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Abstract Agricultural intensification and urban sprawl have led to significant alterations in riverscapes, and one of the critical consequences is the deterioration of water quality with significant implications for public health. Therefore, the objectives of this study were the assessment of the water quality of the Suquía River, the assessment of LULC change at different spatial scales, and the analysis of the potential seasonal correlation among LULC change and Water Quality Index (WQI). The Sample Sites (SS) 1 and 2 before Cordoba city had the highest WQI values while from SS3 the WQI decreased, with the lowest WQI close to the wastewater treatment plant (SS7) after Cordoba city. From SS8 in a agricultural context, the WQI increases but does not reach the original values. In light of analysis carried out, the correlation between water quality variables and the different LULC classes at the local and regional scales demonstrated that WQI is negatively affected by agricultural and urban activities, while natural classes impacted positively. The spatialization of the results can help strongly in assessing and managing the diffusion of point and non-point pollution along the riverscape. The knowledge gained from this research can play a crucial role in water resources management, which supports the provision of river ecosystem services essential for the well-being of local populations.

Chromophoric dissolved organic matter (CDOM) is crucial in aquatic ecosystems, influencing light penetration and biogeochemical processes. This study investigates the CDOM variability in seven oligotrophic lakes of North Andean Patagonia using Landsat 8 imagery. An empirical band ratio model was calibrated and validated for the estimation of CDOM concentrations in surface lake water as the absorption coefficient at 440 nm (acdom440, m−1). Of the five atmospheric corrections evaluated, the QUAC (Quick Atmospheric Correction) method demonstrated the highest accuracy for the remote estimation of CDOM. The application of separate models for deep and shallow lakes yielded superior results compared to a combined model, with R2 values of 0.76 and 0.82 and mean absolute percentage errors (MAPEs) of 14% and 22% for deep and shallow lakes, respectively. The spatio-temporal variability of CDOM was characterized over a five-year period using satellite-derived acdom440 values. CDOM concentrations varied widely, with very low values in deep lakes and moderate values in shallow lakes. Additionally, significant seasonal fluctuations were evident. Lower CDOM concentrations were observed during the summer to early autumn period, while higher concentrations were observed in the winter to spring period. A gradient boosting regression tree analysis revealed that inter-lake differences were primarily influenced by the lake perimeter to lake area ratio, mean lake depth, and watershed area to lake volume ratio. However, seasonal CDOM variation was largely influenced by Lake Nahuel Huapi water storage (a proxy for water level variability at a regional scale), followed by precipitation, air temperature, and wind. This research presents a robust method for estimating low to moderate CDOM concentrations, improving environmental monitoring of North Andean Patagonian Lake ecosystems. The results deepen the understanding of CDOM dynamics in low-impact lakes and its main environmental drivers, enhance the ability to estimate lacustrine carbon stocks on a regional scale, and help to predict the effects of climate change on this important variable.

Nowadays, we witness remarkable technological progress alongside unprecedented challenges that threaten the delicate balance of our planet’s ecological system. Environmental contamination plays a central role in this, with rapid urbanization, industrialization, mining and agricultural practices intensifying the introduction of pollutants into the environment. This article highlights the potential synergy between two fields operating at vastly different scales: satellite technology and nanotechnology. This article delves into the offerings of each of these disciplines and examines how they can mutually contribute to the detection, prevention and mitigation of environmental pollution. Satellites play a crucial role in identifying and monitoring large-scale polluted areas, offering comprehensive insights into environmental challenges. They are indispensable in tracking air, water pollution levels, assessing land degradation, and monitoring changes in ocean health with relatively high spatial and temporal resolution. Nanotechnology leverages the unique properties of materials at sub-micron scale by offering amplified chemical reactivity and new optical, electronic, and magnetic attributes, enabling selective and sensitive sensors and rapid and efficient contaminant capture/degradation strategies. Emerging nanomaterials, along with nature-inspired and self-powered or self-sustaining designs, broaden capabilities for efficient solutions. Advanced nanocharacterization techniques deepen material understanding and quantification, while nanofabrication allows precise design of functional nano-devices. We believe the synergistic relationship between both fields can yield cooperative solutions, expediting effective measures and greatly influencing policy decisions. This article advocates for the collaboration between these two disciplines to foster impactful progress in facing global challenges.

