Automatic machine-learning based water body detection algorithm as part of the hydrological conditioning of the TanDEM-X DEM

Abstract

The generation of a global water body mask (WBM) is part of the hydrological pre-conditioning of the TandDEM-X DEM in order to improve hydrological analyses. Its core is an automatic machine-learning (ML) based water body detection using a Gradient Boosted Decision Tree (GBDT) algorithm as underlying classifier with Bayesian hyperparameter optimization. The features considered by the algorithm are mainly based on the TanDEM-X datasets assisted by a global Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 data. The ML model is sustained by hyperparameter tuning in order to learn the spectral signatures and their varieties for water bodies and land surfaces, enabling accurate detection and classification. As sufficient training data is required and to avoid manual hyperparameter tuning, the automation generates training data by using an initial WBM created from reference data which allows for automatic random sampling throughout the feature space to train the classifier. For each TanDEM-X tile, a classified WBM is generated by the automatic ML algorithm. For quality control, the classification is compared to an alternative WBM based on the WBM quality layer of the Copernicus (COP) DEM modified using the TanDEM-X dataset. First results show that the classified WBM is able to improve the alternative COP WBM in approximately 45% of tiles processed so far, underlining the quality of the used ML algorithm. Compared to the COP WBM, the ML based approach shows large improvements in northern regions containing mostly frozen water as it has the flexibility of including the minimum observed amplitude preferably capturing frozen open water. On the other hand, arid regions often show too little water resulting in an insufficient number of training pixels to perform a classification.