Comparison of phycocyanin concentrations in Chaohu Lake, China, retrieved using MODIS and OLCI images - Wang, Jie; Wang, Zhi-cheng; Cui, Yu-huan; Hao, Shuang; Yi, Hua-yang (Frontiers in Environmental Science, 2022)
Phycocyanin (PC) concentration is used as an indicator to characterize cyanobacteria biomass while monitoring eutrophication in inland water. Remote sensing provides useful methods for quantifying PC concentration; however, there is a shortage of datasets for the long-term monitoring of PC concentration when only a single remote sensing data is used. Therefore, PC concentrations obtained from multi-source remote sensing images should be compared before integrating them for long-term monitoring. In this study, machine learning (ML) regression algorithms are used to develop PC concentration retrieval models suitable for Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel 3 Ocean and Land Colour Instrument (OLCI) images, and their accuracies are compared. The two optimal retrieval models are applied to satellite images acquired on the same days to compare the spatial consistency of the two PC concentration retrieval results. The results show that the sensitive spectral range of PC concentration is 560–680 nm. Among the ML regression algorithms, gradient boosted tree (GBT) regression exhibits the highest PC retrieval accuracy for both the MODIS images (R2 = 0.82, RMSE = 61.9 µg/L) and OLCI images (R2 = 0.86, RMSE = 45.44 µg/L). The PC concentrations retrieved from the MODIS and OLCI images acquired in bloom and no-bloom periods have the high spatial consistency in the most area of Chaohu Lake. Their correlation coefficient also reaches more than 0.7, and the average relative error reaches 0.293 µg/L. However, a large difference exists in areas with high PC concentrations. This is primarily affected by the poor applicability of atmospheric correction algorithms and retrieval models in cyanobacterial bloom areas. The proposed PC concentration retrieval models developed using GBT regression in this paper can expend the idea for the quantitative retrieval of other inland water quality parameters in inland water, and the conclusions should enable the effective integration of MODIS and OLCI images for the time series monitoring of PC concentrations in reservoirs and lakes.