Abstract

Satellite chlorophyll–a (Chla) products are widely used to study coastal productivity, but their performance often degrades in river-influenced and optically complex waters. We evaluated MODIS-Aqua Chla retrievals in the coastal upwelling area off central-southern Chile, a region strongly affected by seasonal river plumes, using monthly in situ Chla and hydrographic observations from Station 18 (August 2002 to September 2011), daily MODIS products, and matchup analyses based on 3 × 3 pixel windows and 1-, 3-, 5-, and 7-day composites. MODIS Chla and normalized Fluorescence Line Height (nFLH) reproduced the broad seasonal cycle, with maxima during spring–summer, but default MODIS Chla systematically exceeded in situ observations, particularly during periods of enhanced turbidity and river-influenced optical complexity. Among the raw satellite products, 1-day MODIS Chla matchups showed the strongest agreement with in situ Chla (r = 0.77, RMSE = 8.5 mg m−3), whereas 5-day composites increased matchup availability to 95% but reduced the correlation (r = 0.46, RMSE = 10.5 mg m−3). In contrast, nFLH showed more stable performance across composite lengths, although it underestimated high Chla values and should therefore be interpreted as a complementary fluorescence-based diagnostic rather than as a direct substitute for locally validated Chla retrievals. A gradient boosting model trained with MODIS remote-sensing reflectances improved the correspondence between satellite and in situ Chla relative to the default MODIS product within the available Station 18 matchup dataset. Because this model was evaluated using cross-validation rather than an independent regional validation dataset, the machine-learning results should be interpreted as a local proof of concept rather than a fully validated regional algorithm. These results indicate that standard MODIS algorithms overestimate Chla in this river-influenced upwelling system and highlight the value of local correction approaches, including machine-learning methods, for improving coastal ocean color products, provided that future applications include independent spatially distributed validation and improved bio-optical characterization of river-influenced waters.

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