Chlorophyll content is a critical indicator of the physiological status and fruit quality of pear trees, with Soil Plant Analysis Development (SPAD) values serving as an effective proxy due to their advantages in rapid and non-destructive acquisition. However, current remote sensing-based SPAD retrieval studies are primarily limited to single phenological stages or rely on a narrow set of input features, lacking systematic exploration of multi-temporal feature fusion and comparative model analysis. In this study, pear leaves were selected as the research object, and Sentinel-2 remote sensing data combined with in situ SPAD measurements were used to conduct a comprehensive retrieval study across multiple growth stages, including flowering, fruit-setting, fruit enlargement, and maturity. First, spectral reflectance and representative vegetation indices were extracted and subjected to Pearson correlation analysis to construct three input feature schemes. Subsequently, four machine learning algorithms—K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and an Optimized Integrated Algorithm (OIA)—were employed to develop SPAD retrieval models, and the performance differences across various input combinations and models were systematically evaluated. The results demonstrated that (1) both spectral reflectance and vegetation indices exhibited significant correlations with SPAD values, indicating strong retrieval potential; (2) the OIA model consistently outperformed individual algorithms, achieving the highest accuracy when using the combined feature scheme; (3) among the phenological stages, the fruit-enlargement stage yielded the best retrieval performance, with R² values of 0.740 and 0.724 for the training and validation sets, respectively. This study establishes a robust SPAD retrieval framework that integrates multi-source features and multiple models, enhancing prediction accuracy across different growth stages and providing technical support for intelligent orchard monitoring and precision management.

Accurate monitoring of leaf chlorophyll-related status (SPAD) and water status is important for evaluating apple growth and supporting orchard management. In this study, SPAD and leaf water content (LWC) were considered complementary indicators of chlorophyll-related and water-related physiological characteristics, respectively. Unmanned aerial vehicle (UAV) multispectral imagery and field measurements were collected during the flowering, fruit-setting, fruit-expansion, and fruit-coloring stages of Aksu red Fuji apples. Sensitive spectral features were screened using correlation analysis, and k-nearest neighbor (KNN), extreme learning machine (ELM), random forest (RF), and backpropagation neural network (BPNN) models were developed for SPAD and LWC estimation. The results showed clear relationships between multispectral features and both traits, with the RF model combining vegetation indices and band reflectance achieving the best overall performance. During the fruit-expansion stage, the optimal RF models achieved independent test-set R² values of 0.767 for SPAD and 0.768 for LWC. These results demonstrate the potential of UAV multispectral remote sensing combined with machine learning for orchard-scale spatial monitoring of apple leaf chlorophyll and water status.