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A review of remote sensing based actual evapotranspiration estimation

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Evapotranspiration is a major component of the global water cycle and provides a critical nexus between terrestrial water, carbon and surface energy exchanges. Evapotranspiration is inherently difficult to measure and predict especially at large spatial scales. Remote sensing provides a cost‐effective method to estimate evapotranspiration at regional to global scales. In the past three decades a large number of studies on remote sensing based evapotranspiration estimation have emerged. This review summarizes the basic theories underpinning current remote sensing based evapotranspiration estimation methods. It also lays out the development history of these methods and compares their advantages and limitations. Several key directions for further study are identified and discussed, including identification of uncertainty sources in remote sensing evapotranspiration models, merging of different remote sensing methods, application of data assimilation and fusion techniques in producing robust evapotranspiration estimates, and utilization of multi‐source remote sensing data and latest sensor technologies. Further advances in the remote sensing of evapotranspiration will enhance capabilities for monitoring of the global water and energy cycles, including water availability and ecosystem responses and feedbacks to climate change and human impacts. WIREs Water 2016, 3:834–853. doi: 10.1002/wat2.1168 This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods
Geographic distribution of primary climatic control factors regulating terrestrial E derived from long‐term climate statistics, including demand = f(Tair, Humidity, Wind Speed), supply = f(Precipitation), and energy = f(all‐sky radiation, clear‐sky radiation) constraints.
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(a) Latitudinal distribution of mean E from six RS E products, (b) mean seasonal cycle of the six E products, (c) time series of annual global land E anomalies from the MTE model and nine other models, and (d) time series of annual global land E anomalies from five RS E model ((a) and (b) Reprinted with permission from Ref . Copyright 2013 IOP Publishing Ltd, Ref . Copyright 2010 Macmillan Publishers Limited, and (d) Ref . Copyright 2013 Copernicus Publications.)
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(a) The spatial patterns of ensemble mean (mean), standard deviation (std), and relative standard deviation (rstd) of 12 global E (latent heat) products, including five RS E products, for 1994 (Reprinted with permission from Ref . Copyright 2011 American Geophysical Union) and (b) the spatial patterns of mean, std, and rstd of multi‐year (1989–2005) mean annual E from seven global RS E products (data are provided by Mueller, et al.)
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Schematic diagrams of different configurations of resistance networks (surface and aerodynamic resistances to heat or water vapor) used in different existing heat and/or evapotranspiration models: (a) one‐source model, (b) two‐layer model, (c) two‐patch model, (d) hybrid dual‐source model, (e) multi‐patch model, and (f) multi‐layer model; subscripts a and s represent atmosphere and surface, while superscripts S and C denote soil and canopy, respectively. Note that is only applied to the resistance to water vapor.
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Selected key publications influencing the course of remote sensing based E science and model development.
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