the Penman–Monteith equation. The Penman–Mon-

teith method is an extension of the Penman equation

that allows the approach to be applied to a range of

vegetated surfaces through the introduction of plant

specific resistance factors (Monteith, 1965):

LEPM ¼DðRN 2 GÞ þ racpðes 2 eaÞ=ra

Dþ gð1 þ rs=raÞð1Þ

where LEPM is the modeled latent heat flux; D, the

slope of the saturation vapor pressure temperature

relationship; g, the psychrometric constant; RN, the

net radiation; G, the ground heat flux; ra; the mean air

density at constant pressure; cp; the specific heat of

air; es 2 ea; the vapor pressure deficit of the air; es; the

saturation vapor pressure of the air; ea; the actual

vapor pressure of the air; rs; the bulk surface

resistance, and ra; the aerodynamic resistance. The

aerodynamic resistance was estimated by

ra ¼ln½ðz 2 dÞ=z0�ln½ðz 2 dÞ=z0v�

k2Uð2Þ

where z is the height at which the wind speed U was

measured, d is the displacement height estimated to be

0.7zveg, where zveg is the vegetation height, z0 is the

roughness height approximated as 0.1zveg, z0v is the

roughness height for water vapor approximated as

0:1z0; and k is the Von Karmen’s constant (0.4). The

surface resistance, determined by back solution of Eq.

(1) during wet periods, was on average 50 s m21 and

was invariant with time of day (Jacobs et al., 2002).

Jacobs et al. (2002) provide further details on the

determination of plant specific resistance factors.

An alternative method to estimate evapotranspira-

tion under potential conditions is the Priestley–Taylor

method. For well-watered conditions, typically found

in wetlands and irrigated agricultural settings, the

Priestley–Taylor method may be advantageous as it

requires significantly less meteorological data than the

Penman–Monteith method. The basis for the Priest-

ley–Taylor method is the theoretical lower limit of

evaporation from a wet surface known as the

‘equilibrium’ evaporation (Priestley 1959; Slatyer

and McIlroy 1961):

LEPT ¼ aD

Dþ gðRN 2 GÞ ð3Þ

where a ¼ 1: Equilibrium conditions reflect evapor-

ation from a wet surface under conditions of minimum

advection that result in the actual vapor pressure of the

air approaching the saturation vapor pressure. Priest-

ley and Taylor (1972) showed that for conditions of

minimum advection with no edge effects, a ¼ 1:26:In this case, the aerodynamic term of the combination

equation is effectively assigned a constant percent of

the radiation term. The Priestley–Taylor equation

with a values ranging from 1 to 1.26 has been

successfully applied in wetland environments (Price

and Woo, 1988; Souch et al., 1998; Thompson et al.,

1999). In this research, the equilibrium form of the

Priestley–Taylor equation was found to give the best

results.

4. Results and discussion

4.1. Incoming solar radiation

The satellite estimates of incoming solar radiation

were compared to measurements made at the Paynes


Fig. 2. The diurnal cycle of solar radiation measured by the pyranometer and estimated from GOES images for (a) day 186, (b) day 187, and (c)

day 188.


Prairie Preserve. The visible GOES imagery was used

to estimate an instantaneous measurement of incom-

ing solar radiation at 15 min past every hour during

the daytime. The ground-based pyranometer provided

half-hour estimates of the mean incoming solar

radiation beginning on the hour and the half-hour.

Fig. 2 shows examples of the diurnal evolution of

incoming solar radiation measurements. The obser-

vations on days 186 and 187 (Fig. 2(a) and (b)) are

typical of most days during the experiment. These

days have an irregular diurnal energy cycle due to the

high cloud cover, that is, typical of the Florida

summer-time convective system patterns. Day 188

(Fig. 2(c)) had the lowest convective activity of any

day during the measurement period. Day 188 exhibits

a well-defined diurnal cycle and demonstrates the

agreement between the satellite and the ground-based

solar radiation measurements typical under low cloud

conditions (Stewart et al., 1999).

During cloudless periods, the pyranometer

measurements and the GOES estimates agree well.

