This webinar introduces sits, an open-source R package for land use and land cover classification of big Earth observation data using satellite image time series. Users build regular data cubes from cloud services such as Amazon Web Services, Microsoft Planetary Computer, NASA Harmonized Landsat-Sentinel, Brazil Data Cube, Swiss Data Cube and Digital Earth Africa. The SITS API includes assessing training sample quality, machine learning and deep learning classification algorithms, and Bayesian post-processing methods for smoothing and uncertainty assessment. To evaluate results, SITS supports best practice accuracy assessments.