The Brazilian Amazon state of Mato Grosso has undergone rapid agricultural expansion and intensification in recent decades, leading to land depletion and high carbon emissions. Since 2010, the low-carbon agricultural Plan (ABC Plan) promotes practices to limit greenhouse gas emissions while increasing productivity. Among these practices, the adoption of intra-annual integrated crop-livestock systems (ICLS) is encouraged and can be monitored by remote sensing, especially through dense satellite image time series (SITS) and advanced analysis tools. In this study, we compare a calibrated Random Forest and three deep learning methods (LSTM, LTAE and TempCNN) suitable for processing SITS for mapping intra-annual ICLS in Mato Grosso. In particular, MODIS time series were used to classify a spatially and temporally rich dataset among four classes (pasture, single-crop, double-crop and intra-annual ICLS). TempCNN achieved slightly higher overall results in terms of precision (85.63%), recall (85.71%) and F1-score (85.32%), even though there is an inter-class and intraclass heterogeneity in the results. For the ICLS class, TempCNN achieved the best precision (85.14%) but Random Forest achieved higher recall (69.63% vs. 58.52% for TempCNN) and F1-Score (70.14% vs. 69.36% for TempCNN).