In cryospheric studies, the most critical variable is estimating the beginning and evolution of the seasonal snow accumulation and its melting process. We propose and evaluate a new method for identifying seasonal snow cover phase changes in the Argentinean Andes using time series of Sentinel-1 synthetic aperture radar data available in the Google Earth Engine platform. We used meteorological and optical Sentinel-2 data to validate snow presence. First, we investigated seasonal snow cover dynamics in different regions of interest (ROIs). We identified three land surface cover periods: bare soil, dry snow, and melting snow. This finding is significant because other studies show that bare soil and dry snow have similar backscattering responses in C-band. Our methodology uses time series derivatives and their positive and negative anomalies. Finally, we compare our results with those obtained with a fixed threshold change detection approach. Our method was able to detect the phase change between bare soil and dry snow period in 75% of the ROIs, while a fixed threshold of $-\text{2} \,\mathrm{dB}$ only detects it in 42% of the cases. Furthermore, the derivative method also detects in 92% of the ROIs time series the beginning of the melting period, showing that it is a promising methodology for operative systems.

En los últimos años, la contaminación y la eutrofización de los recursos hídricos se ha convertido en uno de los problemas ambientales más importantes de mundo, ya que pone en riesgo tanto la cantidad como la calidad del agua para consumo humano. La concentración de Clorofila-a (Cl-a) es un buen indicador de la calidad del agua. El objetivo de este trabajo fue generar una serie de tiempo a partir de un modelo estadístico basado en datos de campo e información satelital de MODIS, para determinar y predecir la distribución temporal de la Cl-a en el embalse Río Tercero. Se obtuvo un modelo de regresión lineal (R2=0,66), el cual tiene en cuenta la relación entre bandas 1 y 2 del satélite MODIS. Con dicho algoritmo, se obtuvo un valor de Cl-a para cada día desde Enero de 2009 a Septiembre de 2019. Se generó una serie de tiempo para el reservorio bajo estudio. Este trabajo es una contribución relevante para estudiar la evolución temporal de la contaminación del agua en reservorios.

La información de precipitación es crítica para la comprensión del equilibrio hidrológico a escala global. La lluvia, junto con otras variables ambientales tales como evapotranspiración, temperatura, humedad relativa, entre otras, representa un factor de interés para la producción agrícola. Debido a esto, surge la necesidad de llevar adelante estudios que posibiliten comprender mejor la variabilidad espacial y temporal de las mismas. En este trabajo se presenta una metodología que permite automatizar la descarga de series temporales de datos de precipitación de la Misión de Medición de la Lluvia Tropical (Tropical Rainfall Measuring Mission (TRMM)) desde la plataforma Google Earth Engine (GEE) y validar los datos obtenidos con una serie de datos históricos de una estación meteorológica. Con este fin se desarrolló un sistema bajo la plataforma GEE para la generación y descarga de datos TRMM. Como caso de estudio se fijó la cuenca del Arroyo Las Conchas de la Provincia de Entre Ríos, Argentina. Para la validación de los resultados, se generó un set de datos con la información de precipitaciones desde el 1 de enero del 2000 al 31 de diciembre del 2015, medida por pluviómetros, para el área de influencia de la estación meteorológica de la Estación Experimental Agropecuaria del INTA de Oro Verde, departamento de Paraná, provincia de Entre Ríos, Argentina. Los resultados obtenidos mediante el proceso de evaluación muestran que existe una estrecha relación entre ambas fuentes de información. La metodología propuesta permitirá generar sets de datos históricos de precipitación para estudiar el régimen hídrico en regiones de difícil acceso o en cuencas extensas y poco pobladas.