Reservoirs are artificial environments built by humans, and the impacts of these environments are not completely known. Retention time and high nutrient availability in the water increases the eutrophic level. Eutrophication is directly correlated to primary productivity by phytoplankton. These organisms have an important role in the environment. However, high concentrations of determined species can lead to public health problems. Species of cyanobacteria produce toxins that in determined concentrations can cause serious diseases in the liver and nervous system, which could lead to death. Phytoplankton has photoactive pigments that can be used to identify these toxins. Thus, remote sensing data is a viable alternative for mapping these pigments, and consequently, the trophic. Chlorophyll-a (Chl-a) is present in all phytoplankton species. Therefore, the aim of this work was to evaluate the performance of images of the sensor Operational Land Imager (OLI) onboard the Landsat-8 satellite in determining Chl-a concentrations and estimating the trophic level in a tropical reservoir. Empirical models were fitted using data from two field surveys conducted in May and October 2014 (Austral Autumn and Austral Spring, respectively). Models were applied in a temporal series of OLI images from May 2013 to October 2014. The estimated Chl-a concentration was used to classify the trophic level from a trophic state index that adopted the concentration of this pigment-like parameter. The models of Chl-a concentration showed reasonable results, but their performance was likely impaired by the atmospheric correction. Consequently, the trophic level classification also did not obtain better results.

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The atmospheric effects that influence on the signal registered by remote sensors might be minimized in order to provide reliable spectral information. In aquatic systems, the application of atmospheric correction aims to minimize such effects and avoid the under or overestimation of remote sensing reflectance (R rs). Accurately R rs provides better information about the state of aquatic system, it means, establishing the concentration of aquatic compounds more precisely. The aim of this study is to evaluate the outputs from several atmospheric correction methods (Dark Object Subtraction-DOS; Quick Atmospheric Correction-QUAC; Fast Line-of-sight Atmospheric Analysis of Hypercubes-FLAASH; Atmospheric Correction for OLI 'lite'-ACOLITE, and Provisional Landsat-8 Surface Reflectance Algorithm-L8SR) in order to investigate the suitability of R rs for estimating total suspended matter concentrations (TSM) in the Barra Bonita Hydroelectrical Reservoir. To establish TSM concentrations via atmospherically corrected Operational Land Imager (OLI) scene, the TSM retrieval model was calibrated and validated with in situ data. Thereby, the achieved results from TSM retrieval model application demonstrated that L8SR is able to provide the most suitable R rs values for green and red spectral bands, and consequently, the lowest TSM retrieval errors (Mean Absolute Percentage Error about 10% and 12%, respectively). Retrieved R rs from near infrared band is still a challenge for all the tested algorithms.

Quasi-analytical algorithm (QAA) was designed to derive the inherent optical properties (IOPs) of water bodies from above-surface remote sensing reflectance (R rs). Several variants of QAA have been developed for environments with different bio-optical characteristics. However, most variants of QAA suffer from moderate to high negative IOP prediction when applied to tropical eutrophic waters. This research is aimed at parametrizing a QAA for tropical eutrophic water dominated by cyanobacteria. The alterations proposed in the algorithm yielded accurate absorption coefficients and chlorophyll-a (Chl-a) concentration. The main changes accomplished were the selection of wavelengths representative of the optically relevant constituents (ORCs) and calibration of values directly associated with the pigments and detritus plus colored dissolved organic material (CDM) absorption coefficients. The re-parametrized QAA eliminated the retrieval of negative values, commonly identified in other variants of QAA. The calibrated model generated a normalized root mean square error (NRMSE) of 21.88% and a mean absolute percentage error (MAPE) of 28.27% for a t (k), where the largest errors were found at 412 nm and 620 nm. Estimated NRMSE for a CDM (k) was 18.86% with a MAPE of 31.17%. A NRMSE of 22.94% and a MAPE of 60.08% were obtained for a u (k). Estimated a u (665) and a u (709) was used to predict Chl-a concentration. a u (665) derived from QAA for Barra Bonita Hydroelectric Reservoir (QAA_BBHR) was able to predict Chl-a accurately, with a NRMSE of 11.3% and MAPE of 38.5%. The performance of the Chl-a model was comparable to some of the most widely used empirical algorithms such as 2-band, 3-band, and the normalized difference chlorophyll index (NDCI). The new QAA was parametrized based on the band configuration of MEdium Resolution Imaging Spectrometer (MERIS), Sentinel-2A and 3A and can be readily scaled-up for spatiotemporal monitoring of IOPs in tropical waters.

In this present research, we assessed the performance of band algorithms in estimating chlorophyll-a (Chl-a) concentration based on bands of two new sensors: Operational Land Imager onboard Landsat-8 satellite (OLI/Landsat-8), and MultiSpectral Instrument onboard Sentinel-2A (MSI/Sentinel-2A). Band combinations designed for Thematic Mapper onboard Landsat-5 satellite (TM/Landsat-5) and MEdium Resolution Imaging Spectrometer onboard Envisat platform (MERIS/Envisat) were adapted for OLI/ Landsat-8 and MSI/Sentinel-2A bands. Algorithms were calibrated using in situ measurements collected in three field campaigns carried out in different seasons. The study area was the Barra Bonita hydroelectric reservoir (BBHR), a highly productive aquatic system. With exception of the three-band algorithm, the algorithms were spectrally few affected by sensors changes. On the other hands, algorithm performance has been hampered by the bio-optical difference in the reservoir sections, drought in 2014 and pigment packaging.

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As of 16 May 2020, the number of confirmed cases and deaths in Brazil due to Covid-19 hit 233,142 and 15,633, respectively, making the country one of the most affected by the pandemic. The State of São Paulo (SSP) hosts the largest number of confirmed cases in Brazil, with over 60,000 cases to date. Here we investigate the spatial distribution and spreading patterns of Covid-19 in the SSP by mapping the spatial autocorrelation and the clustering patterns of the virus in relation to the population density and the number of hospital beds. Clustering analysis indicated that São Paulo City is a significant hotspot for both the confirmed cases and deaths, whereas other cities across the state were less affected. Bivariate Moran’s I showed a low relationship between the number of deaths and population density, whereas the number of hospital beds was less related, implying that the fatality depends substantially on the actual patients’ conditions. Multivariate Local Geary showed a positive relationship between the number of deaths and population density, with two cities near São Paulo City being negatively related; the relationship between the number of deaths and hospital beds availability in the São Paulo Metropolitan Area was basically positive. Social isolation measures throughout the State of São Paulo have been gradually increasing since early March, an action that helped to slow down the emergence of the new confirmed cases, highlighting the importance of the safe-distancing measures in mitigating the local transmission within and between cities in the state.

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