Proximal Hyperspectral Sensing and Machine Learning for Chlorophyll-a Retrieval in Optically Complex Urban Freshwaters

Urban freshwater ecosystems affected by eutrophication and recurrent algal blooms require monitoring approaches capable of representing optical complexity and spatial heterogeneity. This study evaluated an integrated workflow combining proximal in situ hyperspectral sensing, radiometric calibration, spectral filtering, predictor-band selection, data transformation, and machine-learning regression to estimate chlorophyll-a (chl-a) in a tropical eutrophic urban reservoir. Monthly field campaigns were conducted from September 2022 to February 2023, with simultaneous chl-a measurements and hyperspectral image acquisition. After preprocessing, noise removal, and exclusion of anomalous spectra, 82 matched hyperspectral–chl-a observations were retained for model development. Predictor bands were selected using Pearson correlation and F-test analysis, identifying five relevant wavelengths: 530, 535, 682, 687, and 732 nm. Multiple Linear Regression, Random Forest Regressor, Support Vector Regressor, and XGBoost Regressor were tested under different data transformations. The Support Vector Regressor with logarithmic transformation achieved the best performance, with R2 = 0.86 and RMSE = 6.89 µg L−1. The selected wavelengths correspond to spectral regions associated with green reflectance, red chl-a absorption, and red-edge/NIR responses in productive waters. The results indicate that proximal hyperspectral sensing combined with machine learning can support chl-a estimation in optically complex urban reservoirs and provide complementary information for eutrophication monitoring and bloom-management strategies.