Abstract
Abstract. Currently, applications of remote sensing evapotranspiration (ET) products are limited by the coarse resolution of satellite remote sensing data caused by land surface heterogeneities and the temporal-scale extrapolation of the instantaneous latent heat flux (LE) based on satellite overpass time. This study proposes a simple but efficient model (EFAF) for estimating the daily ET of remotely sensed mixed pixels using a model of the evaporative fraction (EF) and area fraction (AF) to increase the accuracy of ET estimate over heterogeneous land surfaces. To accomplish this goal, we derive an equation for calculating the EF of mixed pixels based on two key hypotheses. Hypothesis 1 states that the available energy (AE) of each sub-pixel is approximately equal to that of any other sub-pixels in the same mixed pixel within an acceptable margin of error and is equivalent to the AE of the mixed pixel. This approach simplifies the equation, and uncertainties and errors related to the estimated ET values are minor. Hypothesis 2 states that the EF of each sub-pixel is equal to that of the nearest pure pixel(s) of the same land cover type. This equation is designed to correct spatial-scale errors for the EF of mixed pixels; it can be used to calculate daily ET from daily AE data. The model was applied to an artificial oasis located in the midstream area of the Heihe River using HJ-1B satellite data with a 300 m resolution. The results generated before and after making corrections were compared and validated using site data from eddy covariance systems. The results show that the new model can significantly improve the accuracy of daily ET estimates relative to the lumped method; the coefficient of determination (R2) increased to 0.82 from 0.62, the root mean square error (RMSE) decreased to 1.60 from 2.47 MJ m−2(decreased approximately to 0.64 from 0.99 mm) and the mean bias error (MBE) decreased from 1.92 to 1.18 MJ m−2 (decreased from approximately 0.77 to 0.47 mm). It is concluded that EFAF can reproduce daily ET with reasonable accuracy; can be used to produce the ET product; and can be applied to hydrology research, precision agricultural management and monitoring natural ecosystems in the future.
Highlights
IntroductionLarge-scale remotely sensed evapotranspiration (ET) estimates generally have a resolution that is too coarse for use in critical applications (e.g. drought assessment, water management or agricultural monitoring) (McCabe et al, 2017)
Large-scale remotely sensed evapotranspiration (ET) estimates generally have a resolution that is too coarse for use in critical applications (McCabe et al, 2017)
The EFAF study was performed on crops that mainly grew during June, July, August and September
Summary
Large-scale remotely sensed evapotranspiration (ET) estimates generally have a resolution that is too coarse for use in critical applications (e.g. drought assessment, water management or agricultural monitoring) (McCabe et al, 2017). Classical satellite-based models such as the Surface Energy Balance Algorithm for Land (SEBAL) (Bastiaanssen et al, 1998), Surface Energy Balance System (SEBS) (Su, 2002), Atmosphere-Land Exchange Inverse (ALEXI) and an associated flux disaggregation technique (DisALEXI) (Norman et al, 2003; Anderson et al, 2011, 2012), and temperaturesharpening and flux aggregation scheme (TSFA) (Peng et al, 2016) have been developed to monitor land–atmosphere energy balance flux interactions; and in most cases, spatially variable inputs and parameters are based on assumptions of homogeneity of land and atmospheric surfaces (Sharma et al, 2016). Ershadi et al (2013) reported that input aggregation underestimated ET at the satellite image scale, with up to 15 % fewer retrievals, and at the pixel scale by up to 50 % relative to using an original fine-resolution Landsat image These results suggest that the spatial characteristics obtained from data of a specific resolution can only reflect characteristics observed at that resolution. At the pixel scale, determining whether the physical mechanism is suitable for application, identifying the applicable conditions and determining how to correct the scale effects are the three critical issues for remotely sensed ET estimates (Li and Wang, 2013)
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