We explore a state-of-art architecture based on convolutional recurrent networks, designed to ingest and extract information of a sequence of satellite images, for large area LULC classification in the Brazilian Amazon biome. We fine-tune and evaluate it according to multiple combinations between MODIS archives at 250 m for a single year, providing surface reflectance time series data and derived indices, and external environmental data. Qualitative and quantitative differences between the trained models were evident according the input features assessed. The combination of surface reflectance and auxiliary data yield slightly better performance than only using surface reflectance data. In specific, the auxiliary data contributed to the accuracy of Mixed Forest, Savanna, Grassland and Cropland. In contrast, the arrangement including derived indices showed lower performance and therefore they negatively contribute to the classification task in the area of study.