Addressing field data scarcity in algal bloom surveillance: integrating fuzzy inference and orbital remote sensing in Brazilian reservoirs.

Chronic scarcity of continuous in situ sampling severely hampers trophic monitoring in tropical reservoirs, restricting the application of data-intensive models in regulatory environments. Therefore, the aim of this study was to develop a cross-validation framework integrating a Mamdani Fuzzy Inference System (FIS) with Sentinel-2 Normalized Difference Chlorophyll Index (NDCI) retrievals via Google Earth Engine (GEE) to overcome these limitations. The methods involved parameterizing the FIS with subtropical thresholds using a 16-year limnological dataset from the Billings Reservoir (Brazil). This system was then cross-validated against satellite-derived NDCI distributions using non-parametric statistical tests. Subsequently, it was applied to the Funil Reservoir to evaluate spatial transferability based exclusively on NDCI mapping, without concurrent in situ validation. The results demonstrated a statistically significant validation at the Billings Reservoir (Kruskal-Wallis, p < 0.05 ), confirming that NDCI medians vary systematically across fuzzy-classified trophic states. Additionally, Levene's test revealed homogeneous variance for total phosphorus (TP) ( W = 3.09 , p = 0.051 ) contrasting with heteroscedastic chlorophyll-a (Chl-a) ( p < 0.05 ), characterizing the distinct dispersion regimes of eutrophication drivers and biological responses. Furthermore, the NDCI-based application to the Funil Reservoir yielded synoptic maps that indicated potential algal bloom expansion at resolutions unattainable by conventional monitoring. In conclusion, this approach establishes remote sensing-based surveillance via NDCI, initially calibrated by fuzzy logic, as a valuable screening tool for eutrophication monitoring in data-scarce aquatic systems.