Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression
Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression
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DOI：
10.1111/pce.13718
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发表时间：
2020-02-27
影响因子：
7.3
通讯作者：
Bernacchi, Carl
中科院分区：
文献类型：
--
作者：
Fu, Peng;Meacham-Hensold, Katherine;Bernacchi, Carl
缺乏有效的手段来准确地推断光合特性限制了对全球陆地碳通量的理解和改善光合途径以提高作物产量。在这里，我们调查了是否安装在一个移动的平台上的高光谱成像相机可以提供的能力，以帮助解决这些挑战，重点是三个主要的方法，即反射光谱，光谱指数，和基于数值模型反演的偏最小二乘回归（PLSR）估计光合性状冠层高光谱反射率为11个烟草品种。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R-2为0.8，高于数值反演的PLSR提供的R-2为0.6。与基于反射光谱的PLSR相比，基于光谱指数的PLSR对V（cmax）的预测效果更好（R-2 = 0.84 +/- 0.02，RMSE = 33.8 +/- 2.2 μ mol m（-2）s（-1）），而J（max）的性能相似（R-2 = 0.80 +/- 0.03，RMSE = 22.6 +/- 1.6 μ mol m（-2）s（-1））。对光谱分辨率的进一步分析表明，在小于14.7 nm的光谱分辨率下，可以用10个光谱带预测V（cmax）和J（max）。这些结果对改善光合作用途径和绘制跨尺度光合作用图谱具有重要意义。
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 R-2 of 0.8 for predicting V (cmax) and J (max), higher than an R-2 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) (R-2 = 0.84 +/- 0.02, RMSE = 33.8 +/- 2.2 mu mol m(-2) s(-1)) while a similar performance for J (max) (R-2 = 0.80 +/- 0.03, RMSE = 22.6 +/- 1.6 mu mol 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.