Location: Soil and Water Management ResearchTitle: An intercomparison study of TSM, SEBS, and SEBAL using high-resolution imagery and lysimetric data Author
|Paul, George - Kansas State University|
|Prasad, P.v, Vara - Kansas State University|
|Staggenborg, Scott - Kansas State University|
|Neale, Christopher - Utah State University|
|Hutchinson, Stacy - Kansas State University|
|Aiken, Robert - Kansas State University|
Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 6/28/2012
Publication Date: 10/23/2012
Citation: Paul, G., Gowda, P., Prasad, P., Howell, T.A., Staggenborg, S.A., Colaizzi, P.D., Neale, C.M., Hutchinson, S.L., Aiken, R.M. 2012. An intercomparison study of TSM, SEBS, and SEBAL using high-resolution imagery and lysimetric data [abstract]. ASA-CSSA-SSSA Annual Meeting Abstracts. 2012 CDROM. Paper No. 209-7.
Technical Abstract: Over the past three decades, numerous remote sensing based ET mapping algorithms were developed. These algorithms provided a robust, economical, and efficient tool for ET estimations at field and regional scales. The Two Source Model (TSM), Surface Energy Balance System (SEBS), and Surface Energy Balance Algorithm for Land (SEBAL) cover the major spectrum of the algorithms available for estimating ET. An intercomparison of these models is important for ascertaining the performances under different conditions and preparing for the next generation operational ET mapping program. This study combines high resolution remote sensing data with field measurements of the agro-meteorological variables, and surface energy fluxes acquired during the Bushland Evapotranspiration and Agricultural Remote Sensing Experiment 2007 and 2008 ( BEAREX07, BEAREX08 ) conducted at the USDA-ARS Conservation and Production Research Laboratory (CPRL) in Bushland, Texas. These experiments offer several unique field measurements for stringent evaluation of the energy balance models including, (a) simultaneous evaluation of dryland and irrigated conditions using lysimetric data, (b) use of high resolution (1**3 m) airborne images for acquiring 'pure' pixels of the lysimeter locations, (c) multiple images acquired from emergence to vegetative growth period from two years covering tall and short crops and, (d) evaluating instantaneous ET (mm h**1) values against lysimeter data to provide better representation of algorithm's capabilities.