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Spatial variability in microwave radiometric signatures of growing corn and soybean during SMAPVEX16-microwex., , , , , , , , , and 4 other author(s). IGARSS, page 3953-3956. IEEE, (2017)Impact of Bias Correction Methods on Estimation of Soil Moisture When Assimilating Active and Passive Microwave Observations., , , and . IEEE Trans. Geosci. Remote. Sens., 54 (1): 262-278 (2016)Dielectric Response of Corn Leaves to Water Stress., , , and . IEEE Geosci. Remote. Sens. Lett., 14 (1): 8-12 (2017)Modeling SAR Observables by Combining a Crop-Growth Model With Machine Learning., , , , and . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., (2023)Ground-Based Soil Moisture Retrieval Using the Correlation Between Dual-Polarization GNSS-R Interference Patterns., , , , , , , , and . IEEE Trans. Geosci. Remote. Sens., (2024)Crop-Growth Driven Forward-Modeling of Sentinel-1 Observables Using Machine-Learning., , , and . IGARSS, page 5961-5964. IEEE, (2022)Timely Mapping of Crop Stage and Watering Events Through Sentinel-L Time-Series., , , , and . IGARSS, page 196-199. IEEE, (2018)Using Diurnal Variation in Backscatter to Detect Vegetation Water Stress., , and . IEEE Trans. Geosci. Remote. Sens., 50 (7): 2618-2629 (2012)Sentinel-1 Cross Ratio and Vegetation Optical Depth: A Comparison over Europe., , , , , , , and . Remote. Sens., 12 (20): 3404 (2020)Sentinel-1 SAR Backscatter Response to Agricultural Drought in The Netherlands., , and . Remote. Sens., 14 (10): 2435 (2022)