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Above Ground Biomass Estimation from Passive Microwaves Brightness Temperatures Using Neural Networks.

, , , , , , , и . IGARSS, стр. 7795-7798. IEEE, (2022)

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Towards the Removal of Model Bias from ESA CCI SM by Using an L-Band Scaling Reference., , , , , , , и . IGARSS, стр. 6194-6197. IEEE, (2021)Combining L-Band Radar and Smos L-Band Vod for High Resolution Estimation of Biomass., , , , , , и . IGARSS, стр. 5508-5511. IEEE, (2019)Soil moisture retrieval using SMOS brightness temperatures and a neural network trained on in situ measurements., , , , и . IGARSS, стр. 1574-1577. IEEE, (2017)SMOS Neural Network Soil Moisture Data Assimilation., , , , , , , и . IGARSS, стр. 5548-5551. IEEE, (2018)L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records., , , , , , , и . Remote. Sens., 13 (13): 2480 (2021)Global retrieval of soil moisture using neural networks trained with synthetic radiometric data., , , , , и . IGARSS, стр. 1581-1584. IEEE, (2017)SMOS-IC Vegetation Optical Depth Index in Monitoring Aboveground Carbon Changes in the Tropical Continents During 2010-2016., , , , , , , и . IGARSS, стр. 2825-2828. IEEE, (2018)Smos L-Band Vegetation Optical Depth is Highly Sensitive to Aboveground Biomass., , , , , , , , , и 2 other автор(ы). IGARSS, стр. 9038-9041. IEEE, (2018)Evaluation of the Sensitivity of SMOS L-VOD to Forest Above-Ground Biomass at Global Scale., , , , , , и . Remote. Sens., 12 (9): 1450 (2020)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)