Spectral Mixture Analysis as a Unified Framework for the Remote Sensing of Evapotranspiration
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Description: This analysis proposes a unified framework for estimation of evapotranspiration (ET) using spectral mixture analysis (SMA) based on globally standardized substrate, vegetation, and dark (SVD) endmembers (EMs). Using all available Landsat 8 scenes from a month in the peak growing season (June) in a diverse 90 x 120 km region in northern California, we characterize the relationship between each of the S, V, D land cover fractions versus apparent brightness temperature (T), as well as ET fraction (EF) and moisture availability (Mo) estimated using the Triangle Method [1,2]. V fraction yields accurate, linearly scalable estimates of subpixel vegetation abundance which contain considerably more structure than either the linearly or quadratically normalized spectral indices that are generally used in ET studies. D fraction yields information which is very similar to shortwave broadband albedo. S fraction estimates, at least for this geographic area and season, show a consistent (ρ ~ 0.7 to 0.9) linear relationship to T. Because the SVD approach includes accurate, scalable estimates of both vegetation abundance and albedo, it provides a physically-based conceptual framework that unifies the two most widely used approaches to estimation of ET from remotely sensed observations. The additional information provided by the third (S) fraction is suggestive of a potential avenue for ET model improvement by providing an explicit observational constraint on the exposed soil fraction. Taken together, these results suggest the potential for a single unified framework for ET estimation. The strong linear scaling properties of SMA fraction estimates from meter to kilometer scales also facilitate vicarious validation of ET estimates using multiple resolutions of imagery.