Comparison of satellite reflectance algorithms for estimating turbidity and cyanobacterial concentrations in productive freshwaters using hyperspectral aircraft imagery and dense coincident surface observations

Richard Beck, Min Xu, Shengan Zhan, Richard Johansen, Hongxing Liu, Susanna Tong, Bo Yang, Song Shu, Qiusheng Wu, Shujie Wang, Kevin Berling, Andrew Murray, Erich Emery, Molly Reif, Joseph Harwood, Jade Young, Christopher Nietch, Dana Macke, Mark Martin, Garrett StillingsRichard Stumpf, Haibin Su, Zhaoxia Ye, Yan Huang

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

We analyzed 37 satellite reflectance algorithms and 321 variants for five satellites for estimating turbidity in a freshwater inland lake in Ohio using coincident real hyperspectral aircraft imagery converted to relative reflectance and dense coincident surface observations. This study is part of an effort to develop simple proxies for turbidity and algal blooms and to evaluate their performance and portability between satellite imagers for regional operational turbidity and algal bloom monitoring. Turbidity algorithms were then applied to synthetic satellite images and compared to in situ measurements of turbidity, chlorophyll-a (Chl-a), total suspended solids (TSS) and phycocyanin as an indicator of cyanobacterial/blue green algal (BGA) abundance. Several turbidity algorithms worked well with real Compact Airborne Spectrographic Imager (CASI) and synthetic WorldView-2, Sentinel-2 and Sentinel-3/MERIS/OLCI imagery. A simple red band algorithm for MODIS imagery and a new fluorescence line height algorithm for Landsat-8 imagery had limited performance with regard to turbidity estimation. Blue-Green Algae/Phycocyanin (BGA/PC) and Chl-a algorithms were the most widely applicable algorithms for turbidity estimation because strong co-variance of turbidity, TSS, Chl-a, and BGA made them mutual proxies in this experiment.

Original languageEnglish (US)
Pages (from-to)413-433
Number of pages21
JournalJournal of Great Lakes Research
Volume45
Issue number3
DOIs
StatePublished - Jun 2019

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Aquatic Science
  • Ecology

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