Spectral Mixture Analysis as a Unified Framework for the Remote Sensing of Evapotranspiration

This analysis proposes a unified framework for estimation of evapotranspiration (ET) 8 using spectral mixture analysis (SMA) based on globally standardized substrate, vegetation, and 9 dark (SVD) endmembers (EMs). Using all available Landsat 8 scenes from a month in the peak 10 growing season (June) in a diverse 90 x 120 km region in northern California, we characterize the 11 relationship between each of the S, V, D land cover fractions versus apparent brightness 12 temperature (T), as well as ET fraction (EF) and moisture availability (Mo) estimated using the 13 Triangle Method [1,2]. V fraction yields accurate, linearly scalable estimates of subpixel vegetation 14 abundance which contain considerably more structure than either the linearly or quadratically 15 normalized spectral indices that are generally used in ET studies. D fraction yields information 16 which is very similar to shortwave broadband albedo. S fraction estimates, at least for this 17 geographic area and season, show a consistent (ρ ~ 0.7 to 0.9) linear relationship to T. Because the 18 SVD approach includes accurate, scalable estimates of both vegetation abundance and albedo, it 19 provides a physically-based conceptual framework that unifies the two most widely used 20 approaches to estimation of ET from remotely sensed observations. The additional information 21 provided by the third (S) fraction is suggestive of a potential avenue for ET model improvement by 22 providing an explicit observational constraint on the exposed soil fraction. Taken together, these 23 results suggest the potential for a single unified framework for ET estimation. The strong linear 24 scaling properties of SMA fraction estimates from meter to kilometer scales also facilitate vicarious 25 validation of ET estimates using multiple resolutions of imagery. 26

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