Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data
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 d...
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| Format: | Article |
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/8/1242 |
| _version_ | 1797570722103558144 |
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| author | Sumanta Chatterjee Jingyi Huang Alfred E. Hartemink |
| author_facet | Sumanta Chatterjee Jingyi Huang Alfred E. Hartemink |
| author_sort | Sumanta Chatterjee |
| collection | DOAJ |
| description | 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 (R<sup>2</sup> = 0.68 and RMSE = 0.06 m<sup>3</sup> m<sup>-3</sup> 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. |
| first_indexed | 2024-03-10T20:29:22Z |
| format | Article |
| id | doaj.art-c8f48e96ae3c43febd8f256daf066855 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:29:22Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c8f48e96ae3c43febd8f256daf0668552023-11-19T21:31:38ZengMDPI AGRemote Sensing2072-42922020-04-01128124210.3390/rs12081242Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary DataSumanta Chatterjee0Jingyi Huang1Alfred E. Hartemink2Department of Soil Science, University of Wisconsin-Madison, 1525 Observatory Drive, Madison, WI 53706, USADepartment of Soil Science, University of Wisconsin-Madison, 1525 Observatory Drive, Madison, WI 53706, USADepartment of Soil Science, University of Wisconsin-Madison, 1525 Observatory Drive, Madison, WI 53706, USAProgress 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 (R<sup>2</sup> = 0.68 and RMSE = 0.06 m<sup>3</sup> m<sup>-3</sup> 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.https://www.mdpi.com/2072-4292/12/8/1242remote sensingsoil moisture networksensor synergydata fusionsoil water conservationecological monitoring |
| spellingShingle | Sumanta Chatterjee Jingyi Huang Alfred E. Hartemink Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data Remote Sensing remote sensing soil moisture network sensor synergy data fusion soil water conservation ecological monitoring |
| title | Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data |
| title_full | Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data |
| title_fullStr | Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data |
| title_full_unstemmed | Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data |
| title_short | Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data |
| title_sort | establishing an empirical model for surface soil moisture retrieval at the u s climate reference network using sentinel 1 backscatter and ancillary data |
| topic | remote sensing soil moisture network sensor synergy data fusion soil water conservation ecological monitoring |
| url | https://www.mdpi.com/2072-4292/12/8/1242 |
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