Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: September 4, 2012
Publication Date: N/A
Technical Abstract: Modern practices of water management in agriculture can significantly benefit from accurate mapping of crop water consumption at field scale. Assuming that actual evapotranspiration (ET) is the main water loss in land hydrological balance, remote sensing data represent an invaluable tool for water use assessment on large areas in a cost effective manner. In particular, methodologies that exploit the connection between land surface temperature (LST) and energy required for the evapotranspiration process have been closely investigated in the last few decades. However, currently available high resolution (' 100 m) LST maps are characterized by low temporal frequency (bi-weekly); as a consequence, the adopted approaches for ET mapping at field scale generally rely on temporally sparse high resolution estimates and some sort of temporal interpolation method. These approaches appear to provide reliable results in many study cases; however, they have some obvious limitations, mainly related to discontinuities in ET temporal signal due to rainfall or irrigation that occur between two high resolution acquisitions. Recent progress in data fusion methodologies allows exploration of means for combining high and low spatial resolution data to improve the accuracy in ET estimations at daily scale. Here we report applications of an integrated multi-platform and multi-resolution methodology aiming to assess ET at daily timescales and field spatial scales. The Atmosphere-Land EXchange Inverse (ALEXI) model uses GOES (Geostationary Operational Environmental Satellite) data to obtain coarse resolution (10-km) maps at the U.S. continental scale, whereas the DisALEXI procedure uses ALEXI results as boundary condition to assess ET at finer spatial scales using MODIS (MODerate resolution Imaging Spectroradiometer) 1-km and Landsat 30-m LST imagery. The latter estimates are combined by means of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to obtain continuous daily ET maps at 30-m spatial resolution. Study cases include agricultural areas with typical US crops, such as soybean, corn and cotton.