Maize and sorghum field segregation using multi-temporal Sentinel-2 data in central Mexico

Abstract

Crop segregation in optical remote sensing cannot always be easily achieved among similar species since their spectral response can be similar. We aimed to distinguish fields of two C4 gramineae with similar plant structure and crop calendar, maize (Zea mays) and sorghum (Sorghum bicolor), in an agricultural area of the state of Guanajuato, in central México. One hundred and forty-three fields of these two crops were identified and their average spectral signatures were derived at different phenological stages, using digital information from 11 Sentinel 2A/B images, corresponding to the 2019 spring–summer cycle. We also calculated the temporal profile of six spectral indices for each crop, to evaluate seasonal variations and conducted a cropland classification, using the random forests algorithm and different combinations of dates and indices. Results show that segregation of maize and sorghum fields can be achieved due to small differences in the near-infrared region of their spectral signatures and disparities in the rate of decay during the senescence stage. Classification results also confirmed the separability of these two crops, obtaining a very high overall accuracy and kappa values (98.43% and 97.07%, correspondingly) based on the reflectance of nine images and only three spectral indices: the normalized difference vegetation index, the normalized difference water index, and the structure insensitive pigment index. This investigation demonstrates that the combined use of time series of high-resolution images and spectral indices can contribute to distinguish fields of similar crop species.