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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 #313423
Title: Using multi-satellite data fusion to estimate daily high spatial resolution evapotranspiration over a forested site in North Carolina

Author
item Yang, Yun
item Anderson, Martha
item Semmens, Kathryn
item Gao, Feng
item Kustas, William - Bill
item HAIN, C. - University Of Maryland
item Schull, Mitchell
item NOORMETS, A. - North Carolina State University
item WYNNE, R.H. - Virginia State University

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 1/15/2015
Publication Date: 1/26/2015
Citation: Yang, Y., Anderson, M.C., Semmens, K.A., Gao, F.N., Kustas, W.P., Hain, C., Schull, M.A., Noormets, A., Wynne, R. 2015. Using multi-satellite data fusion to estimate daily high spatial resolution evapotranspiration over a forested site in North Carolina.[abstract]. 2015 North American Carbon Program and Ameriflux joint meeting, Jan 26-30, 2015, Washington, DC.2015 CD-ROM..

Interpretive Summary:

Technical Abstract: Atmosphere-Land Exchange Inverse model and associated disaggregation scheme (ALEXI/DisALEXI). Satellite-based ET retrievals from both the Moderate Resolution Imaging Spectoradiometer (MODIS; 1km, daily) and Landsat (30m, bi-weekly) are fused with The Spatial and Temporal Adaptive Reflective Fusion Model (STARFM) to retrieve high spatial and temporal resolution ET. A Data Mining Sharpener (DMS) methodology is also used in the system to sharpen the native Landsat thermal infrared band (TIR) to 30m resolution. Comparing with Landsat only ET retrievals, this ET modeling system can optimize the usage of multi-satellite data, which are in different temporal and spatial resolution, to maximize the utility of high spatial and temporal ET estimation. Daily high spatial resolution ET retrievals are compared with observations from local flux towers. The model estimated ET matches well with the observed data and shows different annual ET trends for sites with various landcover types and different forest management strategies.