Factors affecting the measurement of CDOM by remote sensing of optically complex inland waters

Patrick L. Brezonik, Leif Olmanson, Jacques C Finlay, Marvin E. Bauer

Research output: Contribution to journalArticlepeer-review

186 Scopus citations


A combination of new measurements and analysis of historical data from several geographic regions was used to address four issues that affect the reliability and interpretation of colored dissolved organic matter (CDOM) measured by remote sensing of inland waters. First, high variability of CDOM levels in lakes and rivers was found at seasonal and multi-year time scales and at shorter intervals in some rivers and lakes. Coefficients of variation (CVs) of 30%-50% for absorptivity at 440nm (a440) were common in historical and new data sets we examined. CDOM values used to calibrate imagery thus should be measured close to the image acquisition date, preferably within 1-2 months in lakes and a few days in large rivers, unless it can be shown that CDOM levels are temporally stable over longer (or shorter) time periods in a given aquatic system. Second, spectral slopes (S) for CDOM in the visible range vary little over time (even over multi-year periods) within sites. Substantial variation was found between sites, however, and most spectra showed a change in slope near 460nm. Values of S400-460 for waters with moderate to high CDOM levels generally were within a narrow range (~0.014-0.018) and similar to reported S values in the near UV. Values of S400-460 for waters with low CDOM generally were smaller and more variable, as were values for S460-650 for all waters. Overall, the variability of spectral slopes in the visible range should not have a large effect on the reliability of a440 estimates made from remote sensing, which in many models involve reflectance measurements at wavelengths>500nm. Third, although a strong correlation (r2=0.925) was found between CDOM levels and DOC concentrations in 34 surface waters sampled in 2013, the standard error of estimate suggests an uncertainty of~±20% in predicting DOC at a440=5m-1 (a moderate CDOM level). Moreover, CDOM-DOC relationships for unpublished data sets we analyzed and those reported in the literature indicate that both the fraction of DOC that is colored and slopes of regressions between CDOM and DOC are highly variable in space and time. Prediction of DOC concentrations in water bodies from CDOM levels (whether measured in the laboratory or by remote sensing) thus is associated with considerable uncertainty. For the present, this implies that field sampling is required to verify DOC concentrations predicted from remotely sensed CDOM measurements until we have a better understanding of variations in DOC-CDOM relationships. Fourth, shapes of reflectance spectra for CDOM-rich waters varied greatly depending on the concentrations of other constituents (suspended solids and chlorophyll) that affect the optical properties of water. Nonetheless, it is not obvious from our results for several predictive models that different remote sensing algorithms are needed to calculate CDOM levels accurately for waters where CDOM is the only variable affecting reflectance versus waters where other constituents also affect the spectra. The best band or band ratio models for simulated Landsat 8, Sentinel-2 and Sentinel-3 bands from field measured reflectance spectra yielded high r2 values (0.84-0.86) for a440. The broader Landsat 8 bands worked nearly as well for a440 as the narrower Sentinel band sets and hyperspectral bands, probably because CDOM is characterized by a broad exponential increase in absorbance with decreasing wavelength rather than specific peaks or troughs in absorbance or reflectance.

Original languageEnglish (US)
Pages (from-to)199-215
Number of pages17
JournalRemote Sensing of Environment
StatePublished - Feb 1 2015

Bibliographical note

Funding Information:
We thank the following individuals for supplying data for our analysis: Emily Stanley, Univ. of Wisconsin, Madison, North Temperate Lakes LTER Program; Bruce Monson, Minnesota Pollution Control Agency, St. Paul; and Bethany Brinkman and Ray Hozalski, Univ. of Minnesota. PLB thanks his former sampling and lab crews and graduate students at the Univ. of Florida, especially R. Yorton, E. Shannon, and C. Hendry, for their dedication in producing the historical databases on Florida lakes. We thank Sandra Brovold, Nolan Kleijan, and Adam Worm, Dept. of Ecology, Evolution, and Behavior, Univ. of Minnesota, Luke Loken, Univ. of Wisconsin-Madison, and Chip Small, Univ. of St. Thomas, for field and lab assistance with the 2013 SLRE data, and the Lake Superior National Estuarine Research Reserve and then reserve manager Shon Schooler for sampling assistance on the St. Louis River Estuary. We also greatly appreciate the assistance of Bryan Leavitt and Rick Perk, CALMIT, University of Nebraska, for the loan of their spectroradiometer system and their helpful advice in operating the system and interpreting the data. PLB thanks the Univ. of Minnesota Office of the Vice President for Research (UMOVPR) and UM Retirees Association for an award from the Professional Development Grants Program for Retirees that provided critical financial support. JCF acknowledges support from the Minnesota Sea Grant College Program supported by the NOAA office of Sea Grant, United States Department of Commerce , under grant No. R/RegHCE-09-12 . LGO and MB also acknowledge support from the UMOVPR for funding under the U-Spatial: Spatial Science and Systems Infrastructure grant.

Publisher Copyright:
© 2014 Elsevier Inc.


  • CDOM
  • Chlorophyll
  • DOC
  • Hyperspectral imagery
  • Lake superior
  • Lakes
  • Landsat
  • Mississippi river
  • Remote sensing
  • Rivers
  • Sentinel
  • Suspended sediment
  • Turbidity
  • Water quality


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