Large-Scale Apple Orchard Identification from Multi-Temporal Sentinel-2 Imagery

Accurately extracting large-scale apple orchards from remote sensing imagery is of importance for orchard management. Most studies lack large-scale, high-resolution apple orchard maps due to sparse orchard distribution and similar crops, making mapping difficult. Using phenological information and multi-temporal feature-selected imagery, this paper proposed a large-scale apple orchard mapping method based on the AOCF-SegNet model. First, to distinguish apples from other crops, phenological information was used to divide time periods and select optimal phases for each spectral feature, thereby obtaining spectral features integrating phenological and temporal information. Second, semantic segmentation models (FCN-8s, SegNet, U-Net) were com-pared, and SegNet was chosen as the base model for apple orchard identification. Finally, to address the issue of the low proportion of apple orchards in remote sensing images, a Convolutional Block Attention Module (CBAM) and Focal Loss function were integrated into the SegNet model, followed by hyperparameter optimization, resulting in AOCF-SegNet. The results from mapping the Yantai apple orchards indicate that AOCF-SegNet achieved strong segmentation performance, with an overall accuracy of 89.34%. Compared to the SegNet, U-Net, and FCN-8s models, AOCF-SegNet achieved an improvement in overall accuracy by 3%, 6.1%, and 9.6%, respectively. The predicted orchard area exhibited an approximate area consistency of 71.97% with the official statistics.