Comparison of Landsat 8 and Landsat 7 for regional measurements of CDOM and water clarity in lakes

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

Research output: Contribution to journalArticlepeer-review

79 Scopus citations


The potential strengths and limitations of the Landsat systems for water clarity and colored dissolved organic matter (CDOM) measurement were evaluated in Minnesota in the summers of 2013 and 2014. Optical water quality characteristics, including chlorophyll a, total suspended solids (TSS), dissolved organic carbon (DOC), and CDOM were collected along with imagery from Landsats 7 and 8. Sites represented a wide range of concentrations of CDOM, chlorophyll, and mineral suspended solids (MSS), the primary factors that affect reflectance. Clear images from September 24, 2013 (Landsat 7) and September 16, 2013 (Landsat 8) acquired for northern Minnesota eight days apart allowed comparison of the respective ETM + and OLI sensors for CDOM measurements. We examined a wide variety of potential band and band ratio models and found some two-variable models that included the NIR band worked well for Landsat 8 (R2 = 0.82) and reasonably well for Landsat 7 (R2 = 0.74). The commonly used green/red model had a poor fit for both sensors (R2 = 0.24, 0.25), and five sites with high MSS were clear outliers. Exclusion of these sites and other sites not included with the Landsat 7 dataset yielded a less optically complex subset of 20 coincident lakes. For this subset strong models were found for many band and band ratio models, including the commonly used green/red model with R2 = 0.79 for Landsat 7 and R2 = 0.81 for Landsat 8. The less optically complex subset may explain why the green/red model has worked well in other areas. For optically complex waters CDOM models that used the new Landsat 8 ultra-blue and narrower NIR band worked best for the full dataset indicating that the new bands and other Landsat 8 characteristics, such as higher radiometric sensitivity and improved signal-to-noise ratios, improve CDOM measurements. For water clarity measured as Secchi depth (SD), we compared September 1, 2008 Landsat 7 and August 22, 2013 Landsat 8 images from path 28 using stepwise regression to identify the best model using all bands and band ratios including the new blue and narrower NIR band. The best water clarity model for Landsat 8 used the OLI 2/4 band ratio plus OLI band 1 and was nearly identical with a model using the OLI 2/4 band ratio plus OLI band 2. The latter model is similar to the model used for previous Landsat water clarity assessments, which used the ETM + 1/3 band ratio plus ETM + band 1. For SD measurements we found strong relationships with both sensors, with only slight improvements for the OLI sensor for the lakes in our datasets. In contrast to some previous reports that indicated Landsat 7's ETM + lacked sufficient sensitivity for reliable retrieval of CDOM, we found that overall both sensors worked well for water clarity and CDOM measurements. This will allow their continued use for current and historical measurements of important water characteristics on a regional scale.

Original languageEnglish (US)
Pages (from-to)119-128
Number of pages10
JournalRemote Sensing of Environment
StatePublished - Nov 1 2016

Bibliographical note

Funding Information:
We thank Sandra Brovold, Michelle Rorer, 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, and others who assisted with sample collection and lab assistance with the 2013 and 2014 field data, and the Lake Superior National Estuarine Research Reserve and research coordinator Shon Schooler for sampling assistance on the St. Louis River Estuary. We also thank Pamela Anderson Supervisor of the Water Quality Monitoring Unit, MPCA and the CLMP volunteers for the SD 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 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 MEB acknowledge support from the UMOVPR for funding under the U-Spatial: Spatial Science and Systems Infrastructure grant and the Univ. of Minnesota Agricultural Experiment Station.

Publisher Copyright:
© 2016 Elsevier Inc.

Copyright 2017 Elsevier B.V., All rights reserved.

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