Evaluating and Optimizing VIIRS Retrievals of Chlorophyll-a and Suspended Particulate Matter in Turbid Lakes Using a Machine Learning Approach

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched to continue the legacy of the MODerate Resolution Imaging Spectroradiometer (MODIS). Despite recent studies demonstrating the use of VIIRS observations over inland waters, VIIRS has not been widely used to generate water quality products (e.g., chlorophyll-a (Chl- a ), suspended particulate matter (SPM)) in relatively large turbid lakes. This study examines the quality of VIIRS-derived remote sensing reflectance (R _rs ) from four different atmospheric-correction processors with matchups from 13 lakes sized between 107 km ² and 2573 km ² across the eastern plain of China. NOAA’s operational R _rs outperforming R _rs retrieved by other state-of-the-art algorithms were shown to contain mean uncertainties of 57%, 33%, 20%, 28% for R _rs (486), R _rs (551), R _rs (671), and R _rs (745), respectively, which induced ~55% uncertainty in satellite-retrieved SPM and Chl- a from recently developed algorithms in the studied lakes. A deep neural network was developed for simultaneous retrievals of Chl- a and SPM from VIIRS Rayleigh-corrected reflectance to improve accuracy. The model with satisfactory accuracy (mean uncertainty of 28% for Chl- a and 20% for SPM) outperformed other machine learning approaches and nearly halved uncertainties compared to those obtained from satellite-derived R _rs products. Within the 2012-2020 period, high-quality VIIRS-derived Chl- a and SPM across 61 lakes in eastern China had evident interannual variability in SPM but insignificant temporal variations in Chl- a . This study provides validated, high-quality, basin-scale VIIRS-derived Chl- a and SPM products in eastern China during the past decade. Our results offer a strategy for improving regional water quality products from VIIRS data.