@article{e81a9a89bfb84fba90b54334931ae767,
title = "Comparison of satellite reflectance algorithms for estimating turbidity and cyanobacterial concentrations in productive freshwaters using hyperspectral aircraft imagery and dense coincident surface observations",
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.",
author = "Richard Beck and Min Xu and Shengan Zhan and Richard Johansen and Hongxing Liu and Susanna Tong and Bo Yang and Song Shu and Qiusheng Wu and Shujie Wang and Kevin Berling and Andrew Murray and Erich Emery and Molly Reif and Joseph Harwood and Jade Young and Christopher Nietch and Dana Macke and Mark Martin and Garrett Stillings and Richard Stumpf and Haibin Su and Zhaoxia Ye and Yan Huang",
note = "Funding Information: This study was funded by the U.S. Army Corps of Engineers and the NASA Glenn Research Center . The U.S. Environmental Protection Agency and the Kentucky Department of Environmental Protection, Division of Water provided valuable in-kind services. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. This article expresses only the personal views of the U.S. Army Corps of Engineers and U.S. EPA employees listed as authors and does not necessarily reflect the official positions of the Corps of the Department of the Army, or of the U.S. EPA, or NASA. Funding Information: This study was funded by the U.S. Army Corps of Engineers and the NASA Glenn Research Center. The U.S. Environmental Protection Agency and the Kentucky Department of Environmental Protection, Division of Water provided valuable in-kind services. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. This article expresses only the personal views of the U.S. Army Corps of Engineers and U.S. EPA employees listed as authors and does not necessarily reflect the official positions of the Corps of the Department of the Army, or of the U.S. EPA, or NASA. Publisher Copyright: {\textcopyright} 2018 International Association for Great Lakes Research",
year = "2019",
month = jun,
doi = "10.1016/j.jglr.2018.09.001",
language = "English (US)",
volume = "45",
pages = "413--433",
journal = "Journal of Great Lakes Research",
issn = "0380-1330",
publisher = "International Association of Great Lakes Research",
number = "3",
}