Modeling bistatic scattering signatures from sources of opportunity in P-Ka bands

Shah, Rashmi; Zuffada, Cinzia; Chew, Clara; Lavalle, Marco; Xu, Xiaolan; Azemati, Amir

doi:10.1109/iceaa.2017.8065616

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…The last two points are mainly because the model assumes no topography. However, these differences have minimal impact on the retrievals, as we used the averaged NBRCS, Equation (17), which uses a 3 × 5 portion of the full DDM.…”

Section: Discussionmentioning

confidence: 99%

“…SoOp includes signals transmitted from global navigation satellite system (GNSS) and communications satellites. Due to the pervasive presence and reliability of L-band Global Positioning System (GPS)/GNSS signals, using SoOp from GPS/GNSS satellites for soil moisture retrieval has been receiving increasing attention in recent years [14][15][16][17]. For instance, NASA Cyclone GNSS (CYGNSS) was originally designed and used for sensing sea level wind speed in tropical cyclones from GNSS signals [18].…”

Section: Introductionmentioning

confidence: 99%

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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

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…The center bin of this DDM represents scattering in specular direction, which has the highest Signal to Noise Ratio (SNR). retrieval techniques by using the available SoOp from these sources [6][7][8][9][10][11][12]. In our previous works [3][4][5], a fully bistatic scattering forward model for vegetated terrain has been developed, which provides the possibility to exploit SoOp for soil moisture retrieval over various land covers including forests.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, bistatic radars are much more resilient in that their transmitters are pervasively present, such as in the case of GPS/GNSS transmitters. Therefore, it is important to advance novel soil moisture retrieval techniques by using the available SoOp from these sources [6][7][8][9][10][11][12]. In our previous works [3][4][5], a fully bistatic scattering forward model for vegetated terrain has been developed, which provides the possibility to exploit SoOp for soil moisture retrieval over various land covers including forests.…”

Section: Introductionmentioning

confidence: 99%

Bistatic Scattering Forward Model Validation Using GNSS-R Observations

Azemati

Moghaddam

Bhat

2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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In this paper we advance a previously developed bistatic scattering forward model to include the circularly polarized incident and scattered waves, which is the case for Global Navigation Satellite System (GNSS) reflectometry. This model development enables retrieval of soil moisture from Signals of Opportunity (SoOp) bistatic observations, e.g., from the Cyclone Global Navigation Satellite System (CYGNSS) observations and GNSS Reflectometer Instrument for Bistatic Synthetic Aperture Radar (GRIBSAR). In order to validate the forward model with measured data, we present a method to construct the Delay Doppler Map (DDM) from simulations of the forward model. The forward model Radar Cross Section (RCS) predictions will be compared with actual measurements. The validated model is intended for use in soil moisture retrievals.

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Relationship Between Bistatic Radar Scattering Cross Sections and GPS Reflectometry Delay-Doppler Maps Over Vegetated Land in Support of Soil Moisture Retrieval

Azemati

Moghaddam

Bhat

2018

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

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Modeling bistatic scattering signatures from sources of opportunity in P-Ka bands

Cited by 5 publications

References 16 publications

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

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

Bistatic Scattering Forward Model Validation Using GNSS-R Observations

Relationship Between Bistatic Radar Scattering Cross Sections and GPS Reflectometry Delay-Doppler Maps Over Vegetated Land in Support of Soil Moisture Retrieval

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