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Fusion of optical and SAR images based on deep learning to reconstruct vegetation NDVI time series in cloud-prone regions

文献类型： 外文期刊

作者： Li, Jingbo 2 ; Li, Changchun 3 ; Xu, Weimeng 2 ; Feng, Haikuan 2 ; Zhao, Fa 2 ; Long, Huiling 2 ; Meng, Yang 2 ; Chen, Weinan 3 ; Yang, Hao 2 ; Yang, Guijun 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Henan Polytech Univ, Sch Surveying & Mapping Land Informat Engn, Jiaozuo 454000, Peoples R China

4.Changan Univ, Sch Geol Engn & Surveying & Mapping, Xian 710054, Peoples R China

关键词： Vegetation Monitoring; Time series; Deep learning; Transformer; SAR data; Optical data; NDVI

期刊名称：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION （ 影响因子：7.672； 五年影响因子：7.332 ）

ISSN： 1569-8432

年卷期： 2022 年 112 卷

页码：

收录情况： SCI

摘要： The normalized difference vegetation index (NDVI) is crucial to many sustainable agricultural practices such as vegetation monitoring and health evaluation. However, optical remote sensing data often suffer from a large amount of missing information due to sensor failures and harsh atmospheric conditions. The synthetic aperture radar (SAR) offers a new approach to filling in missing optical data based on its excessive revisit density and its potential to image without interference from clouds and rain. Due to the difference in imaging mechanisms between SAR and optical sensors, it is very difficult to fuse the data. This paper developed an advanced deep learning Spatio-temporal fusion method, i.e., Transformer Temporal-spatial Model (TTSM), to synergize the SAR and optical time-series to reconstruct vegetation NDVI time series in cloudy regions. The proposed multi-head attention and end-to-end architecture achieved satisfactory accuracy (R-2 greater than 0.88), outperforming the existing deep learning solutions. Extensive experiments were carried out to evaluate the TTSM method on large-scale areas (the spatial scale of megapixels) in northeast China with the main vegetation types of crops and forests. The R-2, SSIM, RMSE, NRMSE, and MAE of our prediction results were 0.88, 0.80, 0.06, 0.16, and 0.05, respectively. The influence of training sample size was investigated through a transfer learning study, and the result indicated that the model had good generalizability. Overall, our proposed method can fill in the gap of optical data at an extensive regional scope over the vegetated area using SAR.

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