Satellite-Based Chlorophyll-a Prediction Reveals Salinity-Dominated Regime Shifts in the East China Sea: A 22-Year Multi-Sensor Analysis with Explainable AI

We developed an explainable machine learning framework combining 22 years (2003–2024) of multi-sensor satellite data (MODIS Aqua, CMEMS, C3S) with zone-specific SHAP attribution to quantify chlorophyll-a (Chl-a) mechanisms in the East China Sea. A geography-free XGBoost model achieved $R^2=0.802$ on 1.4 million pixel-month observations, and counterfactual experiments confirmed its superior environmental sensitivity over location-dependent models. Multi-strategy threshold detection identified two critical salinity boundaries—11.62 psu marking the turbidity-to-productivity transition (Cohen’s $d=-2.92$) and 34.03 psu at the Kuroshio Front ($d=-1.04$)—neither of which coincides with traditional physical definitions. Zone-specific SHAP analysis revealed that sea surface salinity (SSS) dominates Chl-a attribution across all zones but through fundamentally different mechanisms. We propose an “SSS Triple-Role Framework” in which salinity serves as turbidity proxy in estuarine waters, nutrient proxy in transitional waters, and dilution signal offshore, resolving the apparent contradiction of simultaneous positive and negative salinity effects. Non-additive interactions—including SSS × SST coupling (61% modulation) and SST × sea level amplification during Kuroshio intrusions—further demonstrate hierarchical controls missed by additive models. These findings provide quantitative benchmarks for ecosystem monitoring in river-dominated marginal seas.