Surface Soil Moisture Estimation Using a Neural Network Model in Bare Land and Vegetated Areas
Most of the approaches to retrieve surface soil moisture (SSM) by optical and thermal infrared (TIR) spectroscopies are purposed to calculate various characteristic bands/indices and then to establish the regression relationship between them in combination with the measurement data. However, due to...
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| Format: | Article |
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Wiley
2023-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2023/5887177 |
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| author | Dayou Luo Xingping Wen Ping He |
| author_facet | Dayou Luo Xingping Wen Ping He |
| author_sort | Dayou Luo |
| collection | DOAJ |
| description | Most of the approaches to retrieve surface soil moisture (SSM) by optical and thermal infrared (TIR) spectroscopies are purposed to calculate various characteristic bands/indices and then to establish the regression relationship between them in combination with the measurement data. However, due to the combined impact of many factors, the regression relationship often shows nonlinearity. Moreover, the relationship between the single temporal image and the measured data are not transplantable in time and space, which makes it difficult to construct a more general model for the remote sensing (RS) estimation of SSM. In order to solve this problem, the back propagation (BP) neural network (NN) with an excellent nonlinear mapping ability is introduced to determine the relationship between the characteristic band/index and the measurement data. In the BPNN model, the optical and TIR RS data in different periods were taken as the input parameters, and the in situ soil moisture data were treated as the output parameter. There are 12 schemes designed to retrieve SSM. The key findings of study were as follows: (1) the BPNN model could retrieve SSM with a high accuracy that indicates the correlation coefficient between the estimated and measured soil moisture as 0.9001 and (2) the SSM retrieval model based on the BPNN can be applied to estimate the SSM with different spatial resolution values. |
| format | Article |
| id | doaj-art-f678b40f2c8046fdb37f2eccc968e76e |
| institution | OA Journals |
| issn | 2314-4939 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-f678b40f2c8046fdb37f2eccc968e76e2025-08-20T02:22:28ZengWileyJournal of Spectroscopy2314-49392023-01-01202310.1155/2023/5887177Surface Soil Moisture Estimation Using a Neural Network Model in Bare Land and Vegetated AreasDayou Luo0Xingping Wen1Ping He2Faculty of Land Resource EngineeringFaculty of Land Resource EngineeringFaculty of Land Resource EngineeringMost of the approaches to retrieve surface soil moisture (SSM) by optical and thermal infrared (TIR) spectroscopies are purposed to calculate various characteristic bands/indices and then to establish the regression relationship between them in combination with the measurement data. However, due to the combined impact of many factors, the regression relationship often shows nonlinearity. Moreover, the relationship between the single temporal image and the measured data are not transplantable in time and space, which makes it difficult to construct a more general model for the remote sensing (RS) estimation of SSM. In order to solve this problem, the back propagation (BP) neural network (NN) with an excellent nonlinear mapping ability is introduced to determine the relationship between the characteristic band/index and the measurement data. In the BPNN model, the optical and TIR RS data in different periods were taken as the input parameters, and the in situ soil moisture data were treated as the output parameter. There are 12 schemes designed to retrieve SSM. The key findings of study were as follows: (1) the BPNN model could retrieve SSM with a high accuracy that indicates the correlation coefficient between the estimated and measured soil moisture as 0.9001 and (2) the SSM retrieval model based on the BPNN can be applied to estimate the SSM with different spatial resolution values.http://dx.doi.org/10.1155/2023/5887177 |
| spellingShingle | Dayou Luo Xingping Wen Ping He Surface Soil Moisture Estimation Using a Neural Network Model in Bare Land and Vegetated Areas Journal of Spectroscopy |
| title | Surface Soil Moisture Estimation Using a Neural Network Model in Bare Land and Vegetated Areas |
| title_full | Surface Soil Moisture Estimation Using a Neural Network Model in Bare Land and Vegetated Areas |
| title_fullStr | Surface Soil Moisture Estimation Using a Neural Network Model in Bare Land and Vegetated Areas |
| title_full_unstemmed | Surface Soil Moisture Estimation Using a Neural Network Model in Bare Land and Vegetated Areas |
| title_short | Surface Soil Moisture Estimation Using a Neural Network Model in Bare Land and Vegetated Areas |
| title_sort | surface soil moisture estimation using a neural network model in bare land and vegetated areas |
| url | http://dx.doi.org/10.1155/2023/5887177 |
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