Integrating canopy spectra into the optical phenotype under nitrogen deposition in a subtropical evergreen broad-leaved forest
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2026
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Abstract
Abstract The increasing pressure of nitrogen deposition on forest ecosystems is driving the need for large-scale monitoring of plant functional responses. Hyperspectra is a powerful tool for estimating leaf traits, yet key gaps persist in linking canopy spectral signals to plant functional adaptations under nitrogen enrichment. Many traits lack distinct spectral signatures and substantial “non-coding” spectral regions remain poorly interpreted. To address this, we conducted a canopy nitrogen addition experiment (including control, 25 kg N ha−1 y−1 (CN25), 50 kg N ha−1 y−1 (CN50)) at a subtropical forest in China. We collected canopy spectra and 35 leaf functional traits across morphology, resource allocation and physiological metabolism. Key findings include: (1) A statistically significant co-inertia structure was confirmed between canopy spectra and leaf traits; (2) Notably, nitrogen deposition did not alter the trait-spectrum relationship, under CN50 the coupling between leaf traits and canopy spectra was significantly enhanced compared to the control; and (3) Dominant species exhibited limited but discernible shifts in their ecological strategies along nitrogen gradients, with specific spectral features serving as indicators of transitions in resource allocation. This study demonstrates that canopy spectra are associated with multidimensional leaf traits under nitrogen deposition, effectively serving as integrated optical phenotypes that capture functional variations. The consistent relationship between spectrum and plant traits enables the real-time, non-destructive monitoring of ecological responses via hyperspectral remote sensing.
| Reference Key |
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| Authors | Yarong Feng, Zhihui Wang, Fangyan Liu, Long Yang, Zhongyu Sun, Huaiqiang Liu, Dongming Liu, Liping Wei, Nan Liu |
| Journal | journal of plant ecology |
| Year | 2026 |
| DOI |
10.1093/jpe/rtag113
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| URL | |
| Keywords | Keywords not found |
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