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

Douglas Baldwin; Salvatore Manfreda; Henry Lin; Erica  A.H. Smithwick

doi:10.3390/rs11172013

Remote Sensing (Aug 2019)

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

Douglas Baldwin,
Salvatore Manfreda,
Henry Lin,
Erica A.H. Smithwick

Affiliations

Douglas Baldwin: Department of Geography, The Pennsylvania State University, University Park, PA 16802, USA
Salvatore Manfreda: Department of European and Mediterranean Cultures: Architecture, Environment, and Cultural Heritage (DiCEM), University of Basilicata, Matera 75100, Italy
Henry Lin: Department of Ecosystem Science and Management, The Pennsylvania State University, University Park, PA 16802, USA
Erica A.H. Smithwick: Department of Geography, The Pennsylvania State University, University Park, PA 16802, USA

DOI: https://doi.org/10.3390/rs11172013
Journal volume & issue: Vol. 11, no. 17
p. 2013

Abstract

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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.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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