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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 #296731
Title: Data fusion techniques for mapping daily water use and vegetation stress at field scales

Author
item Anderson, Martha
item CAMMALLERI, C - Collaborator
item Gao, Feng
item WANG, P - Collaborator
item HAIN, C - University Of Maryland
item YILMAZ, M - Collaborator
item Kustas, William - Bill

Submitted to: American Meteorological Society
Publication Type: Abstract Only
Publication Acceptance Date: 10/2/2013
Publication Date: 2/2/2014
Citation: Anderson, M.C., Cammalleri, C., Gao, F.N., Wang, P., Hain, C., Yilmaz, M.T., Kustas, W.P. 2014. Data fusion techniques for mapping daily water use and vegetation stress at field scales [abstract]. 28th Conference on Hydrology at 94th American Meteorological Society Annual Meeting, February 2-6, 2014, Atlanta, GA.

Interpretive Summary:

Technical Abstract: Satellite retrievals of land-surface temperature derived from thermal infrared (TIR) imagery have proven to have significant value in constraining diagnostic models of surface energy balance and evapotranspiration (ET). TIR-based ET retrievals capture important hydrologic features that are typically missed by standard prognostic land-surface models constrained by water balance, such as local ET enhancements due to irrigation, shallow groundwater tables, or sub-pixel surface water bodies. Polar orbiting systems like Landsat collect 60-100 m resolution TIR imagery every 8-16 days, providing spatiotemporal capabilities for monitoring 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 (~daily) ET retrievals using TIR data from the Moderate Resolution Imaging Spectroradiometer (MODIS) systems on board the Terra and Aqua satellite platforms. We describe implementations of a prototype Landsat-MODIS ET data fusion over rainfed and irrigated agricultural fields in the U.S. and demonstrate added value in comparison with a simple Landsat-only interpolation scheme – particularly when a rainfall event occurs between Landsat overpasses. Potential applications for fused ET datasets will be discussed, with societal benefits in the areas of food and water security.