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After Almost 10 Years in Orbit: First Glance at Synergisms and New Results.

, , , , , , , , , , и . IGARSS, стр. 6154-6157. IEEE, (2019)

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Philippe Dreuw

Philippe Zysset

Philippe Kersting

Philippe Bopp

Philippe Moisan

 

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Above-Ground Biomass Estimation Based on Multi-Angular L-Band Measurements of Brightness Temperatures., , , , , , , , и . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., (2023)SMOS Third Mission Reprocessing after 10 Years in Orbit., , , , , , , , , и 5 other автор(ы). Remote. Sens., 12 (10): 1645 (2020)A Follow-Up for the Soil Moisture and Ocean Salinity Mission., , , , , , , , , и 15 other автор(ы). IGARSS, стр. 7831-7834. IEEE, (2021)Is vegetation optical depth needed to estimate biomass from passive microwave radiometers? A statistical study using neural networks., , , , , , и . IGARSS, стр. 5496-5499. IEEE, (2019)Analysis of Vegetation Optical Depth and Soil Moisture Retrieved by SMOS Over Tropical Forests., , , , , и . IEEE Geosci. Remote Sensing Lett., 16 (4): 504-508 (2019)SMOS RFI Experience in the 1400-1427 MHz Passive Band: Case of Extended Interference Caused By Broadcasting Satellite Home-TV Receivers., , , , , и . IGARSS, стр. 4455-4458. IEEE, (2019)Effect of the Polarization Leakage on the SMOS Image Reconstruction Algorithm: Validation Using Ocean Model and In Situ Soil Moisture Data., , , , и . IEEE Trans. Geosci. Remote. Sens., 53 (9): 4961-4971 (2015)Soil moisture retrieval using SMOS brightness temperatures and a neural network trained on in situ measurements., , , , и . IGARSS, стр. 1574-1577. IEEE, (2017)Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data., , , , , , , , , и . Remote. Sens., 8 (11): 959 (2016)Advanced algorithms of the ADEOS-2/POLDER-2 land surface process line: application to the ADEOS-1/POLDER-1 data., , , , , , , , и . IGARSS, стр. 3260-3262. IEEE, (2003)