CLWC-375: A national-scale lake water clarity dataset for China derived from VIIRS 375-m observations (2012&ndash;2025)

Chen, Tianzi; Cao, Zhigang; Wang, Menghua; Jiang, Lide; Shen, Ming; Liu, Dong; Duan, Hongtao

doi:10.5194/essd-2026-314

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https://doi.org/10.5194/essd-2026-314

Preprints

22 Jun 2026

| 22 Jun 2026

Status: this preprint is currently under review for the journal ESSD.

CLWC-375: A national-scale lake water clarity dataset for China derived from VIIRS 375-m observations (2012–2025)

Tianzi Chen, Zhigang Cao, Menghua Wang, Lide Jiang, Ming Shen, Dong Liu, and Hongtao Duan

Abstract. Lake water clarity, quantified as Secchi disk depth (SDD), serves as a useful indicator of aquatic ecosystem health and sensitive proxy for environmental shifts. As the MODIS mission nearly ends, maintaining long-term, high-frequency monitoring of inland waters becomes an urgent Earth observation challenge. Current alternatives face trade-offs between spatial resolution and temporal frequency. Addressing this critical observational gap, this study presents a lake water clarity dataset for China derived from VIIRS 375-m observations (CLWC-375), the first national-scale inland water clarity dataset (2012–2025) for 767 lakes larger than 10 km² across China, derived from the high-resolution VIIRS 375-m imagery band (I-band).

Our methodological framework utilizes a robust semi-analytical empirical model optimized for the remote sensing reflectance (R_rs(λ)) of the VIIRS Image-band (I1). Extensive independent validation against 954 in situ measurements from 74 lakes across a broad optical range (0.1 m to 16.0 m) demonstrates good retrieval accuracy. The customized regional algorithm achieves a high coefficient of determination (R² = 0.85) and uncertainty of 29.5 %, effectively eliminates systematic overestimation in existing global operational VIIRS products. The resulting 14-year, high-frequency dataset captures dynamic aquatic processes and reveals spatiotemporal trajectories. Morphology is related to optical properties: deep plateau lakes consistently have high clarity, whereas shallow lowland lakes have considerable turbidity. Chinese lakes had a significant overall clearing trend (0.28 m decade^-1) since 2012, driven predominantly by rapid improvements in the Tibetan and Yunnan-Guizhou plateaus. By successfully overcoming the spatial-temporal resolution trade-off, the CLWC-375 dataset provides a sustainable observational baseline for decoupling the complex optical responses of inland waters to intense anthropogenic activities and global climate change in the post-MODIS era. The dataset is publicly available at the ScienceDB repository (https://doi.org/10.57760/sciencedb.31441).

Received: 26 Apr 2026 – Discussion started: 22 Jun 2026

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Tianzi Chen, Zhigang Cao, Menghua Wang, Lide Jiang, Ming Shen, Dong Liu, and Hongtao Duan

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RC1:
'Comment on essd-2026-314', Anonymous Referee #1, 23 Jul 2026 reply
This study provides an alternative approach for monitoring inland lakes with higher spatial and temporal resolution compared with VIIRS-M and Sentinel-3 OLCI, especially considering the recent degradation of MODIS observations. Overall, the methodology is reasonable, the results are reliable, and the current organization of the dataset is appropriate. The data format is well designed, with the products provided in NetCDF format following common practices in the community, which facilitates data accessibility, interoperability, and further applications. Providing the original source data and related information is also helpful for users to understand, evaluate, and reuse the dataset. However, several aspects, particularly regarding model accuracy and limitations, need further clarification. Below are my comments and suggestions.
Major comments:
I understand that the VIIRS-I band reflectance was generated through interpolation from VIIRS-M bands following Wang et al. (2018). Although the validation results appear satisfactory, more details regarding the interpolation procedure would be helpful. For example, how is the 375 m VIIRS-I reflectance derived using the 750 nm information? Are there any assumptions involved in this process? In addition, providing some examples of lake images comparing the 638 nm VIIRS-I band and the 671 nm VIIRS-M band would be helpful to evaluate the spatial patterns and reflectance characteristics.

The Secchi Disk Depth (SDD) model uses only one spectral band, and the suitability of the I1 band for this purpose is both interesting and somewhat fortunate. From a physical perspective, the red band has a strong relationship with suspended matter concentration, particularly in medium- to highly turbid waters (Neil et al., 2011, Remote Sensing of Environment, 115(12), 3719–3730). However, some Chinese lakes are relatively clear, where phytoplankton-related optical properties are generally considered dominant and blue-green bands are commonly used. Beyond the current validation results, it would be helpful to provide a stratified evaluation of model performance for high, medium, and low SDD levels.

