Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application

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Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data (2015, methodology): “Srivastava et al. [129] compared three artiﬁcial intelligence techniques along with the generalized linear model (GLM) to improve the spatial resolution of soil moisture and ocean salinity (SMOS)-derived soil moisture products, which are currently available at a very coarse scale of ≈ 40 km.…”
Deep learning in environmental remote sensing: Achievements and challenges (2020, methodology): “Srivastava et al. (2013) ﬁ rst attempted to downscale the SMOS SM by applying the ANN to combine the SMOS SM and MODIS surface temperature at a local or regional scale.”
Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, methodology): “Due to the substantial time cost of running such complex models, there is an increasing interest in using ML models as surrogate models (a model approximating simulation-driven input–output data) to predict target variables at a higher resolution [90, 237].”