During cloudy periods, larger discrepancies exist

between the pyranometer measurements and the

GOES estimates. These discrepancies may be

explained by the differences in the observation scales

between the two instruments. The satellite’s larger

spatial scale serves as a spatial smoothing filter, while

the pyranometer smoothes the solar radiation data

temporally. Under convective cloud cover conditions,

a mix of clear and cloudy sky, the satellite

measurement reflects the regional mix, while the

local measurement can range from completely clear to

completely cloudy. Some relationship between the

temporal and the spatial averaging may exist and

serve to reduce the discrepancy between the two

measurements. However, the dynamic wind patterns

and convective cloud development suggest a highly

non-stationary field that reduces the likelihood of a

simple scaling relationship.

The two data sets were compared using only those

30-min periods during which a GOES image was

available. The 30-min solar radiation pyranometer

measurements and the GOES estimated values are

plotted in Fig. 3. A linear regression model with a zero

intercept between the pyranometer measurements and

the GOES values gave the relationship Rs;GOES ¼

Fig. 3. Measured 30-min average incoming solar radiation versus estimated incoming solar radiation from the GOES images. The solid line is

the 1:1 line.


0:99Rs;pyro with a coefficient of determination ðr2Þ of

0.75. The regression slope was not significantly

different than 1 ðp , 0:0001Þ: However, the GOES

values on average were somewhat larger than the

pyranometer values 477 and 448 W m22, respect-

ively. Inspection of Fig. 3 and the linear regression

residuals versus the fitted values (not shown) indicate

that there is a slight non-linear relationship in which

the GOES values underestimate those measured solar

radiation values, which exceed 900 W m22.

The Root Mean Squared Error (RMSE) of the 30-

min values is 135 W m22 or 28.3% of the mean daily

values of solar radiation. These differences are

slightly larger than those found in previous studies;

Dedieu et al. (1987) obtained RMSE values of 20% in

France, while Garatuza-Payan et al. (2001), Stewart

et al. (1999) reported RMSE of 14.2 and 20.2%,

respectively, for the Yaqui Valley. The maximum

observed radiation for these earlier studies was less

than 900 W m22. As the RMSE is highly sensitive to

extreme values, the Mean Absolute Error (MAE) may

provide a better measure for comparison to the earlier

studies. In this experiment, the MAE is 97.5 W m22

or 20.4% of the mean daily solar radiation values. On

a daily basis, the RMSE of 46 W m22 (9.9%) is

consistent with the results from earlier studies

(Raphael and Hay, 1984; Stewart et al., 1999;

Garatuza-Payan et al., 2001).

4.2. Net radiation

Net radiation at the surface was estimated from the

incoming solar radiation, surface parameters, and

surface measurements by

RN ¼ Rsð1 2 aÞ þ 1a1ssT4a2 1ssT4

s ð4Þ

where Rs is the incoming solar radiation, a is the

surface albedo, 1a is the atmospheric emissivity, 1s is

the surface emissivity, s is the Stefan–Boltzman

constant, Ta is the air temperature and Ts is the surface

temperature. The surface albedo for a wetland surface

was estimated to be 0.20, a value representative of

tall, green vegetated surfaces (Eagleson, 1970). The

atmospheric emissivity was calculated as a function of

air temperature and atmospheric vapor pressure

(Brutsaert, 1975):

1a ¼ 1:24ea

Ta

� �1=7

ð5Þ

The surface emissivity was estimated to be 0.98. Ta

was used in Eq. (4) instead of Ts: This approach is

commonly applied, since Ts is rarely measured

(Brutsaert, 1982) and is most reliable for well-

watered, high coverage vegetation surfaces as com-

pared to arid regions or areas of lower canopy cover

(Diak et al., 2000).

Net radiation was estimated using Eqs. (4) and (5)

with both the solar radiation from the pyranometer

and the GOES solar radiation estimates. The net

radiation estimates were compared to those measured

by the net radiometer at the Paynes Prairie Preserve

using 30-min intervals and cumulative daily totals.

The 30-min measurements plotted in Fig. 4 are from

the daytime periods during which GOES estimates

were available (7:00–18:30 Eastern Standard Time

(EST)). The cumulative totals are the sum of all

periods having positive net radiation measurements.

