Estimating Soil Evaporation Using Drying Rates Determined from Satellite-Based Soil Moisture Records

Abstract

We describe an approach (ESMAP; Evaporation–Soil Moisture Active Passive) to estimate direct evaporation from soil, Esoil, by combining remotely-sensed soil drying rates with model calculations of the vertical fluxes in and out of the surface soil layer. Improved knowledge of Esoil can serve as a constraint in how total evapotranspiration is partitioned. The soil drying rates used here are based on SMAP data, but the method could be applied to data from other sensors. We present results corresponding to ten SMAP pixels in North America to evaluate the method. The ESMAP method was applied to intervals between successive SMAP overpasses with limited precipitation (<2 mm) to avoid uncertainty associated with precipitation, infiltration, and runoff. We used the Hydrus 1-D model to calculate the flux of water across the bottom boundary of the 0 to 50 mm soil layer sensed by SMAP, qbot. During dry intervals, qbot typically transfers water upwards into the surface soil layer from below, usually <0.5 mm day−1. Based on a standard formulation, transpiration from the surface soil layer, ET_s, is usually < 0.1 mm day−1, and, thus, generally not an important flux. Soil drying rates (converted to equivalent water thickness) are typically between 0 and 1 mm day−1. Evaporation is almost always greater than soil drying rates because qbot is typically a source of water to the surface soil and ET_s is negligible. Evaporation is typically between 0 and 1.5 mm day−1, with the highest values following rainfall. Soil evaporation summed over SMAP overpass intervals with precipitation <2 mm (60% of days) accounts for 15% of total precipitation. If evaporation rates are similar during overpasses with substantial precipitation, then the total evaporation flux would account for ~25% of precipitation. ESMAP could be used over spatially continuous domains to provide constraints on Esoil, but model-based Esoil would be required during intervals with substantial precipitation.

A continental-scale soil evaporation dataset derived from Soil Moisture Active Passive satellite drying rates

“…In this study, we apply the methodology developed by Small et al . 11 to estimate soil evaporation using soil moisture drying rates observed by the Soil Moisture Active Passive (SMAP) satellite. This continental-scale gridded dataset is unique from other datasets and has the potential to improve the representation of ET partitioning in hydrologic models and climate studies.…”

“…Yet, simulated fluxes are dependent on imperfect model structure and parameters that are difficult to estimate, resulting in large differences in E soil estimates from different LSMs 4 , 5 , 11 . Total ET simulated by LSMs in the Global Land Data Assimilation System (GLDAS 20 ), North American Land Data Assimilation System phase 2 (NLDAS-2 21 , 22 ) and experimental NLDAS-Testbed have been evaluated through comparison with remotely sensed ET 23 , 24 and networks of eddy covariance flux towers 11 , 25 , but there has been no similar effort to evaluate E soil , E T or E c as few datasets exist for this purpose 4 , 5 . Without observationally-based estimates of how ET is partitioned into the component fluxes, it is not possible to improve the representation of E soil , E T or E c in hydrologic models.…”

“…We extend the initial work of Small et al . 11 that developed and evaluated E-SMAP at several in situ observation locations, to provide a continental-scale, 4-year, 9 km soil evaporation dataset. In this data descriptor, we first describe calculation of E soil and a data screening procedure, followed by an exposition into the components of soil evaporation.…”

Soil Moisture Remote Sensing across Scales

“…Finally, Small et al [7] described an approach to estimate direct evaporation from soil by combining soil drying rates computed from SMAP soil moisture with model calculations of the vertical fluxes.…”

An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping

“…For either the first or second category, the most widely and successfully used spatio-temporal data fusion models are the STARFM and ESTARFM, respectively. However, it should be noted that existing methods also have the following limitations: most fusion methods applied to ET are initially used to integrate the land surface reflectance, spectral index and LST; thus, these methods cannot completely consider the influencing factor of ET including remote sensing and atmospheric characteristics [31] (especially some critical issues, such as soil moisture [32] and vegetation distribution) [33,34].…”