The lack of efficient means to accurately infer photosynthetic traits constrains understanding global land carbon fluxes and improving photosynthetic pathways to increase crop yield. Here, we investigated whether a hyperspectral imaging camera mounted on a mobile platform could provide the capability to help resolve these challenges, focusing on three main approaches, that is, reflectance spectra-, spectral indices-, and numerical model inversions-based partial least square regression (PLSR) to estimate photosynthetic traits from canopy hyperspectral reflectance for 11 tobacco cultivars. Results showed that PLSR with inputs of reflectance spectra or spectral indices yielded an R2 of ~0.8 for predicting V cmax and J max , higher than an R2 of ~0.6 provided by PLSR of numerical inversions. Compared with PLSR of reflectance spectra, PLSR with spectral indices exhibited a better performance for predicting V cmax (R2 = 0.84 ± 0.02, RMSE = 33.8 ± 2.2 μmol m-2  s-1 ) while a similar performance for J max (R2 = 0.80 ± 0.03, RMSE = 22.6 ± 1.6 μmol m-2  s-1 ). Further analysis on spectral resampling revealed that V cmax and J max could be predicted with ~10 spectral bands at a spectral resolution of less than 14.7 nm. These results have important implications for improving photosynthetic pathways and mapping of photosynthesis across scales.

中文翻译：

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从反射光谱估计光合性状：光谱指数、数值反演和偏最小二乘回归的综合。


缺乏准确推断光合性状的有效手段限制了对全球陆地碳通量的了解和改善光合途径以提高作物产量。在这里，我们研究了安装在移动平台上的高光谱成像相机是否能够提供帮助解决这些挑战的能力，重点关注三种主要方法，即基于反射光谱、光谱指数和基于数值模型反演的偏最小二乘法回归（PLSR）根据冠层高光谱反射率估计 11 个烟草品种的光合性状。结果表明，输入反射光谱或光谱指数的 PLSR 预测 V cmax 和 J max 的 R2 约为 0.8，高于数值反演 PLSR 提供​​的约为 0.6 的 R2。与反射光谱的 PLSR 相比，具有光谱指数的 PLSR 在预测 V cmax 方面表现出更好的性能（R2 = 0.84 ± 0.02，RMSE = 33.8 ± 2.2 μmol m-2 s-1 ），而在预测 J max 方面表现出类似的性能（R2 = 0.80） ± 0.03，RMSE = 22.6 ± 1.6 μmol m-2 s-1 )。对光谱重采样的进一步分析表明，可以在小于 14.7 nm 的光谱分辨率下用约 10 个光谱带来预测 V cmax 和 J max。这些结果对于改善光合作用途径和跨尺度的光合作用绘图具有重要意义。