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

J Great Lakes Res. 2019 Jun 1;45(3):413-433. doi: 10.1016/j.jglr.2018.09.001.


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.

Keywords: Algorithm; Cyanobacteria; Harmful algal bloom; Hyperspectral; Satellite; Turbidity.