Two-Step Deep Learning Approach for Estimating Vegetation Backscatter: A Case Study of Soybean Fields
Precisely predicting vegetation backscatter involves various challenges, such as complex vegetation structure, soil–vegetation interaction, and data availability. Deep learning (DL) works as a powerful tool to analyze complex data and approximate the nonlinear relationship between variables, thus ex...
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/41 |
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| author | Dong Zhu Peng Zhao Qiang Zhao Qingliang Li Jinpeng Zhang Lixia Yang |
| author_facet | Dong Zhu Peng Zhao Qiang Zhao Qingliang Li Jinpeng Zhang Lixia Yang |
| author_sort | Dong Zhu |
| collection | DOAJ |
| description | Precisely predicting vegetation backscatter involves various challenges, such as complex vegetation structure, soil–vegetation interaction, and data availability. Deep learning (DL) works as a powerful tool to analyze complex data and approximate the nonlinear relationship between variables, thus exhibiting potential applications in microwave scattering problems. However, few DL-based approaches have been developed to reproduce vegetation backscatters owing to the lack of acquiring a large amount of training data. Motivated by a relatively accurate single-scattering radiative transfer model (SS-RTM) and radar measurements, we, for the first time to our knowledge, introduce a transfer learning (TL)-based approach to estimate the radar backscatter of vegetation canopy in the case of soybean fields. The proposed approach consists of two steps. In the first step, a simulated dataset was generated by the SS-RTM. Then, we pre-trained two baseline networks, namely, a deep neural network (DNN) and long short-term memory network (LSTM), using the simulated dataset. In the second step, limited measured data were utilized to fine-tune the previously pre-trained networks on the basis of TL strategy. Extensive experiments, conducted on both simulated data and in situ measurements, revealed that the proposed two-step TL-based approach yields a significantly better and more robust performance than SS-RTM and other DL schemes, indicating the feasibility of such an approach in estimating vegetation backscatters. All these outcomes provide a new path for addressing complex microwave scattering problems. |
| format | Article |
| id | doaj-art-96f3f3626fae4b03b20de8ee7aaea33d |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-96f3f3626fae4b03b20de8ee7aaea33d2025-01-10T13:20:02ZengMDPI AGRemote Sensing2072-42922024-12-011714110.3390/rs17010041Two-Step Deep Learning Approach for Estimating Vegetation Backscatter: A Case Study of Soybean FieldsDong Zhu0Peng Zhao1Qiang Zhao2Qingliang Li3Jinpeng Zhang4Lixia Yang5Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, ChinaNational Key Laboratory of Electromagnetic Environment, China Research Institute of Radiowave Propagation, Qingdao 266108, ChinaInformation Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, ChinaNational Key Laboratory of Electromagnetic Environment, China Research Institute of Radiowave Propagation, Qingdao 266108, ChinaNational Key Laboratory of Electromagnetic Environment, China Research Institute of Radiowave Propagation, Qingdao 266108, ChinaInformation Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, ChinaPrecisely predicting vegetation backscatter involves various challenges, such as complex vegetation structure, soil–vegetation interaction, and data availability. Deep learning (DL) works as a powerful tool to analyze complex data and approximate the nonlinear relationship between variables, thus exhibiting potential applications in microwave scattering problems. However, few DL-based approaches have been developed to reproduce vegetation backscatters owing to the lack of acquiring a large amount of training data. Motivated by a relatively accurate single-scattering radiative transfer model (SS-RTM) and radar measurements, we, for the first time to our knowledge, introduce a transfer learning (TL)-based approach to estimate the radar backscatter of vegetation canopy in the case of soybean fields. The proposed approach consists of two steps. In the first step, a simulated dataset was generated by the SS-RTM. Then, we pre-trained two baseline networks, namely, a deep neural network (DNN) and long short-term memory network (LSTM), using the simulated dataset. In the second step, limited measured data were utilized to fine-tune the previously pre-trained networks on the basis of TL strategy. Extensive experiments, conducted on both simulated data and in situ measurements, revealed that the proposed two-step TL-based approach yields a significantly better and more robust performance than SS-RTM and other DL schemes, indicating the feasibility of such an approach in estimating vegetation backscatters. All these outcomes provide a new path for addressing complex microwave scattering problems.https://www.mdpi.com/2072-4292/17/1/41backscattering coefficientradiative transfer modelsoybean fieldtransfer learningdeep neural networklong short-term memory network |
| spellingShingle | Dong Zhu Peng Zhao Qiang Zhao Qingliang Li Jinpeng Zhang Lixia Yang Two-Step Deep Learning Approach for Estimating Vegetation Backscatter: A Case Study of Soybean Fields Remote Sensing backscattering coefficient radiative transfer model soybean field transfer learning deep neural network long short-term memory network |
| title | Two-Step Deep Learning Approach for Estimating Vegetation Backscatter: A Case Study of Soybean Fields |
| title_full | Two-Step Deep Learning Approach for Estimating Vegetation Backscatter: A Case Study of Soybean Fields |
| title_fullStr | Two-Step Deep Learning Approach for Estimating Vegetation Backscatter: A Case Study of Soybean Fields |
| title_full_unstemmed | Two-Step Deep Learning Approach for Estimating Vegetation Backscatter: A Case Study of Soybean Fields |
| title_short | Two-Step Deep Learning Approach for Estimating Vegetation Backscatter: A Case Study of Soybean Fields |
| title_sort | two step deep learning approach for estimating vegetation backscatter a case study of soybean fields |
| topic | backscattering coefficient radiative transfer model soybean field transfer learning deep neural network long short-term memory network |
| url | https://www.mdpi.com/2072-4292/17/1/41 |
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