Regional measurements and spatial/temporal analysis of CDOM in 10,000+ optically variable Minnesota lakes using Landsat 8 imagery

Leif G Olmanson; Benjamin P Page; Jacques C Finlay; Patrick L Brezonik; Marvin E Bauer; Claire G Griffin; Raymond M Hozalski

doi:10.1016/j.scitotenv.2020.138141

Regional measurements and spatial/temporal analysis of CDOM in 10,000+ optically variable Minnesota lakes using Landsat 8 imagery

Sci Total Environ. 2020 Jul 1:724:138141. doi: 10.1016/j.scitotenv.2020.138141. Epub 2020 Mar 23.

Authors

Leif G Olmanson¹, Benjamin P Page², Jacques C Finlay³, Patrick L Brezonik⁴, Marvin E Bauer⁵, Claire G Griffin⁶, Raymond M Hozalski⁴

Affiliations

¹ Department of Forest Resources, University of Minnesota, St. Paul, MN 55108, USA. Electronic address: olman002@umn.edu.
² Water Resources Center, University of Minnesota, St. Paul, MN 55108, USA.
³ Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, MN 55108, USA.
⁴ Department of Civil, Environmental, and Geo-Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
⁵ Department of Forest Resources, University of Minnesota, St. Paul, MN 55108, USA.
⁶ Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, MN 55108, USA; Department of Environmental Sciences, University of Virginia, Charlottesville, VA 22905, USA.

PMID: 32247976
DOI: 10.1016/j.scitotenv.2020.138141

Abstract

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, 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 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 a₄₄₀ by ≥3 m^-¹. In two ecoregions with high forest and wetlands cover, a₄₄₀ increased by >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.

Keywords: Atmospheric correction; Inland waters; Lake management; Satellite remote sensing; Water color; Water quality monitoring.