Reliable satellite-based estimation of rice grain total amino acid content (TAA) remains challenging because TAA is an indirect grain-quality trait with weak expression in broad-band canopy spectra, while multiband feature construction generates a high-dimensional predictor space relative to the limited number of field samples. This study therefore examined whether TAA-related multispectral information could be effectively enhanced, retained, and predicted under this small-sample, high-dimensional setting. Sentinel-2 Level-1C imagery and 31 composite rice grain samples collected in the Daying River Valley, Yingjiang County, China, in 2019 were used to develop a framework based on fractional-order derivative (FOD) transformation, multiband spectral-index construction, feature selection, and machine learning prediction. FOD orders of 1.2–1.8 generally strengthened spectral associations with TAA, and three-band indices showed stronger associations than two-band indices, with TBI1 (B12, B3, B7) reaching the highest correlation at order 1.6 (r = 0.641). Ablation analysis further showed that predictive performance improved progressively as FOD-transformed and multiband features were incorporated beyond the original Sentinel-2 bands and conventional vegetation indices. Elastic Net + SVM achieved the highest stratified out-of-fold performance (R² = 0.886, RMSE = 0.264%, RPD = 2.96), whereas Boruta + Stacking performed best under both four- and five-block spatial cross-validation (R² = 0.7489 and 0.7750, respectively). These results indicate that FOD-based multiband feature construction can recover useful information associated with rice grain TAA from broad-band Sentinel-2 observations, while the reduction in performance under spatial separation highlights the need for the cautious interpretation of within-study cross-validation accuracy. The resulting maps should therefore be regarded as model-assisted estimates of within-region TAA variation that can support candidate quality-zone identification and targeted field sampling under the sampled 2019 Yingjiang conditions. Validation across additional years, cultivars, and regions is required before broader operational application.

Mineral prospectivity mapping of weathering-crust-type ilmenite placer deposits is challenged by the limited number of known mineral occurrences, the lack of reliable barren-site labels, and spatial autocorrelation. This study focuses on the Wuding–Luquan area of Yunnan Province, China, and develops an eight-predictor framework comprising elevation, slope, fault kernel density, an interpolated Ti-concentration predictor, FeO_x, Al–OH and Mg/Fe–OH mineral anomalies, and distance to mafic rocks. Forty-three known ilmenite occurrences were used as positive samples, and 86 pseudo-absence/background samples were generated under spatial constraints. Random forest, XGBoost, and a multilayer perceptron, together with an equal-weight soft-voting ensemble, were used for mineral prospectivity mapping. Model performance and predictor contributions were evaluated using nested spatial cross-validation, SHapley Additive exPlanations (SHAP), permutation importance, and spatial-block bootstrap resampling. The equal-weight ensemble achieved the highest out-of-fold ROC-AUC (0.9202) and average precision (0.8580), while yielding the lowest log loss and Brier score. Ti was the most important predictor across all four modeling schemes, whereas Al–OH mineral anomalies, slope, and Mg/Fe–OH mineral anomalies also showed relatively high contributions in the equal-weight ensemble. A total of 379 measured TiO₂ records showed weak positive and scale-dependent rank associations with predicted prospectivity, with median Spearman coefficients ranging from 0.133 to 0.325 across aggregation scales of 250–2000 m. This comparison was treated as an exploratory geochemical comparison rather than as independent validation of model performance. By integrating model probabilities with the distributions of mafic rocks, known mineral occurrences, faults, and measured TiO₂, eight prospective areas (T1–T8) were delineated. The results provide a quantitative basis for regional mineral prospectivity assessment and exploration-target prioritization in the Wuding–Luquan area.