Abstract
Analyzing time-series or big data is much being improved by triggering a deep learning approach. Bayesian Regularization Neural Network (BRNN) algorithm is an excellent opportunity to make a better performance on retrieving soil moisture content. Soil moisture studying is a crucial concept for enhancing the sustainability of the earth's system and process. Retrieving soil moisture from spaceborne Global Navigation Satellite System Reflectometry(GNSS-R) has been a challenge to the system, improving model, and geophysical parameters. Currently, GNSS- R constellation satellites mainly focus on sea applications, and likewise, the received signal could detect snow and land information. Practically in this paper, UK TechDemoSat-1 mission (TDS-1) Delay Doppler Map (DDM) fused with the AVHRR Normalized Difference Vegetation Index (NDVI) imagery as input and the Soil Moisture and Ocean Salinity (SMOS) data as a reference to retrieve SM globally daily. The results have shown the feasibility of this approach as it provides excellent performance analysis in extracting soil moisture. If the GNSS bistatic radar receivers can quantify dielectric impacts and surface roughness more precisely, spaceborne GNSS-R technique would be very promising to retrieve soil moisture on relatively better spatial and temporal resolution.
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