Bulletin of Chinese Academy of Sciences (Chinese Version)


Big Earth Data, clean water and sanitation, earth observation, sustainable development

Document Type

Strategy & Practice


Clean water and sanitation (SDG 6) is one of the 17 Sustainable Development Goals (SDGs) of the United Nations, but so far, the world is not on the right track to achieve SDG 6 targets. In order to change this situation and lead the world to achieve the goal of sustainable water resources management, the United Nations initiated the SDG 6 global acceleration framework including financing, data and information, capacity development, innovation, and governance. From the perspective of data and information that serving SDG 6 target monitoring and evaluation, this study analyzes the current global data progress, the role of Big Earth Data, technology, and the integrated application in SDG 6 target monitoring and evaluation. The study summarizes two problems in the global SDG 6 monitoring and evaluation:1) There is still a lack of sustainably provided high-precision indicator monitoring data set. 2) Lack of operational system integrating data acquisition, indicator calculation and target evaluation. On this basis, the study proposes to establish standardized statistical forms and technical guidelines, to build a system platform for the monitoring and evaluation of all the SDG 6 targets and indicators.

First page


Last Page





Bulletin of Chinese Academy of Sciences


1 UN-Water. Summary Progress Update 2021:SDG 6-Water and sanitation for all. Geneva:UN-Water, 2021.

2 Sadoff C W, Borgomeo E, Uhlenbrook S. Rethinking water for SDG 6. Nature Sustainability, 2020, 3(5):346-347.

3 UN Water. The Sustainable Development Goal 6 Global Acceleration Framework. Geneva:UN-Water, 2020.

4 WHO, UNICEF. Progress on drinking water, sanitation and hygiene:2017 update and SDG baselines. Geneva:WHO, UNICEF, 2017.

5 WHO, UN-HABITAT. Piloting the Monitoring Methodology and Initial Findings for SDG Indicator 6.3.1. Geneva:WHO, UNHABITAT, 2018.

6 UN Environment. Progress on Integrated Water Resources Management. Global Baseline for SDG 6 Indicator 6.5.1:Degree of IWRM Implementation. Nairobi:United Nations Environment Programme, 2018.

7 Hakimdavar R, Hubbard A, Policelli F, et al. Monitoring waterrelated ecosystems with earth observation data in support of Sustainable Development Goal (SDG) 6 reporting. Remote Sensing, 2020, 12(10):1634.

8 WHO, UN-Water. UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) 2017 Report:Financing Universal Water, Sanitation and Hygiene under the Sustainable Development Goals. Geneva:WHO, UN-Water, 2017.

9 UNOOSA. European Global Navigation Satellite System and Copernicus:Supporting the Sustainable Development Goals. Vienna:United Nations, 2018.

10 Chuvieco E. Fundamentals of Satellite Remote Sensing. 2nd Ed. Boca Raton:CRC Press, 2016.

11 Sun A Y. Predicting groundwater level changes using GRACE data. Water Resources Research, 2013, 49(9):5900-5912.

12 Sheffield J, Wood E F, Pan M, et al. Satellite remote sensing for water resources management:Potential for supporting sustainable development in data-poor regions. Water Resources Research, 2018, 54(12):9724-9758.

13 Wang S L, Li J S, Zhang B, et al. Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index. Remote Sensing of Environment, 2018, 217:444-460.

14 Wang S L, Li J S, Zhang B, et al. Changes of water clarity in large lakes and reservoirs across China observed from long-term MODIS. Remote Sensing of Environment, 2020, 247:111949.

15 Guzinski R, Kass S, Huber S, et al. Enabling the use of earth observation data for integrated water resource management in Africa with the water observation and information system. Remote Sensing, 2014, 6(8):7819-7839.

16 Amani M, Mahdavi S, Afshar M, et al. Canadian wetland inventory using Google Earth Engine:The first map and preliminary results. Remote Sensing, 2019, 11(7):842.

17 Mao D H, Wang Z M, Du B J, et al. National wetland mapping in China:A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 164:11-25.

18 Guo H D. Big Earth data:A new frontier in Earth and information sciences. Big Earth Data, 2017, 1(1/2):4-20.

19 Pekel J F, Cottam A, Gorelick N, et al. High-resolution mapping of global surface water and its long-term changes. Nature, 2016, 540:418-422.

20 Tottrup C, Druce D, Tong X, et al. The Global Wetland Extent:Towards A High-resolution Global-level Inventory of the Spatial Extent of Vegetated Wetlands. Nairobi:United Nations Environment Programme, 2020.

21 Bunting P, Rosenqvist A, Lucas R, et al. The global mangrove watch-A new 2010 global baseline of mangrove extent. Remote Sensing, 2018, 10(10):1669.

22 郭华东. 地球大数据支撑可持续发展目标报告(2019). 北京:科学出版社, 2019.

23 郭华东. 地球大数据支撑可持续发展目标报告(2020):中国篇. 科学出版社, 2020.

24 郭华东. 地球大数据支撑可持续发展目标报告(2020):"一带一路"篇. 科学出版社, 2020.

25 Liu J, Wang W, Zhong H. EarthDataMiner:A cloud-based Big Earth Data Intelligence Analysis Platform. IOP Conference Series:Earth and Environmental Science, 2020, 509:012032.