Models implemented in the land surface temperature and vegetation indexes time series analysis: a taxonomic proposal in the context of the global climate change

Abstract

Climate change and global warming are caused principally by anthropogenic activities. For this reason, understanding the research lines that relate Land Surface Temperature and Vegetation Index time series is of great importance, given the amplitude of different open scientific areas on global warming. The result of this classification is presented to the academic community, which divides the studies into two main representative areas in the study of climate change: (1) Geodata Modeling and Analysis and (2) Remote Sensing. From the last one, two types are derived, some constructed with Linnear Regression Analysis (RL) and others with Nonlinear Regression Analysis (RNL). On the Geodata Modeling and Analysis, the Global Climate Models (GCM) are not the right tool for these analyzes due to their coarse spatial resolution. This implies the development of hybrid models with remote sensing, which are also limited by differences in resolution. On the other hand, remote sensing is the most widely disseminated tool for this type of studies. Finally, a promising window for development in the time series opens with non-linear regression analysis.