Estimating Root Zone Soil Moisture Across the Eastern United States with Passive Microwave Satellite Data and a Simple Hydrologic Model

Abstract

Root zone soil moisture (RZSM) affects many natural processes and is an important component of environmental modeling, but it is expensive and challenging to monitor for relatively small spatial extents. Satellite datasets offer ample spatial coverage of near-surface soil moisture content at up to a daily time-step, but satellite-derived data products are currently too coarse in spatial resolution to use directly for many environmental applications, such as those for small catchments. This study investigated the use of passive microwave satellite soil moisture data products in a simple hydrologic model to provide root zone soil moisture estimates across a small catchment over a two year time period and the Eastern U.S. (EUS) at a 1 km resolution over a decadal time-scale. The physically based soil moisture analytical relationship (SMAR) was calibrated and tested with the Advanced Microwave Scanning Radiometer (AMSRE), Soil Moisture Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) data products. The SMAR spatial model relies on maps of soil physical properties and was first tested at the Shale Hills experimental catchment in central Pennsylvania. The model met a root mean square error (RMSE) benchmark of 0.06 cm3 cm−3 at 66% of the locations throughout the catchment. Then, the SMAR spatial model was calibrated at up to 68 sites (SCAN and AMERIFLUX network sites) that monitor soil moisture across the EUS region, and maps of SMAR parameters were generated for each satellite data product. The average RMSE for RZSM estimates from each satellite data product is <0.06 cm3 cm−3. Lastly, the 1 km EUS regional RZSM maps were tested with data from the Shale Hills, which was set aside for validating the regional SMAR, and the RMSE between the RZSM predictions and the catchment average is 0.042 cm3 cm−3. This study offers a promising approach for generating long time-series of regional RZSM maps with the same spatial resolution of soil property maps.

Performance evaluation of soil moisture profile estimation through entropy-based and exponential filter models

“…However, the MW-based SM measurements only account for the top few (∼0-5) cm of the soil column. There have been multiple studies exploring the assimilation/ forcing of the surface SM data from MW sensors into LSMs and crop models with some success in reducing the model errors (Bolten and Crow 2012, Ridler et al 2014, Lievens et al 2015, Yang et al 2016, Baldwin et al 2017, 2019.…”

Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau

“…Second, the water exchange happens immediately and ends within one day when soil water in the first layer exceeds field capacity, with an infinite permeability. Third, the soil water loss of the second layer decreases linearly from a relatively humid (does not include the real humid condition with a significant non-linear water loss function) condition to the wilting point [24,29,52].…”

“…However, based on the non-linear relationship between SSM and RZSM [26], it is possible to estimate RZSM that is based on SSM data. The RZSM can be estimated from SSM by using filtering techniques [28] and analytical models [24,29]. Furthermore, Cumulative Distribution Function (CDF) matching could be used to derive RZSM with the depth scaling method [30].…”

High-Resolution SMAP-Derived Root-Zone Soil Moisture Using an Exponential Filter Model Calibrated per Land Cover Type

“…Remote sensing SSM observations have been assimilated or used as forcing into different land surface models (LSM) and crop models, in order to derive RZSM, with the model errors being partially reduced [21][22][23][24][25][26]. Nevertheless, the studies reported that using SSM alone is limited by the disconnection between surface and subsurface dynamics, which in turn leads to uncertainties in the root-zone model.…”