For each combination method, Table 1 lists the

analysis results including the sample counts (N ), the

net radiation values’ mean and standard deviation

(S.D.), the fitted linear regression models RNcak ¼

ARNobs þ B and their corresponding r2; the MAE, the

RMSE, the systematic RMSE (RMSEs) and the

unsystematic error RMSE (RMSEu) of the estimated

30-min and the daily total net radiation in comparison

to the in situ measured net radiation. While the

regression model and the coefficient of determination

provide the correct measures to evaluate model

performance (Krzysztofowicz, 1992), the RMSE and

MAE values are included to facilitate intercomparison

among studies.

The net radiation results using pyranometer data

indicate that Eqs. (4) and (5) provide a reasonable

approach to estimate net radiation. The pyranometer

linear correlation results show excellent agreement;

the slope is close to one, the intercept is close to zero

and the coefficient of determination is very high. The

30-min pyranometer derived values overestimate the

net radiation slightly, while the daily total values are

nearly identical. The RMSEs are fairly small; 12.4 and

1.9% of the mean 30-min and daytime total values,

respectively.


The GOES results have considerably more varia-

bility than the pyranometer estimates and also appear

to have a low bias for the highest net radiation values.

On average, the GOES data overestimate net radiation

by 11.6% and have a RMSE of 107.7 W m22 (33.7%)

and 0.50 MJ m22 (3.7%) for the 30-min and the

daytime total values, respectively. The regression

results show a fairly strong relationship based on the

coefficient of determination (r2 ¼ 0:75 and

r2 ¼ 0:88) for the 30-min and the daily total values,

respectively. In addition, a large portion of the GOES

RMSE is systematic indicating that it may be possible

to significantly improve the accuracy of the net

radiation estimates (Willmott, 1982). Few studies are

available that have evaluated the calculated net

radiation using GOES data using measured values.

Our results compare favorably to those of Gu et al.

(1999) whose 30-min GOES RN values for sites in the

Table 1

Sample counts (N ), mean and S.D., linear regression slope (A ), intercept (B ) and coefficient of determination ðr2Þ; MAE, RMSE, RMSEs and

RMSEu of estimated 30-min and total daily net radiation in comparison to in situ measured net radiation. The terms N, A, and r2 are

dimensionless, the remaining terms for the 30-min and the total daily measures have the units W m22 and MJ m22, respectively. The linear

regression model is RNcalc ¼ ARNobs þ B: Thirty-minute measurements were made during the daytime (7:00–18:30 EST). Total daily values

are the sum of all periods with positive net radiation measurements

N RNcalc

(mean)

RNobs

(mean)

RNcalc

(S.D.)

RNobs

(S.D.)

A B r2 MAE RMSE RMSEs RMSEu

30-min

Pyranometer 210 344.7 319.1 214.6 200.9 1.05 212.77 0.97 24.7 39.5 11.0 38.0

GOES 217 356.4 319.3 190.3 204.7 0.81 90.36 0.75 73.5 107.7 49.4 95.7

Total daily

Pyranometer 19 13.7 13.7 3.7 3.8 0.96 0.65 0.97 0.05 0.26 0.05 0.25

GOES 19 14.7 13.7 3.0 3.8 0.73 4.69 0.88 0.11 0.50 0.41 0.29

Fig. 4. Measured net radiation versus estimated net radiation with 30-min average pyranometer measurements of incoming solar radiation and

estimated incoming solar radiation from the GOES images. The solid line is the 1:1 line.


Fig. 5. The diurnal cycle of evapotranspiration measured with the eddy flux system and modeled using Eq. (1) with net radiation estimated using

pyranometer measurements and GOES solar radiation estimates for (a) day 186, (b) day 187, and (c) day 188.


Boreal Ecosystem-Atmosphere Study (BOREAS)

underestimated measured values by 7.7% and had a

RMSE of 68% and a coefficient of determination of

46%.

4.3. Modeled evapotranspiration

The evapotranspiration fluxes were estimated

using both the Penman–Monteith method and the

Priestley–Taylor method. For both evapotranspira-

tion equations, three different sets of net radiation data

were analyzed; the measured net radiation values, the

modeled net radiation from the pyranometer data and

the modeled net radiation from the GOES data. All

modeled fluxes were compared to the fluxes measured

by the eddy-correlation method using the 30-min

measurements and the daytime total values.

