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-teractio...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(R^(2))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.展开更多
基金supported by the National Natural Science Foundation of China(Grant number 42371341)the Natural Science Foundation of Guangxi Zhuang Autonomous Region(CN)(Grant number 2025GXNSFFA069008,2024GXNSFAA010351)+1 种基金the Key Laboratory of Tropical Marine Ecosystem and Bioresource,Ministry of Natural Resources(Grant Number 2023ZD02)the Innovation Project of Guangxi Graduate Education(Grant Number YCSW2025397).
文摘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(R^(2))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.
