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Is vegetation optical depth needed to estimate biomass from passive microwave radiometers? A statistical study using neural networks., , , , , , and . IGARSS, page 5496-5499. IEEE, (2019)Enhancements of SMOS level 2 soil moisture products over Canada., , , , , , , , and . IGARSS, page 2435-2438. IEEE, (2014)SMOS forest optical depth intercomparisons over pan-tropical biomes., , , , , and . IGARSS, page 5311-5314. IEEE, (2016)Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data., , , , , , , , , and . Remote Sensing, 8 (11): 959 (2016)Soil Moisture Retrieval Using Neural Networks: Application to SMOS., , , , , , , , , and . IEEE Trans. Geoscience and Remote Sensing, 53 (11): 5991-6007 (2015)Effect of the Polarization Leakage on the SMOS Image Reconstruction Algorithm: Validation Using Ocean Model and In Situ Soil Moisture Data., , , , and . IEEE Trans. Geoscience and Remote Sensing, 53 (9): 4961-4971 (2015)Soil moisture retrieval using SMOS brightness temperatures and a neural network trained on in situ measurements., , , , and . IGARSS, page 1574-1577. IEEE, (2017)Optimizing the algorithm for retrieving soil moisture from SMOS data., , , , , , , , , and . IGARSS, page 3952-3954. IEEE, (2007)After Almost 10 Years in Orbit: First Glance at Synergisms and New Results., , , , , , , , , and 1 other author(s). IGARSS, page 6154-6157. IEEE, (2019)Analysis of Vegetation Optical Depth and Soil Moisture Retrieved by SMOS Over Tropical Forests., , , , , and . IEEE Geosci. Remote. Sens. Lett., 16 (4): 504-508 (2019)