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Assessing the relationship between microwave vegetation optical depth and gross primary production.

, , , , , , , , , , , and . Int. J. Applied Earth Observation and Geoinformation, (2018)

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A non-linear data-driven approach to reveal global vegetation sensitivity to climate., , , , , , and . MultiTemp, page 1-3. IEEE, (2017)Advances in the Remote Sensing of Terrestrial Evaporation., , , and . Remote Sensing, 11 (9): 1138 (2019)Global climatic drivers of vegetation based on wavelet analysis., , , , and . MultiTemp, page 1-3. IEEE, (2017)Improving terrestrial evaporation estimates over continental Australia through assimilation of SMOS soil moisture., , , , and . Int. J. Applied Earth Observation and Geoinformation, (2016)Assessing the relationship between microwave vegetation optical depth and gross primary production., , , , , , , , , and 2 other author(s). Int. J. Applied Earth Observation and Geoinformation, (2018)A Quasi-Global Evaluation System for Satellite-Based Surface Soil Moisture Retrievals., , and . IEEE Trans. Geoscience and Remote Sensing, 48 (6): 2516-2527 (2010)Towards Estimating Land Evaporation at Field Scales Using GLEAM., , , , , and . Remote Sensing, 10 (11): 1720 (2018)Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models., , , , , , and . Remote Sensing, 10 (10): 1601 (2018)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)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)