The high biomass productivity recorded in eutrophic waters leads to qualitative and quantitative changes in the phytoplankton community, resulting in massive algal blooms. This study evaluates the use of Acolite software on Sentinel-2 (S2) imagery for atmospheric and glint correction and its implications for monitoring algal blooms in a eutrophic reservoir. We analyse two dates comparing the corrected reflectance with the field reflectance data and find that glint correction improves the results in one but has the opposite effect in another. By analysing the spectra of the image, we can identify algal scum as a problem for this correction and propose to use the Floating Algae Index to to flag as suspected floating algae and in consequence not correct that pixel for remnant sunglint.

Seasonal snow is one of the most dynamic components of the cryosphere. The structure and composition of snowpacks is complex and highly variable, both in space and also in time. This study evaluates a new method for identifying phase changes of seasonal snow cover in the Argentinean Andes using Sentinel-1 Synthetic Aperture Radar (SAR) data available in Google Earth Engine (GEE) through time series derivatives and positive and negative anomalies. The results were compared with an approach based on fixed threshold time series phase change detection.

The high productivity of biomass registered in eutrophic water bodies, leads to qualitative and quantitative changes in the phytoplankton community, resulting in massive algae blooms. This research evaluates different atmospheric cor-rection methods, Sen2cor and Acolite, over Sentinel-2 (S2) images and its effects for monitoring algae blooms in an eu-trophic reservoir. Specifically, we analyze two dates com-paring the corrected reflectance to field reflectance data and laboratory results to study the turbid and productive water of San Roque reservoir, Argentina.

Remote sensing community is making enormous efforts to implement early warning systems capable for following spatio-temporal patterns of water quality and climate change risk indicators, being Horizon 2030 EOXPOSURE project one of them. This work presents first results of surface temperature Landsat 8 Level 2 Collection 2 products analysis for a reservoir and compare them with field data measurements. A Root Mean Square Error (RMSE) of 1.7°C and a Mean Absolute Percentage Error (MAPE) of 7% were obtained for these products but validation curve resulted not confident at a 95% level. A semiempirical linear model with 94% accuracy, RMSE of 1.1°C and a MAPE of 5% is presented. It was successfully validated with a control group data set obtaining 94% accuracy. A Water Surface Temperature temporal series is shown for the 2013–2020 period and spatio temporal patterns are analyzed and discussed. Water surface temperature behavior in zones with algal bloom occurrence present greater significant values, up to 3°C, than those with clearer water, indicating that water emissitiviy must be revised for these cases.

Mapping Human Exposure to Risky Environmental Conditions is key to quantify the vulnerability of population and economic assets. In order to develop novel tools, implementing the use of information layers from current and future Earth Observation (EO) missions is necessary. The EOxposure project that has been created and funded by the European Commission's Horizon 2020 research and innovation program covers this porpoise. Several topics involving human exposure to environmental risks are being studied, including water quality and pollution, which is addressed in this paper. The high productivity of biomass registered in eutrophic water bodies, leads to qualitative and quantitative changes in the phytoplankton community, resulting in massive algae blooms. This research work proposes a methodology that takes advantage of the temporal and spectral resolution of Sentinel-2 (S2) for monitoring eutrophic reservoir. Specifically, it uses large temporal series of S2 images and advanced data mining techniques to study the turbid and productive water of San Roque reservoir, Argentina. The spatial patterns and the temporal tendencies of these aquatic indicators are analysed and evaluated in order to assess their contribution to water quality models and a local water management program.

As part of a collaborative effort among researchers of several institutions and organizations, this project takes advantage of the Google Earth Engine (GEE) cloud computing environment to map algae bloom over the main water bodies and reservoirs of Latin America using Sentinel-2 imagery (2015 to present). The methodology based on the Normalized Difference Chlorophyll Index (NDCI) for chlorophyll-a and Trophic State Index (TSI) detection provided promising results. NDCI responds well to high levels of chlorophyll-a and, therefore, can be used as an indicator for algae blooms. The image processing as well as the display of maps and charts are being implemented into a GEE App to be freely available for general public use.

