02051nam a2200265 a 450000100080000000500110000800800410001902400590006010000200011924501260013926001720026550000170043752011100045465000160156465000190158065000250159965000160162465000100164065000250165065300340167565300170170965300120172670000210173870000260175921836972026-01-21       2025    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1109/MIGARS67156.2025.112320652DOI1 aSILVA, T. L. da  aElevation-based clustering and spatiotemporal analysis of coffee crop agro-environmental dynamics.h[electronic resource]  aIn: INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE FOR GEOANALYTICS AND REMOTE SENSING, 2025, Bucharest. Proceedings [...]. Piscataway: IEEE, 2025. p. 119-122.c2025  aMIGARS 2025.  aAbstract— Agricultural monitoring via remote sensing offers key insights into crop dynamics. This study analyzed the influence of elevation on the agro-environmental dynamics of coffee crops in Caconde, São Paulo, Brazil, from 2013 to 2023. K-means clustering identified two elevation groups: lowland (up to 1,017.91 meters) and highland (1,018 meters and above). Statistical analyses revealed significant differences in the normalized difference vegetation index (NDVI), root-zone soil moisture, temperature, and evapotranspiration between the two groups. Temperature had a moderate effect, with lowland coffee plots consistently warmer. Both elevation zones were thermally suitable for Arabica coffee. Spatially, the lowlands were centralized, while the highlands were located to the north and south. Our results highlight how elevation shapes agroenvironmental factors and raises the possibility of elevationbased management. To advance the understanding of coffee crop dynamics across the elevation gradient, future research should integrate more environmental variables and biophysical indicators.  aAgriculture  aRemote sensing  aTime series analysis  aAgricultura  aCafé  aSensoriamento Remoto  aAnálise de séries temporais  aGeoanalytics  aK-means1 aROMANI, L. A. S.1 aMASSRUHA, S. M. F. S.