Many clear-water lakes are large lakes, particularly those on the Tibetan Plateau. The blue-green bands from VIIRS-M may potentially provide higher accuracy for SDD retrieval in these waters. Although the manuscript mentioned that the performance may not be satisfactory (Line 506), I wonder whether a combined strategy using both VIIRS-M and VIIRS-I bands could improve SDD estimation for clear lakes. Since clear lakes usually have relatively homogeneous spatial characteristics, slightly reduced spatial resolution with improved spectral information may be a reasonable strategy. This approach does not necessarily need to be included in the current dataset, but it could be discussed as a potential direction for improving future products.

The current organization of the dataset is appropriate. Regarding data uncertainty, it would be helpful to include additional information in the distributed NetCDF files. Although empirical models do not provide formal uncertainty estimates, an additional layer describing the number of valid observations would be useful for users to evaluate data quality and determine whether the dataset is suitable for subsequent analyses.

Section 6.2 presents long-term SDD variations, and the results appear generally reasonable. It would be helpful to compare these trends with previously published studies to demonstrate whether the observed patterns are consistent or different. In addition, briefly discussing the potential drivers of long-term SDD changes would improve the interpretation of these results.

There are several spelling errors and inconsistencies throughout the manuscript. In addition, some references appear repeatedly in the text (e.g., Lines 65–67 and Lines 494–498), which may be related to EndNote formatting issues. Please carefully check and correct these problems. Specific suggestions are listed below.

Minor comments:
Line 34, the reported performance should be revised to “determination (R² = 0.87) and uncertainty of 27.34%” according to the main text.

Line 55, suggest correcting the typographical error “occu” to “occur”.

Lines 121–122, suggest revising the lake-zone names and abbreviations to “the Eastern Plain Lake Zone (EPL), the Northeast Plain and Mountain Lake Zone (NPML), the Yunnan-Guizhou Plateau Lake Zone (YGPL), the Inner Mongolia-Xinjiang Lake Zone (IMXL), and the Tibetan Plateau Lake Zone (TPL)” to ensure consistency with Fig. 1 and the terminology used elsewhere in the manuscript.

Line 124, suggest changing “The EPL and NPL” to “The EPL and NPML”.

Line 127, suggest changing “the MXPL is dominated” to “the IMXL is dominated” to use the revised abbreviation consistently.

Line 132, suggest deleting the stray backslash before the caption of Fig. 1 and changing “TPGL” to “YGPL”.

Line 164, suggest changing “Fig. 1” to “Fig. 3”, because the distributions of Chl-a, SPM, and SDD described here are presented in Fig. 3.

Line 172, suggest revising the caption to “Table 1. Statistical results of in situ water transparency (SDD) in different lake zones in China...”.

Line 242, suggest changing “Fig. 2” to “Fig. 4”.

Line 318, suggest changing the variable name “Zsd” to “SDD” and revising “Secchi Disc Depth” to “Secchi Disk Depth” in Table 2.

Line 352, suggest changing “Zhang et al. (2025)” to “Zhang et al. (2021)”.

Line 363, the font size of “a” in Fig. 7 is inconsistent with other plots.

Line 380, suggest changing “in the east China Plain (a and b)” to “in the east China Plain (a)”; changing “Tibetan Plateau (c and d) (August 8, 2015)” to “Tibetan Plateau (b) (August 8, 2015)”.

Line 397, “Changan” and “Bositeng” should be corrected to “Chagan” and “Bosten”.

Line 397, “January SDD” is unclear.

Line 424, suggest changing the study period from “2012 to 2021” to “2012 to 2025”.

Line 448, suggest adding the missing unit and revising “0.36 decade⁻¹” to “0.36 m decade⁻¹”.

Line 469, “Lui et al. (2020)” should be corrected to “Liu et al. (2020)”; “(Shen et al. 2020)” should be revised to “Shen et al. (2020)”.

Line 505, suggest correcting “the Tibetan Platea lakes” to “the Tibetan Plateau lakes”.

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Citation: https://doi.org/10.5194/essd-2026-314-RC1

Tianzi Chen, Zhigang Cao, Menghua Wang, Lide Jiang, Ming Shen, Dong Liu, and Hongtao Duan

Data sets

CLWD-375: China's lake water clarity dataset from VIIRS 375-m observations (2012–2025) Zhigang Cao https://www.scidb.cn/s/mIz6ji

Tianzi Chen, Zhigang Cao, Menghua Wang, Lide Jiang, Ming Shen, Dong Liu, and Hongtao Duan

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Short summary

This study provides a new, long-term record of lake clarity across China from 2012 to 2025, covering 767 lakes. We carried out this work because existing satellite records are ending or lack the detail or frequency needed for continuous monitoring. Using daily observations from VIIRS (375 m), we developed a method to improve tracking. Results show many lakes have become clearer, while shallow lakes remain more turbid, providing a consistent basis to track lake change and inform water management.


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