the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Mar 2026
Observations of Coherent L-Band Emission from Snow-Covered Arctic Sea Ice
Abstract. Radiometric measurements at L-band (1.4 GHz), collected in the Canadian Arctic in 2024, are used to study which type of model best reproduces the observations. While incoherent radiative transfer models are standard for sea ice thickness retrievals, they neglect phase interference effects. However, the observations analyzed here can only be explained when interference phenomena are explicitly included, requiring a coherent approach. To reduce uncertainties and ensure the robustness of the models, an optimal estimation method is used to determine snow and sea ice parameters consistent with the measured brightness temperatures and in situ measurements. The results show that the coherent model reproduces the observations substantially better than the incoherent formulation, yielding less than half the total cost with respect to the in situ measurements and being approximately 30,000 times more likely to explain the observations. These findings highlight the relevance of coherence effects at L-band, which are commonly neglected, at least in the context of local in situ measurements.
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Status: open (until 27 May 2026)
- RC1: 'Comment on egusphere-2026-1336', Anonymous Referee #1, 04 May 2026 reply
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- 1
Review of “Observations of Coherent L-Band Emission from Snow-Covered Arctic Sea Ice”
General Comments
In this paper, the authors compare coherent and incoherent L-band emission models by simulating radiometric measurements conducted in the Canadian Arctic, applying an optimal estimation method to reduce uncertainties.
While the idea of using a coherent model to explain in-situ measurement with a small footprint is novel and potentially feasible, its extension to satellite-scale footprints remains unclear. Overall, the method and results require further investigation and validation before the work reaches the standard expected for publication in a journal such as The Cryosphere. I therefore recommend major revision.
Specific Comments
Introduction
It is stated that coherence is often neglected in practical modelling and satellite retrievals. In contrast to local measurements with ARIEL, which have a footprint as small as 1m, satellite footprints typically extend over several kilometers, implying that coherent effects are likely averaged out at larger scales. This raises an important question regarding to the applicability of the coherent approach beyond local measurement. A more detailed discussion on the scale dependency of coherence and its relevance for different observation platforms is necessary.
Field study and data preparation
A detailed description of ice conditions surrounding the measurement sites would be beneficial. For instance, what is the distance between the three sites, and what is the spatial extent of the SAR image used? Are the measurements done on first-year ice floes? What do the different backscatter responses indicate, do they correspond to different ice types? Referring to table 1, there is no clear gradient in ice and snow thickness across the three sites. What about the ice salinity and ice temperature? In which range do they vary?
Emission modelling
The axis ratio appears to have a strong influence on the penetration depth. It would be therefore be helpful to quantify the sensitivity of the brightness temperature to this parameter. For example, how much does TB change when the axis ratio changes from 4 to 6 keeping the other parameters constant?
Model-based optimization of measurements
The quality of Optimal Estimation depends strongly on the quality of the forward model and parameter sensitivities. If the forward model itself has many assumptions and simplifications, the estimated values can be biased. Furthermore, the error covariance and prior covariance can strongly affect the results. It would be interesting to see how the coherent and incoherent models perform without OE? Could they reproduce the measured TBH and TBV? Does the coherent model still outperform the incoherent model without OE method? If so, it would be useful to quantify the difference in performance and clarify the physical reasons.
Results and discussion
In Figure 4, optimized snow density varies strongly in site FAR. What is the reason for such strong variability in snow density compared to other sites? Is there any physical explanation why the optimized axis ratio varies strongly from measurement to measurement?
Page 11 “ even though the incoherent model shows more variation as it relies on these variables, alongside the axis ratio, to change the permittivity trying to match the low PD of some measurements. For the same reason, the optimized axis ratio presents more variation for the incoherent model.“ I can’t agree with this statement. The low PDs appear when snow depth is about 5 cm (e.g. in Site CLO), for these measurements Coherent model shows more variation in axis ratio. This might be the reason that Coherent model produces low PD and high TBH.
It might be interesting to plot axis ratio and PD in both models. The parameter axis ratio is not measured and cannot be validated. What happens if axis ratio is not included in the parameter list for OE? Do the results change?
Technical Corrections
Page 2 Line 49: a opening -> an opening
Page 9 Line 200: 0.005-0.01 cm -> m
Page 11 Line 220: even thought -> even though
Page 11 Table 2: Snow Uncertainty unit should be m here