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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 #333413

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Mapping evapotranspiration at fine spatial and temporal resolution by fusing multiple satellite resources

item Anderson, Martha
item HAIN, C. - University Of Maryland
item Gao, Feng
item Yang, Yun
item SUN, L. - Collaborator
item YANG, YANG - Collaborator
item Kustas, William - Bill

Submitted to: Ninth International Symposium on Biological Control
Publication Type: Abstract Only
Publication Acceptance Date: 9/16/2016
Publication Date: 10/11/2016
Citation: Anderson, M.C., Hain, C., Gao, F.N., Yang, Y., Sun, L., Yang, Y., Kustas, W.P. 2016. Mapping evapotranspiration at fine spatial and temporal resolution by fusing multiple satellite resources [abstract]. Ninth International Symposium on Biological Control. 2016 CD-ROM.

Interpretive Summary:

Technical Abstract: Given growing pressures on freshwater resources due to increasing populations, evolving landuse and changing climate, there is a need for timely information on water availability and drought over a wide spectrum of spatial scales: from individual farm fields to continental and global scales. To support these monitoring needs, satellite retrievals of land-surface temperature (LST) derived from thermal infrared (TIR) imagery have demonstrated significant value for multi-scale mapping of surface moisture conditions, consumptive water use (or evapotranspiration; ET) and vegetation health. In land-surface modeling, TIR imagery can serve as an effective substitute for precipitation data, providing much-needed water information in data-poor regions of the world. This paper discusses a multi-scale remote sensing modeling system that fuses flux assessments generated with TIR imagery collected by multiple satellite platforms to estimate daily surface fluxes from field to global scales. The Landsat series of polar orbiting systems has collected TIR imagery at 60-100 m resolution (8-16 day revisit) since the 1980s, providing spatiotemporal capabilities for monitoring historical and realtime ET and vegetation stress/drought globally at the scale of human management – nominally, the field scale. Recent experiments have demonstrated that the temporal sampling of high resolution TIR imaging systems can be further enhanced by fusing lower spatial (1 km) but higher temporal resolution ET retrievals using TIR data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments (1 km; ~daily) and from geostationary (GEO) weather satellites (3-10 km; 15 minute intervals). We describe implementations of a prototype Landsat-MODIS-GEO ET data fusion over agricultural landscapes under rainfed and irrigated water management, with societal benefits in the areas of food and water security.