Extensive eutrophication process in water body may lead to the creation of algal blooms, reduction in oxygen supplies, death of aquatic life and danger to human health. Monitoring eutrophic processes is therefore mandatory to the aquatic environment and human health. However, the changes in the spatial and seasonal distribution of the phenomena are difficult to be resolved using sparse water sampling or acquisition of remote sensing data. Therefore, this research work proposes a methodology that takes advantage of the temporal and spectral resolution of Sentinel-2 (S2) for monitoring eutrophic reservoir. Specifically, it uses large temporal series of S2 images and advanced temporal unmixing model to study the spectral response of the turbid and productive water of San Roque reservoir, Argentina. The spatial patterns and the temporal tendencies of these aquatic indicators are analysed and evaluated in order to assess their contribution to the local water management.

The air pollution caused by the immense wildfire that occurred in Northeast Patagonia, Argentina in the summer 2016-2017 is presented in this work through the assessment of ground-based data, satellite measurements and modeled concentrations of NO, NO2, CO, PM2.5, PM10 and AOD. The data was obtained from a monitoring station placed in Bahia Blanca city; the OMI (Aura), MOPITT and MODIS (Terra) satellite sensors, and the APIFLAME-WRF-CHIMERE modeling system was used to estimate the chemical processes and atmospheric transport of pollutants emitted. A concentration higher than usual was found for December 20th and 21st 2016 and January 1st 2017 for every species, at Bahía Blanca's station. Satellite daily data was acquired for a specific day and modeled results were also exhibited for a qualitative assessment. The models obtained satisfactory results, according to the satellite survey. Finally, it was possible to assess air pollution from a wildfire by means of the combination of these different data sources.

Eutrophication is a process in which elevated organic matter and nutrients raises the primary production of a water body. As a result, the productivity of phytoplankton and biomass are very high at all trophic levels. During bloom event, the spatial and temporal distribution of this phenomena is difficult to be observed using conventional water sampling methods. This work advance the state of the art by using Sentinel-2 (S2) images to estimate chlorophyll-a (chl-a) concentration with an empirical model. Specifically, the model uses band 8 (NIR) and band 4 (red) to predict chl-a concentration during an algal bloom event in San Roque lake, Córdoba, Argentina. Nevertheless, novel spectral ratio for algae composition patterns has also been created using bands 8a and 9. The results show that S2 has the potential to monitor bloom events in eutrophic lakes.

Water pollution is an important problem around the world as it is closely related to human and environmental health. Field campaigns are expensive, time consuming and may provide little information. Remote sensing provides synoptic spatio-temporal views and can lead to a better understanding of lake ecology. In this work an extreme algal bloom event which occurred in a reservoir is characterized by LANDSAT8-OLI sensor and in situ sampling. Chlorophyll-a concentration and algae abundance data are measured on samples collected simultaneously with satellite pass and used to build semiempirical models. Two linear functions to calculate chlorophyll-a from satellite data are presented and compared. A linear model from band 2 (blue) and band 5 (NIR) presents the best performance with a determination coefficient equal to 0,89. In situ and satellite chlorophyll-a lead comparable trophic class assessment, hypertrophic. Both Models fail to predict chlorophyll-a concentration near river intrusion (North), where low values of reflectance are recorded.

Eutrophication is a phenomenon that affects many water bodies around the world. In severe cases, eutrophication can lead to large algal blooms. This study presents a method to detect algae blooms based on a time series of chlorophyll-a (Chl-a) concentration in the period 2001-2014. This time series is obtained from a semi-empirical algorithm generated with MODIS satellite data and in situ data from the Ministry of Water Resources of Cordoba Province. By detecting algae bloom dates and their statistic characterization, it is possible to define the range of Chl-a values in which the San Roque Dam is going through a bloom event.