2026-04-14T04:51:53Zhttps://recercat.cat/oai/request

oai:recercat.cat:20.500.12327/28202025-10-22T11:33:42Zcom_2072_4428com_2072_4427col_2072_487898

00925njm 22002777a 4500 dc Yépez, Santiago author Velásquez, Germán author Torres, Daniel author Saavedra-Passache, Rodrigo author Pincheira, Martin author Cid, Hayleen author Rodríguez-López, Lien author Contreras, Frédéric author Frappart, Frédéric author Cristóbal, Jordi author Pons, Xavier author Flores, Neftali author Bourrel, Luc author 2024-01-22 This study aims to develop and implement a methodology for retrieving bio-optical parameters in a lagoon located in the Biobío region, South-Central Chile, by analyzing time series of Landsat-8 OLI satellite images. The bio-optical parameters, i.e., chlorophyll-a (Chl-a, in mg·m−3) and turbidity (in NTU) were measured in situ during a satellite overpass to minimize the impact of atmospheric distortions. To calibrate the satellite images, various atmospheric correction methods (including ACOLITE, C2RCC, iCOR, and LaSRC) were evaluated during the image preprocessing phase. Spectral signatures obtained from the scenes for each atmospheric correction method were then compared with spectral signatures acquired in situ on the water surface. In short, the ACOLITE model emerged as the best fit for the calibration process, reaching R2 values of 0.88 and 0.79 for Chl-a and turbidity, respectively. This underlies the importance of using inversion models, when processing water surfaces, to mitigate errors due to aerosols and the sun-glint effect. Subsequently, reflectance data derived from the ACOLITE model were used to establish correlations between various spectral indices and the in situ data. The empirical retrieval models (based on band combinations) yielding superior performance, with higher R2 values, were subjected to a rigorous statistical validation and optimization by applying a bootstrapping approach. From this process the green chlorophyll index (GCI) was selected as the optimal choice for constructing the Chl-a retrieval model, reaching an R2 of 0.88, while the red + NIR spectral index achieved the highest R2 value (0.79) for turbidity analysis, although in the last case, it was necessary to incorporate data from several seasons for an adequate model training. Our analysis covered a broad spectrum of dates, seasons, and years, which allowed us to search deeper into the evolution of the trophic state associated with the lake. We identified a striking eight-year period (2014–2022) characterized by a decline in Chl-a concentration in the lake, possibly attributable to governmental measures in the region for the protection and conservation of the lake. Additionally, the OLI imagery showed a spatial pattern varying from higher Chl-a values in the northern zone compared to the southern zone, probably due to the heat island effect of the northern urban areas. The results of this study suggest a positive effect of recent local regulations and serve as the basis for the creation of a modern monitoring system that enhances traditional point-based methods, offering a holistic view of the ongoing processes within the lake. Yépez, Santiago, Germán Velásquez, Daniel J. Torres, Rodrigo Saavedra-Passache, Martin Pincheira, Hayleen Cid, Lien Rodríguez‐López, et al. 2024. “Spatiotemporal Variations in Biophysical Water Quality Parameters: An Integrated in Situ and Remote Sensing Analysis of an Urban Lake in Chile.” Remote Sensing 16 (2): 427. https://doi.org/10.3390/rs16020427. 2072-4292 http://hdl.handle.net/20.500.12327/2820 https://doi.org/10.3390/rs16020427 Spatiotemporal Variations in Biophysical Water Quality Parameters: An Integrated In Situ and Remote Sensing Analysis of an Urban Lake in Chile