the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Nov 2025
A lake salinity dataset produced via microwave and optical imageries
Abstract. Lake salinity is an important parameter to characterize physical and biogeochemical processes and a fundamental indicator to evaluate lake water quality. However, its estimation in inland waters has been challenging because passive microwave salinity satellites lack sufficient spatial resolution, and optical satellites cannot directly measure it. To address it, we constructed a framework for estimating lake salinity by combining Synthetic Aperture Radar (SAR) and Multi-Spectral Instrument (MSI) data. It can be summarized in step 1: construct a salinity mechanism model based on SAR data using the Elfouhaily spectrum, dielectric constant, and small perturbation method (SPM) models; step 2: develop four machine learning (ML) salinity algorithms using quasi-synchronous salinity and MSI with SAR imagery; and step 3: build an ensemble model to estimate salinity by coupling the mechanism and ML models via a generalized additive model. The proposed integrated algorithm (N = 84, RMSE = 0.60 ppt, and MAPE = 2.3 %) outperformed single-satellite microwave mechanistic or ML models across all eleven lakes in the Inner Mongolia Xinjiang Lake zone. On this basis, we reconstructed the lake salinity dataset for 2016–2024 and conducted independent validation (N = 65, R2 = 0.97, and RMSE = 0.89 ppt) and pixel-level histogram validation confirmed dataset quality, with no significant systematic bias across lake types. The reconstruction revealed a spatial pattern of smooth transition from the nearshore to the center and trends with significant increases in Lake Daihai and Lake Dalinor. The dataset and its development framework will facilitate exploration of salinity status and trends in inland lakes, providing scientific evidence and methodological support for salinization prevention and global lake salinity budget research. The dataset (10 m spatial resolution, TIF format) is publicly available via Zenodo (https://doi.org/10.5281/zenodo.17638099, Deng et al., 2025a) and includes annual/seasonal salinity rasters and statistical files.
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Status: open (until 16 Jan 2026)
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RC1: 'Comment on essd-2025-671', Anonymous Referee #1, 24 Nov 2025
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AC1: 'Reply on RC1', mingming deng, 11 Dec 2025
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Thank you referees for your useful and constructive comments. We have made additions and adjusted language and figures in the manuscript based on your comments that have improved the practicality and clarity of the study. Our detailed responses to each comment are in the attached supplement.
Thank you,
Mingming Deng and co-authors
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AC1: 'Reply on RC1', mingming deng, 11 Dec 2025
reply
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RC2: 'Comment on essd-2025-671', Anonymous Referee #2, 04 Dec 2025
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The study fully leverages the physical mechanisms underlying passive remote sensing of water salinity and the high spatial resolution advantages of optical remote sensing, enabling salinity estimation for inland lakes across China. The lake salinity dataset provided by the research is highly valuable for monitoring biogeochemical processes within lake ecosystems. The manuscript is clearly structured, but a few minor issues still require revision. It could be considered for publication after these revisions are made. These small issues are as follows:
- Line 90: The citation order of the figures jumps from Fig. 1 directly to Fig. 10. You may need to move Fig. 10 to the position of Fig. 2, or remove the citation of Figure 10 at this point.
- Please move the word “Catchment” in Table 1 onto a single line to ensure the integrity of the term.
- The section number 2.5 seems it should be changed to 2.6.
- In the caption of Figure 3, please remove the comma ‘,’ after “RMSE = 0.82 ppt”.
- Line 443: Should the square brackets be changed to parentheses?
Citation: https://doi.org/10.5194/essd-2025-671-RC2 -
AC2: 'Reply on RC2', mingming deng, 11 Dec 2025
reply
Thank you referees for your useful and constructive comments. We have made additions and adjusted language and figures in the manuscript based on your comments that have improved the practicality and clarity of the study. Our detailed responses to each comment are in the attached supplement.
Thank you,
Mingming Deng and co-authors
Data sets
A lake salinity dataset produced via microwave and optical imageries Mingming Deng et al. https://doi.org/10.5281/zenodo.17638099
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The manuscript I reviewed is already a revised version. I like it very much. Not only is the research original, but the data products are unique and significant for various stakeholders. To my knowledge no one has published similar datasets in the literature. It's a pleasant read and I also learned from this reading. I have only a few editorial comments, which can be made during galley proof corrections.
Fig. 1. Need to explain (a) in the caption. Also explain the meaning of “frequency of surface water occurrence” and the cross symbols in each panel.
Fig. 2 caption: Please add one sentence similar to this: “The details of individual steps and the meaning of the symbols are described below.”
Figs. 3 and 13. Change “Measure Salinity (ppt)” to “Measured Salinity (ppt)” in all x-axis labels.
Fig. 6. I like this figure as it makes visual inspection straightforward. The lower salinity around the land-lake boundaries may be due to runoff, but what caused the high salinity points (reddish colors) in c7?
Fig. 11. Put some space between column 3 and column 4, and between column 6 and column 7.
Eq. (8) – what’s the unit of g? Need to list.