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Potential of Multi-Temporal ALOS-2 PALSAR-2 ScanSAR Data for Vegetation Height Estimation in Tropical Forests of Mexico., , , , , и . Remote Sensing, 10 (8): 1277 (2018)Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree-Grass Ecosystems., , , , , , , , , и 8 other автор(ы). Remote. Sens., 10 (8): 1293 (2018)EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts., , , , и . CoRR, (2020)Double machine learning for causal hybrid modeling - applications in the Earth sciences., , , , и . CoRR, (2024)The imprint of plants on ecosystem functioning: A data-driven approach., , , , , , , , , и 1 other автор(ы). Int. J. Appl. Earth Obs. Geoinformation, (2015)Deep learning and process understanding for data-driven Earth system science., , , , , , и . Nat., 566 (7743): 195-204 (2019)Identifying Dynamic Memory Effects on Vegetation State Using Recurrent Neural Networks, , , , , и . Frontiers in Big Data, (2019)Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs., , , , , , и . CoRR, (2022)EarthNet2021: A Large-Scale Dataset and Challenge for Earth Surface Forecasting as a Guided Video Prediction Task., , , , и . CVPR Workshops, стр. 1132-1142. Computer Vision Foundation / IEEE, (2021)ADVANCING DEEP LEARNING FOR EARTH SCIENCES: FROM HYBRID MODELING TO INTERPRETABILITY., , , и . IGARSS, стр. 3979-3982. IEEE, (2020)