Downscaling of SMAP Soil Moisture Data by Using a Deep Belief Network
The spatial resolution of current soil moisture (SM) products is generally low, consequently limiting their applications. In this study, a deep belief network-based method (DBN) was used to downscale the Soil Moisture Active Passive (SMAP) L4 SM product. First, the factors affecting soil surface moi...
| Main Authors: | , , , , , |
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| Format: | Article |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/22/5681 |
| _version_ | 1827643768424103936 |
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| author | Yulin Cai Puran Fan Sen Lang Mengyao Li Yasir Muhammad Aixia Liu |
| author_facet | Yulin Cai Puran Fan Sen Lang Mengyao Li Yasir Muhammad Aixia Liu |
| author_sort | Yulin Cai |
| collection | DOAJ |
| description | The spatial resolution of current soil moisture (SM) products is generally low, consequently limiting their applications. In this study, a deep belief network-based method (DBN) was used to downscale the Soil Moisture Active Passive (SMAP) L4 SM product. First, the factors affecting soil surface moisture were analyzed, and the significantly correlated ones were selected as predictors for the downscaling model. Second, a DBN model was trained and used to downscale the 9 km SMAP L4 SM to 1 km in the study area on 25 September 2019. Validation was performed using original SMAP L4 SM data and in situ measurements from SM and temperature wireless sensor network with 34 sites. Finally, the DBN method was compared with another commonly used machine learning model-random forest (RF). Results showed that (1) the downscaled 1 km SM data are in good agreement with the original SMAP L4 SM data and field measured data, and (2) DBN has a higher correlation coefficient and a lower root mean square error than those of RF. The coefficients of determination for fitting the two models with the measured data at the site were 0.5260 and 0.4816, with relative mean square errors of 0.0303 and 0.0342 m<sup>3</sup>/m<sup>3</sup>, respectively. The study also demonstrated the applicability of the DBN method to AMSR SM data downscaling besides SMAP. The proposed method can provide a framework to support future hydrological modeling, regional drought monitoring, and agricultural research. |
| first_indexed | 2024-03-09T18:01:41Z |
| format | Article |
| id | doaj.art-161ec96796dc4b22922ccf117f18b6f1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:01:41Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj.art-161ec96796dc4b22922ccf117f18b6f12023-11-24T09:48:50ZengMDPI AGRemote Sensing2072-42922022-11-011422568110.3390/rs14225681Downscaling of SMAP Soil Moisture Data by Using a Deep Belief NetworkYulin Cai0Puran Fan1Sen Lang2Mengyao Li3Yasir Muhammad4Aixia Liu5Shandong 3S Engineering Technology Research Center, College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaShandong 3S Engineering Technology Research Center, College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaShandong 3S Engineering Technology Research Center, College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaShandong 3S Engineering Technology Research Center, College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, ChinaLand Satellite Remote Sensing Application Center, China Ministry of Natural Resources, Beijing 100048, ChinaThe spatial resolution of current soil moisture (SM) products is generally low, consequently limiting their applications. In this study, a deep belief network-based method (DBN) was used to downscale the Soil Moisture Active Passive (SMAP) L4 SM product. First, the factors affecting soil surface moisture were analyzed, and the significantly correlated ones were selected as predictors for the downscaling model. Second, a DBN model was trained and used to downscale the 9 km SMAP L4 SM to 1 km in the study area on 25 September 2019. Validation was performed using original SMAP L4 SM data and in situ measurements from SM and temperature wireless sensor network with 34 sites. Finally, the DBN method was compared with another commonly used machine learning model-random forest (RF). Results showed that (1) the downscaled 1 km SM data are in good agreement with the original SMAP L4 SM data and field measured data, and (2) DBN has a higher correlation coefficient and a lower root mean square error than those of RF. The coefficients of determination for fitting the two models with the measured data at the site were 0.5260 and 0.4816, with relative mean square errors of 0.0303 and 0.0342 m<sup>3</sup>/m<sup>3</sup>, respectively. The study also demonstrated the applicability of the DBN method to AMSR SM data downscaling besides SMAP. The proposed method can provide a framework to support future hydrological modeling, regional drought monitoring, and agricultural research.https://www.mdpi.com/2072-4292/14/22/5681deep learningdeep belief networksoil moistureSMAP L4Shandian River Basin |
| spellingShingle | Yulin Cai Puran Fan Sen Lang Mengyao Li Yasir Muhammad Aixia Liu Downscaling of SMAP Soil Moisture Data by Using a Deep Belief Network Remote Sensing deep learning deep belief network soil moisture SMAP L4 Shandian River Basin |
| title | Downscaling of SMAP Soil Moisture Data by Using a Deep Belief Network |
| title_full | Downscaling of SMAP Soil Moisture Data by Using a Deep Belief Network |
| title_fullStr | Downscaling of SMAP Soil Moisture Data by Using a Deep Belief Network |
| title_full_unstemmed | Downscaling of SMAP Soil Moisture Data by Using a Deep Belief Network |
| title_short | Downscaling of SMAP Soil Moisture Data by Using a Deep Belief Network |
| title_sort | downscaling of smap soil moisture data by using a deep belief network |
| topic | deep learning deep belief network soil moisture SMAP L4 Shandian River Basin |
| url | https://www.mdpi.com/2072-4292/14/22/5681 |
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