A MWCMLAI-Net method for LAI inversion in maize and rice using GF-3 and Lutan radar data
ABSTRACTThis study aimed at alleviating the problems of unsatisfactory inversion accuracy and weak model stability in LAI remote sensing quantitative inversion. The properties and complex scattering mechanism of SAR data specify the polarization combinations and frequencies. This paper proposes an i...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2341128 |
| _version_ | 1797214456110907392 |
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| author | Xiaoxuan Wang Xiaoping Lu Zenan Yang |
| author_facet | Xiaoxuan Wang Xiaoping Lu Zenan Yang |
| author_sort | Xiaoxuan Wang |
| collection | DOAJ |
| description | ABSTRACTThis study aimed at alleviating the problems of unsatisfactory inversion accuracy and weak model stability in LAI remote sensing quantitative inversion. The properties and complex scattering mechanism of SAR data specify the polarization combinations and frequencies. This paper proposes an improved water cloud model combined with a deep neural network (MWCMLAI-Net) for high-precision inversion. The polarized GF-3 (C-band) and Lutan (L-band) were used to investigate the potential of SAR images to estimate LAI, a strong indicator of crop productivity. The study selected xiangfu district in the eastern part of Kaifeng City, Henan Province, as the test area and investigated the LAI of maize and rice. The [Formula: see text] Model, backward scattering coefficient extracted by the modified cloud and water model (MWCM), and LAI obtained by the inversion of the MWCM were used as the inputs, and the MWCMLAI-Net inversion of the LAI was constructed. The results showed that the model’s inverted LAI fitting accuracies of maize and rice for the three fertility periods were better than the other models, with R2 above 0.8516 and RMSE below 0.3999 m2/m2. The addition of noise did not affect the results. |
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| format | Article |
| id | doaj.art-ec5e1551e92e48739a6d88f416838e94 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-04-24T11:14:27Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
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| series | International Journal of Digital Earth |
| spelling | doaj.art-ec5e1551e92e48739a6d88f416838e942024-04-11T11:34:19ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552024-12-0117110.1080/17538947.2024.2341128A MWCMLAI-Net method for LAI inversion in maize and rice using GF-3 and Lutan radar dataXiaoxuan Wang0Xiaoping Lu1Zenan Yang2Key Laboratory of Spatio-Temporal Information and Ecological Restoration of Mines of Natural Resources of the People's Republic of China, Henan Polytechnic University, Jiaozuo, People’s Republic of ChinaKey Laboratory of Spatio-Temporal Information and Ecological Restoration of Mines of Natural Resources of the People's Republic of China, Henan Polytechnic University, Jiaozuo, People’s Republic of ChinaKey Laboratory of Spatio-Temporal Information and Ecological Restoration of Mines of Natural Resources of the People's Republic of China, Henan Polytechnic University, Jiaozuo, People’s Republic of ChinaABSTRACTThis study aimed at alleviating the problems of unsatisfactory inversion accuracy and weak model stability in LAI remote sensing quantitative inversion. The properties and complex scattering mechanism of SAR data specify the polarization combinations and frequencies. This paper proposes an improved water cloud model combined with a deep neural network (MWCMLAI-Net) for high-precision inversion. The polarized GF-3 (C-band) and Lutan (L-band) were used to investigate the potential of SAR images to estimate LAI, a strong indicator of crop productivity. The study selected xiangfu district in the eastern part of Kaifeng City, Henan Province, as the test area and investigated the LAI of maize and rice. The [Formula: see text] Model, backward scattering coefficient extracted by the modified cloud and water model (MWCM), and LAI obtained by the inversion of the MWCM were used as the inputs, and the MWCMLAI-Net inversion of the LAI was constructed. The results showed that the model’s inverted LAI fitting accuracies of maize and rice for the three fertility periods were better than the other models, with R2 above 0.8516 and RMSE below 0.3999 m2/m2. The addition of noise did not affect the results.https://www.tandfonline.com/doi/10.1080/17538947.2024.2341128Leaf area index (LAI)GF-3LutanMWCMLAI-Netmaizerice |
| spellingShingle | Xiaoxuan Wang Xiaoping Lu Zenan Yang A MWCMLAI-Net method for LAI inversion in maize and rice using GF-3 and Lutan radar data International Journal of Digital Earth Leaf area index (LAI) GF-3 Lutan MWCMLAI-Net maize rice |
| title | A MWCMLAI-Net method for LAI inversion in maize and rice using GF-3 and Lutan radar data |
| title_full | A MWCMLAI-Net method for LAI inversion in maize and rice using GF-3 and Lutan radar data |
| title_fullStr | A MWCMLAI-Net method for LAI inversion in maize and rice using GF-3 and Lutan radar data |
| title_full_unstemmed | A MWCMLAI-Net method for LAI inversion in maize and rice using GF-3 and Lutan radar data |
| title_short | A MWCMLAI-Net method for LAI inversion in maize and rice using GF-3 and Lutan radar data |
| title_sort | mwcmlai net method for lai inversion in maize and rice using gf 3 and lutan radar data |
| topic | Leaf area index (LAI) GF-3 Lutan MWCMLAI-Net maize rice |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2341128 |
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