Christian-Albrechts-Universität zu Kiel, Department of Geography, Earth Observation and Modelling, Ludewig-Meyn-Str. 14, 24098 Kiel, Germany.
EOMAP GmbH & Co.KG, Castle Seefeld, Schlosshof 4a, 82229 Seefeld, Germany.
Sci Total Environ. 2018 Jan 15;612:1200-1214. doi: 10.1016/j.scitotenv.2017.08.219. Epub 2017 Sep 8.
Phytoplankton indicated by its photosynthetic pigment chlorophyll-a is an important pointer on lake ecology and a regularly monitored parameter within the European Water Framework Directive. Along with eutrophication and global warming cyanobacteria gain increasing importance concerning human health aspects. Optical remote sensing may support both the monitoring of horizontal distribution of phytoplankton and cyanobacteria at the lake surface and the reduction of spatial uncertainties associated with limited water sample analyses. Temporal and spatial resolution of using only one satellite sensor, however, may constrain its information value. To discuss the advantages of a multi-sensor approach the sensor-independent, physically based model MIP (Modular Inversion and Processing System) was applied at Lake Kummerow, Germany, and lake surface chlorophyll-a was derived from 33 images of five different sensors (MODIS-Terra, MODIS-Aqua, Landsat 8, Landsat 7 and Sentinel-2A). Remotely sensed lake average chlorophyll-a concentration showed a reasonable development and varied between 2.3±0.4 and 35.8±2.0mg·m from July to October 2015. Match-ups between in situ and satellite chlorophyll-a revealed varying performances of Landsat 8 (RMSE: 3.6 and 19.7mg·m), Landsat 7 (RMSE: 6.2mg·m), Sentinel-2A (RMSE: 5.1mg·m) and MODIS (RMSE: 12.8mg·m), whereas an in situ data uncertainty of 48% needs to be respected. The temporal development of an index on harmful algal blooms corresponded well with the cyanobacteria biomass development during summer months. Satellite chlorophyll-a maps allowed to follow spatial patterns of chlorophyll-a distribution during a phytoplankton bloom event. Wind conditions mainly explained spatial patterns. Integrating satellite chlorophyll-a into trophic state assessment resulted in different trophic classes. Our study endorsed a combined use of satellite and in situ chlorophyll-a data to alleviate weaknesses of both approaches and to better characterise and understand phytoplankton development in lakes.
浮游植物通过其光合作用色素叶绿素 a 指示，是湖泊生态学的一个重要指针，也是欧洲水框架指令中定期监测的参数。随着富营养化和全球变暖，蓝藻对人类健康的影响越来越大。光学遥感可以支持监测湖泊表面浮游植物和蓝藻的水平分布，并减少与有限水样分析相关的空间不确定性。然而，仅使用一颗卫星传感器的时间和空间分辨率可能会限制其信息价值。为了讨论多传感器方法的优势，在德国库默罗湖应用了独立于传感器的、基于物理的模型 MIP（模块化反演和处理系统），并从五个不同传感器（MODIS-Terra、MODIS-Aqua、Landsat 8、Landsat 7 和 Sentinel-2A）的 33 张图像中得出了湖泊表面叶绿素 a。遥感湖泊平均叶绿素 a 浓度显示出合理的发展，从 2015 年 7 月到 10 月，浓度在 2.3±0.4 到 35.8±2.0mg·m 之间变化。实地和卫星叶绿素 a 的匹配显示，Landsat 8（RMSE：3.6 和 19.7mg·m）、Landsat 7（RMSE：6.2mg·m）、Sentinel-2A（RMSE：5.1mg·m）和 MODIS（RMSE：12.8mg·m）的性能不同，而实地数据的不确定性为 48%。有害藻类 bloom 指数的时间发展与夏季蓝藻生物量的发展非常吻合。卫星叶绿素 a 地图允许在浮游植物 bloom 期间跟踪叶绿素 a 分布的空间模式。风况主要解释了空间模式。将卫星叶绿素 a 纳入营养状态评估会导致不同的营养类别。我们的研究支持将卫星和实地叶绿素 a 数据结合使用，以缓解两种方法的弱点，并更好地描述和理解湖泊中浮游植物的发展。
