Orchard plantation mapping using remote sensing phenological feature fusion and interpretable ML algorithms in Hunan Province, China

Orchard plantations play a crucial role in the rural economy of southern China, making accurate orchard surveys essential for effective management and resource allocation. Owing to the distinct seasonal growth patterns of orchards, extracting phenological features from multi-temporal remote sensing data has become a primary approach for obtaining orchard information. However, the subtropical monsoon climate of southern China brings frequent cloud cover and rainfall. This poses major challenges to constructing continuous, high-resolution optical remote sensing datasets. To overcome these limitations, this study integrates high-temporal-resolution MODIS data with medium-spatial-resolution Landsat imagery to generate monthly composite images that capture key stages of orchard growth. Based on more than 9000 sampling sites across the province, phenological information was extracted from three conventional features, including spectral reflectance, vegetation indices, and texture features, to build multiple machine learning classification models for high-precision orchard mapping. The results demonstrate that the proposed multi-feature fusion framework yields a substantial accuracy gain of up to 17 percentage points compared to traditional methods. While the baseline method relying solely on single-phase spectral features achieved 72.2% accuracy, the optimal combination of spectral, texture, and phenological features using the LightGBM model reached an accuracy of 89.2% (F1-score: 88.6%).Furthermore, SHAP analysis enhanced model interpretability by revealing the key factors influencing the decision-making process. The results indicate that orchards in Hunan Province are primarily distributed in hilly regions, where large- and small-scale orchards coexist, with Huaihua and Yongzhou containing the largest orchard areas. From 1995 to 2022, the province's orchard area expanded significantly, growing from approximately 45,000 ha to nearly 140,000 ha, which represents an increase of more than 200%. This study demonstrates the effectiveness of spatiotemporal data fusion in mitigating cloud-related challenges in subtropical regions and underscores the novel role of texture features in capturing key phenological information. It provides a reliable framework for large-scale orchard mapping and supports protective land utilization strategies and sustainable agricultural development in the region. • Spatio-temporal fusion mitigates cloud–rain impacts on orchard phenology monitoring. • First proposed the temporal features of texture features. • Enhance orchard classification accuracy using temporal features of texture features. • Quantifying the Classification Contribution of Texture Features Using SHAP in Phenology.