Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing

Tsang, Leung; Durand, Michael; Derksen, Chris; Barros, Ana P.; Kang, Do-Hyuk; Lievens, Hans; Marshall, Hans-Peter; Zhu, Jiyue; Johnson, Joel; King, Joshua; Lemmetyinen, Juha; Sandells, Melody; Rutter, Nick; Siqueira, Paul; Nolin, Anne; Osmanoglu, Batu; Vuyovich, Carrie; Kim, Edward; Taylor, Drew; Merkouriadi, Ioanna; Brucker, Ludovic; Navari, Mahdi; Dumont, Marie; Kelly, Richard; Kim, Rhae Sung; Liao, Tien-Hao; Borah, Firoz; Xu, Xiaolan

doi:https://doi.org/10.5194/tc-16-3531-2022

Articles | Volume 16, issue 9

https://doi.org/10.5194/tc-16-3531-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-16-3531-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 9

Review article

|

02 Sep 2022

Review article |

| 02 Sep 2022

Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing

Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu

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Final revised paper (published on 02 Sep 2022)
Preprint (discussion started on 30 Sep 2021)

Interactive discussion

Status: closed

RC1:
'Comment on tc-2021-295', Anonymous Referee #1, 09 Oct 2021

The paper is a very thorough review of the remote sensing methodology for assessing water equivalent in the seasonal snow pack. The primary focus is on technique which utilize X to Ku band radars. There is a detailed discussion of theory concerning scattering from the evolving snow grains, scattering from rough interfaces, the effects of vegetation and complications from snow wetness. In situ and airborne data are used to illustrate generally good agreement between theory and observations.

The paper includes discussion of different retrieval algorithms and their limitations. Suggestions for improving retrievals by augmenting with other data sets (such as passive microwave) as well as incorporation of other radar techniques (such as tomography and InSAR) are included.

The paper concludes with a discussion of two recently proposed satellite missions. Of the two, the Canadian TSSM-Explorer is an early stage of development.

I think this is a useful, stand alone review of current progress in using radar to measure snow water equivalent. The scientific justifcation and the rational for high resolution radar (as opposed to solely passive microwave techniques from space) are well justified. One quibble I have is the short statement at the end of section 3.1.1. There, the authors briefly mention the role of layering in the snow pack on backscatter. Given the efforts by several of the authors on the impact of layering, I am surprised that there was not more discussion of a point that could present a problem for thick snow packs and well into the winter season when layering might be expected to develop.

More generally, papers of this sort often set up the scientific and engineering arguments for a satellite mission. Because TSSM seems to be moving along, it is not clear to me whether the paper is designed to influence development beyond the completed Phase 0 study. If so, then the point might be emphasized with a discussion of recommended mission and instrument requirements. If not, then the paper losses some of its justification in my opinion.

Citation: https://doi.org/10.5194/tc-2021-295-RC1
- AC1: 'Reply on RC1', Leung Tsang, 02 Feb 2022
  
  reply on RC1 is in .pdf
  
  Citation: https://doi.org/10.5194/tc-2021-295-AC1
RC2:
'Comment on tc-2021-295', Helmut Rott, 03 Nov 2021

Please find my commenst in the attached file

Citation: https://doi.org/10.5194/tc-2021-295-RC2
- AC2: 'Reply on RC2', Leung Tsang, 02 Feb 2022
  
  Reply to RC 2 is in attached .pdf
  
  Citation: https://doi.org/10.5194/tc-2021-295-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (11 Feb 2022) by Christian Haas

AR by Leung Tsang on behalf of the Authors (24 Mar 2022) Author's response

EF by Sarah Buchmann (29 Mar 2022)

EF by Sarah Buchmann (31 Mar 2022) Manuscript

EF by Sarah Buchmann (31 Mar 2022) Author's tracked changes

ED: Referee Nomination & Report Request started (02 Apr 2022) by Christian Haas

RR by Helmut Rott (19 Apr 2022)

