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10m datasets of remote sensing monitoring in Hainan Island inland water ecology from 2019 to 2021 (FUI、transparency、trophic status)

Dataset Overview

As an important resource, inland water is of great significance to human production and life. As a typical island independent water system, Hainan Province is generally rich in surface inland water resources and abundant in river runoff. However, due to the influence of dry and wet monsoon and terrain, it has the characteristics of uneven spatial and temporal distribution. At present, there are relatively few studies on the quality of inland water in Hainan Island. This product is based on the surface reflectance data of sentinel 2 on GEE cloud computing platform, combined with the measured data set, and uses three models of Fui, qaav6 and Fui based nutrient status to respectively retrieve the Fui, transparency and nutrient status of the inland water body of Hainan Island in the dry and wet seasons from 2019 to 2021. This data set can provide important scientific basis for the inland water quality monitoring, water pollution control and water ecological protection of Hainan Island.

Dataset Details

Spatial Resolution: 10m

Time Resolution: 6 months

Product Number: XDA19030105_002

Create Institution: International Research Center of Big Data for Sustainable Development Goals

Created By: Li Junsheng; Wang Shenglei

Creation Date: 2022-12-13T08:50:13.187Z

File Size: 108

Data Format: shape

Type Of Data: Vector

Data Label:

Data Citation

Li Junsheng, Wang Shenglei. 10m datasets of remote sensing monitoring  in Hainan Island  inland water ecology from 2019 to 2021 (FUI, Transparency, Trophic status). 2022

Data Licence Agreement

Users of this product shall clearly indicate the source as " 10-m datasets of remote sensing monitoring  in Hainan Island  inland water ecology from 2019 to 2021 (FUI、transparency、trophic status)" in all forms of research output, including, but not limited to, published and unpublished papers, theses, manuscripts, books, reports, data products, and other academic output. The data producers are not responsible for any losses caused by the use of the data. The boundaries and marks used in the maps do not represent any official endorsement by or opinion of the data producers.

Funded Projects

Supported by Hainan Provincial Department of Science and Technology under Grant No. ZDKJ2019006 and the "Strategic Priority Research Program" of the Chinese Academy of Science, Grant No. XDA19030105