Water clarity, as a first-order indicator that reflects the optical characteristics of water bodies, represents a comprehensive proxy for aquatic ecosystems’ trophic state. Optical remote sensing technology makes it possible to monitor water clarity changes of lakes (including reservoirs) at large scales. Water clarity annual dynamics dataset of lakes (>1 ha) across China covers the period from 1990 to 2018, with a time resolution of 5-year and spatial resolution of 30 meters, which sources from the Landsat top of air reflectance data embedded in the GEE platform. Three in-situ SDD measurement datasets were used for model calibration and validation. The first dataset was obtained from 37 field campaigns by our team during 2004-2018. Three quarters of this dataset (N= 976) were used to calibrate the model, for which the R2 and rRMSE were 0.79 and 61.9%, respectively; the remaining dataset (N= 325) was used to validate the model, and the validation results indicated stable performance by showing comparative errors (R2=0.80, rRMSE = 57.6%). The second and the third datasets were both used to validate model performance with a major focus on testing the temporal transferability of the model. The second dataset (340 samples), collected as part of the Chinese lakes survey conducted by Nanjing Institute of Geography and Limnology from 2007 to 2009, also indicated a good model performance (R2=0.78, rRMSE% = 59.1%); the third dataset (229 samples) was assembled by the first lake surveys conducted in the 1980s, demonstrating a stable performance for lake SDD before 1990s (R2=0.81, rRMSE = 50.6%). Comparison of validation results for these different periods and datasets demonstrated the stable performance of the SDD model. Finally, based on the water clarity estimation model, the algorithms of cloud mask and water index were conducted on the GEE platform to accomplish the water clarity of lakes across China. The water clarity information could assist local, provincial or even national level decision-making on policies/management for protecting or improving inland water quality.

File naming and required software

File name: the water clarity dataset of Chinese lakes is stored in vector file format, and the file name is "water_ clarity_ of_ lakes. shp", in which the lake is displayed in the form of the center point of the lake, and the field "ID_Number" in the attribute table of the SHP file is used to represent the code number of the lake. The field" SDD_1990_m "is used to represent the annual mean value of water clarity in 1990, the unit of which is measured in meters. Similar fields represent the similar meaning.
Data reading mode: the dataset could be opened by ArcGIS to display the spatial distribution of the center of the lake.

Data Citations Data citation guideline What's data citation?

Cite as:

Tao, H., Song, K., Liu, G., Wang, Q., Wen, Z. (2021). Water clarity annual dynamics dataset across China (1990-2018). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Hydro.tpdc.271571. CSTR: 18406.11.Hydro.tpdc.271571. (Download the reference： RIS | Bibtex )

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

References literature

1.Song, K.S., Liu, G., Wang, Q., Wen, Z.D., Lyu, L.L., Du, Y.X., Sha, L.W., & Fang, C. (2020). Quantification of lake clarity in China using Landsat OLI imagery data. Remote Sensing of Environment, 243,111800. (View Details )

2.Tao, H., Song, K.S., Liu, G., Wang, Q., Wen, Z.D., Jacinthe, P.A., Xu, X.F., Du, J., Shang Y.X., Li, S.J., Wang, Z.M., Lyu, L., Hou, J.B., Wang, X., Liu, D., Shi, K., Zhang, B.H., and Duan, H.T. (2022). A Landsat-derived annual inland water clarity dataset of China between 1984 and 2018. Earth System Science Data, 14(1), 79-94. (View Details )

Support Program

CASEarth:Big Earth Data for Three Poles（grant No. XDA19070000） (No:XDA19000000)

Copyright & License

To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.

License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)