The Salto Grande Reservoir of the Uruguay River experiences recurrent increases of cyanobacteria bloom, which negatively impacts water quality, with adverse consequences for public health and tourism. This study uses images from the Sentinel-2 satellite to investigate different methods for detecting and monitoring cyanobacteria. Two indices are calculated and two models are applied for the detection of cyanobacteria: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI) and Chlorophyll-a models M1 and M2, which have not previously been evaluated with images from Sentinel-2. NDVI is commonly used to identify areas with vegetation cover. The NDWI, using green and near-infrared (NIR) bands, is used as a filter for the application of Chlorophyl-A models since it is very useful for the identification of aquatic areas. Both M1 and M2 estimate the concentration of chlorophyll in the water, with model M2 demonstrating greater efficiency in the detection of cyanobacteria compared to model M1. Sentinel-2 images allow the correct observation of variations in the concentration of cyanobacteria in the riverbed, facilitating the monitoring of seasonal changes and cyanobacteria blooms. The results of our investigation underlines the effectiveness of NDWI when used with the Sentinel-2 images for identifying surface water areas and the M2-Chlorophyll-a model for detecting cyanobacteria in them. Combining these indices with on-site measurements is likely to offer a robust approach to monitoring and managing of the Salto Grande Reservoir.

Agricultural systems are under growing pressure, as soil degradation threatens food security and sustainable land use. Early detection through soil monitoring and precision agriculture is vital to prevent irreversible damage and enable timely conservation. This study evaluates a combined procedure based on electrical resistivity tomography and frequency-domain electromagnetic induction measurements, together with discrete soil sampling, to electrically characterize the soil, identify layers, and map the A horizon depth in a non-disturbing way. This work includes the design and implementation of a mounting electrode system, which reduces the installation time of electrical resistivity tomography surveys by 60% while maintaining data quality. The data were acquired in the oldest long-term agronomic experiment in South America, comprising seven rotation systems with three replicates each, totaling 21 rainfed plots, and representing contrasting management scenarios. Soil A horizon thickness maps of the entire experiment were obtained through two procedures. A comparison between mapping inputs, including all plots and only bare-soil plots, revealed minimal differences in unvegetated areas but notable discrepancies under plant cover, where vegetation increased fluctuations and noise. The present study provides a methodology for accurately assessing the spatial variability of the A horizon thickness by means of proximal sensing techniques. This contributes to the challenge of gathering fundamental soil information in a fast and cost-effective manner, critical for precision agricultura.