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Science and Technology of Kaifeng City","award":["222102110439"],"award-info":[{"award-number":["222102110439"]}]},{"name":"Plan of Science and Technology of Kaifeng City","award":["2102005"],"award-info":[{"award-number":["2102005"]}]},{"name":"Plan of Science and Technology of Kaifeng City","award":["KLSMNR-202204"],"award-info":[{"award-number":["KLSMNR-202204"]}]},{"name":"Foundation of Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People\u2019s Republic of China","award":["61871175"],"award-info":[{"award-number":["61871175"]}]},{"name":"Foundation of Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People\u2019s Republic of China","award":["42101386"],"award-info":[{"award-number":["42101386"]}]},{"name":"Foundation of Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People\u2019s Republic of 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Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People\u2019s Republic of China","award":["KLSMNR-202204"],"award-info":[{"award-number":["KLSMNR-202204"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil moisture is an important parameter affecting environmental processes such as hydrology, ecology, and climate. Synthetic aperture radar (SAR) microwave remote sensing is an important means of farmland surface soil moisture (SSM) measurement. The inversion of farmland SSM by microwave remote sensing is greatly affected by vegetation cover. To address this problem, a multisource remote sensing inversion method of farmland SSM based on feature optimization and machine learning is proposed in this paper. Six typical machine learning algorithms suitable for small sample training, including random forest, radial basis function neural network, generalized regression neural network, support vector regression, genetic algorithm\u2013back propagation neural network, and extreme learning machine, were selected in this paper. The features extracted from Sentinel-1\/2 and Radarsat-2 remote sensing data were analyzed by Pearson correlation, and those with high correlation coefficients were selected to form the optimal feature subset as the input for the subsequent machine learning models. Then, the SSM collaborative inversion models under different machine learning algorithms were constructed, and comparative experiments were set up to select the optimal prediction model. The models\u2019 accuracy under different feature parameters were studied, and the difference in the performance between the dual-polarization SAR data and the quad-polarization SAR data in SSM inversion was explored. The experimental results showed that among the six models, the random forest model had a higher inversion accuracy, with a coefficient of determination of 0.6395 and a root mean square error of 0.0264 cm3\/cm3. Meanwhile, the inversion accuracy could be greatly improved after feature optimization, and the inversion accuracy of the quad-polarization SAR data combined with optical remote sensing data, was better than that of the dual-polarization SAR data combined with optical remote sensing data.<\/jats:p>","DOI":"10.3390\/rs14205102","type":"journal-article","created":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T22:45:29Z","timestamp":1665614729000},"page":"5102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Retrieval of Farmland Surface Soil Moisture Based on Feature Optimization and Machine Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Jianhui","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Min","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengwei","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4358-6449","authenticated-orcid":false,"given":"Ning","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,12]]},"reference":[{"key":"ref_1","first-page":"1192","article-title":"Review on soil moisture retrieval by satellite remote sensing","volume":"27","author":"Chen","year":"2012","journal-title":"Prog. 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