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Title: Performance of five surface energy balance models for estimatng daily evapotranspiration in high biomass sorghum

Author
item Wagle, Pradeep
item BHATTARAI, NISHAN - University Of Michigan
item Gowda, Prasanna
item KAKANI, VIJAYA - Oklahoma State University

Submitted to: Journal of Photogrammetry and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/1/2017
Publication Date: 4/9/2017
Citation: Wagle, P., Bhattarai, N., Gowda, P., Kakani, V. 2017. Performance of five surface energy balance models for estimatng daily evapotranspiration in high biomass sorghum. Journal of Photogrammetry and Remote Sensing. 128:192-203.

Interpretive Summary: Numerous thermal remote sensing-based models have been developed to estimate ET using surface energy balance (SEB) equation. It is necessary to evaluate their performance at the same site to identify robust models. However, none of the SEB models have been evaluated to predict ET in rain-fed high biomass sorghum grown for biofuel production. We compared the performance of five widely used single-source SEB models, namely Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET with Internalized Calibration (METRIC), Surface Energy Balance System (SEBS), Simplified Surface Energy Balance Index (S-SEBI), and operational Simplified Surface Energy Balance (SSEBop), for estimating ET over a high biomass sorghum field during the 2012 and 2013 growing seasons. The estimated ETs were evaluated against eddy covariance (EC) measured ET (ETEC) for 19 cloud-free Landsat image acquisition days. We used several model performance measures such as Nash-Sutcliffe efficiency, the coefficient of determination, root mean square error, and percent bias for the evaluation. Results indicated that S-SEBI performed the best followed by SEBAL and SEBS. In general, SEB models overestimated ET during dry periods, indicating the necessity of inclusion of soil moisture or plant water stress component in SEB models.

Technical Abstract: Robust evapotranspiration (ET) models are required to predict water usage in a variety of terrestrial ecosystems under different geographical and agrometeorological conditions. As a result, numerous remote sensing-based ET models have been developed to estimate large-scale ET based on the surface energy balance (SEB) equation. However, comparison of the performance of several SEB models at the same site is lacking. In addition, none of the SEB models have been evaluated to predict ET in rain-fed high biomass sorghum grown for biofuel production. In this paper, we evaluated the performance of five widely used single-source SEB models, namely Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET with Internalized Calibration (METRIC), Surface Energy Balance System (SEBS), Simplified Surface Energy Balance Index (S-SEBI), and operational Simplified Surface Energy Balance (SSEBop), for estimating ET over a high biomass sorghum field during the 2012 and 2013 growing seasons. The estimated ETs were compared against eddy covariance (EC) measured ET (ETEC) for 19 cloud-free Landsat image acquisition days. Several model performance measures such as Nash-Sutcliffe efficiency (NSE), the coefficient of determination (R2), root mean square error (RMSE), and percent bias (PBias) were used in the evaluation. Results indicated that S-SEBI performed the best followed by SEBAL and SEBS. METRIC overestimated ET for most of the growing season, while SSEBop underestimated until peak crop growth and overestimated during senescence. In general, SEB models overestimated ET during dry periods, indicating the necessity of inclusion of soil moisture or plant water stress component in SEB models.