Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method

Pan, Jinmei; Durand, M. T.; Lemmetyinen, Juha; Liu, Desheng; Shi, Jiancheng

doi:10.5194/tc-2023-85

Cited by 4 publications

(7 citation statements)

References 21 publications

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“…This confirms that a statistical approach is better suited to reproduce the measured radar backscatter from ground geophysical properties (Pan et al, 2023) rather than using a single set of values to represent a larger domain such as TVC.…”

Section: Discussionsupporting

confidence: 53%

“…With the saturation effect of the modelled background properties within the Ku-Band range, the volume scattering will only become more dominant compared to the background surface scattering. These results show that a distributed, statistical approach, for all the retrieved parameters, is more suited to forward modelling of the Ku-Band signal (Pan et al, 2023), even though this approach is less preferable for satellite observation SWE retrievals (Durand et al, 2023). To improve efficiency in forward modelling computation time, the snowpacks of January were reduced to two layers, i.e.…”

Section: Forward Modeling Of Ku-band Backscattermentioning

confidence: 99%

“…The synergistic radiometer measurements were effective in providing first-guess information on effective snow grain size which was used within the SWE retrieval approach developed and evaluated by Lemmetyinen et al (2018). These tower measurements have also been used to support other algorithm development experiments (Merkouriadi et al, 2021;Zhu et al, 2021;Durand et al, 2023;Pan et al, 2023), with the daily temporal resolution proving to be particularly insightful. However, a major limitation was the lack of spatial sampling, so efforts in the community shifted to the acquisition of airborne Ku-band radar data.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we utilized measurements from this UMass instrument, deployed during the 2018/19 winter over the Trail Valley Creek study area in the Northwest Territories, Canada, to advance science readiness activities of TSMM (Siqueira et al, 2021). This paper focuses on the forward radiative transfer modeling component of a future SWE retrieval algorithm similar to what is proposed by (Rott et al, 2012;Pan et al, 2023). The data collected during the TVC experiment are used to validate SMRT as the forward model used for such SWE algorithm and will provide insight on the model parameterization needed to link snow properties to the Ku-Band signal, i.e.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Retrieval of airborne Ku-Band SAR Using Forward Radiative Transfer Modeling to Estimate Snow Water Equivalent: The Trail Valley Creek 2018/19 Snow Experiment

Montpetit,

King,

Meloche

et al. 2024

Preprint

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Abstract. Accurate snow information at high spatial and temporal resolution is needed to support climate services, water resource management, and environmental prediction services. However, snow remains the only element of the water cycle without a dedicated Earth Observation mission. The snow scientific community has shown that Ku-Band radar measurements provide quality snow information with its sensitivity to snow water equivalent and the wet/dry state of snow. With recent developments of tools like the Snow MicroPenetrometer (SMP) to retrieve snow microstructure data in the field and radiative transfer models like the Snow Microwave Radiative Transfer Model (SMRT), it becomes possible to properly characterize the snow and how it translates into radar backscatter measurements. An experiment at Trail Valley Creek (TVC), Northwest Territories, Canada was conducted during the winter of 2018/19 in order to characterize the impacts of varying snow geophysical properties on Ku-Band radar backscatter at a 100-m scale. Airborne Ku-Band data was acquired using the University of Massachusetts radar instrument. This study shows that it is possible to calibrate SMP data to retrieve statistical information on snow geophysical properties and properly characterize a representative snowpack at the experiment scale. The tundra snowpack measured during the campaign can be characterize by two layers corresponding to a rounded snow grain layer and a depth hoar layer. Using Radarsat-2 and TerraSAR-X data, soil background roughness properties were retrieved (msssoil = 0.010±0.002) and it was shown that a single value could be used for the entire domain. Microwave snow grain size polydispersity values of 0.74 and 1.11 for rounded and depth hoar snow grains, respectively, was retrieved. Using the Geometrical Optics surface backscatter model, the retrieved effective soil permittivity increased from C-Band (εsoil = 2.47) to X-Band (εsoil = 2.61), to Ku-Band (εsoil = 2.77) for the TVC domain. Using SMRT and the retrieved soil and snow parameterizations, an RMSE of 2.6 dB was obtained between the measured and simulated Ku-Band backscatter values when using a global set of parameters for all measured sites. When using a distributed set of soil and snow parameters, the RMSE drops to 0.9 dB. This study thus shows that it is possible to link Ku-Band radar backscatter measurements to snow conditions on the ground using a priori knowledge of the snow conditions to retrieve SWE at the 100 m scale.

