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Remote Sens. 2018, 10(12), 1961; https://doi.org/10.3390/rs10121961

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

Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA
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Received: 24 October 2018 / Revised: 1 December 2018 / Accepted: 1 December 2018 / Published: 5 December 2018
(This article belongs to the Special Issue Advances in the Remote Sensing of Terrestrial Evaporation)
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Abstract

This study illustrates a unified, physically-based framework for mapping landscape parameters of evapotranspiration (ET) using spectral mixture analysis (SMA). The framework integrates two widely used approaches by relating radiometric surface temperature to subpixel fractions of substrate (S), vegetation (V), and dark (D) spectral endmembers (EMs). Spatial and temporal variations in these spectral endmember fractions reflect process-driven variations in soil moisture, vegetation phenology, and illumination. Using all available Landsat 8 scenes from the peak growing season in the agriculturally diverse Sacramento Valley of northern California, we characterize the spatiotemporal relationships between each of the S, V, D land cover fractions and apparent brightness temperature (T) using bivariate distributions in the ET parameter spaces. The dark fraction scales inversely with shortwave broadband albedo (ρ < −0.98), and show a multilinear relationship to T. Substrate fraction estimates show a consistent (ρ ≈ 0.7 to 0.9) linear relationship to T. The vegetation fraction showed the expected triangular relationship to T. However, the bivariate distribution of V and T shows more distinct clustering than the distributions of Normalized Difference Vegetation Index (NDVI)-based proxies and T. Following the Triangle Method, the V fraction is used with T to compute the spatial maps of the ET fraction (EF; the ratio of the actual total ET to the net radiation) and moisture availability (Mo; the ratio of the actual soil surface evaporation to potential ET at the soil surface). EF and Mo estimates derived from the V fraction distinguish among rice growth stages, and between rice and non-rice agriculture, more clearly than those derived from transformed NDVI proxies. Met station-based reference ET & soil temperatures also track vegetation fraction-based estimates of EF & Mo more closely than do NDVI-based estimates of EF & Mo. The proposed approach using S, V, D land cover fractions in conjunction with T (SVD+T) provides a physically-based conceptual framework that unifies two widely-used approaches by simultaneously mapping the effects of albedo and vegetation abundance on the surface temperature field. The additional information provided by the third (Substrate) fraction suggests a potential avenue for ET model improvement by providing an explicit observational constraint on the exposed soil fraction and its moisture-modulated brightness. The structures of the T, EF & Mo vs SVD feature spaces are complementary and that can be interpreted in the context of physical variables that scale linearly and that can be represented directly in process models. Using the structure of the feature spaces to represent the spatiotemporal trajectory of crop phenology is possible in agricultural settings, because variations in the timing of planting and irrigation result in continuous trajectories in the physical parameter spaces that are represented by the feature spaces. The linear scaling properties of the SMA fraction estimates from meter to kilometer scales also facilitate the vicarious validation of ET estimates using multiple resolutions of imagery. View Full-Text
Keywords: spectral mixture analysis; evapotranspiration; surface energy balance spectral mixture analysis; evapotranspiration; surface energy balance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sousa, D.; Small, C. Spectral Mixture Analysis as a Unified Framework for the Remote Sensing of Evapotranspiration. Remote Sens. 2018, 10, 1961.

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