Retrieval of Farmland Surface Soil Moisture Based on Feature Optimization and Machine Learning
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 sens...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/20/5102 |
| _version_ | 1827648075947048960 |
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| author | Jianhui Zhao Chenyang Zhang Lin Min Zhengwei Guo Ning Li |
| author_facet | Jianhui Zhao Chenyang Zhang Lin Min Zhengwei Guo Ning Li |
| author_sort | Jianhui Zhao |
| collection | DOAJ |
| description | 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–back 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’ 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 cm<sup>3</sup>/cm<sup>3</sup>. 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. |
| first_indexed | 2024-03-09T19:31:21Z |
| format | Article |
| id | doaj.art-0f517e749c934b978acaf24053db0187 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T19:31:21Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0f517e749c934b978acaf24053db01872023-11-24T02:19:12ZengMDPI AGRemote Sensing2072-42922022-10-011420510210.3390/rs14205102Retrieval of Farmland Surface Soil Moisture Based on Feature Optimization and Machine LearningJianhui Zhao0Chenyang Zhang1Lin Min2Zhengwei Guo3Ning Li4College of Computer and Information Engineering, Henan University, Kaifeng 475004, ChinaCollege of Computer and Information Engineering, Henan University, Kaifeng 475004, ChinaCollege of Computer and Information Engineering, Henan University, Kaifeng 475004, ChinaCollege of Computer and Information Engineering, Henan University, Kaifeng 475004, ChinaCollege of Computer and Information Engineering, Henan University, Kaifeng 475004, ChinaSoil 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–back 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’ 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 cm<sup>3</sup>/cm<sup>3</sup>. 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.https://www.mdpi.com/2072-4292/14/20/5102surface soil moisturemultisource remote sensingfeature optimizationmachine learning |
| spellingShingle | Jianhui Zhao Chenyang Zhang Lin Min Zhengwei Guo Ning Li Retrieval of Farmland Surface Soil Moisture Based on Feature Optimization and Machine Learning Remote Sensing surface soil moisture multisource remote sensing feature optimization machine learning |
| title | Retrieval of Farmland Surface Soil Moisture Based on Feature Optimization and Machine Learning |
| title_full | Retrieval of Farmland Surface Soil Moisture Based on Feature Optimization and Machine Learning |
| title_fullStr | Retrieval of Farmland Surface Soil Moisture Based on Feature Optimization and Machine Learning |
| title_full_unstemmed | Retrieval of Farmland Surface Soil Moisture Based on Feature Optimization and Machine Learning |
| title_short | Retrieval of Farmland Surface Soil Moisture Based on Feature Optimization and Machine Learning |
| title_sort | retrieval of farmland surface soil moisture based on feature optimization and machine learning |
| topic | surface soil moisture multisource remote sensing feature optimization machine learning |
| url | https://www.mdpi.com/2072-4292/14/20/5102 |
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