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Section: Discussionsupporting

confidence: 53%

Section: Forward Modeling Of Ku-band Backscattermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Retrieval of airborne Ku-Band SAR Using Forward Radiative Transfer Modeling to Estimate Snow Water Equivalent: The Trail Valley Creek 2018/19 Snow Experiment

Montpetit,

King,

Meloche

et al. 2024

Preprint

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show abstract

“…Indeed, a priori information is pivotal for the retrieval algorithms of many satellite missions. For example, prior information is critical to estimation of river discharge from the recently-launched Surface Water and Ocean Topography satellite mission (Durand et al, 2023). However, it is vital that studies characterize the sensitivity of retrievals to priors.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of SWE from dual-frequency radar measurements: Usingtimeseries to overcome the need for accurate a priori information

Durand,

Johnson,

Dechow

et al. 2023

Preprint

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Abstract. Measurements of radar backscatter are sensitive to snow water equivalent (SWE) across a wide range of frequencies, motivating proposals for satellite missions to measure global distributions of SWE. However, radar backscatter measurements are also sensitive to snow stratigraphy, microstructure and to surface roughness, complicating SWE retrieval. A number of recent advances have created new tools and datasets with which to address the retrieval problem, including a parameterized relationship between SWE, microstructure, and radar backscatter, and methods to characterize surface scattering. Although many algorithms also introduce external (prior) information on SWE or snow microstructure, the precision of the prior datasets used must be high in some cases in order to achieve accurate SWE retrieval. We hypothesize that a time series of radar measurements can be used to solve this problem, and demonstrate that SWE retrieval with acceptable error characteristics is achievable by using previous retrievals as priors for subsequent retrievals. We demonstrate the accuracy of three configurations of the prior information: using a global SWE model, using the previously retrieved SWE, and using a weighted average of the model and the previous retrieval. We assess the robustness of the approach by quantifying the sensitivity of the SWE retrieval accuracy to SWE biases artificially introduced in the prior. We find that the retrieval with the weighted averaged prior demonstrates SWE accuracy better than than 20 %, and an error increase of only 3 % relative RMSE per 10 % change in prior bias; the algorithm is thus both accurate and robust. This finding strengthens the case for future radar-based satellite missions to map SWE globally.

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Retrieval of snow water equivalent from dual-frequency radar measurements: using time series to overcome the need for accurate a priori information

Durand,

Johnson,

Dechow

et al. 2024

The Cryosphere

View full text Add to dashboard Cite

Abstract. Measurements of radar backscatter are sensitive to snow water equivalent (SWE) across a wide range of frequencies, motivating proposals for satellite missions to measure global distributions of SWE. However, radar backscatter measurements are also sensitive to snow stratigraphy, to microstructure, and to ground surface roughness, complicating SWE retrieval. A number of recent advances have created new tools and datasets with which to address the retrieval problem, including a parameterized relationship between SWE, microstructure, and radar backscatter, and methods to characterize ground surface scattering. Although many algorithms also introduce external (prior) information on SWE or snow microstructure, the precision of the prior datasets used must be high in some cases in order to achieve accurate SWE retrieval. We hypothesize that a time series of radar measurements can be used to solve this problem and demonstrate that SWE retrieval with acceptable error characteristics is achievable by using previous retrievals as priors for subsequent retrievals. We demonstrate the accuracy of three configurations of prior information: using a global SWE model, using the previously retrieved SWE, and using a weighted average of the model and the previous retrieval. We assess the robustness of the approach by quantifying the sensitivity of the SWE retrieval accuracy to SWE biases artificially introduced in the prior. We find that the retrieval with the weighted averaged prior demonstrates SWE accuracy better than 20 % and an error increase of only 3 % relative RMSE per 10 % change in prior bias; the algorithm is thus both accurate and robust. This finding strengthens the case for future radar-based satellite missions to map SWE globally.

show abstract

Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method

Cited by 4 publications

References 21 publications

Retrieval of airborne Ku-Band SAR Using Forward Radiative Transfer Modeling to Estimate Snow Water Equivalent: The Trail Valley Creek 2018/19 Snow Experiment

Retrieval of airborne Ku-Band SAR Using Forward Radiative Transfer Modeling to Estimate Snow Water Equivalent: The Trail Valley Creek 2018/19 Snow Experiment

Retrieval of SWE from dual-frequency radar measurements: Usingtimeseries to overcome the need for accurate a priori information

Retrieval of snow water equivalent from dual-frequency radar measurements: using time series to overcome the need for accurate a priori information

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