Super-resolution deep neural networks for water classification from free multispectral satellite imagery

Recent years have seen rapid progress in the adoption of fully convolutional neural networks (FCN) to classify optical satellite imagery, made possible by a combination of new FCN architectures, next-generation GPUs, and publicly available satellite imagery from, e.g., the Landsat and Sentinel missions. These satellites offer repeat global coverage at intervals of only a few days at a spatial resolution of ≥10 m, which is sufficient for some but not all applications of interest. A smaller body of literature considers similar tools to classify commercial satellite imagery that offer 1 – 2 orders of magnitude higher spatial resolutions but with limited spatial and temporal coverage. In this work, we develop a super-resolution FCN to achieve the best of both worlds: land cover classification at commercial-level spatial resolutions but with the spatiotemporal coverage of public satellite imagery. To do so, we label 1 – 2 m resolution commercial imagery and use this as training data for super-resolution FCN. As a specific application, we focus on the segmentation of rivers, with the goal of tracking changes in reach-averaged river widths, depths, and discharges over time. We present detailed performance analyses and demonstrate that, surprisingly, we achieve ≳ 90% classification accuracy at meter-scale resolutions from free Sentinel-2 imagery. We extensively validate our model through in situ USGS gage data and ground-truth GPS tracing of river shorelines. By making our super-resolution FCN codes and training weights publicly available, we hope that these tools will be of use to the broader hydrology community and beyond, as the models can be re-trained for other segmentation tasks.