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

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

Content Provider	MDPI
Author	Azemati, Amir Melebari, Amer Campbell, James D. Walker, Jeffrey P. Moghaddam, Mahta
Copyright Year	2022
Abstract	This paper presents a soil moisture retrieval scheme from Cyclone Global Navigation Satellite System (CYGNSS) delay-Doppler maps (DDMs) 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 bistatic radar cross section DDM of the land surface. This method was tested on both simulated DDMs and CYGNSS DDMs over the Soil Moisture Active Passive (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 DDMs were for grassland and forest vegetation types. The vegetation type of the Yanco validation site was grassland. The vegetation water content (VWC) was $0.19 kg$ $m^{- 2}$ and $4.89 kg$ $m^{- 2}$ 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 $0.03 m^{3}$ $m^{- 3}$ while it was approximately $0.06 m^{3}$ $m^{- 3}$ and $0.09 m^{3}$ $m^{- 3}$ 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 an ubRMSE of less than $0.12 m^{3}$ $m^{- 3}$ .
Starting Page	3129
e-ISSN	20724292
DOI	10.3390/rs14133129
Journal	Remote Sensing
Issue Number	13
Volume Number	14
Language	English
Publisher	MDPI
Publisher Date	2022-06-29
Access Restriction	Open
Subject Keyword	Remote Sensing Imaging Science Soop Gnss-r Cygnss Soil Moisture Retrieval Ddm Brcs Smap
Content Type	Text
Resource Type	Article

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