Research Article | Open Access

Volume 2025 |Article ID 100059 | https://doi.org/10.1016/j.plaphe.2025.100059

A novel constrained spectral matching approach for extending UAV multispectral reflectance measurements and estimating nitrogen and phosphorus contents in wetland vegetation species

Zhinan Lao,^1,2,7 Bolin Fu ,^1,7 Weiwei Sun,³ Yeqiao Wang,⁴ Yuyu Zhou,^5,6 Hongchang He,¹ Tengfang Deng,¹ and Ertao Gao¹

¹College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, China
²School of Geography, South China Normal University, Guangzhou, 510631, China
³Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo, 315211, China
⁴Department of Natural Resources Science, University of Rhode Island, Kingston, RI, USA
⁵Department of Geography, The University of Hong Kong, Hong Kong, China
⁶Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong, China
⁷These authors contributed equally to this paper.

Abstract

Canopy nitrogen content (CNC) and canopy phosphorus content (CPC) of vegetation in wetlands are key physiological traits, which can be associated with the process of wetland ecosystems. Because of the spectral signals obscured by pigments and water content, it is challenging to accurately estimate CNC and CPC of vegetation species in wetlands using multispectral images. Therefore, we developed the constrained PROSAIL-PRO spectra matching (CPSM) approach to extend multispectral reflectance of unmanned aerial vehicle measurements to 400 ∼ 2500 nm. We verified the matched accuracy and spectral reliability of CPSM's spectra from two aspects of reflectance and vegetation spectral characteristic based on field-measured spectral data. We proposed a novel hybrid retrieval strategy to achieve the high-precision estimation of CNC and CPC for seven karst wetland vegetation species. Finally, we evaluated the applicability of combining CPSM with our strategy to estimate CNC and CPC for two typical species in mangrove wetlands. Our results proved that CPSM-based spectra had good consistency with original reflectance of UAV images (R2 = 0.82 ∼ 0.86), and they could maintain similar spectral characteristics to measured spectra. Besides, this study found that the optimal spectral features of CNC and CPC were distributed near the red edge position and water-absorption valley of vegetation spectra. We obtained high-precision estimation of CNC and CPC in karst wetland using CPSM and our hybrid retrieval strategy (R2 = 0.60 ∼ 0.98, MRE = 5.91 % ∼ 26.25 %). The approach also showed a better transferring performance in estimating CNC and CPC of mangrove species (R2 = 0.77 ∼ 0.89, MRE = 9.65 % ∼ 16.87 %). The CPSM approach is effective to achieve high-precision estimation of vegetation CNC and CPC.