基于GEE的中国湖泊浮游植物生物量时空动态分析

随着全球变暖和社会经济发展,中国湖泊富营养化情况时有发生,迫切需要对中国湖泊的浮游植物生物量进行有效监测。本文选择了中国756个面积超过10 km²的湖泊进行研究,基于Google Earth Engine（GEE）云端运算平台,反演2003—2018年间叶绿素a（chl-a）浓度数据,以此来分析研究各个湖泊的营养状态及其时空变化,探索了中国五大湖区内湖泊各季节与年均chl-a浓度时空分布特征与气象、社会经济及湖泊特征等影响因素之间的关系。结果表明： ① 中国湖泊的营养状态变化具有明显的季节性与地域性,研究时段内处于中营养状态的湖泊约占90%,春季时大多数位于东部平原湖区与东北平原与山区湖区的湖泊表现为贫营养状态,而青藏高原湖区与云贵高原湖区的湖泊在春季多呈现富营养状态。由各个湖泊年均chl-a浓度变化可以看出中国约82%的湖泊年均chl-a浓度的变化率小于0.5,呈现出轻微变化,18%的湖泊chl-a浓度呈现剧烈变化趋势。② 温度和降水对湖表chl-a浓度影响较大,超过70%湖泊的chl-a浓度与其表面温度和降水存在正相关性,其中大部分分布在中国北部与东部。缓冲区人口和草地占比、湖泊海拔和湖泊地理位置也对湖泊浮游植物生物量具有一定影响。

Spatio-temporal dynamic analysis of phytoplankton biomass in Chinese lakes based on Google Earth Engine

With the impact of global warming and socio-economic development, eutrophication has been observed frequently in Chinese lakes. Therefore, there is an urgent need to monitor the phytoplankton biomass of the lakes. In this paper, 756 lakes with an area more than 10 km² were selected as research objects. With the help of Google Earth Engine platform, we retrieved the chlorophyll-a (chl-a) concentration from 2003 to 2018, revealed the seasonal and annual nutritional status, and examined the spatio-temporal changes of the lakes. The relationship between spatio-temporal characteristics of lake trophic status and meteorological phenomena, socio-economy and lake features wers analyzed. The main conclusions are as follows: (1) The change of lake trophic states in China has obvious seasonality and regionality. About 90% of lakes were mesotrophic within 15 years of the study. In spring, most lakes in the plain areas of eastern China, the Northeast China Plain and mountain regions were oligotrophic, while in summer, many lakes turned into eutrophication. In comparison, most lakes on the Qinghai-Tibet Plateau and the Yunnan-Guizhou Plateau were eutrophic in spring. The interannual variations in chl-a concentration show that 82% of lakes in China had slight changes in chl-a concentration (the absolute annual rate is <0.5), and the rest showed dramatic variations. (2) The lake surface temperature and precipitation had strong influences on chl-a concentration. For more than 70% of the lakes, the concentration of chl-a had a positive correlation with the lake surface temperature and precipitation, most of which are located in the eastern and northern China. The population in buffer zone, altitude, and geographical location of the lakes also exert influence on the biomass of the phytoplankton.