关键词: 高光谱遥感, 冬小麦, 叶绿素含量, 迁移学习, 域偏移

Abstract:

[Objective] Hyperspectral remote sensing enables accurate estimation of crop chlorophyll content, a key indicator of photosynthetic capacity and nitrogen nutrient status. However, models trained in one region (source domain) often suffer significant performance degradation when applied to another region (target domain) due to "domain shift" caused by differences in soil, climate, and management practices. This study proposes a robust adaptive transfer learning framework (RATL) to mitigate domain shift in cross-regional chlorophyll estimation of winter wheat and achieve accurate inversion. [Methods] A total of 1 491 paired samples of canopy hyperspectral reflectance and leaf soil and plant analyzer development (SHAP) values were collected during the late grain filling stage of winter wheat in the 2023–2024 growing season from Xinxiang and Zhoukou in Henan province. The degree of domain shift between the two regions was quantified using multiple metrics, including maximum mean discrepancy (MMD), Wasserstein distance, and correlation differences. The RATL framework consists of three synergistic modules: (1) Adaptive feature selection, which combines source-target correlation weighting and stability constraints to select 100 core features from the original 1 724-dimensional feature set, with preferential selection of red-edge bands (680–750 nm); (2) adaptive feature weight calculation, which fuses random forest importance scores from both domains and feature stability metrics to assign weights to each feature, guiding the model to focus on domain-invariant features; and (3) domain adaptation training, which employs a two-stage XGBoost regressor (pre-training on source domain followed by fine-tuning with 30% of target domain samples) and incorporates a domain discriminator loss (balancing parameter λ=0.1) to encourage learning of domain-invariant representations. Four experimental scenarios were designed: source-only validation (Scenario A), direct transfer (Scenario B), transfer learning comparison with Transfer Component Analysis and Correlation Alignment(Scenario C), and the ideal upper bound using combined data from both regions (Scenario D). SHapley Additive exPlanations analysis was employed to interpret model decision mechanisms, and quantile regression forests were used to generate 90% prediction intervals for uncertainty assessment. [Results and Discussions] Quantitative domain shift analysis revealed significant distribution differences between Xinxiang and Zhoukou Maximum Mean Discrepancy was 0.20, P<0.001), confirming the challenge of domain shift in cross-regional modeling. Direct transfer exhibited substantial performance degradation on the target domain (R²=0.61). Traditional transfer learning methods TCA and CORAL achieved only marginal improvements (R²≈0.64–0.65, transfer gains<7%), proving insufficient for addressing complex domain shift. In contrast, RATL achieved optimal performance on the target domain (R²=0.75, Root Mean Square Error was 7.49), representing a 23.4% improvement over direct transfer and reaching of the ideal performance achieved with combined data from both regions. SHAP analysis demonstrated that RATL successfully shifted the model's reliance from environmentally sensitive features to red-edge vegetation indices (modified Simple Ratio at 705 nm, Normalized Difference Vegetation Index,Green Index) that exhibit consistent responses across regions, enhancing both physiological rationality and regional adaptability of model decisions. Uncertainty quantification showed that RATL achieved higher prediction interval coverage (79.52%) compared to direct transfer (77.94%), while adaptively widening intervals for extreme SPAD values to provide more realistic and reliable uncertainty estimates. These results demonstrate that RATL's multi-module collaborative strategy effectively mitigates agricultural hyperspectral domain shift, significantly improving model interpretability and reliability while maintaining prediction accuracy, outperforming traditional linear alignment methods. [Conclusions] The proposed RATL framework achieves high-precision and high-reliability cross-regional chlorophyll inversion with only a small set of target samples through the synergistic effects of adaptive feature selection, feature weight adjustment, and domain adaptation training. The framework effectively focuses on physiologically meaningful domain-invariant features and provides reliable uncertainty estimates, offering a practical technical solution for regional-scale agricultural remote sensing monitoring. Future work will extend RATL to address temporal domain shift (e.g., across different growth stages) and explore lightweight model implementations for real-time applications.

Key words: hyperspectral remote sensing, winter wheat, chlorophyll content, transfer learning, domain migration