Investigating the Potential of Downscaling Approaches for SMAP Radiometer Soil Moisture in Agroforestry Areas, China
Accurate high-spatial-resolution soil moisture content (SMC) datasets are crucial for applications, such as erosion modelling, flood forecasting, and agricultural production. Downscaling is an effective way to convert coarse satellite observations to a finer spatial resolution. Two downscaling appro...
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IEEE
2022-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/9926093/ |
| _version_ | 1798022923864244224 |
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| author | Huizhen Cui Lingmei Jiang Menxin Wu Jian Wang Fangbo Pan Wanjin Liao |
| author_facet | Huizhen Cui Lingmei Jiang Menxin Wu Jian Wang Fangbo Pan Wanjin Liao |
| author_sort | Huizhen Cui |
| collection | DOAJ |
| description | Accurate high-spatial-resolution soil moisture content (SMC) datasets are crucial for applications, such as erosion modelling, flood forecasting, and agricultural production. Downscaling is an effective way to convert coarse satellite observations to a finer spatial resolution. Two downscaling approaches were proposed to improve the spatial resolution of the soil moisture active passive (SMAP) radiometer SMC. A downscaling method (method 1) based on the triangular feature space concept was developed to express SMAP L3 SMC as a polynomial function of the Global Land Surface Satellite leaf area index, preprocessed synthetic land surface temperature, and microwave polarization difference index. A second downscaling method (method 2) based on the simulated datasets was developed to express high-resolution SMC as a function of coarse-resolution SMC and sentinel-1 synthetic aperture radar observations. Downscaled SMC (1 km) was evaluated by the <italic>in situ</italic> measurements and compared by the SMAP L2 active and passive SMC product in agroforestry areas, China. The results showed that the two downscaling methods could effectively capture the spatial variability of soil moisture at 1-km spatial scales. The root-mean-square error (RMSE) of downscaled SMC for grass, shrub, and forestland is 0.052–0.055 cm<sup>3</sup> cm<sup>−3</sup>, 0.063–0.069 cm<sup>3</sup> cm<sup>−3</sup>, and 0.067–0.073 cm<sup>3</sup> cm<sup>−3</sup>, respectively. The accuracies of method 1, method 2, and SMAP L2 SMC in the grassland were higher than those in the shrubland and forestland. Overall, the <italic>R</italic> and RMSE between the downscaled soil moisture from method 1, method 2, and SMAP L2 SMC were 0.613, 0.626, and 0.619 and 0.051 cm<sup>3</sup> cm<sup>−3</sup>, 0.041 cm<sup>3</sup> cm<sup>−3</sup>, and 0.45 cm<sup>3</sup> cm<sup>−3</sup>, respectively. The active and SMAP passive microwave combination method has great potential for soil moisture downscaling in agroforestry areas in China. |
| first_indexed | 2024-04-11T17:37:55Z |
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| id | doaj.art-a3bb26c361a94c8b9f6291e13a23c749 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-11T17:37:55Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
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| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-a3bb26c361a94c8b9f6291e13a23c7492022-12-22T04:11:33ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-01159369938010.1109/JSTARS.2022.32162679926093Investigating the Potential of Downscaling Approaches for SMAP Radiometer Soil Moisture in Agroforestry Areas, ChinaHuizhen Cui0https://orcid.org/0000-0002-1165-1998Lingmei Jiang1https://orcid.org/0000-0002-9847-9034Menxin Wu2Jian Wang3https://orcid.org/0000-0001-6811-8425Fangbo Pan4Wanjin Liao5https://orcid.org/0000-0001-7928-9043State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaNational Meteorological Center, Beijing, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, ChinaAccurate high-spatial-resolution soil moisture content (SMC) datasets are crucial for applications, such as erosion modelling, flood forecasting, and agricultural production. Downscaling is an effective way to convert coarse satellite observations to a finer spatial resolution. Two downscaling approaches were proposed to improve the spatial resolution of the soil moisture active passive (SMAP) radiometer SMC. A downscaling method (method 1) based on the triangular feature space concept was developed to express SMAP L3 SMC as a polynomial function of the Global Land Surface Satellite leaf area index, preprocessed synthetic land surface temperature, and microwave polarization difference index. A second downscaling method (method 2) based on the simulated datasets was developed to express high-resolution SMC as a function of coarse-resolution SMC and sentinel-1 synthetic aperture radar observations. Downscaled SMC (1 km) was evaluated by the <italic>in situ</italic> measurements and compared by the SMAP L2 active and passive SMC product in agroforestry areas, China. The results showed that the two downscaling methods could effectively capture the spatial variability of soil moisture at 1-km spatial scales. The root-mean-square error (RMSE) of downscaled SMC for grass, shrub, and forestland is 0.052–0.055 cm<sup>3</sup> cm<sup>−3</sup>, 0.063–0.069 cm<sup>3</sup> cm<sup>−3</sup>, and 0.067–0.073 cm<sup>3</sup> cm<sup>−3</sup>, respectively. The accuracies of method 1, method 2, and SMAP L2 SMC in the grassland were higher than those in the shrubland and forestland. Overall, the <italic>R</italic> and RMSE between the downscaled soil moisture from method 1, method 2, and SMAP L2 SMC were 0.613, 0.626, and 0.619 and 0.051 cm<sup>3</sup> cm<sup>−3</sup>, 0.041 cm<sup>3</sup> cm<sup>−3</sup>, and 0.45 cm<sup>3</sup> cm<sup>−3</sup>, respectively. The active and SMAP passive microwave combination method has great potential for soil moisture downscaling in agroforestry areas in China.https://ieeexplore.ieee.org/document/9926093/Agroforestry areadownscalinghigh resolutionsentinel-1soil moisture active passive (SMAP)soil moisture content (SMC) |
| spellingShingle | Huizhen Cui Lingmei Jiang Menxin Wu Jian Wang Fangbo Pan Wanjin Liao Investigating the Potential of Downscaling Approaches for SMAP Radiometer Soil Moisture in Agroforestry Areas, China IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Agroforestry area downscaling high resolution sentinel-1 soil moisture active passive (SMAP) soil moisture content (SMC) |
| title | Investigating the Potential of Downscaling Approaches for SMAP Radiometer Soil Moisture in Agroforestry Areas, China |
| title_full | Investigating the Potential of Downscaling Approaches for SMAP Radiometer Soil Moisture in Agroforestry Areas, China |
| title_fullStr | Investigating the Potential of Downscaling Approaches for SMAP Radiometer Soil Moisture in Agroforestry Areas, China |
| title_full_unstemmed | Investigating the Potential of Downscaling Approaches for SMAP Radiometer Soil Moisture in Agroforestry Areas, China |
| title_short | Investigating the Potential of Downscaling Approaches for SMAP Radiometer Soil Moisture in Agroforestry Areas, China |
| title_sort | investigating the potential of downscaling approaches for smap radiometer soil moisture in agroforestry areas china |
| topic | Agroforestry area downscaling high resolution sentinel-1 soil moisture active passive (SMAP) soil moisture content (SMC) |
| url | https://ieeexplore.ieee.org/document/9926093/ |
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