This study developed a model based on Support Vector Machines (SVM) and Normalized Difference Vegetation Index (NDVI) time series to classify citrus areas in Álamo, Veracruz, Mexico. MODIS images (MOD13Q1, 250 m resolution) from 2003 to 2022 were used, processed using radiometric correction, noise filtering, and temporal harmonization. Training areas were classified into four categories: citrus, natural vegetation, grasslands, and urban areas, using 3,759 time series, 50 % of which were positive for citrus. The SVM model (RBF kernel: γ = 0.1, C = 10) achieved an accuracy of 91.4 % using 5-fold cross-validation, with 88% success in citrus and 93.9 % in non-citrus samples. The results showed an average NDVI of 0.74 for citrus, distinguishable from weeds (0.87), although with challenges in small plots due to spatial resolution. The estimates coincided with official data (SIACON) in 2021 (...

This study developed a model based on Support Vector Machines (SVM) and Normalized Difference Vegetation Index (NDVI) time series to classify citrus areas in Álamo, Veracruz, Mexico. MODIS images (MOD13Q1, 250 m resolution) from 2003 to 2022 were used, processed using radiometric correction, noise filtering, and temporal harmonization. Training areas were classified into four categories: citrus, natural vegetation, grasslands, and urban areas, using 3,759 time series, 50 % of which were positive for citrus. The SVM model (RBF kernel: γ = 0.1, C = 10) achieved an accuracy of 91.4 % using 5-fold cross-validation, with 88% success in citrus and 93.9 % in non-citrus samples. The results showed an average NDVI of 0.74 for citrus, distinguishable from weeds (0.87), although with challenges in small plots due to spatial resolution. The estimates coincided with official data (SIACON) in 2021 (...