Research Article | Open Access

Volume 2026 |Article ID 100130 | https://doi.org/10.1016/j.plaphe.2025.100130

Combining rEW-2DCOS and mechanism-guided adaptive ensemble learning to improve the retrieval of leaf nitrogen, phosphorus, and potassium contents

Bolin Fu ,¹ Yawei Zhu,¹ Yeqiao Wang,² Keyue Huang,¹ Hongyuan Kuang,¹ and Tengfang Deng¹

¹College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, China
²Department of Natural Resources Science, University of Rhode Island, Kingston, RI, USA

Abstract

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 interactions. 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 traditional 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.