Accurate estimation of crop biophysical traits is essential for optimizing crop management. Solarinduced chlorophyll fluorescence (SIF), as a direct indicator of photosynthesis, has large potential in monitoring plant growth status. This study aimed to enhance the estimation of wheat key biophysical traits, including aboveground biomass (AGB), canopy relative chlorophyll content (RCC), and nitrogen balance index (NBI), by integrating reflectance-based SIF-sensitive indices with a multi-task sparse variational Gaussian process (SVGP) model. The results showed that the model utilizing the indices as the exclusive predictors demonstrated superior performance compared to other models. This study confirmed the efficacy of the indices in the estimation of winter wheat biophysical traits, offering a precise method for monitoring the growth status of winter wheat.

Introduction: Shading regulation is crucial for cultivating and restoring endangered Hopea hainanensis saplings. However, the high variability in light conditions introduced by shading gradients can increase noise in visible and near-infrared (VNIR) canopy hyperspectral reflectance and exacerbate the domain shift between measured spectra and those simulated by radiative transfer models (RTMs). In addition, dry leaf density (DLD) retrieval from VNIR spectra remains challenging. These limitations restrict the robust nondestructive monitoring of physiological and structural responses of saplings to shading. Methods: We developed an inversion framework that integrates multiple spectral scale-normalization methods with a hybrid RTM–deep learning modeling approach under controlled shading gradients. This framework was designed to improve cross-domain spectral consistency under shading-induced variability and to introduce a new carbon-based constituent index, DLD _CBC , as a VNIR-compatible proxy for DLD. Results: Inter-group shading differences in original reflectance were easily obscured by noise, whereas scale normalization substantially enhanced the shading response in the green peak and red-edge regions and better-preserved physical consistency between RTM-simulated and field-measured data. The hybrid modeling achieved the highest accuracy for chlorophyll (R ² = 0.820; RMSE = 3.390 μg·cm ^-2 ), while nitrogen retrieval remained limited under VNIR (R ² = 0.404; RMSE = 0.207). DLD _CBC exhibited a consistent trend with measured DLD (r ² = 0.747; RMSE = 4.867 mg·cm ^-2 ). Both the inverted chlorophyll and DLD _CBC can effectively reproduce the directional response of measured traits along the shading gradient. Discussion: This study provides a practical VNIR-based pathway for nondestructive and robust retrieval of key biophysical traits of H. hainanensis saplings under highly variable shading conditions.

Shade management, which is widely adopted in cultivation and understory regeneration, alters plant light environments, thereby degrading the trait inversion performance and posing a key challenge in plant phenotyping. To address this issue, this study reframed chlorophyll retrieval of Hopea hainanensis under shade management as an illumination-regime-dependent conditional domain shift problem, and developed a condition-aware domain adaptation framework (CAI-DAI) tailored to this setting. The results showed that chlorophyll content increased with shading intensity, accompanied by clear differences in canopy spectral distributions among shading levels, supporting the presence of condition-dependent variation under shade management. Model comparisons showed that CA-IE and CAI-DAI, which integrate conditional encoding and conditional alignment, performed better than the comparative models across fine-tuning ratios from 30% to 70%. Among them, CAI-DAI achieved the best and most stable performance, with test MAE ranging from 4.355 to 4.774 μg·cm⁻² and nRMSE ranging from 16.4% to 18.2%, and R² ranging from 0.456 to 0.585. Further evaluation at individual shading levels (S1–S4) showed that CAI-DAI produced narrower error ranges than CA-IE. It also showed smaller error fluctuations under most fine-tuning ratios. These results demonstrate that the proposed framework effectively improves robustness under heterogeneous shading conditions and limited labeled samples, providing methodological support for chlorophyll monitoring and decision-making related to shade management.

Cephalotaxus hainanensis, a valuable medicinal and endangered conifer, requires scientific conservation and precision management to ensure the sustainable utilization of its genetic and ecological resources. Nitrogen (N) is a key nutrient that regulates plant growth and metabolism; rapid and accurate nitrogen diagnosis is vital for optimizing fertilization, reducing nutrient losses, and promoting healthy plant development. This study employed a combined approach integrating stepwise regression, correlation analysis, and Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify leaf color features strongly correlated with leaf nitrogen content (LNC). A support vector regression (SVR) model, suitable for small-sample datasets, was then employed to accurately estimate LNC across canopy layers. Nine color variables were found to be highly associated with LNC, among which the Green Minus Blue index (GMB) consistently appeared across all correlation methods, demonstrating strong robustness and generality. Color features effectively reflected LNC variations among nitrogen treatments—especially between N1 and N4—and across canopy layers, with the most pronounced contrasts observed between upper and lower leaves. The Spearman-based SVR model revealed that the middle canopy maintained the highest and most stable LNC. However, the lower leaves were most sensitive to nitrogen deficiency, while the upper leaves were more sensitive to nitrogen excess. Comprehensive analysis identified N2 as the optimal nitrogen treatment, representing a balanced nutrient state. Overall, this study confirms the reliability of color features for LNC estimation and highlights the importance of vertical canopy LNC distribution in nitrogen diagnostics, providing a theoretical and methodological foundation for color-based nitrogen diagnosis and precision nutrient management in evergreen conifers.