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A non-linear data-driven approach to reveal global vegetation sensitivity to climate., , , , , , and . MultiTemp, page 1-3. IEEE, (2017)Investigating the control of ocean-atmospheric oscillations over global terrestrial evaporation using a simple supervised learning method., , , , and . MultiTemp, page 1-3. IEEE, (2017)GLEAMv3: satellite-based land evaporation and root-zone soil moisture, , , , , , , , and . Geoscientific Model Development, 10 (5): 1903--1925 (May 17, 2017)Analyzing Granger Causality in Climate Data with Time Series Classification Methods., , , , , and . ECML/PKDD (3), volume 10536 of Lecture Notes in Computer Science, page 15-26. Springer, (2017)Towards Estimating Land Evaporation at Field Scales Using GLEAM., , , , , and . Remote Sensing, 10 (11): 1720 (2018)Improving terrestrial evaporation estimates over continental Australia through assimilation of SMOS soil moisture., , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2016)A Quasi-Global Evaluation System for Satellite-Based Surface Soil Moisture Retrievals., , and . IEEE Trans. Geosci. Remote. Sens., 48 (6): 2516-2527 (2010)Sentinel-1 Backscatter Assimilation Using Support Vector Regression or the Water Cloud Model at European Soil Moisture Sites., , , , , and . IEEE Geosci. Remote. Sens. Lett., (2022)Observation-based assessment of secondary water effects on seasonal vegetation decay across Africa., , , , , and . Frontiers Big Data, (2022)Global climatic drivers of vegetation based on wavelet analysis., , , , and . MultiTemp, page 1-3. IEEE, (2017)