the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Snow Water Equivalent Retrieval and Analysis Over Altay Using 12-Day Repeat-Pass Sentinel-1 Interferometry
Abstract. Accurate Snow Water Equivalent (SWE) estimation is significant for understanding global climate change, surface energy balance, and regional water cycles. However, although there have been many studies on the inversion of SWE using active and passive microwave remote sensing, it remains challenging to assess the global distribution of SWE with sufficient temporal and spatial resolution and accuracy. Interferometric Synthetic Aperture Radar (InSAR) has become a promising technique for SWE change estimation, which is limited by the optimal radar frequencies and revisit intervals that have not been available until recently. In this study, 12-day Sentinel-1 C-band InSAR data from 2019 to 2021 are used to retrieve ΔSWE (SWE changes in one InSAR pair) and cumulative SWE in the Altay region of Xinjiang, China. The correlation between the retrieved ΔSWE and in-situ observations reaches R=0.48, with a low RMSE of 15.5 mm (n=241) throughout the two whole snow seasons, improving to R=0.47 and RMSE of 15.9 mm for 2019–2020, and R=0.51 and RMSE of 14.8 mm for 2020–2021. These results are achieved without filtering for low coherence or high temperatures. Heavy snowfall leads to decorrelation and phase unwrapping errors, which affect ΔSWE retrieval and are propagated into cumulative SWE. Validation of the cumulative SWE after removing wet snow yields an RMSE of 36.5 mm, which improves to 28.4 mm when high-elevation stations with unwrapping errors due to heavy snowfall are also excluded. InSAR-derived cumulative SWE time series show consistency with ground observations at some stations, though slight overestimations and underestimations are observed due to error accumulation. Various factors combined with validation results show that higher coherence, lower air temperature, and reliable snow density improve the retrieval accuracy. The proposed phase calibration method demonstrates that selecting at least half of the available in-situ ΔSWE values for calibration yields reliable ΔSWE estimates. Calibrating only the integer multiples of 2π provides reasonable accuracy, but is still inferior to the full calibration method, indicating that residual modulo 2π phase has a noticeable contribution to the final inversion accuracy, which highlights that phase calibration plays a key role in the accurate ΔSWE retrieval. This study provides a valuable reference and processing prototype for applying 12-day revisit Sentinel-1 and future NISAR InSAR data to SWE monitoring.
- Preprint
(6416 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on egusphere-2025-2329', Anonymous Referee #1, 29 Jul 2025
This study addresses an important gap in the application of InSAR for snow water equivalent (SWE) retrieval by leveraging Sentinel-1 C-band SAR pairs and in situ measurements collected in Altay, Xinjiang. While the theoretical foundations of InSAR for SWE estimation are well established, validation remains limited. This paper contributes valuable insight by providing a matchup dataset and a processing prototype, offering practical guidance and highlighting key limitations and uncertainties for future InSAR-based SWE applications, including those involving upcoming satellite missions.
However, I have several comments and suggestions that may improve the clarity and robustness of the study:
- Calibration Strategy:
It appears that the InSAR retrieval algorithm requires prior in situ information for calibration. Could the authors clarify whether calibration points were randomly selected, and whether the remaining data were used for validation? I suggest an alternative approach—using data from one year for calibration and another year for validation. If the algorithm performs well under this split-sample approach, it would indicate temporal stability in the model parameters, which would be valuable for operational applications. - Assumption of Dry Snow Conditions:
The theoretical model underlying the ΔSWE–Δφ relationship assumes dry snow, where the dominant reflective interface is between the snow and the ground. This assumption does not hold under wet snow conditions, where signal penetration is strongly affected. Therefore, I recommend filtering out data corresponding to wet snow conditions during the validation process. If the authors wish to explore the wet snow regime, the InSAR pair selection should be constrained to periods between two dry snow events, and then for wet snow, two acquisition from wet snow period can be selected for comparison. - Snow Wetness Screening:
Although direct in situ measurements of snow wetness may not be available, backscatter signatures (e.g., sudden increase of σ⁰ ) can provide useful indicators. At a minimum, the authors could stratify the analysis by using a temporal threshold—for example, separating SAR pairs acquired before and after April—to distinguish predominantly dry versus wet snow conditions. This stratification would help reduce confounding effects and enhance the interpretability of the results.
Overall, this paper presents a promising step toward operational SWE retrieval using InSAR, but addressing the calibration methodology and the influence of wet snow conditions would strengthen the findings significantly.
