Improving SPM retrieval across diverse transitional waters through satellite-derived OWT classification and per-class algorithm optimization

Satellite remote sensing provides synoptic coverage and frequent revisit capabilities, making it an essential tool for estimating suspended particulate matter (SPM) in optically complex aquatic environments. Recent advances in satellite-derived optical water type (OWT) classification offer new opportunities for improving SPM retrieval across diverse water bodies, yet a systematic per-OWT recalibration and comparative evaluation of existing SPM algorithms has not been fully assessed. In this study, we employ a comprehensive framework that recalibrates 17 widely used SPM algorithms for each of 11 satellite-derived OWT classes and uses a Round-Robin evaluation to identify the optimal algorithm for each class while minimizing cross-type performance variability. Using Sentinel-2 MultiSpectral Instrument (MSI) reflectance, the resulting OWT-based blending scheme achieves substantially improved accuracy across optically diverse transitional waters, with a Root Mean Square Error ( Ψ ) of ∼4.6–8.2 g/m 3 and a coefficient of determination ( r 2 ) of 0.87–0.91 when validated against in-situ observations, including measurements from regions not used for calibration. The framework is further demonstrated through spatial mapping and multi-year trend analysis of SPM in two transitional coastal systems. The results highlight the value of satellite-derived OWTs for enhancing the transferability and robustness of SPM retrievals and provide a scalable approach for water-quality monitoring and ecosystem-assessment applications.