Novel method for measuring and processing field spectral data for water quality applications

The accelerated eutrophication of water bodies is a global problem and requires monitoring with high spatial and temporal resolution. Satellite remote sensing techniques have proven to be useful to detect and quantify an indicator of this phenomenon, chlorophyll-a concentration, where the associated error propagation is crucial to understand these data. This work presents a novel method for measuring and processing field spectral data for water quality applications with a special focus on statistical uncertainties. The radiometric data obtained from San Roque reservoir in Cordoba, Argentina, was processed using a bootstrap algorithm to obtain the mean reflectance and its standard deviation. The proposed algorithm includes flagging of unfavorable water reflectance measurements due to sky conditions and floating algae, as well as remnant sunglint correction using the reflectance of the 1350 nm. The results showed that the proposed algorithm improved the accuracy of radiometric data by reducing the standard deviation and the remnant sunglint effect. Linear regression analysis showed a higher correlation between the measured reflectances with total and water quality variables for the new method. The proposed algorithm yielded smaller prediction errors for total chlorophyll and turbidity compared to the current algorithm by a factor of 1.9 ± 0.2 and 3.7 ± 0.3.