A novel MODIS algorithm to estimate chlorophyll a concentration in eutrophic turbid lakes

A novel approach was developed to estimate phytoplankton biomass in eutrophic turbid lakes, using MODIS bands designed for land and atmospheric studies. The Baseline Normalized Difference Bloom Index (BNDBI) uses the difference of remote-sensing reflectance (Rrs, sr(-1)) at 555 nm (band 4) and 645 nm (band 1) after baseline correction using bands at 469 nm and 859 nm: (Rrs'(555) Rrs'(645))/(Rrs'(555)+ Rrs'(645)). BNDBI takes advantage of the Chl-a's absorption minimum near 572 nm and absorption maximum near 667 nm. Using data from Lake Chaohu, the index showed a strong relationship with Chl-a concentrations in conditions that would normally saturate more sensitive ocean-color sensors. Extensive field measurements were used to calibrate and validate the algorithm with unbiased root-mean-square-error (URMSE) of 47.9% when compared to in situ Rrs data. A reduced sensitivity to atmospheric effects was accomplished by using a baseline correction approach, anchored at 469 nm and 859 nm to correct the radiances at 555 nm and 645 nm. Radiative transfer simulations showed that the algorithm can be applied directly to MODIS Rayleigh-corrected reflectance (Rrc) after adjusting algorithm coefficients (URMSE uncertainty of 56.4% for MODIS Rrc data) for Chl-a concentrations <1000 mu g L-1. Comparative analyses showed that the index was resistant to changes in turbidity and organic matter concentrations. Theoretical simulations, image comparisons and spectral analyses demonstrated that the index was robust in a range of complex atmospheric and surface conditions, with different aerosol types, optical thickness (tau(a)555), solar/viewing geometry, sun glint and thin clouds. A comparison with other MODIS and MERIS Chl-a algorithms for turbid waters showed that BNDBI provided consistent results with the advantage of using MODIS wavebands that remain unsaturated in high turbidity conditions. The BNDBI opens new possibilities to explore bio-optical dynamics in turbid eutrophic lakes using data from a range of satellite sources. (C) 2016 Elsevier Ltd. All rights reserved.