Year-round sea ice and snow characterization from combined passive and active microwave observations and radiative transfer modeling

Soriot, Clément; Picard, Ghislain; Prigent, Catherine; Frappart, Frédéric; Dominé, Florent

doi:10.1016/j.rse.2022.113061

Cited by 9 publications

(11 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In these regions of low T B at 18 and 36 GHz, the microwave signal is likely scattered, within the snow pack (volume scattering due to the formation of depth hoar for instance), at the snow surface and snow/sea ice interface (scattering over rough surfaces), and possibly within the sea ice (volume scattering in the desanilized multi‐year sea ice). The microwave scattering signatures have been analyzed recently, using a state‐of‐the‐art radiative transfer model to explore the co‐variability of the microwave observations (passive and active) (Soriot et al., 2022). This study underlined the complexity of the scattering responses, and the difficulty to propose consistent and quantified explanations for all signatures, across frequencies.…”

Section: Methodsmentioning

confidence: 99%

“…A database with observations at CIMR frequencies is built, merging the SMAP observations at 1.4 GHz and the AMSR2 ones at 6, 10, 18, and 36 GHz, to characterize sea ice and snow (Soriot et al., 2022). The statistical analysis between the PMW measurements and the SIT estimates are conducted for the ICESat‐2 SIT (Petty et al., 2022), for the CS2SMOS SIT (Ricker et al., 2017), and for Nucleus for European Modelling of the Ocean (NEMO) (Madec & Team, 2008; Rousset et al., 2015) modeled SIT.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Arctic Sea Ice Thickness Estimation From Passive Microwave Satellite Observations Between 1.4 and 36 GHz

Soriot

Prigent

Jiménez³

et al. 2023

Earth and Space Science

Self Cite

View full text Add to dashboard Cite

Arctic sea ice thickness (SIT) has been mostly retrieved from lidar and radar altimeters since the 2000s. However, the repeatability of altimeters and their spatial coverage limit SIT estimates spatially and temporally. On the other hand, the passive microwave (PMW) radiometers have daily basin‐scale coverage of the Arctic. In this study, we propose a SIT retrieval from PMW observations, derived from a statistical inversion technique. It is based on the evidence of high correlations between PMW observations and existing altimetric satellite‐derived SIT, especially at 36 GHz. Lidar ICESat‐2 SIT products are used to train a neural network with multiple combinations of brightness temperatures between 1.4 and 36 GHz as inputs over the 2018–2019 time period. The PMW retrieved SIT can mimic the lidar SIT product over the full winter over the Arctic, with a correlation of 0.85, and a root mean square difference (RMSD) of 0.54 cm. Results are also compared with the SIT product CS2SMOS (CryoSat‐2 and Soil Moisture Ocean Salinity merged product), with SIT from a coupled ice/ocean reanalysis model and with the Operation IceBridge QuickLook airborne SIT measurements. The PMW SIT retrieval with all frequencies from 1.4 to 36 GHz shows a correlation of 0.72 and a RMSD of 57 cm when compared to OIB‐QL measurements, for large SIT (mostly above 3 m), under multi‐year ice environments. The PMW SIT retrieval using only 18 and 36 GHz has similar performances and could allow the calculation of long time series, these microwave frequencies being available from satellites since the 1980s.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Arctic Sea Ice Thickness Estimation From Passive Microwave Satellite Observations Between 1.4 and 36 GHz

Soriot

Prigent

Jiménez³

et al. 2023

Earth and Space Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…A full description of the model can be found in Picard et al (2018). The model also allows for the inclusion of freshwater and saline ice layers that can be combined with snowpacks to properly represent observed conditions (Soriot et al, 2022;Murfitt et al, 2022Murfitt et al, , 2023.…”

Section: Snow Microwave Radiative Transfer (Smrt) Modelmentioning

confidence: 99%

Forward Modelling of SAR Backscatter during Lake Ice Melt Conditions using the Snow Microwave Radiative Transfer (SMRT) Model

Murfitt

Duguay

Picard

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. Monitoring of lake ice is important to maintain transportation routes but in recent decades the number of in situ observations have declined. Remote sensing has worked to fill this gap in observations, with active microwave, particularly synthetic aperture radar (SAR), being a crucial technology. However, the impact of wet conditions on radar and how interactions change under these conditions has been largely ignored. It is important to understand these interactions as warming conditions are likely to lead to an increase in the occurrence of slush layers. This study works to address this gap using the snow microwave radiative transfer (SMRT) model to conduct forward modelling experiments of backscatter for Lake Oulujärvi in Finland. Experiments were conducted under dry conditions, under moderate wet conditions, and under saturated conditions. These experiments reflected field observations during the 2020–2021 ice season. Results of the dry snow experiments support the dominance of surface scattering from the ice-water interface. However, conditions where layers of wet snow are introduced show that the primary scattering interface changes depending on the location of the wet layer. The addition of a saturated layer at the ice surface results in the highest backscatter values due to the larger dielectric contrast created between the overlying dry snow and the slush layer. Improving the representation of these conditions in SMRT can also aid in more accurate retrievals of lake ice properties such as roughness, which is key for inversion modelling of other properties such as ice thickness.

show abstract

“…A database with observations at CIMR frequencies is built, merging the SMAP observations at 1.4 GHz and the AMSR2 ones at 6, 10, 18, and 36 GHz, to characterize sea ice and snow (Soriot et al, 2022). The statistical analysis between the PMW measurements and the SIT estimates are conducted for the ICESat-2 SIT (Petty et al, 2022), for the CS2SMOS SIT (Ricker et al, 2017), and for Nucleus for European Modelling of the Ocean (NEMO) (Madec & Team, 2008;Rousset et al, 2015) modeled SIT.…”

Section: 1029/2022ea002542mentioning

confidence: 99%

“…In these regions of low T B at 18 and 36 GHz, the microwave signal is likely scattered, within the snow pack (volume scattering due to the formation of depth hoar for instance), at the snow surface and snow/sea ice interface (scattering over rough surfaces), and possibly within the sea ice (volume scattering in the desanilized multi-year sea ice). The microwave scattering signatures have been analyzed recently, using a state-of-the-art radiative transfer model to explore the co-variability of the microwave observations (passive and active) (Soriot et al, 2022). This study underlined the complexity of the scattering responses, and the difficulty to propose consistent and quantified explanations for all signatures, across frequencies.…”

Section: Initial Analysis Of the Datamentioning

confidence: 99%

Arctic Sea Ice Thickness estimation from passive microwave satellite observations between 1.4 and 36 GHz

Soriot

Prigent

Jiménez³

et al. 2022

Preprint

View full text Add to dashboard Cite

High correlation is evidenced over the Arctic between passive microwave (PMW) satellite signatures and sea ice thickness (SIT) derived from lidar and radar altimeters • A Neural Network inversion is developed to mimic lidar-derived SIT, with PMW observations • SIT estimates from passive microwaves show good performances when compared to other satellite or modeled SIT, and to campaign measurements

show abstract

Year-round sea ice and snow characterization from combined passive and active microwave observations and radiative transfer modeling

Cited by 9 publications

References 85 publications

Arctic Sea Ice Thickness Estimation From Passive Microwave Satellite Observations Between 1.4 and 36 GHz

Arctic Sea Ice Thickness Estimation From Passive Microwave Satellite Observations Between 1.4 and 36 GHz

Forward Modelling of SAR Backscatter during Lake Ice Melt Conditions using the Snow Microwave Radiative Transfer (SMRT) Model

Arctic Sea Ice Thickness estimation from passive microwave satellite observations between 1.4 and 36 GHz

Contact Info

Product

Resources

About