Modeling of the land surface temperature as a function of the soil-adjusted vegetation index

Land surface temperature is a physical-environmental variable that is the target of studies in climatology and heat island phenomena resulting from the urbanization model of the 21st century. It is known that each object has a different thermal capacity, which results in higher or lower temperatures. The modeling of temperature as a function of objects on the land surface can allow understanding between these variables. It can corroborate temperature forecasts with the alteration of objects in an area. The objects on the Earth's surface can be computed with geoprocessing techniques that aim to detail Land Use and Occupation. This paper evaluates the Linear, Exponential, and Sinusoidal models to determine which of these models is more expressive for the study of the land surface temperature as a function of land surface objects. For this purpose, images from the Landsat 8 satellite were used to calculate the Earth's surface temperature and determine the scene's objects. To determine the scene's objects, the Soil Adjusted Vegetation Index (SAVI) was used, which is one of the techniques for obtaining Land Use and Occupation. For model analysis, Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), and Sum of Square of Residuals (SSR). According to the AIC, BIC, and SSR criteria, the sinusoidal model presented better performance when compared to the other models. However, there are large variations in SSR between classes, especially for the pasture class, which makes the models not highly explanatory.