A review of remote sensing based actual evapotranspiration estimation

Abstract

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. FIGURE 5 | (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 118.

Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate

“…MOD16,GLEAM v2A,GLEAM v2B and GLEAM v3A and PML). Constructing an ET product by combining different approaches can take advantage of their desirable features and reduce their limitations (Zhang et al, 2016). This is indeed reflected by the enhanced performance of DOLCE over all the biome types against the reference products.…”

“…Together with the reasonable density of in-situ surface observations, this has 10 meant that reanalysis products -usually the basis of gridded land surface forcing data sets at regional and global scales -are reasonably well constrained. On the other hand, very few LSM outputs (such as evapotranspiration or sensible heat flux) can be directly observed by remote sensing, and the only currently available way to derive them is by the use of modelling schemes or empirical formulations that use satellite based datasets (Jung et al, 2011;Fisher et al, 2008;Miralles et al, 2011b;Martens et al, 2016;Mu et al, 2007;Su, 2002;Zhang et al, 2016;. This approach leads to large and 15 usually unquantifiable uncertainties.…”

“…As a result, gridded LSM evaluation products show considerable differences (Ershadi et al, 2014;Jiménez et al, 2011;McCabe et al, 2016 ;Michel et al, 2016;Vinukollu et al, 2011). If an accurate description of these uncertainties was available, then it might be possible to evaluate gridded LSM simulations in certain circumstances, but until now very few studies quantify LSM uncertainties at regional or global scales (Badgley et al, 2015;Loew et al, 2016;Zhang et al, 2016). 20 Several gridded ET products have already been developed (see Table 1).…”

“…Indeed, all the evaluation studies that compared gridded estimates against flux tower observations highlighted this point as a limitation in the evaluation approach. Zhang et al (2016) pointed out that merging an ensemble of gridded ET products using a sophisticated data fusion method is 15 likely to generate a better ET product with reduced uncertainty. Ershadi et al (2014) and McCabe et al (2016) noted in their studies that the multi-product mean produces improved estimates relative to individual ET products.…”

Evapotranspiration and Surface Energy Fluxes Estimation Using the Landsat-7 Enhanced Thematic Mapper Plus Image over a Semiarid Agrosystem in the North-West of Algeria

“…Remote sensing technology can provide land surface parameters such as albedo, vegetation indices and surface temperature, which are indispensable to remote sensing-based energy balance models for scaling up evapotranspiration and surface energy fluxes to larger spatial and longer temporal scales. It is recognized as the only way to retrieve evapotranspiration at several temporal and spatial scales (Zhang et al, 2016;Liou et al, 2014). To this end, there has been a major effort over the past several years to develop remote sensing-based methods that provide spatially distributed surface fluxes maps using airborne and satellite data (Khaldi et al, 2011).…”