Fig. 5 shows examples of the diurnal evolution of

the evapotranspiration estimates and measurements.

Typically, the GOES estimated values agreed well

with the measured values until midday. However, as

exhibited on Days 186 and 187 (Fig. 5(a) and (b)) the

afternoon convective patterns can result in significant

differences between local and regional evapotran-

spiration estimates. The differences result in both

overestimation and underestimation errors over the

course of a day. During the relatively cloud free

conditions on Day 188 (Fig. 6(c)), the GOES data

slightly underestimate afternoon evapotranspiration

measurements. These underestimates may be due to

the development of clouds outside the region sampled

by the flux instruments, but within the GOES

sampling grid.

Tables 2 and 3 summarize the relationships among

the estimated evapotranspiration and the measured

evapotranspiration values for the 30-min and the

daytime total values, respectively. Excellent agree-

ment was found between the measured and the

modeled evapotranspiration values using the net

radiometer values for both the Penman–Monteith

and the Priestley–Taylor models. Based on the

regression relationship and the RMSE, the Penman–

Monteith is slightly preferable to the Priestley–Taylor

type model. For data-limited sites, the Priestley–

Taylor model appears to be quite suitable. Both

models may be applied at either the 30-min or the

daily time interval. As expected from the net

radiation results, the pyranometer measurements

also provide a reasonable means to estimate evapo-

transpiration. Although, the pyranometer results

Fig. 6. Measured 30-min average evapotranspiration versus modeled evapotranspiration using Eq. (1) with measured net radiation, estimated net

radiation from pyranometer measurements, and estimated net radiation from the GOES images. The solid line is the 1:1 line.


slightly overestimate the evapotranspiration at both

the 30-min and the daily time scales, the regression

results are quite good with r2 values from 0.88 to 0.95,

slopes near to one, and intercepts near to zero. The

RMSEs appear to reflect the error in estimating RN

using Rs measurements.

As Fig. 6 shows, the pyranometer measurements

clearly are preferable to GOES measurements for

estimating LE on a 30-min basis. However, even at

this high temporal resolution, the GOES measure-

ments are able to provide LE estimates that slightly

overestimate the measured values on average

(15.9 W m22), that provide good, albeit somewhat

skewed, estimates overall ðr2 ¼ 0:67Þ and that result

in reasonable RMSE errors on the order of 30%.

Overall, these results appear to be largely a reflection

of the solar radiation errors caused by sampling scale

differences. Higher temporal and spatial resolution

should improve the solar radiation estimates and, in

turn, enhance the potential evapotranspiration results.

On a daily basis as shown in Fig. 7, the correlation

between the measured evapotranspiration and the

GOES modeled evapotranspiration is dramatically

improved and the scatter and the skew are signifi-

cantly reduced. Based on a visual inspection, it is

difficult to distinguish whether the GOES data or the

pyranometer data provide the better evapotranspira-

tion estimates. The statistical summary presented in

Table 3 indicates that for daily measurements, the

GOES data set can provide nearly comparable results

to those of the pyranometer based on the r2 values of

0.90 and 0.89, respectively. While the skew is still

present for the GOES LE estimates, the large portion

of the systematic GOES LE RMSE as compared to

Table 2

Sample counts (N ), mean and S.D., linear regression slope (A ), intercept (B ) and coefficient of determination ðr2Þ; MAE, RMSE, RMSEs and

RMSEu of estimated 30-min evapotranspiration rates in comparison to in situ measured evapotranspiration. The terms N, A, and r2 are

dimensionless, the remaining terms have the units W m22. The linear regression model is LEcalc ¼ ALEobs þ B: Measurements were made

during the daytime (7:00–18:30 EST)

N LEcalc

(mean)

LEobs

(mean)

LEcalc

(S.D.)

LEobs

(S.D.)


