Department of Forest Resources, University of Minnesota, St. Paul, MN 55108, USA.
Water Resources Center, University of Minnesota, St. Paul, MN 55108, USA.
Sci Total Environ. 2020 Jul 1;724:138141. doi: 10.1016/j.scitotenv.2020.138141. Epub 2020 Mar 23.
Information on colored dissolved organic matter (CDOM) is essential for understanding and managing lakes but is often not available, especially in lake-rich regions where concentrations are often highly variable in time and space. We developed remote sensing methods that can use both Landsat and Sentinel satellite imagery to provide census-level CDOM measurements across the state of Minnesota, USA, a lake-rich landscape with highly varied lake, watershed, and climatic conditions. We evaluated the error of satellite derived CDOM resulting from two atmospheric correction methods with in situ data, and found that both provided substantial improvements over previous methods. We applied CDOM models to 2015 and 2016 Landsat 8 OLI imagery to create 2015 and 2016 Minnesota statewide CDOM maps (reported as absorption coefficients at 440 nm, a) and used those maps to conduct a geospatial analysis at the ecoregion level. Large differences in a among ecoregions were related to predominant land cover/use; lakes in ecoregions with large areas of wetland and forest had significantly higher CDOM levels than lakes in agricultural ecoregions. We compared regional lake CDOM levels between two years with strongly contrasting precipitation (close-to-normal precipitation year in 2015 and much wetter conditions with large storm events in 2016). CDOM levels of lakes in agricultural ecoregions tended to decrease between 2015 and 2016, probably because of dilution by rainfall, and 7% of lakes in these areas decreased in a by ≥3 m. In two ecoregions with high forest and wetlands cover, a increased by >3 m in 28 and 31% of the lakes, probably due to enhanced transport of CDOM from forested wetlands. With appropriate model tuning and validation, the approach we describe could be extended to other regions, providing a method for frequent and comprehensive measurements of CDOM, a dynamic and important variable in surface waters.
有关有色溶解有机物 (CDOM) 的信息对于理解和管理湖泊至关重要，但通常无法获得，尤其是在湖泊丰富的地区，那里的浓度在时间和空间上经常高度变化。我们开发了遥感方法，可以使用 Landsat 和 Sentinel 卫星图像为美国明尼苏达州提供普查级别的 CDOM 测量值，该州是一个湖泊丰富的地区，具有高度变化的湖泊、流域和气候条件。我们评估了两种大气校正方法与现场数据相结合从卫星获取 CDOM 产生的误差，并发现这两种方法都比以前的方法有了很大的改进。我们将 CDOM 模型应用于 2015 年和 2016 年 Landsat 8 OLI 图像，以创建 2015 年和 2016 年明尼苏达州全州 CDOM 地图（报告为 440nm 处的吸收系数，a），并使用这些地图在生态区水平上进行地理空间分析。生态区之间的 a 值存在很大差异，这与主要的土地覆盖/利用有关；湿地和森林面积大的生态区的湖泊 CDOM 水平明显高于农业生态区的湖泊。我们将两年的区域湖泊 CDOM 水平进行了比较，这两年的降水差异很大（2015 年接近正常降水，2016 年降水较多，有大暴雨事件）。农业生态区湖泊的 CDOM 水平在 2015 年至 2016 年之间趋于下降，可能是由于降雨稀释所致，这些地区有 7%的湖泊 a 值下降了≥3m。在两个森林和湿地覆盖率高的生态区，有 28%和 31%的湖泊的 a 值增加了>3m，这可能是由于森林湿地中 CDOM 的传输增强所致。通过适当的模型调整和验证，我们描述的方法可以扩展到其他地区，为频繁和全面测量 CDOM 提供一种方法，CDOM 是地表水的一个动态且重要的变量。
