Conference Agenda

Lakes and reservoirs are aquatic systems that play a crucial role in both environmental balance and human well-being. However, these systems are exposed to eutrophication and deterioration of water quality. Therefore, comprehensive monitoring programs are essential to establish effective strategies for its improvement. The main objective of this study was to characterize an algal and cyanobacteria bloom (HAB) recorded in the Río Tercero Reservoir (Córdoba, Argentina) by integrating In-situ monitoring techniques with satellite-based remote sensing data. Water quality variables and phytoplankton composition were measured in the reservoir and related with a Landsat 9 image. Results revealed the presence of a HAB occurring in the central-western region of the reservoir. This founding was also demonstrated by Landsat data which was used to predict the total chlorophyll-a concentration (Chl-aTotal), and cyanobacterial chlorophyll-a (Chl-aCyano) across the entire reservoir surface.

Water pollution in the Bolívar department of Colombia is a critical environmental problem. This problem affects aquatic ecosystems, water quality, and access to clean water for domestic, agricultural, and industrial use. Conventional water contamination monitoring methods are often costly, laborious, and limited in spatial and temporal coverage. This makes accurate and timely assessments, particularly in large or difficult to access regions. Moreover, the dependence on spot sampling and laboratory analysis limits the availability of the results. This limits the ability to respond to unexpected contamination events. This limits the ability to respond to unexpected contamination events. These limitations have prompted the adoption of alternative approaches. These include the use of real-time sensors, remote sensing, and satellite imagery. Predictive models based on artificial intelligence have also been developed. These tools allow optimizing water body monitoring and extend the spatial and temporal coverage of water contamination monitoring. In this context, this study aims to address the challenges of conventional monitoring. This study proposes a deep learning-based model to estimate and predict contamination in strategic wetlands, with a particular focus on \textit{Ciénaga de la Virgen}. The methodology integrates Sentinel-2 remote sensing data with \textit{in-situ} measurements of key indicators of water contamination. A dataset was constructed by combining spectral information and water contamination parameters from monitoring stations. The results achieved a promising prediction of the contamination variables. The proposed framework facilitates sustainable management of water resources and contributes to understanding the dynamics of contamination.

Bi or quarterly monitoring at some sampling stations provides relevant water quality (WQ) management information on the watershed scale. However, it does not provide enough spatial coverage and temporal frequency to support effective public policies for the sustainable use of local aquatic systems. Due to the high variability in the optical properties of aquatic systems, the monitoring frequency should allow for continuous tracking of WQ temporal dynamics, enabling regulatory agencies to issue alerts to managers when necessary. Despite the availability of orbital optical sensor constellations for systematic remote sensing of aquatic systems, there is currently no platform for continuous and automated WQ monitoring. This paper introduces the prototype of the MAPAQUALI platform, a modular continuous monitoring platform that integrates three aquatic systems.

Combining sampling campaigns with satellite spectral data a single band algorithm was developed to retrieve water turbidity, in Paraná River middle basin, North-East Argentina. Water samples were collected and characterized by in situ and laboratory physicochemical measurements. ACOLITE software was used to atmospherically correct Sentinel-2 products, obtaining spectral water surface reflectance. Several models were trained and tested, calculating performance metrics as the selection criteria. The best model was a linear expression between turbidity and spectral band at 704 nm, both transformed by natural logarithm. Turbidity maps followed the same sediment gradient across Paraná River, with high values in the West shore and low presence in the East side. A turbidity regression model can be used to describe water quality, a relevant parameter for water potabilization.

This study presents an optical water type (OWT) classification approach to characterize the spatial and temporal variability of water quality in Mar Chiquita Lagoon, a large inland RAMSAR site in Argentina. Multispectral (Sentinel-2, Sentinel-3) and hyperspectral (PACE) satellite imagery were used to derive three spectral descriptors: apparent visible wavelength, a normalized difference index, and the trapezoidal area under the RGB bands. These metrics were used to classify each water pixel into predefined OWTs based on spectral similarity. The results highlight consistent spatial patterns across sensors, with the central and southern lagoon dominated by moderately turbid, particle-rich waters (class 5a), and more variable classes (4a, 4b, 5b) concentrated near northern tributary inflows. Temporal analysis using PACE revealed seasonal shifts in OWTs, with class 4b becoming dominant during the spring–summer period. This multi-sensor framework demonstrates the effectiveness of spectral classification for monitoring optically complex inland waters in the absence of in situ data. The findings underscore the value of satellite-based remote sensing for tracking water quality and delineating the spatial influence of tributaries across temporal and spectral dimensions.

Harmful cyanobacterial blooms, detrimental to both environmental health and human well-being, are a growing global concern. Addressing this challenge, the present study adopts a dual approach, combining in situ measurements with satellite remote sensing imagery for effective monitoring. The primary objective of this research is to initiate the development of a semi-empirical algorithm specifically designed to quantify cyanobacteria chlorophyll-a concentration in San Roque Lake, Córdoba, Argentina. Notably, this represents the first algorithm of its kind to be developed for this specific water body, which frequently experiences severe blooms. The methodology involved leveraging satellite images from Landsat 8 and 9, acquired concurrently with comprehensive in situ field campaigns. During these campaigns, chlorophyll-a concentration was directly measured, radiometric data were collected, and microscopic algal abundance counts were performed to validate cyanobacteria presence. Among the various relationships explored, the most promising model emerged from the B3/B4 band ratio, yielding a strong coefficient of determination (R2 of 0.6). This developed model was subsequently applied to a satellite image captured during a significant algal bloom event on February 20, 2025. The results demonstrated a clear consistency between the model's chlorophyll predictions and the visual representation of the bloom in the RGB satellite composite. For future work, it is essential to further calibrate and refine this model using a more extensive and diverse dataset, ensuring its robustness and broader applicability for routine monitoring and management of San Roque Lake.