Potential errors in the application of thermal-based energy balance models with coarse resolution data

William P. Kustas, Nurit Agam, Martha C. Anderson, Fuqin Li, Paul D. Colaizzi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A thermal sharpening algorithm (TsHARP) providing fine resolution land surface temperature (LST) to the Two-Source-Model (TSM) for mapping evapotranspiration (ET) was applied over two agricultural regions in the U.S. One site is a rainfed corn and soybean production region in central Iowa, while the other is an irrigated agricultural area in the Texas High Plains. Application of TsHARP to coarse (1 km) resolution thermal data over the rainfed agricultural area is found to produce reliable fine/within-field (60 m) resolution ET estimates, while in contrast, the TsHARP algorithm applied to the irrigated area does not perform as well, possibly due to significant sub-pixel moisture variations from irrigation. As a result, there may be little benefit in applying TsHARP for generating TSM-derived 60 m ET maps for the irrigated compared to the rainfed region. Consequently, reliable estimation of fine/within-field ET and crop stress still requires fine native resolution thermal imagery in areas with significant sub-pixel moisture variations.

Original languageEnglish
Title of host publicationRemote Sensing for Agriculture, Ecosystems, and Hydrology IX
DOIs
StatePublished - 1 Dec 2007
Externally publishedYes
EventRemote Sensing for Agriculture, Ecosystems, and Hydrology IX - Florence, Italy
Duration: 18 Sep 200720 Sep 2007

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6742
ISSN (Print)0277-786X

Conference

ConferenceRemote Sensing for Agriculture, Ecosystems, and Hydrology IX
Country/TerritoryItaly
CityFlorence
Period18/09/0720/09/07

Keywords

  • Crop stress
  • Evapotranspiration
  • Rainfed and irrigated agriculture
  • Surface energy balance
  • Thermal remote sensing
  • Two-source model

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