Fusion of Medium- and High-Resolution Remote Images for the Detection of Stress Levels Associated with Citrus Sooty Mould

Citrus sooty mould caused by Capnodium spp. alters the quality of fruits on the tree and affects their productivity. Past laboratory and hand-held spectrometry tests have concluded that sooty mould exhibits a typical spectral response in the near-infrared spectrum region. For this reason, this study aims at developing an automatic method for remote sensing of this disease, combining 10 m spatial resolution Sentinel-2 satellite images and 0.25 m spatial resolution orthophotos to identify sooty mould infestation levels in small orchards, common in Mediterranean conditions. Citrus orchards of the Comunitat Valenciana region (Spain) underwent field inspection in 2022 during two months of minimum (August) and maximum (October) infestation. The inspectors categorised their observations according to three levels of infestation in three representative positions of each orchard. Two synthetic images condensing the monthly information were generated for both periods. A filtering algorithm was created, based on high-resolution images, to select informative pixels in the lower resolution images. The data were used to evaluate the performance of a Random Forest classifier in predicting intensity levels through cross-validation. Combining the information from medium- and high-resolution images improved the overall accuracy from 0.75 to 0.80, with mean producer’s accuracies of above 0.65 and mean user’s accuracies of above 0.78. Bowley–Yule skewness coefficients were +0.50 for the overall accuracy and +0.28 for the kappa index.