Twin Cities WRS Seminar

Using Remote Sensing to Monitor Dissolved Organic Matter in 10,000+ Minnesota Lakes Using an Automated High Performance Computing Environment


Leif Olmanson
Dept. of Forest Resources

February 21, 2020


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 using an automated high performance computing environment (Google Earth Engine) 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 tested the MAIN and Surface Reflectance (SR) atmospheric correction methods with in situ data, and found they provided substantial improvement over previous methods with model R2 of 0.85 and 0.83, respectively. We applied the MAIN model to 2015 and 2016 Landsat 8 OLI imagery to create 2015 and 2016 Minnesota statewide CDOM maps (reported as absorption coefficients at 440 nm, a440) and used those maps to conduct a geospatial analysis at the ecoregion level. Large differences in a440 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 in 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 a440 by ≥ 3 m-1. In two ecoregions with high forest and wetlands cover, a440 increased ≥ 3 m-1 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.