Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation

By S. Paloscia, S. Pettinato, E. Santi, C. Notarnicola, L. Pasolli, A. Reppucci. 2013

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Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas (2017, methodology): “[21] proposed an approach based on the neural network technique for operational soil moisture mapping from Sentinel-1 images.”
Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data (2015, methodology): “More recently, Paloscia et al. [121] have adopted different conﬁgurations of ANN for the estimation of soil moisture from ASAR and RADARSAT2 images, simulating also conditions that will be available with Sentinel 1 data.”
Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles (2019, methodology): “In this paper, we report on the Sentinel-1 mission and its suitability for global soil moisture monitoring, continuing the legacy of ERS-1/2 and Envisat ASAR into the next decade.”
Deep learning in environmental remote sensing: Achievements and challenges (2020, methodology): “This approach has been extensively used to investigate the potential of ANN in estimating regional SM from AMSR-E (Santi et al., 2016; Santi et al., 2012), Sentinel-1 SAR (Paloscia et al., 2013), and the Soil Moisture Active Passive (SMAP) observations (Santi et al., 2014).”