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Considering combined or separated roughness and vegetation effects in soil moisture retrievals.

, , , , , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2017)

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Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index., , , , and . Remote. Sens., 9 (1): 35 (2017)Considering combined or separated roughness and vegetation effects in soil moisture retrievals., , , , , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2017)Global Scale IB AMSR2 Vegetation Optical Depth at X-Band., , , , , , , , , and 3 other author(s). IGARSS, page 6028-6031. IEEE, (2021)Evaluating roughness effects on C-band AMSR-E observations., , , , , , , and . IGARSS, page 3311-3314. IEEE, (2014)SMOS-IC: Current Status and Overview of Soil Moisture and VOD Applications., , , , , , , , , and 4 other author(s). IGARSS, page 1451-1453. IEEE, (2018)The Added-Value of Satellite Soil Moisture Observations Over Irrigated Areas to Support Land Surface Model Developments., , , , , , , and . IGARSS, page 3715-3718. IEEE, (2018)The Effect of Three Different Data Fusion Approaches on the Quality of Soil Moisture Retrievals from Multiple Passive Microwave Sensors., , , , , , , and . Remote Sensing, 10 (1): 107 (2018)First Retrievals of ASCAT IB VOD (Vegetation Optical Depth) at Global Scale., , , , , , , , , and 6 other author(s). IGARSS, page 6403-6406. IEEE, (2021)SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product., , , , , , , , , and . Remote. Sens., 9 (5): 457 (2017)Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data., , , , , , , , , and . Remote. Sens., 8 (11): 959 (2016)