Exploring Convolutional Neural Networks for Predicting Sentinel-C Backscatter Between Image Acquisitions

Abstract

Sentinel-1 C-band radar backscatter satellite images provide a repeating sequence of fine-resolution (10-m) observations that can be used for a number of applications, but the 12-day interval between satellite observations is too infrequent for many applications, such as measuring moisture dynamics. For a variety of applications, moisture information is demanded at high temporal frequency and fine spatial resolution over large areas. Machine learning approaches have been used to predict higher spatial resolutions than the original satellite images, but little effort has been made to increase the temporal resolution of acquired backscatter images. This study extends machine learning approaches to infer fine-resolution backscatter between observations relying on auxiliary data observations, including elevation and daily gridded weather. Several variations of Multi-modal Fully Convolutional Neural Network architectures, problem setup, and training methods are explored for a predominantly rural area in southwest Oklahoma near the transition between humid subtropical and semiarid climates. The training area lies in the overlap zone for adjacent Sentinel-1 satellite tracks, allowing for training with several different temporal offsets. We find that the UNET architecture produced the most accurate and robust estimated backscatter patterns, with superior prediction compared to a prior observation baseline in nearly all cases investigated when geography was included in the training data. This superior performance also generalized to nearby areas when training data for a given geography was not available, where 86% of predictions performed superior compared to a prior observation baseline.