2025-02-072025-02-072024https://hdl.handle.net/10883/35520CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purposeAGRICULTURAL SCIENCES AND BIOTECHNOLOGYWorking PaperThis study aimed to assess whether radar (Sentinel-1) and optical (Sentinel-2) satellite data could detect residue management practices and differentiate between conventional, minimal, and no tillage fields in Guanajuato, Mexico. The study used in-situ data collected by the CIMMYT-led MasAgro Guanajuato project, which tracks land preparation and crop management. Various tillage and residue indices were tested, including NDSVI, NDTI, and NDI5, based on Sentinel-2 bands. The conclusion suggests that most successful remote sensing applications for tillage detection and residue management rely on survey data. These data can then be used to train machine learning based algorithms.REMOTE SENSINGCROP RESIDUE MANAGEMENTCONSERVATION AGRICULTURETILLAGEOpen AccessSustainable Agrifood Systems