Identifying the optimal phenological period for discriminating subtropical fruit tree crops using multi-temporal Sentinel-2 data and Google Earth Engine

Abstract

 The accurate and appropriate monitoring of the spatial distribution of fruit tree crops is crucial for crop management and yield forecasting. Owing to both inter- and intra-farm fragmentation and overlapping phenological cycles, the classification of fruit tree crops in subtropical agriculture using single-date images is challenging. Therefore, this research aimed to identify the optimal temporal window in which the crucial phenological stages can be used to classify fruit tree crops in Levubu, Limpopo province, using a random forest (RF) classifier. Phenological metrics were extracted from 12-month Multispectral Instrument (MSI) images from Sentinel-2 (S2). The RF classification algorithm attained an overall accuracy of 84.89% and a kappa coefficient of 83%. The user accuracy ranged from 62  to 100%, while the producer accuracy ranged from 60 to 100%. An analysis of variance was used to assess whether the overall accuracies among the S2 monthly composites were statistically significant. The results showed distinct spectral differences between fruit trees. In April, there were differences observed during the harvesting and senescence of the mango and macadamia nut crops. In May, there were differences observed during the senescence of the macadamia nut, mango, and guava crops. In June and July, there were distinct spectral differences during the peak flowering stage of the avocado, macadamia nut, and mango crops, as well as in the fruiting stage of the banana crops. Followed by the red-edge bands, the shortwave infrared bands were significant in differentiating between the respective fruit tree crops. The results of this research provide evidence-based information that can assist farm managers and horticulturists in making informed decisions. This is critical in achieving effective agricultural management and in ensuring the sustainability of local horticultural systems.