Cloud gap-filling with deep learning for improved grassland monitoring

arXiv:2403.09554v1 Announce Type: new
Abstract: Uninterrupted optical image time series are crucial for the timely monitoring of agricultural land changes. However, the continuity of such time series is often disrupted by clouds. In response to this challenge, we propose a deep learning method that integrates cloud-free optical (Sentinel-2) observations and weather-independent (Sentinel-1) Synthetic Aperture Radar (SAR) data, using a combined Convolutional Neural Network (CNN)-Recurrent Neural Network (RNN) architecture to generate continuous Normalized Difference Vegetation Index (NDVI) time series. We emphasize …

abstract arxiv challenge cloud continuity cs.cv deep learning eess.iv free gap however image independent monitoring optical sentinel series time series type weather