Large tropical forests like the Amazon, important in carbon and water cycling, constantly are targets for climate change research and studies on the seasonality of the vegetation that seek to correlate the various types of data in order to develop theoretical models, obtain projections or thresholds for different phenomena, vegetation mechanisms, climate feedback from changes in forest behavior and possible relationships with global climate change, however, all currently used techniques have limitations that may hinder complex analysis. Data obtained locally are generally known as field truth, but imply high costs and therefore are not performed on a large scale. As far as the data obtained by remote orbital sensors are concerned, the limitations generally lie in the temporal and spatial scales covered by the sensors, in the interferences and noise of the signal received by the sensor and also in its ability to indicate, with relative fidelity, processes of operation of the terrestrial system occurring in situ. This study aimed to identify and assess the main variables used in the study of seasonal dynamics of the Amazon rainforest. The variables identified were the vegetation indices NDVI and EVI, the Gross Primary Productivity (GPP), the Solar-Induced Fluorescence (SIF) and the Evapotranspiration