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Learning main drivers of crop progress and failure in Europe with interpretable machine learning., , , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2021)Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine., , , , , , , , , and 3 other author(s). IGARSS, page 2320-2323. IEEE, (2020)Living in the Physics and Machine Learning Interplay for Earth Observation., , , , , , , , , and . CoRR, (2020)Growing stock volume from multi-temporal landsat imagery through google earth engine., , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2019)Inference over radiative transfer models using variational and expectation maximization methods., , , , , and . Mach. Learn., 112 (3): 921-937 (March 2023)Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources., , , , , , , , , and . Inf. Fusion, (2020)Interpretability of Recurrent Neural Networks in Remote Sensing., , , , , , and . IGARSS, page 3991-3994. IEEE, (2020)Interpolation and Gap Filling of Landsat Reflectance Time Series., , , , , , and . IGARSS, page 349-352. IEEE, (2018)Global Estimation of Biophysical Variables from Google Earth Engine Platform., , , , , , and . Remote. Sens., 10 (8): 1167 (2018)Physics-Aware Machine Learning for Geosciences and Remote Sensing., , , , , , , , , and . IGARSS, page 2086-2089. IEEE, (2021)