Improved leaf area index reconstruction in heavily cloudy areas: A novel deep learning approach for SAR-Optical fusion integrating spatiotemporal features
The Leaf Area Index (LAI) is an essential parameter for assessing vegetation growth. LAI derived from optical data can suffer from gaps caused by cloud cover. Synthetic Aperture Radar (SAR) presents a solution with its all-weather observation capability. To address these issues, this study proposes...
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| Language: | English |
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Elsevier
2025-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225003929 |
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| author | Mingqi Li Pengxin Wang Kevin Tansey Fengwei Guo Ji Zhou |
| author_facet | Mingqi Li Pengxin Wang Kevin Tansey Fengwei Guo Ji Zhou |
| author_sort | Mingqi Li |
| collection | DOAJ |
| description | The Leaf Area Index (LAI) is an essential parameter for assessing vegetation growth. LAI derived from optical data can suffer from gaps caused by cloud cover. Synthetic Aperture Radar (SAR) presents a solution with its all-weather observation capability. To address these issues, this study proposes a new deep learning approach for reconstructing time series LAI using SAR and optical data in two steps. Firstly, the two-dimensional Convolutional Neural Network-Transformer (2D CNN-Transformer) is applied to bridge SAR and optical data. Secondly, the 2D CNN-Transformer predicted LAI and the Sentinel-2 LAI are input into the Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion (EDCSTFN) model to further improve the accuracy. The novelty lies in a two-step framework combining a 2D CNN-Transformer for spatiotemporal feature extraction and a deep learning fusion algorithm refining accurate LAI reconstruction. Results showed that the 2D CNN-Transformer achieved a higher accuracy (R2 = 0.64, RMSE = 0.38 m2/m2) in establishing a relationship between SAR and optical data, compared to 1D CNN, 2D CNN-LSTM, and 1D CNN-Transformer. In the second step, the EDCSTFN reconstructed LAI achieved the highest accuracy of an R2 of 0.81 and an RMSE of 0.22 m2/m2, with an average R2 of 0.61 and RMSE of 0.37 m2/m2 across croplands and forests in millions of pixels, further improving the accuracy based on the first step. The approach effectively fills gaps in spatial details and achieves a more continuous spatial distribution. The proposed approach demonstrates good generalizability in millions of pixels under frequent cloud cover and complex surface conditions and provides a new strategy for the fusion of optical and SAR data. |
| format | Article |
| id | doaj-art-8828d33fae9b4083ba68ecf03e4ed61c |
| institution | DOAJ |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-8828d33fae9b4083ba68ecf03e4ed61c2025-08-20T02:57:35ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-08-0114210474510.1016/j.jag.2025.104745Improved leaf area index reconstruction in heavily cloudy areas: A novel deep learning approach for SAR-Optical fusion integrating spatiotemporal featuresMingqi Li0Pengxin Wang1Kevin Tansey2Fengwei Guo3Ji Zhou4College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China; Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, PR ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China; Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, PR China; Corresponding author at: P.O. Box 116, China Agricultural University, East Campus, Qinghua East Road, No. 17, Haidian, Beijing 100083, PR ChinaSchool of Geography, Geology and the Environment, University of Leicester, Leicester LE1 7RH, UKCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China; Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, PR ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, PR ChinaThe Leaf Area Index (LAI) is an essential parameter for assessing vegetation growth. LAI derived from optical data can suffer from gaps caused by cloud cover. Synthetic Aperture Radar (SAR) presents a solution with its all-weather observation capability. To address these issues, this study proposes a new deep learning approach for reconstructing time series LAI using SAR and optical data in two steps. Firstly, the two-dimensional Convolutional Neural Network-Transformer (2D CNN-Transformer) is applied to bridge SAR and optical data. Secondly, the 2D CNN-Transformer predicted LAI and the Sentinel-2 LAI are input into the Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion (EDCSTFN) model to further improve the accuracy. The novelty lies in a two-step framework combining a 2D CNN-Transformer for spatiotemporal feature extraction and a deep learning fusion algorithm refining accurate LAI reconstruction. Results showed that the 2D CNN-Transformer achieved a higher accuracy (R2 = 0.64, RMSE = 0.38 m2/m2) in establishing a relationship between SAR and optical data, compared to 1D CNN, 2D CNN-LSTM, and 1D CNN-Transformer. In the second step, the EDCSTFN reconstructed LAI achieved the highest accuracy of an R2 of 0.81 and an RMSE of 0.22 m2/m2, with an average R2 of 0.61 and RMSE of 0.37 m2/m2 across croplands and forests in millions of pixels, further improving the accuracy based on the first step. The approach effectively fills gaps in spatial details and achieves a more continuous spatial distribution. The proposed approach demonstrates good generalizability in millions of pixels under frequent cloud cover and complex surface conditions and provides a new strategy for the fusion of optical and SAR data.http://www.sciencedirect.com/science/article/pii/S1569843225003929SAR dataOptical dataSpatiotemporal featuresLAIDeep learningSpatiotemporal fusion |
| spellingShingle | Mingqi Li Pengxin Wang Kevin Tansey Fengwei Guo Ji Zhou Improved leaf area index reconstruction in heavily cloudy areas: A novel deep learning approach for SAR-Optical fusion integrating spatiotemporal features International Journal of Applied Earth Observations and Geoinformation SAR data Optical data Spatiotemporal features LAI Deep learning Spatiotemporal fusion |
| title | Improved leaf area index reconstruction in heavily cloudy areas: A novel deep learning approach for SAR-Optical fusion integrating spatiotemporal features |
| title_full | Improved leaf area index reconstruction in heavily cloudy areas: A novel deep learning approach for SAR-Optical fusion integrating spatiotemporal features |
| title_fullStr | Improved leaf area index reconstruction in heavily cloudy areas: A novel deep learning approach for SAR-Optical fusion integrating spatiotemporal features |
| title_full_unstemmed | Improved leaf area index reconstruction in heavily cloudy areas: A novel deep learning approach for SAR-Optical fusion integrating spatiotemporal features |
| title_short | Improved leaf area index reconstruction in heavily cloudy areas: A novel deep learning approach for SAR-Optical fusion integrating spatiotemporal features |
| title_sort | improved leaf area index reconstruction in heavily cloudy areas a novel deep learning approach for sar optical fusion integrating spatiotemporal features |
| topic | SAR data Optical data Spatiotemporal features LAI Deep learning Spatiotemporal fusion |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225003929 |
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