Suggestions for revision or reasons for rejection

General Comment:
I wish to thank the authors for their efforts in revising the manuscript and preparing the detailed response to the reviews. The revised version thoroughly addresses and clarifies issues raised in the review. In particular the revisions and material added to the sections on radar signal interaction, the retrieval approach and on the test cases, as well as related discussions are essential contributions for comprehensive understanding and are enhancing the impact of the topic addressed in the article. However, there are a still some issues that need to be checked and clarified, as addressed below.
Details:
Line 362ff: The RMSE value and correlation coefficient are lacking statistical significance, being based on a very limited sample with only two different soil moisture values (Fig. 6a).
Fig. 9 and related text: Rather than an example for wheat (which is usually harvested many weeks before the first snowfall) the readership of a review paper on snow remote sensing techniques would expect reference or discussion on the impact of vegetation as prevalent during the snow cover season.
L552 and Fig 10: Only a short statement refers to this figure, lacking a message on the observed SnowSAR backscatter signatures. For example, as follow-up to the extensive discussion on the impact of forest on the backscatter signals and the sensitivity to SWE, information on differences in sigma-0 between open land and forested areas would be of interest for the reader. The add-on of forest cover map along the track (e.g. forest density, as shown in the Appendix to ESA 2012), or an outline of the forested areas, would be useful.
Fig, 15b: This figure was replaced by a less informative one (showing only binary scattering albedo values) whereas the original one shows observed and simulated values (which is of interest).
Fig. 16 and Fig. 17: The data base and message of these figures, which were not shown in the original version of the paper, are unclear. Besides, in Fig. 16 the assignment of the two point clouds at each frequency, displayed with the same symbol, is not obvious.
Table 4: Please provide also the mean SWE values for each data set, in addition to the SWE range.
L1040: Please provide a reference on observed difference in saturation between the co- and cross-polarized signals, and specify the Ku-band frequency, incidence angle and snow type for the 300 mm saturation limited quoted here. Depending on microstructure, the saturation limit may vary by one order of magnitude. NoSRex Snowscat data show similar sensitivity in terms of SWE for co- and cross-polarized Ku-band data (e.g. Lemmetyinen et al., 2014), suggesting a similar saturation limit.
Section 5.2, C-band SAR: The revision of this section does not adequately address the issues mentioned in the comments to the first version of the paper. As motivation for including this section it is mentioned “ that some recent studies have demonstrated the possibility of snow depth estimation for deep snow using C-band radar …”. The material and reference presented in this section suggest that the snow microstructure does not play a role for deducing the depth of deep snow from C-band backscatter data. A balanced account, as to be expected for a review paper, should present a wider view, referring also to publications on the impact of snow microstructure. An in-depth study on this respect is reported by Naderpour et al. (2022), showing a dense time series on L- to K-band backscatter (including C-band and Ku-band) for Alpine snow and discussing the impact of microstructure on the observed signatures and SWE.
Fig. 20A: Please check the equation for computing the height and the geometric properties of an interferometric baseline. The height above a reference surface is proportional to the wavelength, the phase difference, and the radar range times the sin of the incidence angle and inversely proportional to the perpendicular baseline. Besides, it is not obvious that this figure is needed, considering that similar graphics have ben shown in many publications and textbooks over the years.
L1203: Radarsat and Envisat (a multi-sensor mission) were not the first C-band SAR mission, ERS was years ahead.
L2010: CryoSat-2 has a Ku-band sensor featuring a SAR mode and a SAR Interferomeric (SARIn) mode.
L1238 to 1241: “ Based on the ground-based measurements and the airborne measurements, the Ku-band at 17.2 GHz has a dynamic range from -16 dB to -6 dB “ , ”The X-band at 9.6 GHz has co-polarization dynamic range from -20 dB to -14dB”. I wonder where these numbers come from and to which snow type, background medium and incidence angle they refer. For example, at the snow-covered Alpine valley and the tundra site the SnowSAR data show at 40 deg. inc. angle KuVV sigma-0 values up to -4 dB, and X-VV up to -6 dB.
L1239: “ … Ku-band 17.2 GHz .… shows good correlation with SWE “. This is a simplified statement, can be omitted. For deep, fine-grained snow the sensitivity is rather low, as for example evident from the SnowSAR data of the Alpine tundra site.
L1247 -L1249: Please provide a reference to experimental data on the sensitivity of Ku-band cross-pol sigma-0 for snow depth below one meter.
L1284: “ ….seasonal snow melt is a commodity of the utmost importance for human health and well-being, supports nearly all sectors of the economy …” This seems to be slightly exaggerated.
Reference:
Naderpour, R., Schwank, M., Houtz, D., Werner, C. and Mätzler, C. 2022: Wideband backscattering from Alpine snow cover: A full-season study, IEEE Trans. Geosc. Rem. Sens., vol. 60, pp. 1-15, 2022, Art no. 4302215, doi: 10.1109/TGRS.2021.3112772, (date of publication October 5, 2021; date of current version January 21, 2022).

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ED: Publish subject to minor revisions (review by editor) (24 Apr 2022) by Christian Haas

AR by Leung Tsang on behalf of the Authors (18 May 2022) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (11 Jun 2022) by Christian Haas

AR by Leung Tsang on behalf of the Authors (18 Jun 2022) Author's response Manuscript

Short summary

Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.