Abstract
ABSTRACT Analysis of the suitability of water colour products for the reliability of subsequent applications, or improvements of the algorithm at later stages, is important. We used three types of water bodies: Type 1 (Southern Ocean and Qinghai Lake) clean oceanic water bodies dominated by chlorophyll and clean inland water bodies dominated by chlorophyll and coloured dissolved organic matter; Type 2 (Taihu Lake and Chagan Lake) turbid inland water bodies dominated by suspended matter and chlorophyll and Type 3 (Xiaoxingkai Lake and Daxingkai Lake) turbid inland water bodies dominated by suspended matter. The accuracies of two Chl a concentration products data for synchronous Sentinel-3 OLCI Neural Network (NN) and ocean colour 4 band ratio (OC4Me) algorithms were verified using two metrics combined with in situ data (to compare the applicability of Chl a concentration algorithms for the three types of water bodies). The optimal Chl-a concentration algorithm was selected to analyse the spatial and temporal variation characteristics of monthly concentrations of Chl a. Based on the analysis, (1) the NN algorithm had better applicability to Type 1 water bodies ; the NN algorithm had better applicability to Type 2 water bodies, ; and both algorithms had poor applicability to Type 3 water bodies, (2) Limitations: An overall low derivation value was obtained for the Sentinel-3 OLCI NN algorithm with water concentrations of Chl a, higher than 25 mg m−3. The OC4Me Chl a concentration product only applies to the concentrations of Chl a in Type 2 during the summer. (3) When the Sentinel-3 OLCI Chl a concentration product was compared with the moderate-resolution imaging spectroradiometer (MODIS) and visible infrared imaging radiometer (VIIRS) Chl a concentration products, the distribution trends of the Chl a concentrations were consistent. Furthermore, the Sentinel-3 OLCI Chl a concentration product was more dominant in spatial resolution.
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