Cartografía del aguacate en el sur del estado de México mediante tratamiento digital de imágenes sentinel-2

The avocado crop (Persea americana Mill.) is one of the most important in Mexico, among the states with the highest production is the State of Mexico, which is the third producing state nationwide. Coatepec Harinas and Donato Guerra are two of the most representative municipalities regarding this activity; however, there is no census that specifies the surface of the crop, so the objective of this research was to test vegetation index methods, spectral angle mapper (SAM) and spectral information divergence (SID) algorithms and the combination of these in Sentinel-2 sensor images to evaluate its performance in identifying areas planted with the avocado crop. The results were validated with a confusion matrix and the comparison of the training and validation reference data. The SID algorithm achieved an accuracy of 97.5% to detect avocado, while the SAM treatment obtained an accuracy of 63.1%. The combination of SID with the Anthocyanin Reflectance Index 1 (ARI1), provided a better result on regional validation mapping with 85% accuracy. Other combinations of indices and treatments gave results less than 50% of the precision, so they are not recommended. This methodology could be tested for the detection of other crops of commercial interest, since Sentinel-2 shows to be a viable alternative for this type of study, having a good spectral resolution, as well as being easily accessible and manipulated.