Fruit trees are typically organized at the orchard level, where the tree-based ecosystem is characterized by high homogeneity, leading to clustered distributions with distinct boundaries. While remote sensing-based classification techniques are well established, most studies have not treated fruit orchards as a distinct category. Whether remote sensing can effectively address orchard classification and distribution remains uncertain. This study focused on the Guanzhong Plain on the southern part of the Loess Plateau as a representative drought-vulnerable region in China, characterized by mixed orchard-cropland landscapes. Sentinel-2 imagery was used as the primary classification feature, supplemented by topographic characteristics. A Random Forest classifier was trained and validated using 1980 ground samples across major planting regions in May 2024. The final classification results were satisfactory, with an overall accuracy of 0.86. Meanwhile, a comparison against statistical data demonstrated the reasonableness of fruit orchard area: the correlation coefficients for three major fruit types (apple, grape, and kiwi) are greater than 0.75. Compared with existing land cover products, which often misclassify fruit trees as cropland or forestland, our results demonstrated that combining band reflectance time series, vegetation index time series, and topographic features can effectively differentiate fruit orchards from spectrally similar cropland and forestland. This study facilitates precise fruit orchard mapping, supporting targeted production management and ecological carbon sequestration estimation in similar regions with drought-vulnerable agroforestry systems.