摘要Leaf nitrogen,phosphorus,and potassium content(LNC,LPC,LKC)are core nutrient elements and measurable trait parameters essential for assessing vegetation growth status and understanding hydrology-vegetation in-teractions.However,the spectral characteristics of these elements remain poorly understood,posing a significant challenge for quantitative remote sensing inversion.This study analyzed 303 samples and 21,210 full-spectrum hyperspectral measurements across seven vegetation species,revealing inherent interspecific heterogeneity in their spectra.We quantified spectral heterogeneity using Enhanced Spectral Information Divergence(ESID)and developed a novel r-Enhanced Wavelet Two-Dimensional Correlation Spectroscopy(rEW-2DCOS)method to identify spectral bands exhibiting synergistic correlations with each nutrient element.Validation against tradi-tional CSPA and full-spectrum data confirmed the method's feasibility.The results revealed the density peaks of sensitive bands for LNC(600-860 nm,1230 nm,2080-2250 nm),LPC(600-750 nm,1930-2380 nm),and LKC(580-830 nm,1680-2350 nm).Furthermore,we established a mechanism-guided adaptive ensemble learning regression model(M-AEL)for inversion.The average inversion accuracy(R2)using rEW-2DCOS reached 0.71 for LNC,0.73 for LPC,and 0.71 for LKC across the seven vegetation species,representing improvements of 14.6％,14.9％,and 3.1％over CSPA-based results and 83.3％,83.8％,and 88.7％over full-spectrum results.Finally,the Mantel test assessed relationships between LNC,LPC,LKC,and hydrology-vegetation factors across species,identifying key drivers for each element.This research advances hyperspectral remote sensing for estimating key nutrient elements in karst wetlands,providing a scientific foundation for monitoring vegetation health and maintaining the equilibrium within these fragile hydrology-vegetation ecosystems.