Citation: https://doi.org/10.5194/egusphere-2025-2329-RC1 - AC1: 'Reply on RC2', Jingtian Zhou, 11 Sep 2025
- Calibration Strategy:
-
RC2: 'Comment on egusphere-2025-2329', Anonymous Referee #2, 12 Aug 2025
The manuscript presents a two-year retrieval of SWE using 12-day repeat pass InSAR from Sentinel-1 C-band over the Altay region in China. The method is sound, and the results are interesting for the InSAR SWE community. However, I consider that there are several things that require attention before being accepted.
General Comments:
The manuscript could improve presentation and better explanation of the ideas. Also, I think you should consider adding more references on how your results compare to previous work and how they contribute to them. Starting from Section 3.4.1 I could only find two references for the rest of the manuscript. Some of the topics you comment (coherence, lost phase cycles/ambiguities, calibration…) have been discussed in the literature.
I think the phase calibration method is correct, however:
- As it is now explained is a bit confusing. My suggestion is describing first the method for a single InSAR image, then extend to multiple images (redundant images as you say). Up to equation 13, is there a benefit in calibrating all images in the same step, with respect to calibrating them one by one?
- I think one natural consideration here is weighting based on coherence. Consider adding a term to the method where observed int.phases from high coherence pixels have more weight than low coherence pixels. This way you could decrease the uncertainty derived from the noise on those pixels.
- Specify clearly that y is calculated from the ground data. What is the point of Eq (6)? Same for Eq (15).????
The results from the coherence are interesting but limited. Adding discussion on topography or coherence against in-situ dSWE could broaden the discussion.
Some plots are way too packed with information, making them very hard to read. E.g., fig 22.
Specific Comments:
In Section 2.2.1, maybe comment at what time is the flight pass already here.
Lines 147 to 150: do you mean that the spatial resolution from ERA-5 is too coarse? It is not clear for me what’s the meaning of these sentences…
Line 165: This is correct, but the sentence is hard to understand as it is now. Consider splitting it.
Line 188: two things here, what do you mean that dPhi is estimated from the unwrapped InSAR phase? I guess you use Eq. 3 for retrieve dSWE?
Line 189 to 192: I’ll argue that the main limitation, in particular for C-band and 12 days temporal baseline is decorrelation, and vegetation if there is any on the study area. If you are going to comment on phase unwrapping (lost phase cycles) you could indicate the amount of mm of dSWE that are equivalent to dPhi=2π.
Figure 3: If you want to refer to distances CA, DE, etc… I think these should be explained in the text (?). Also theta_s appears only in the drawing. Better simplify the figure.
Line 218: Phase calibration is explained in Section 3.4!
Lines 253 to 255: not sure what is the point about the local incidence angle here. Both sentences seem to contradict each other.
Line 295: can you explain what “Monte Carlo random selection” is, and how it is applied?
Section 4.1:
- It is true that phase ambiguities can occur but based on the calibration methodology this may not be a problem in practice. Can authors comment on it?
- Can authors comment on why same interferograms show a very similar decorrelation pattern from (assuming these are georeferenced) north-west to south-east? Is it related to topography?
Section 4.2:
- I suggest you introduce Eq (22) already here as Figs 8 and 9 are showing that.
- The titles of subfigures in 8 and 9 should be a single date, from the secondary.
Line 340 to 343: This is not really obvious on the plots…
Section 4.3.1:
Carefully consider when is dSWE and when is SWE.
Are the points used for calibration excluded for Fig. 10? In my opinion you should.
Section 4.4:
I thnk the sign for the complex dielectric permitivitty is incorrect.
Can you comment on the decrease on SWE in the late season? How can you calculate it since assuming wet snow the retrieval is not possible?
Grammar:
The manuscript could use a revision of English and format (justifications). Remove duplicated definition of acronyms.
I have write down some catches, but there are more:
Page 2, Line 53: This sentence is hard to understand, consider rewriting.
Page 2, Line 59: No need for point in parenthesis.
Page 2, Line 60: Check reference from Guneriussen is duplicated.
Page 3, Line 67: was explored.
Page 3, Line 73: was…
Line 105: by the?
Line 163: I don’t know if algorithm is the correct word here.
Line 256: as follows
Line 261: why this equitation has a different form than EQ (3)?
Citation: https://doi.org/10.5194/egusphere-2025-2329-RC2 - AC1: 'Reply on RC2', Jingtian Zhou, 11 Sep 2025
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
460 | 94 | 14 | 568 | 10 | 23 |
- HTML: 460
- PDF: 94
- XML: 14
- Total: 568
- BibTeX: 10
- EndNote: 23
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1