RESUMO
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.
RESUMO
A procedure that uses Landsat imagery to estimate Secchi disk transparency (SDT) of lakes was developed and applied to approximately 500 lakes with surface areas > 10 ha in the seven-county metropolitan area of Minneapolis and St. Paul, MN, USA, to assess spatial patterns and temporal trends in lake clarity. Thirteen Landsat MSS and TM images over the period 1973-1998 were used for the analysis. Satellite brightness values from lake surfaces were calibrated against available historical data on SDT (n = approximately 20-40) measured nearly contemporaneously with the acquisition date of each image. Calibration regression equations for the late-summer TM images had a range of r2 from 0.72 to 0.93. Regression analysis for three late-summer MSS images yielded r2 values ranging from 0.60 to 0.79. Results indicate that a single late-summer image yields a reliable estimate of regional lake clarity and reasonably accurate estimates of SDT for individual lakes. An analysis of seasonal patterns on a large lake water-quality database was used to develop a model that adjusts synoptic satellite SDT estimates from different dates to a common reference, making them more comparable from year-to-year. Analysis of long-term trends shows that in spite of the large land-use changes within the region over the study period, only 49 (about 10%) of assessed lakes in the region showed significant temporal trends in SDT over the period, and more lakes had increasing SDT (34) than decreasing SDT (15).
