4.7 Article

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

Journal

REMOTE SENSING
Volume 11, Issue 17, Pages -

Publisher

MDPI
DOI: 10.3390/rs11172013

Keywords

AMSR-E; SMOS; SMAP; soil moisture; root zone; SMAR; SCAN; MCMC; CONUS soil

Funding

  1. NASA Terrestrial Carbon Program [NNX11AJ02G]
  2. Water JPI
  3. U.S. NSF Hydrologic Sciences Program [EAR-1416881]
  4. Pennsylvania State Geography Department
  5. NASA [NNX11AJ02G, 143916] Funding Source: Federal RePORTER

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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 cm(3) 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 cm(3) 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 cm(3) 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.

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