4.7 Article Data Paper

A dataset of remote-sensed Forel-Ule Index for global inland waters during 2000-2018

期刊

SCIENTIFIC DATA
卷 8, 期 1, 页码 -

出版社

NATURE RESEARCH
DOI: 10.1038/s41597-021-00807-z

关键词

-

资金

  1. National Key Research and Development Program of China [2016YFB0501502]
  2. National Natural Science Foundation of China [41901272, 41971318, 41701402]
  3. China Postdoctoral Science Foundation [8206300163]
  4. Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19080304]
  5. Science and Technology Service Network Initiative of Chinese Academy of Sciences [KFJ-STS-ZDTP-077]

向作者/读者索取更多资源

This paper provides the first time series Forel-Ule Index dataset for large global lakes from 2000-2018 based on MODIS observations, which could be valuable for studies investigating the drivers and interaction mechanisms of lake colour change.
Water colour is the result of its constituents and their interactions with solar irradiance; this forms the basis for water quality monitoring using optical remote sensing data. The Forel-Ule Index (FUI) is a useful comprehensive indicator to show the water colour variability and water quality change in both inland waters and oceans. In recent decades, lakes around the world have experienced dramatic changes in water quality under pressure from both climate change and anthropogenic activities. However, acquiring consistent water colour products for global lakes has been a challenge. In this paper we present the first time series FUI dataset for large global lakes from 2000-2018 based on MODIS observations. This dataset provides significant information on spatial and temporal changes of water colour for global large lakes during the past 19 years. It will be valuable to studies in search of the drivers of global and regional lake colour change, and the interaction mechanisms between water colour, hydrological factors, climate change, and anthropogenic activities.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据