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.

Monitoring the United Nation Sustainable Development Goals (UN-SDGs) calls for adequate methodologies to extract specific indicators of status for each realm (e.g., air, water, land) and socio-economic-environmental issue. Earth observation has been considered, since the beginning, one of the pillars of this task, but the use of Synthetic Aperture Radar is still limited. This work report about the results achieved by a joint research project between Italy and Argentina, whose goal was to design and implement data processing techniques able to exploit the data from the COSMO-SkyMed and SAOCOM constellations, in conjunction with those by the Sentinel constellation by the European Space Agency and provide country-wide indicators for some of the UN-SDGs.

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.

Oviposition measurement with ovitraps is one of the most widely used methods to monitor Aedes aegypti mosquito activity in the world. Egg counting is however very time consuming. This paper presents the semi -automatic counting of mosquito eggs laid on ovitrap sticks in images acquired by cellular phones. In Cordoba, Argentina, 150 ovitraps were distributed in the city to measure the evolution of the Aedes aegypti population, estimated indirectly by the number of laid eggs. An important increase in the counts is a potential indicator of an imminent outbreak, alerting the health services to warn the population and recall the good sanitary practices. Bringing image processing to this proj ect is a way to relieve the technician from the tedious egg counting behind a magnifier and to reduce the count errors due to distraction or fatigue. We developed a fast semiautomatic counting solution with tools to focus on the useful image area, to show the confidence of automatic count numbers and to handle the collection of results.

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.

Here, we present a method for temporal modeling of the oviposition activity of Ae. aegypti mosquitoes based on a weighted generalised linear model (GLM) with explanatory environmental effects extracted from freely available remotely sensing (satellite) images. Our results show potential for operational applications. Experimental results are provided using field collected Ae. aegypti eggs count data in Córdoba, Argentina.

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.

The Central Florida Remote Sensing Lab has developed a rain impact model (RIM) to estimate the changes in Aquarius sea surface salinity (SSS) due to the accumulation of precipitation prior to the satellite observation time. RIM uses HYCOM (Hybrid Coordinate Ocean Model) ocean surface salinities and the NOAA global rainfall product CMORPH, to model transient changes in the near-surface salinity profile. The original version of RIM, a ID diffusion model, neglects the effects of wind and wave mixing. However, it was shown the mechanical mixing of the ocean caused by wind and waves rapidly reduces the salinity stratification caused by rain. Also, previous results using RIM, in the presence of moderate/high wind speeds, show that the model overestimates the rain effect on SSS. To address this issue, previous work (RIM-2) focused on the parameterization of the effects of wind on the vertical diffusivity (Ks), Preliminary results did not show great improvement, probably due to the fact that the mixing depth (do) also varies with wind speed. Therefore, this paper will account for the wind speed effects on both K, and do that result in a new version of the model, RIM-3. Results will be presented that compare RIM and RIM-3 at different depths for several parametrizations. Also, comparisons, between RIM-3 at depths of several meters with measurements from in-situ salinity instruments, will be presented.

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.

Based upon research with the Aquarius (AQ) satellite remote sensor, a rain impact model (RIM) has been developed which estimates the occurrence of sea surface salinity (SSS) stratification. RIM uses global salinity (HYCOM) and rainfall (CMORPH) products to estimate the transient change in SSS due to rainfall. Previously SSS predicted by RIM have exhibited good correlations with AQ, but the choice for the duration window (24 h) was arbitrary. In this paper, we examine the effect on RIM of different time duration windows.

We designed an integrated platform for early prediction of meteorological fire risk. The system is operative since the end of 2014 and estimates the fire danger index automatically, based on the 72 hours forecast of the Weather Research and Forecast (WRF) model. Though the system is in experimental phase, the first results showed a quite acceptable performance. Moreover, this index is capable of continuously improving the algorithms to produce enhanced risk estimation. Thus, in the short term, the system would also include geospatial information and satellite based products to help firefighting activities during all phases: early warning, pre-disaster planning, preparing and forecasting, response and assistance, recovering and reconstruction throughout a web map service.

Segmentation of multispectral remote sensing images is a key competence for a great variety of applications. Many of the applied segmentation algorithms are generative models based on Markov random fields. These approaches are generally limited to multivariate probability densities such as the normal distribution. In addition, it is usually impossible to adjust the contextual parameters separately for each frequency band. In this letter, we present a new segmentation algorithm that avoids the aforementioned problems and allows the use of any univariate density function as emission probability in each band. The approach consists of three steps: first, calculate feature vectors for every frequency band; second, estimate contextual parameters for every band and apply local smoothing; and third, merge the feature vectors of the frequency bands to obtain final segmentation. This procedure can be iterated; however, experiments show that after the first iteration, most of the pixels are already in their final state. We call our approach successive band merging (SBM). To evaluate the performance of SBM, we segment a Landsat 8 and an AVIRIS image. In both cases, the k̂ coefficients show that SBM outperforms the benchmark algorithms.

We analyze the information content in COSMO-SkyMed data with different acquisition modes and polarizations. A set of discrimination problems ranging from difficult to simple using samples from different land cover types is presented. Several separability measures, i.e. stochastic distances and their derived hypothesis tests, are applied to pairs of samples, and their ability to discriminate is assessed. From the studied modes, class separability of water, pasture, forest and urban is enhanced if the lowest resolution mode is used. Both, ascending and left looking acquisition geometry yield better classification results. Distance measurement tests between samples of the same class give better results for HH polarization than for VV polarization suggesting that the analyzed cover properties are better described by that microwave configuration.