Estimation and comparison of evapotranspiration from MODIS and AVHRR sensors for clear sky days over the Southern Great Plains

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Abstract

Evapotranspiration (ET) cannot be measured directly from satellite observations but remote sensing can provide a reasonably good estimate of evaporative fraction (EF), defined as the ratio of ET and available radiant energy. It is feasible to estimate EF using a contextual interpretation of radiometric surface temperature (To) and normalized vegetation index (NDVI) from multiple satellites. Recent studies have successfully estimated net radiation (Rn) over large heterogeneous areas for clear sky days using only remote sensing observations. With distributed maps of EF and Rn, it is now possible to explore the feasibility and robustness of ET estimation from multiple satellites. Here we present the results of an extensive inter-comparison of spatially distributed ET and related variables (NDVI, To, EF and Rn) derived from MODIS and AVHRR sensors onboard EOS Terra, NOAA14 and NOAA16 satellites respectively. Our results show that although, NDVI and To differ with the sensor response functions and overpass times, contextual space of NDVI–To diagram gives comparable estimates of EF. The utility of different sensors is demonstrated by validating the estimated ET results to ground flux stations over the Southern Great Plains with a root mean square error of 53, 51 and 56.24 Wm 2, and a correlation of 0.84, 0.79 and 0.77 from MODIS, NOAA16 and NOAA14 sensors respectively.

Introduction

Evapotranspiration (ET) is an important variable for water and energy balances on the earth's surface. Understanding the distribution of ET is essential for many environmental monitoring applications including water resources management, agricultural efficiency, global vegetation analysis, climate dynamics, and ecological applications. The three major factors controlling ET are availability of water, amount of available radiant energy and transport mechanism to remove the water vapor away from the surface. The above mentioned factors in turn depend on other variables such as soil moisture, land surface temperature, air temperature, vegetation cover, vapor pressure, wind speed, etc., which again may vary with region, season, and time of day. The general approach to account for all such factors is to use a combination of remote sensing data, ancillary surface data and atmospheric data for the estimation of ET (Bastiaanssen et al., 1996, Jackson et al., 1977, Holwill and Stewart, 1992, Moran et al., 1994, Nishida et al., 2003, Norman et al., 2003). Accurate characterization of global ET distribution with satellite remote sensing with little or no ground observations is a challenging task. A direct estimation of evaporative fraction (EF), defined as the ratio of ET and available radiant energy, has been shown to work well with AVHRR sensor onboard NOAA14 satellite and MODIS sensor onboard EOS-Terra satellite (Jiang and Islam, 2001, Jiang and Islam, 2003, Islam et al., 2002, Islam et al., 2003, Venturini et al., 2004).

Earlier studies focused on estimation of EF rather than ET because accurate estimation of available radiant energy was difficult (Nishida et al., 2003, Venturini et al., 2004). Here we propose an algorithm that uses a net radiation (Rn) estimation methodology, recently proposed by Bisht et al. (2005), in combination with a distributed map of EF to obtain ET maps over large areas.

Spectral response function (SRF) is defined as the sensor response of a channel as a function of wavelength. There are important differences in SRF, sensor characteristics, calibration techniques and atmospheric corrections (Venturini et al., 2004, Huete et al., 2002, Justice et al., 2002, Trishchenko et al., 2002) among the sensors used to estimate EF, Rn and ET. To provide a continuous monitoring capability for ET, it is important to explore the utility of an ET estimation algorithm with different sensors. Here, we will test and validate the adequacy of our ET estimation algorithm over Southern Great Plains (SGP) of the United States for clear sky days using MODIS and AVHRR sensors onboard EOS Terra, NOAA14 and NOAA16 satellites respectively.

Section snippets

Estimation of ET using remote sensing

In this study, we will use an ET estimation methodology based on an extension of the Priestley–Taylor equation and a relationship between remotely sensed surface temperature and vegetation index proposed by Jiang and Islam (2001) as:λET=ϕ(ΔΔ+γ)(RnG)where λET is the representative of ET (Wm 2), Rn is the net radiation (Wm 2), G is the soil heat flux (Wm 2), Φ is the parameter that accounts for aerodynamic and canopy resistances, Δ is the slope of saturated vapor pressure at air temperature (Ta

An overview of sensors

For continual, comprehensive global coverage, a series of advanced satellite sensors are being designed, developed and launched over the last several decades. Among various satellite sensors, AVHRR, onboard NOAA polar orbiting satellites has one of the longest records of operation. The AVHRR sensor is a broadband, 5- or 6-channel scanning radiometer, sensing in the visible, near-infrared (NIR), and thermal-infrared (TIR) portions of the electromagnetic spectrum. The AVHRR sensor provides for

Study region

The Southern Great Plains (SGP) region of US was chosen as our study site because of its relatively flat terrain, heterogeneous land cover, easy accessibility, wide variability of climate cloud cover type and surface flux properties, and large seasonal variation in temperature and specific humidity. It covers most of the Oklahoma and southern part of Kansas, extending in longitude from 95.3°W to 99.5°W and in latitude from 34.5°N to 38.5°N. Fig. 2 shows the study region. It is a heterogeneous

Inter-comparison of To, NDVI, Φ and ET by MODIS, NOAA14 and NOAA16 sensors

We compare and contrast the spatially varied parameters used in the determination of ET over SGP, keeping in mind the distinct characteristics of multiple sensors and the respective spectral response functions of their channels. The adopted methodology is based on a contextual interpretation of the triangle space of To and NDVI. Hence, we begin with the detailed analysis of remotely sensed parameters: To and NDVI, followed by the coefficient ‘Φ’ which gives us an estimate of EF. A spatially

Diurnal cycle of ET

Remote sensing measurements are essentially instantaneous and so are the estimated rates of ET. It is therefore necessary to develop techniques which can convert instantaneous values into daily estimates of ET. A common approach is to use a weighting technique based on the similarity between the diurnal course of ET and that of other components of the surface energy balance (Brutsaert & Sugita, 1992). Jackson et al. (1983) assumed that the diurnal course of ET would generally follow the course

Ground validation of ET

ET estimates from remote sensing are validated to ground estimates at MODIS overpass time since EF remains fairly constant during the daytime period, while available energy derived from MODIS data products vary with the time of overpass. By checking the reliability of scattered EBBR stations in SGP for each study day, we do validation as per two sets of comparisons given in Sections 5.2 and 5.3. It should be noted that cloud pixels are masked out from satellite derived ET maps. Satisfying such

Summary and discussion

Evapotranspiration cannot be measured directly from satellite observations but a reasonably good estimate of evaporative fraction (EF) has been obtained by using a contextual interpretation of radiometric surface temperature and normalized vegetation index from multiple satellites. Recent studies have successfully estimated net radiation over large heterogeneous areas for clear sky days using only remote sensing observations. With distributed maps of evaporative fraction and net radiation, it

Acknowledgements

This research was supported, in part, by grants from the National Research Institute Competitive Grants Program (MASR-2004-00888) and the National Aeronautics and Space Administration (NAG5-11694; NNG05M16G) of the United States of America.

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