We report the discovery of a novel and fundamental dual synchronization relationship between the scattering efficiency (Q$_{\text{sca}}$) and a specifically formulated angular distribution complexity parameter ($\widetilde{C}_{\text{p}}$) in spherical particle systems. Through extensive numerical simulations using the rigorous Multiple Sphere T-Matrix (MSTM) method, we found that Q$_{\text{sca}}$ exhibits a strong positive correlation with (1-$\widetilde{C}_{\text{p}}$) when the real part of the refractive index is varied, while it synchronizes strongly and positively with $\widetilde{C}_{\text{p}}$ when the imaginary part is varied. Our analysis reveals that this duality arises from the distinct ways the real and imaginary parts of the refractive index \textbf{perturb vs.~dampen electromagnetic resonances} within the particles, leading to different coupled responses in the total scattered energy and the angular distribution. This discovery provides unprecedented insights into how phase contrast and absorption processes distinctly modulate scattering properties and the angular distribution of scattered light, particularly in regimes dominated by resonance. It establishes that the specific formulation of $\widetilde{C}_{\text{p}}$ used here is sensitive to the overall balance of multipole contributions, making it a valuable parameter for capturing refractive index-driven changes. }.

Accurate satellite retrieval of oceanic particulate organic nitrogen (PON) vertical profile is essential for understanding global biogeochemical processes; however, no dedicated retrieval models currently exist. This study developed a novel PON profile retrieval model using the eXtreme Gradient Boosting (XGBoost) algorithm, based on a comprehensive global dataset that includes in situ PON measurements, MODIS-Aqua bio-optical data, and 3D reanalysis physical data. The XGBoost-retrieved PON profiles were compared with those derived from Copernicus particulate backscattering coefficient ($b_{\mathit{bp}}$) profiles and were further used to estimate the euphotic-zone PON stocks through an optimally performing regression model. The results showed that the proposed model significantly outperformed models constructed without physical inputs, achieving $R^2$ of 0.83, RMSE of 1.49 $\mathrm{mg} {\mathrm{m}}^{-3}$ and MAPE of 18.07%. Compared to the $b_{\mathit{bp}}$-based profiles, the XGBoost-retrieved profiles exhibited higher accuracy. The model also provided reliable estimates of euphotic-zone PON stocks, with $R^2$ of 0.76, RMSE of 200.31 mg ${\mathrm{m}}^{-2}$ and MAPE of 15.09%. These findings demonstrate the potential of the proposed retrieval model for investigating oceanic nitrogen dynamics and biogeochemical cycles.