A Hybrid EOF Algorithm to Improve MODIS Cyanobacteria Phycocyanin Data Quality in a Highly Turbid Lake: Bloom and Nonbloom Condition

摘要

Extensive monitoring of cyanobacterial blooms in lakes and reservoirs can provide important protection for drinking water sources. In most inland waterbodies, phycocyanin (PC) concentrations are the best indicator of cyanobacteria distribution. PC has a characteristic absorption peak near 620 nm; however, reflectance at this wavelength is only available from MEdium Resolution Imaging Spectrometer (MERIS) and Ocean and Land Colour Instrument (OLCI) sensors. MERIS stopped providing data after 2012 and OLCI was only recently launched (February 2016). The Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua is currently the only satellite instrument that can provide well-calibrated top-of-atmosphere radiance data over an extended number of years to the present. In this study, we develop and validate a new approach based on empirical orthogonal function (EOF) to quantify PC concentrations in a turbid inland lake (Lake Chaohu, China). Based on Rayleigh-corrected reflectance data ( ) at 469, 555, 645, and 859 nm, the concentrations of PC were estimated by regression of 87 concurrent MODIS-field measurements for bloom and nonbloom conditions. The validation (N = 93) showed R = 0.40 and unbiased RMS = 60.86%. Application of the algorithm from 2000 and 2014 showed spatial distribution patterns and seasonal changes that confirmed in situ and MERIS-based studies of floating algae mats. The spatial information on PC concentrations in Lake Chaohu had a reduced sensitivity to perturbations from thin aerosols and high sediments. This EOF approach allows us for new insights in the long-term dynamics of shallow lakes and reservoirs where having a better understanding of cyanobacterial blooms is important.