收稿：2021-04-29，
纸质出版：2022-01-07
移动端阅览
湖泊水体透明度是湖泊水环境状况的综合表征，与多种水质参数关系密切，对湖泊水环境监测具有重要意义。本文的目的是介绍一种中国大型湖泊（
>
20 km
2
）月平均透明度遥感监测数据集的生成流程、主要特征和应用价值。数据集生产方法是将Liu等（2020）构建的透明度遥感算法应用于GEE（Google Earth Engine）云平台存储的MODIS地表反射率数据。对云、云阴影和陆地等非水体像元，直接利用MODIS反射率数据状态波段所包含的像元状态信息进行快速去除。数据以GeoTiff栅格格式存储，同时保存了像元透明度值和坐标信息，便于各种软件平台读取。结果显示：数据集覆盖了全国不同湖区的412个大型湖泊，中国湖泊透明度整体上呈现出“东低西高”空间分布，湖泊透明度季节变异呈现“夏高冬低”和“夏低冬高”两种典型类型。研究表明：基于GEE可以实现中国大型湖泊月份平均透明度的快速计算与制图，月尺度透明度对高动态湖泊水环境监测具有突出优势。中国湖泊透明度数据集不仅可以应用于不同湖区、不同湖泊透明度/清澈度的时空变异研究，还可应用于可持续发展目标下的湖泊水环境评估与预测，该数据集的公开共享对中国湖泊水环境研究发展具有重要意义。
Lake water transparency can comprehensively reflect the lake water environment
has significant relationships to some water quality parameters
and greatly important for water environment monitoring. This study aims to introduce the generation processes
characteristics
and application values of a new monthly mean water transparency dataset for large lakes in China with a water area of
>
20 km
2
. The remote sensing algorithm for retrieving water transparency proposed by Liu et al. (2020) was applied to MODIS surface reflectance data and stored on the Google Earth Engine cloud platform to realize rapid calculation and mapping of monthly mean water transparency in different lakes in China from 2000 to 2020. The MODIS surface reflectance data contain one state band
which was used to remove nonwater pixels such as cloud
cloud shadow
and land. The output data were stored in GeoTIFF grid format
which saved the pixel-based water transparency value and the geographic coordinate information. The GeoTIFF format file was also convenient for different software platforms. The dataset covers 412 large lakes in different lake zones of China. Specifically
Inner Mongolia-Xinjiang Lake Zone (IMXL)
the Tibetan Plateau Lake Zone (TPL)
the Yunnan-Guizhou Plateau Lake Zone (YGPL)
the Northeast Plain and Mountain Lake Zone (NPML)
and the Eastern Plain Lake Zone (EPL) have 40
262
11
20
and 79 lakes
respectively.
This study also provided some application examples of the dataset. First
the dataset indicates that the lakes in China had high water transparency values in the west but low values in the east. In 2019
the area-weighted water transparency values in the IMXL
TPL
YGPL
NPML
and EPL zones were 174.54 cm
276.67 cm
254.93 cm
43.41 cm
and 53.93 cm
respectively. Second
the comparison results of lakes Fuxian and Poyang show the two typical types of seasonal variations in water transparency. For the deep Lake Fuxian
water transparency was determined by phytoplankton content; it had low values in summer. On the contrary
for the shallow Lake Poyang
water transparency was controlled by sediment resuspension; it had low values in winter with strong wind. Third
according to water transparency
we divided the Chinese lakes into four types. Lakes in Type I with high water clarity were majorly located in the west. Lakes in Type IV with low water clarity were mainly distributed in the east. Fourth
water transparency was applied to assess the lake water environment under the sustainable development goals. In the previous two decades
water transparency values in the IMXL
TPL
and NPML zones showed a significantly increasing trend
but water transparency values in the EPL and YGPL zones showed a decreasing trend.
To our knowledge
this dataset is the first monthly mean water transparency dataset
which covers nearly all large lakes in China. The monthly scale time resolution allows the dataset to obtain outstanding advantages for dynamically monitoring the lake water environment in China. In the future
the open sharing dataset is greatly important to promote the development of lake water environment research in China.
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