Temporal Upsampling of NDVI Time Series by RNN-Based Fusion of Sparse Optical and Dense SAR-Derived NDVI Data

This research paper presents a novel approach for fusing time series of cloud-affected optical normalized difference vegetation index (NDVI) data with more regularly sampled synthetic aperture radar (SAR)-estimated NDVI data. We employ a deep learning model based on gated recurrent units (GRU), a recurrent neural network (RNN) cell, that can handle missing data and variable sequence lengths efficiently. Numerical results demonstrate that our method achieves dense and accurate NDVI time series with a mean absolute error (MAE) of 0.05, closely resembling the optical NDVI values. Visual comparison also shows the high performance.