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

Batra, N.; Islam, Shafiqul; Venturini, Virginia; Bisht, Gautam; Jiang, Lide

doi:10.1016/j.rse.2006.02.019

Cited by 184 publications

(149 citation statements)

References 45 publications

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“…More than 60% of the areas in the MODIS LST products are contaminated by weather effects, especially cloud cover [29]. Thus, various applications based on MODIS data are restricted to clear-sky conditions [30,31]. For the latter method, because PMW radiation from the land surface can penetrate atmosphere and clouds, few instances of missing data appear in the retrieved LST.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method

Kou

Jiang

et al. 2016

Remote Sensing

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Land surface temperature (LST) plays a major role in the study of surface energy balances. Remote sensing techniques provide ways to monitor LST at large scales. However, due to atmospheric influences, significant missing data exist in LST products retrieved from satellite thermal infrared (TIR) remotely sensed data. Although passive microwaves (PMWs) are able to overcome these atmospheric influences while estimating LST, the data are constrained by low spatial resolution. In this study, to obtain complete and high-quality LST data, the Bayesian Maximum Entropy (BME) method was introduced to merge 0.01˝and 0.25˝LSTs inversed from MODIS and AMSR-E data, respectively. The result showed that the missing LSTs in cloudy pixels were filled completely, and the availability of merged LSTs reaches 100%. Because the depths of LST and soil temperature measurements are different, before validating the merged LST, the station measurements were calibrated with an empirical equation between MODIS LST and 0~5 cm soil temperatures. The results showed that the accuracy of merged LSTs increased with the increasing quantity of utilized data, and as the availability of utilized data increased from 25.2% to 91.4%, the RMSEs of the merged data decreased from 4.53˝C to 2.31˝C. In addition, compared with the filling gap method in which MODIS LST gaps were filled with AMSR-E LST directly, the merged LSTs from the BME method showed better spatial continuity. The different penetration depths of TIR and PMWs may influence fusion performance and still require further studies.

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Section: Introductionmentioning

confidence: 99%

Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method

Kou

Jiang

et al. 2016

Remote Sensing

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“…It has been proven that the remote sensing-based models could generate reasonable ET distribution across a wide range of land covers due to assimilating remotely sensed land surface temperature, which could signal the variation in evaporative fraction (Jiang and Islam, 2001;Batra et al, 2006), a key element affecting the variation in actual ET. However, the heat flux and ET amount at pixel scale have been viewed with some skepticism despite validation performed with tower-based flux observations or more regionally with aircraft for several retrievals because: (1) the accuracy of land surface variables/parameters retrieved from remotely sensed data has not achieved a completely perfect level; (2) the remote sensing-based models are usually based on the energy balance equation.…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of remote sensing‐based models for estimation of evapotranspiration and accuracy assessment based on SWAT

Gao

Long

2008

Hydrological Processes

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Abstract:An intercomparison of daily actual evapotranspiration (ET) estimates from the single-source models (SEBAL and SEBS) and the two-source models (P-TSEB and S-TSEB) using remotely sensed data was performed to examine their utilities and limitations under a wide range of land covers and different meteorological conditions. The accuracy of ET estimates from remote sensing-based models of a selected watershed on 23 June 2005 (DOY 174) presenting large air drying power and marked contrast in underlying surface characteristics, and 25 July 2005, (DOY 206) in contrast to the meteorological and underlying surface conditions of DOY 174, were evaluated in terms of SWAT-based ET. The ET estimates from the two methodologies are shown to be comparable, indicating that S-TSEB has the highest accuracy with relative errors of 2Ð2% and 5Ð6% with reference to SWAT-based ET for DOY 174 and 206, respectively. Hence it was selected to be the basis for performing an intercomparison with other remote sensing-based models. Single-source models are very sensitive to KB 1 parameter, rendering marked differences in ET estimates because of different treatments of roughness length for heat transfer. SEBAL and SEBS yielded mean absolute percentage difference (MAPD) versus S-TSEB within 26Ð22% and 26Ð56% for DOY 174 and within 9Ð11% and 11Ð69% for DOY 206, respectively, indicating their applicability to higher vegetation cover and soil moisture availability areas under small air drying power condition. P-TSEB generated MAPD versus S-TSEB within 41Ð15% for DOY 174 and 8Ð79% for DOY 206, implying that P-TSEB is highly consistent with S-TSEB under small air drying power and less contrast in soil moisture conditions, whereas noticeable discrepancies occur under large air drying power and marked contrast in soil moisture circumstances, in particular for sparse cover. The performance of P-TSEB largely depends on the meteorological and underlying surface conditions, both exerting significant influences on the extent of coupling between vegetation and soil.

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“…To be able to compare error levels from different studies only papers that report errors in terms of mean error were included in the review. Thus, some of the valuable papers on this topic that use RMSE (root mean square error) to describe errors without including mean error could not be included in the review (e.g., Batra et al, 2006;Cleugh et al, 2007;Guerschman et al, 2009;Venturini et al, 2008). The data sources consulted are summarized in Appendix A.…”

Section: Accuracy Of Spatial Evapotranspiration Datamentioning

confidence: 99%

Spatial evapotranspiration, rainfall and land use data in water accounting – Part 1: Review of the accuracy of the remote sensing data

Karimi

Bastiaanssen

2015

Hydrol. Earth Syst. Sci.

128

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Abstract. The scarcity of water encourages scientists to develop new analytical tools to enhance water resource management. Water accounting and distributed hydrological models are examples of such tools. Water accounting needs accurate input data for adequate descriptions of water distribution and water depletion in river basins. Ground-based observatories are decreasing, and not generally accessible. Remote sensing data is a suitable alternative to measure the required input variables. This paper reviews the reliability of remote sensing algorithms to accurately determine the spatial distribution of actual evapotranspiration, rainfall and land use. For our validation we used only those papers that covered study periods of seasonal to annual cycles because the accumulated water balance is the primary concern. Review papers covering shorter periods only (days, weeks) were not included in our review. Our review shows that by using remote sensing, the absolute values of evapotranspiration can be estimated with an overall accuracy of 95 % (SD 5 %) and rainfall with an overall absolute accuracy of 82 % (SD 15 %). Land use can be identified with an overall accuracy of 85 % (SD 7 %). Hence, more scientific work is needed to improve the spatial mapping of rainfall and land use using multiple space-borne sensors. While not always perfect at all spatial and temporal scales, seasonally accumulated actual evapotranspiration maps can be used with confidence in water accounting and hydrological modeling.

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

Cited by 184 publications

References 45 publications

Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method

Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method

Intercomparison of remote sensing‐based models for estimation of evapotranspiration and accuracy assessment based on SWAT

Spatial evapotranspiration, rainfall and land use data in water accounting – Part 1: Review of the accuracy of the remote sensing data

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