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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 #303211
Title: Data fusion techniques for mapping daily water use and vegetation stress at field scales

Author
item Anderson, Martha
item CAMMALLERI, C. - European Commission-Joint Research Centre (JRC)
item Semmens, Kathryn
item Gao, Feng
item HAIN, C. - University Of Maryland
item Kustas, William - Bill

Submitted to: International Symposium on Recent Advances in Quantitative Remote Sensing
Publication Type: Abstract Only
Publication Acceptance Date: 5/5/2014
Publication Date: 9/22/2014
Citation: Anderson, M.C., Cammalleri, C., Semmens, K.A., Gao, F.N., Hain, C., Kustas, W.P. 2014. Data fusion techniques for mapping daily water use and vegetation stress at field scales [abstract]. 4th International Symposium on Recent Advances in Quantitative Remote Sensing, September 22-26, 2014, Torrent (Valencia), Spain.

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., in both temperature and semiarid climate zones. Relative errors in seasonal ET retrieved using the fusion algorithm are on the order of 5% in comparison with observations of cumulative ET acquired using eddy correlation systems. In particular, the value of data fusion is most evident when a rainfall event occurs between clear-sky Landsat overpasses. Potential applications for fused ET datasets will be discussed, with societal benefits in the areas of food and water security.