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.

Hyperspectral remote sensing can provide information about species richness over large areas and may be useful for species discrimination in tropical environments. Here, we analyze the main sources of variability in leaf spectral signatures of tropical trees and examine the potential of spectroscopic reflectance measurements (450 to 2450 nm) for tree species discrimination. We assess within- and among-species spectral variability and perform a feature selection procedure to identify wavebands in which the species most differ from each other. We assess the discriminative power of these wavebands by calculating a separability index and then classifying the species. Finally, leaf chemical and structural parameters of each species are retrieved by inversion of the leaf optical model PROSPECT-5. Among-species spectral variability is almost five times greater than within-species spectral variability. The feature selection procedure reveals that wavebands, where species most differ, are located at the visible, red edge, and shortwave infrared regions. Classification of the species using these wavebands reaches 96% overall accuracy. Leaf chemical and structural properties retrieve by model inversion show that differences in leaf pigment concentrations and leaf internal structure are the most important parameters controlling the spectral variability of species. These parameters also contribute to the variation in red edge position among species.

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.