Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 8; p. 1242
• Main Authors: Chatterjee, Sumanta, Huang, Jingyi, Hartemink, Alfred E.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2020
• Subjects: Algorithms; artificial intelligence; Backscattering; climate; crops; data collection; data fusion; Datasets; ecological monitoring; empirical models; Environmental monitoring; Hydrology; Land cover; landscapes; Learning algorithms; Machine learning; Mathematical models; model validation; Moisture content; monitoring; Parameters; Pasture; pastures; Precipitation; regression analysis; Remote sensing; Retrieval; Satellites; sensor synergy; Sensors; shrublands; shrubs; Soil moisture; soil moisture network; Soil properties; Soil surfaces; Soil water; soil water conservation; soil water content; Soils; spatial data; Spatial discrimination; Spatial resolution; Stations; Terrain; Topography; United States; Vegetation; Water content; United States > US; United States
• DOI: 10.3390/rs12081242
• ISSN: 2072-4292

Progress in sensor technologies has allowed real-time monitoring of soil water. It is a challenge to model soil water content based on remote sensing data. Here, we retrieved and modeled surface soil moisture (SSM) at the U.S. Climate Reference Network (USCRN) stations using Sentinel-1 backscatter data from 2016 to 2018 and ancillary data. Empirical machine learning models were established between soil water content measured at the USCRN stations with Sentinel-1 data from 2016 to 2017, the National Land Cover Dataset, terrain parameters, and Polaris soil data, and were evaluated in 2018 at the same USCRN stations. The Cubist model performed better than the multiple linear regression (MLR) and Random Forest (RF) model (R2 = 0.68 and RMSE = 0.06 m3 m-3 for validation). The Cubist model performed best in Shrub/Scrub, followed by Herbaceous and Cultivated Crops but poorly in Hay/Pasture. The success of SSM retrieval was mostly attributed to soil properties, followed by Sentinel-1 backscatter data, terrain parameters, and land cover. The approach shows the potential for retrieving SSM using Sentinel-1 data in a combination of high-resolution ancillary data across the conterminous United States (CONUS). Future work is required to improve the model performance by including more SSM network measurements, assimilating Sentinel-1 data with other microwave, optical and thermal remote sensing products. There is also a need to improve the spatial resolution and accuracy of land surface parameter products (e.g., soil properties and terrain parameters) at the regional and global scales.