Code and Data for "A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery"
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Here we share the data and code for “A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery” Corresponding author: Devon Dunmire devon.dunmire@kuleuven.be ‘model_training’ - contains script to train the ML model, and training data sets from (1) in-situ snow measurement sites (training_data.p) and (2) photogrammetry snow depth maps (map_training_data.p) ‘Cross_val_predictions’ contains model predictions for our cross-validation of all the in-situ snow measurement sites ‘run_model’ contains the trained model (final_model_xg.pkl) and scripts to retrieve snow depth with our ML model. ‘SD_*’ zip folders contains daily ML snow depth output over the European Alps for each snow year from Sept. 1 2015 - Apr. 30 2023. Data from multiple orbits is averaged. Naming convention: ‘S1_ml_SD_{yyyymmdd}_.nc’
