Seasonal monitoring of river and lake water surface areas at global scale with deep learning

Abstract Much progress has been made over the last decade to build global inventories of freshwater resources. However, existing freshwater inventories are generally produced for a fixed period in time and/or do not discriminate lakes and rivers. The emergence of deep learning methods and Big Data platforms such as Google Earth Engine offers a potential solution. Here we presenta unique global raster dataset of 6.7 terapixels at a spatial resolution of 10 m produced with a deep learning workflow. This dataset uses Sentinel-2 imagery downloaded from Google Earth Engine to delineatefreshwater as separate semantic classes of rivers and lakes. Ourstudy site covers the non-polar globe, 89% of the terrestrial landmass, with repeat surveys for the months of April, July and October 2021. This gives us the first global-scale direct count of lakes larger than 1 hectare as ~7.3 million. Also the repeat surveys allow us topresent the firstintra-annual ranges for the areas distinctly occupied by rivers and lakes where we find that basins influenced by the Asian summer monsoon (e.g. the Ganges) closely followed by the Amazon display the largest intra-annual range of river area per unit basin area. Finally, we find that whilst maximum of lake water surface occurs in October 2021, the maximum river surfaceis in July 2021 leading us to conclude that the global maximum occurrences of river waterand lake water are not synchronous.