GNSS-R Soil Moisture Retrieval for Flat Vegetated Surfaces Using a Physics-Based Bistatic Scattering Model and Hybrid Global/Local Optimization

Abstract

This paper presents a soil moisture retrieval scheme from Cyclone Global Navigation Satellite System (CYGNSS) DDM over land. The proposed inversion method consists of a hybrid global and local optimization method and a physics-based bistatic scattering forward model. The forward model was developed for bare-to-densely vegetated terrains, and it predicts the circularly polarized BRCS DDM of the land surface. This method was tested on both simulated DDM and CYGNSS DDM over the SMAP Yanco core validation site in Australia. About 250 CYGNSS DDMs from 2019 and 2020 over the Yanco site were used for validation. The simulated DDM were for grassland and forest vegetation types. The vegetation type of the Yanco validation site was grassland. The VWC was 0.19kg/m2 and 4.89kg/m2 for the grassland and forest terrains, respectively. For the case when the surface roughness is known to the algorithm, the unbiased root mean square error (ubRMSE) of soil moisture estimates was less than 0.03m3/m3 while it was approximately 0.06m3/m3 and 0.09m3/m3 for the validation results from 2019 and 2020, respectively. The retrieval algorithm generally had enhanced performance for smaller values of soil moisture. For the case when both the soil moisture and surface roughness are unknown to the algorithm and only a single DDM is used for retrieval, the validation results showed an expected reduced performance, with an ubRMSE of less than 0.12m3/m3.

Detection and Evaluation of Flood Inundation Using CYGNSS Data during Extreme Precipitation in 2022 in Guangdong Province, China

“…The soil moisture data of the SMAP satellite L3 was selected as the comparative data for flood detection. The SMAP satellite was launched by NASA in January 2015 to obtain global soil moisture information by measuring brightness, temperature, and geophysical inversion [32,33]. Its revisiting time is 2-3 d with good space-time coverage of the world [34].…”

A Novel Dual-Branch Neural Network Model for Flood Monitoring in South Asia Based on CYGNSS Data

“…The recently developed Global Navigation Satellite System Reflectometry (GNSS-R) is a novel remote sensing technology for physical parameter inversion by means of GNSS signals reflected from the Earth's surface [9][10][11]. The Cyclone Global Navigation Satellite System (CYGNSS), launched by NASA, provides openly accessed GNSS-R data, which has been successfully employed in the inversion of sea surface wind speed [12][13][14], soil moisture estimation [15][16][17], flood dynamics monitoring [7,18,19], and other features. The CYGNSS constellation constitutes eight small satellites, on which the receivers are mounted to capture the direct and reflected signals from the navigation satellites.…”