| Sumario: | This 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.
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