Penman–Monteith

Net radiometer 167 273.8 272.0 140.88 134.54 1.04 28.14 0.98 16.03 20.68 4.95 20.08

Pyranometer 167 279.0 272.0 150.92 134.54 1.09 218.04 0.95 27.58 37.18 13.84 34.51

GOES 167 287.9 272.0 131.87 134.54 0.80 70.87 0.67 62.72 82.28 30.61 76.37

Priestley–Taylor

Net radiometer 167 270.0 272.0 140.73 134.54 1.04 211.58 0.98 17.57 22.36 4.95 21.80

Pyranometer 167 275.2 272.0 151.12 134.54 1.09 221.67 0.94 29.18 39.08 13.84 36.54

GOES 167 284.6 272.0 132.12 134.54 0.79 69.13 0.65 64.32 84.12 30.61 78.35

Table 3

Same as Table 2, but for daily totals that sum all periods with positive net radiation measurements. The terms N, A, and r2 are dimensionless, the

remaining terms have the units MJ m22

N LEcalc

(mean)

LEobs

(mean)

LEcalc

(S.D.)

LEobs

(S.D.)


Penman–Monteith

Net radiometer 18 11.67 11.60 2.69 2.57 1.04 20.35 0.98 0.03 0.13 0.03 0.12

Pyranometer 18 11.91 11.60 2.72 2.57 1.00 0.32 0.89 0.07 0.30 0.10 0.29

GOES 18 12.38 11.60 2.32 2.57 0.86 2.18 0.90 0.10 0.32 0.21 0.24

Priestley–Taylor

Net radiometer 18 11.55 11.60 2.62 2.57 1.01 20.15 0.97 0.04 0.13 0.02 0.13

Pyranometer 18 11.78 11.60 2.65 2.57 0.97 0.54 0.88 0.07 0.30 0.07 0.30

GOES 18 12.28 11.60 2.23 2.57 0.82 2.74 0.89 0.10 0.35 0.27 0.23


that of the pyranometer suggests that an enhanced

GOES LE model to provide a higher quality daily

evapotranspiration estimates is feasible. Overall, the

daily RMSEs 1.1–3.1% are quite low. The GOES LE

estimates are 0.78 MJ m22 (0.32 mm) on average

higher than the flux measurements and the pyran-

ometer LE estimates are 0.31 MJ m22 (0.13 mm)

higher than the flux measurements. This is compar-

able to Garatuza-Payan et al.’s (2001) reported

GOES-based LE estimates of 0.8 and 0.25 mm for a

cotton crop and a wheat crop, respectively.

5. Summary and conclusions

The Florida environment, with small cumulus

clouds and potential rapid development and decay,

is a challenge for the GOES system when comparing

point insolation measurements to the GOES areally

averaged estimates. Under these conditions, the solar

radiation estimated from satellite measurements can

be used to calculate evaporative fluxes successfully on

short time scales ðr2 ¼ 0:67Þ and with confidence on

daily times scales ðr2 ¼ 0:90Þ: The methods of

calculation employed were the Penman–Monteith

and the Priestley–Taylor method and a simple net

radiation estimation procedure. The satellite measure-

ments were made using the GOES-8 satellite visible

imagery and coupled with local meteorological

measurements.

The GOES solar radiation data tended to somewhat

underestimate the highest rates of evapotranspiration

and to overestimate the more moderate rates of

evapotranspiration. These errors may be due to the

difference in observation scales between the satellite

and the ground-truthing instrument. The errors may

also have been compounded by the significant

convective activity. An improved relationship may

be possible by using a smaller grid spacing and higher

frequency sampling. Finally, as the GOES solar

radiation cloudy model assumes plane-parallel clouds,

error reduction may be possible through an enhanced

cloud model.

Acknowledgments

This work was performed while D. Myers held a

Florida Space Grant Consortium Summer Scholar-

ship. Additional support for this work was provided

by the NASA NIP Grant NAG5-10567. Development

of the GOES insolation product was supported by

RESAC Grant NAG13-990008. We thank

B. Whitfield, J. Andres, A. Lopera, and

S. Mergelsberg for their role in collecting the field

data set. We thank the Florida Department of

Environmental Protection Division of Recreation

and Parks for providing access to the field sites with

special thanks to J. Weimer for assisting in site

selection and ongoing field support. Two anonymous

reviewers are thanked for their helpful comments and

suggestions.

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