Abstract

Lake Villarrica, located in southern Chile, is a vital freshwater resource whose ecological status requires continuous evaluation. Chlorophyll-a (Chl-a) is a key indicator of phytoplankton biomass and estimating it using satellite sensors enables efficient and large-scale monitoring. This study compared the performance of different empirical models based on reflectance data obtained from atmospherically corrected satellite images using ACOLITE software (Generic Version 20231023.0), calibrated with in situ measurements of Chl-a collected during the spring and summer seasons between 2014 and 2024. For each sensor, the best combination of spectral bands was selected, and retrieval models were generated using a bootstrapping procedure with 1000 iterations to obtain robust regression coefficients; the final models were defined using the median of these coefficients. The top-performing model for Landsat-8 and 9 was based on a blue-red band combination (R-2 = 0.79, RMSE = 2.1 & micro;g & centerdot;L-1, MAE = 1.2 & micro;g & centerdot;L-1, n = 74). In contrast, the optimal model for Sentinel-2A utilized green and blue bands, yielding higher precision (R-2 = 0.75, RMSE = 0.8 & micro;g & centerdot;L-1, MAE = 0.72 & micro;g & centerdot;L-1, n = 112). In general, the results obtained through remote sensing reveal a gradual increase in Chl-a levels over the last decade, reflected in recurrent summer biomass increases primarily along the shoreline near the urban area of Puc & oacute;n and in the vicinity of the Puc & oacute;n River inflow into Lake Villarrica. These results support the development of an operational satellite-based monitoring framework for inland lake water quality assessment.