Fusion of Optical and SAR Images Via NDVI-Like Images to Reconstruct NDVI Time Series in Cloud-Prone Regions

Normalized Difference Vegetation Index (NDVI) data from optical satellites have been widely used in the field of remote sensing. However, due to cloud cover and other extreme weather reasons, NDVI time series are often missing. In order to fill the observation gap under severe weather conditions, an NDVI time series reconstruction method that fuses optical and synthetic aperture radar (SAR) images has been developed. However, optical images often suffer from long-term data missing, severely limiting the usability of this reconstruction method. To solve this problem, this paper proposes a NDVI-like image strategy to avoid reliance on optical data. Specifically, the strategy obtains NDVI-like images by fitting the multivariate linear relationship between VV, VH polarization bands of SAR images and NDVI images. Then, the optical image information is represented by NDVI-like images, and the SAR and NDVI-like time series are input into the time series reconstruction network to reconstruct the NDVI time series in cloudy areas. Experimentation has revealed that the NDVI-like image strategy can effectively supplements optical image information for the NDVI reconstruction network, reduce the the influence of SAR image speckle noise on reconstruction outcomes, and obtain better reconstruction effects.