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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #320083
Title: A thermal-based remote sensing modeling system for estimating daily evapotranspiration from field to global scales

Author
item Kustas, William - Bill
item Anderson, Martha
item HAIN - University Of Maryland
item Gao, Feng
item Yang, Yun

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/30/2015
Publication Date: 11/20/2015
Citation: Kustas, W.P., Anderson, M.C., Hain, Gao, F.N., Yang, Y. 2015. A thermal-based remote sensing modeling system for estimating daily evapotranspiration from field to global scales [abstract]. https://scisoc.confex.com/scisoc/2015am/webprogram/paper94723.html.

Interpretive Summary:

Technical Abstract: Thermal-infrared (TIR) remote sensing of land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition as well as providing useful information for constraining prognostic land surface models. This presentation describes a robust but relatively simple TIR-based land surface model called the Two-Source Energy Balance (TSEB) model. The original TSEB modeling framework, published 20 years ago, is one of the developments responsible for revitalizing TIR remote sensing research and advancing the capabilities in the use of LST for generating reliable ET mapping products from field to regional scales. The TSEB algorithms solve for the soil/substrate and canopy temperatures that achieves a balance in the radiation and turbulent heat flux exchange for the soil/substrate and vegetation elements coupled to the lower atmosphere. As a result, the TSEB modeling framework is applicable to a wide range of environmental and canopy cover conditions, which has been a limitation in many other LST-based energy balance approaches. An overview of applications of the TSEB modeling framework to a variety of landscapes will be presented. In addition, a modeling system will be described called the Atmosphere-Land Exchange Inverse (ALEXI) that couples the TSEB scheme with an atmospheric boundary layer model in time-differencing mode to routinely map continental-scale daily ET at 5 to 10-km resolution using geostationary satellites. A related algorithm (DisALEXI) spatially disaggregates ALEXI output down to 'ner spatial resolutions using polar orbiting satellites such as Landsat, which provides pixel resolutions at the scale of human management activities affecting land use\land cover. Unfortunately, such fine resolution data is only available on a biweekly basis at best, not very useful in monitoring daily ET for water management and irrigation scheduling. However, recent studies have demonstrated that the temporal sampling of high resolution TIR imaging systems can be further enhanced by fusing lower spatial (1-5 km) but higher temporal resolution (~hourly to daily) ET retrievals using LST data from the Moderate Resolution Imaging Spectroradiometer (MODIS) systems on board the Terra and Aqua satellite platforms and from geostationary (GEO) satellites. Applications of a Landsat-MODIS-GEO ET data fusion prototype over rainfed and irrigated agricultural fields in the U.S. will be presented and its advantage in comparison with a simple Landsat-only interpolation scheme will be described. Potential applications of this data fusion modeling system for assessing the impacts of human activities and climate change on water resources and agricultural production, particularly in data poor regions, will be discussed.