At the end of 2019, the outbreak of COVID-19 was reported in Wuhan, China. The outbreak spread quickly to several countries, becoming a public health emergency of international interest. Without a vaccine or antiviral drugs, control measures are necessary to understand the evolution of cases. Here, we report through spatial analysis the spatial pattern of the COVID-19 outbreak. The study site was the State of São Paulo, Brazil, where the first case of the disease was confirmed. We applied the Kernel Density to generate surfaces that indicate where there is higher density of cases and, consequently, greater risk of confirming new cases. The spatial pattern of COVID-19 pandemic could be observed in São Paulo State, in which its metropolitan region standed out with the greatest cases, being classified as a hotspot. In addition, the main highways and airports that connect the capital to the cities with the highest population density were classified as medium density areas by the Kernel Density method.It indicates a gradual expansion from the capital to the interior. Therefore, spatial analyses are fundamental to understand the spread of the virus and its association with other spatial data can be essential to guide control measures.

Land surface temperature (LST) plays an important role in a wide variety of scientific studies. Several methodologies to retrieve LST and correct the atmospheric effects for thermal infrared satellite imagery have been developed and all of them require prior knowledge of the land surface emissivity (LSE). The techniques developed for LSE and LST retrieval need to be validated with field measurements. However, in situ measurements are a challenge, being essential to investigate the particularities of each instrument to verify the best approach to collect data and validate the algorithms. Fourier Transform Infrared (FT-IR) spectrometer has been widely used to obtain emissivity of different targets and calculate temperature. The instrument may be used to validate remote sensing data. Moreover, FT-IR allows to collect both emissivity and temperature at the laboratory, thus being an alternative to field validation. We investigated the emissivity dependence of the temperature. In addition, we evaluated the possibility of replacing in situ measurements by laboratory-controlled measurements. We have chosen two single-channel methods to calculate LST and perform the analysis in a Landsat 8 image. We also performed field measurements at the same time as the satellite overpass. FT-IR showed great potential to validate remotely sensed data. However, the instrument requires some time to acquire stability and attention in the calibration process. Laboratory measurements can replace field data, producing approximately 2% of the difference in the LSE. Both single-channel methods provide good accuracy for LST retrieval. Nonetheless, the improved single-channel has superior performance for the study area conditions.

The inverse correlation between NDVI and LST is widely known for its long time series. However, when more specific statistical tests were performed, subtle differences in the correlation behavior over time are more clearly observed. In this work, regression analyses were performed between NDVI and LST at intervals of approximately 10 years, quantifying this relationship for an area of transition from vegetation to urban occupation from 1985 to 2018. The removal of vegetation cover (reduction of 51% to 7% in grassland and 14.4% to 0.6% in forest) to occupy impermeable surfaces ( increase of 31% to 91% in urban areas) caused an average LST increase of 4.18 °C when compared to the first and last decades of the historical series. Temporal analysis allowed us to verify the increase in temperature in the four seasons. The largest difference was 6.36 °C between the first and last decade of autumn, 4.40 °C in spring, 4.09 °C in summer, and 2.41 °C in winter. The results also show that LST has a negative correlation with NDVI, especially in urban areas, with an increase in this correlation during the period (1989: R = −0.55; 1999: R = −0.58; 2008: R = −0.59; 2018: R = −0.76). Our study results will help policymakers understand the dynamics of temperature increases by adding scientifically relevant information on the sustainable organization of the urban environment.

Evapotranspiration (ET) is one of the least understood components of the hydrological cycle. Its applications are varied, from agricultural, ecological and hydrological monitoring, to control of the evolution of climate change. The goal of this work was to analyze the influence that uncertainties in the estimate of land surface temperature (Ts) can cause on ET estimates by S-SEBI model in the Pampa biome area. Also, the specificities of native grassland of Pampa biome related to energy balance were analyzed. The results indicate that the daily evapotranspiration is higher when the pixel Ts is lower, which also shows the influence of land use on the variability of ET. The results demonstrated that the S-SEBI is less dependent on Ts estimation than other models reported in the literature, such as the SEBS, which not exceed 0.5 mm/day in grasslands. The evapotranspiration variability between forest and grassland were lower than expected, demonstrating that the Pampa biome have in Rio Grande do Sul the same importance that forests regarding to the processes of the hydrological cycle, since it covers 63% of the State.

Evapotranspiration (ET) quantification improves the comprehension of the water, heat, and carbon interactions and the feedback to the climate, which is essential for global change research. We aimed to model ET using artificial neural networks (ANNs) based on Landsat-8 and reanalysis data from the National Centers for Environmental Prediction over the grasslands of the Pampa biome. The output variable was the ET trained by eddy covariance (EC) measurements acquired from a flux tower located in Santa Maria, Brazil. ANN was performed using the backpropagation algorithm with four remote sensing input variables (albedo, normalized difference vegetation index, land surface temperature, and surface net radiation). In addition, four meteorological variables from the Environmental Prediction Climate Forecast System Version 2 hourly product were included in the model (air temperature, atmospheric pressure, relative humid, and wind speed). We analyzed 67 clear-sky scenes between 2014 and 2019. Results produced very robust daily ET estimates. ANN exhibited a correlation of 0.88 relative to in situ EC data, demonstrating a good linear relationship between ET estimated and measured and producing a root-mean-square error (mean absolute error) of 0.75 (0.58) mm/day. The ANN model was also compared with the widely known simplified surface energy balance index (S-SEBI) model. S-SEBI exhibited lower correlation with the ET in situ compared to the ANN model. Furthermore, the ANN model had a superior performance in summer and winter seasons in which S-SEBI was found to outperform the ET in situ. The model developed in our research is an alternative to approaches that need a great number of input variables or in situ data since it is only dependent on freely available data. Therefore, it should support future integrated strategies of water resources allocation over the natural grasslands of the Brazilian Pampa.