Spatially Downscaling a Global Evapotranspiration Product for End User Using a Deep Neural Network: A Case Study with the GLEAM Product

1. Introduction

Actual evapotranspiration (ET) is a key variable in the water–heat–carbon exchanges between the land surface and the atmosphere [1,2,3]. Many local applications, such as irrigation and water resources management, rely on high spatiotemporal resolution ET estimates [4,5,6]. Many researchers, such as Wang and Dickinson [7] and Zhang, et al. [8], reviewed common ET modeling methods based on remote sensing and meteorological data, including land surface temperature (LST)-based methods (e.g., two-source energy balance model), Penman–Monteith methods and water balance methods. However, these methods are difficult to master for most end users, for they focus on how to make better use it not how to obtain it. Obtaining high spatiotemporal resolution ET estimates is a big challenge due to the complexity of ET processes.

Currently, there are several published global ET products, such as GLDAS (Global Land Data Assimilation System) ET from National Aeronautics and Space Administration (0.25°, daily) and GLEAM (Global Land Evaporation Amsterdam Model) ET from Vrije University Amsterdam (0.25°, daily), among others [9,10]. Most of these ET products are based on the water balance method and have a high temporal resolution (even sub-daily), but low spatial resolution. Few published global ET products have high spatiotemporal resolution (i.e., 1 km and daily). Because most end users prefer to use an existing ET product rather than running a physical model to estimate it, it is necessary to develop an end user-centered method that can provide high spatiotemporal resolution ET to make full use of existing global ET products.

The GLEAM ET, based on the Priestley–Taylor equation, combines a set of algorithms that estimate ET separately for soil and vegetation [10,11]. GLEAM provides daily actual ET, which has the advantage of higher temporal resolution than the MODIS ET product, which has a temporal resolution of 8 days and suffers from numerous gaps due to clouds [12]. GLEAM also tries to correct random forcing errors by assimilating observed surface soil moisture into the soil profile. However, the GLEAM ET product has lower spatial resolution (0.25°) just like other water balance-based global ET products. To meet the requirements of end users, it is necessary to develop a downscaling method to improve the spatial resolution of GLEAM ET products from 0.25° to 0.01°.

Machine learning-based spatially downscaling methods...