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

Azemati, Amir; Moghaddam, Mahta; Bhat, Arvind

doi:10.1109/igarss.2018.8517345

Cited by 2 publications

(9 citation statements)

References 6 publications

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“…1) for Tonzi ranch area in California. The accuracy of the bistatic model will be assessed with data available from bistatic platforms such as from CYGNSS and GRIBSAR [2][3][4].…”

Section: Rhcp (mentioning

confidence: 99%

“…The measurements taken from SoOp reflections from the Earth surface are represented as Delay Doppler Maps (DDMs) (Fig. 1) [2][3]. It is shown in section 2 that it is possible to use a polarimetric bistatic scattering model to compute the relationship between circularly and linearly polarized radar cross sections (𝝈 𝑹𝑹 , 𝝈 𝑳𝑹 ) and (𝝈 𝑯𝑯 , 𝝈 𝑽𝑽 , 𝝈 𝑯𝑽 , 𝝈 𝑽𝑯 ).…”

Section: Constructing Ddm By Using the Advanced Bistatic Modelmentioning

confidence: 99%

“…Global land surface observations of soil moisture are essential for better understanding of how water, carbon, and energy cycles are linked. Soil moisture mapping has crucial impacts on many areas of human interest including weather and climate forecasting, wildfire and flood prediction, agriculture and drought analysis, and human health [1][2][3][4][5]. Soil moisture can be retrieved using active or passive microwave observations.…”

Section: Introductionmentioning

confidence: 99%

“…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. The proposed bistatic scattering model has three main scattering mechanisms: direct ground scattering, vegetation volume scattering, and double bounce scattering from ground and vegetation layer.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed bistatic scattering model has three main scattering mechanisms: direct ground scattering, vegetation volume scattering, and double bounce scattering from ground and vegetation layer. It also includes the Right-Hand Circularly Polarized (RHCP) incident and linearly polarized scattered wave cases [3][4][5]. The RHCP GNSS incident waves produce both RHCP (𝝈 𝑹𝑹 ) and Left-Hand Circularly Polarized (LHCP) scattered waves (𝝈 𝑳𝑹 ) upon reflection from the ground surface.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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“…1) for Tonzi ranch area in California. The accuracy of the bistatic model will be assessed with data available from bistatic platforms such as from CYGNSS and GRIBSAR [2][3][4].…”

Section: Rhcp (mentioning

confidence: 99%

Section: Constructing Ddm By Using the Advanced Bistatic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Cited by 2 publications

References 6 publications

Bistatic Scattering Forward Model Validation Using GNSS-R Observations

Bistatic Scattering Forward Model Validation Using GNSS-R Observations

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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