A Deep Learning Data Fusion Model using Sentinel-1/2, SoilGrids, SMAP-USDA, and GLDAS for Soil Moisture Retrieval

نویسندگان

چکیده

Abstract We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in top ~5 cm of soil. Input predictors include Sentinel-1 (active radar), and Sentinel-2 (multispectral imagery) as well geophysical variables from SoilGrids modelled fields SMAP-USDA GLDAS. The was trained evaluated on data ~1000 in-situ sensors globally over period 2015 - 2021 obtained an average per-sensor correlation 0.707 ubRMSE 0.055 m 3 /m , can be used to produce map at nominal 320m resolution. These results are benchmarked against 14 other evaluation research works different locations, ablation study identify important predictors.

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ژورنال

عنوان ژورنال: Journal of Hydrometeorology

سال: 2023

ISSN: ['1525-7541', '1525-755X']

DOI: https://doi.org/10.1175/jhm-d-22-0118.1