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.
Highlights
Spatial estimates of volumetric soil moisture content within the top one meter of soil are important for simulating hydrologic and ecosystem processes [1,2,3], such as forecasting agricultural productivity [4] and providing a more precise representation of evapotranspiration (ET) feedbacks for climate change projections [5]
The technological advancement of satellite-based microwave scanning platforms has improved the prospects of producing high quality, globally distributed root-zone soil moisture data products [9], but in situ data is still important for validating root zone soil moisture models [5,10]
Our approach to using terrain and soil characteristics for running the spatial soil moisture analytical relationship (SMAR) with an Advanced Microwave Scanning Radiometer (AMSRE) pixel and the resulting average accuracy across the catchment indicates that the digital soil–landscape units are important in predicting root zone soil moisture across the Shale Hills catchment
Summary
Spatial estimates of volumetric soil moisture content (cm cm−3) within the top one meter of soil (i.e., the ‘root zone’) are important for simulating hydrologic and ecosystem processes [1,2,3], such as forecasting agricultural productivity [4] and providing a more precise representation of evapotranspiration (ET) feedbacks for climate change projections [5]. Mapped estimates of root zone soil moisture content support military trafficability assessments [6], improve flood predictions [7], and can assist with predicting the severity of forest fires [8]. Satellite-derived soil moisture data products are composed of estimates with up to a 53 km average areal spatial resolution for the longest-running satellite datasets [14], while most soil hydrologic processes that impact life and meteorological processes occur at scales
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