Evaluating the Performance of Hyperspectral Leaf Reflectance to Detect Water Stress and Estimation of Photosynthetic Capacities
Evaluating the Performance of Hyperspectral Leaf Reflectance to Detect Water Stress and Estimation of Photosynthetic Capacities
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评估高光谱叶片反射率检测水分胁迫和估计光合能力的性能
DOI：
10.3390/rs13112160
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发表时间：
2021-05
期刊：
影响因子：
5
通讯作者：
Yuanyong Dian
中科院分区：
文献类型：
--
作者：
Jing-Jing Zhou;Ya-Hao Zhang;Ze-Min Han;Xiao-Yang Liu;Yong-Feng Jian;Chun-Gen Hu;Yuanyong Dian
先进的技术能够早期，快速，无损检测干旱对果树的影响和大规模的光合特性的测量是必要的，以满足精准农业和全面预测产量增加的挑战。我们测试了应用高光谱反射率作为一个高通量的表型分析方法，早期识别水分胁迫和快速评估叶片光合性状在柑橘树进行温室实验。为此，光合CO2同化率（Pn），气孔导度（Cond）和蒸腾速率（Trmmol）与气体交换的方法测量叶片高光谱反射率从柑橘生长在土壤干旱水平的梯度6倍，在20天的胁迫诱导和13天的复水。水分胁迫导致Pn、Cond和Trmmol在整个干旱期迅速持续下降。上层土壤对干旱的敏感性高于中、下层土壤。水分胁迫也会引起平均光谱反射率和吸收率随时间的连续动态变化。树木复水后，这些差异就不明显了。四个水分胁迫的原始反射光谱的多样性低，令人惊讶的是，不能跟踪干旱响应，而特定的高光谱光谱植被指数（SVI）和吸收功能或波长位置变量呈现出巨大的潜力。以下机器学习算法：随机森林（RF），支持向量机（SVM），梯度增强（GDboost），和自适应增强（Adaboost）被用来开发一个衡量光合作用的叶片反射光谱。四种机器学习算法的性能进行了评估，和RF算法产生了最高的预测能力预测光合参数（R2为0.92，0.89和0.88的Pn，Cond和Trmmol，分别）。结果表明，叶片高光谱反射率是一种可靠、稳定的监测水分胁迫和增产的方法，在大规模果园中具有很大的应用潜力。
Advanced techniques capable of early, rapid, and nondestructive detection of the impacts of drought on fruit tree and the measurement of the underlying photosynthetic traits on a large scale are necessary to meet the challenges of precision farming and full prediction of yield increases. We tested the application of hyperspectral reflectance as a high-throughput phenotyping approach for early identification of water stress and rapid assessment of leaf photosynthetic traits in citrus trees by conducting a greenhouse experiment. To this end, photosynthetic CO2 assimilation rate (Pn), stomatal conductance (Cond) and transpiration rate (Trmmol) were measured with gas-exchange approaches alongside measurements of leaf hyperspectral reflectance from citrus grown across a gradient of soil drought levels six times, during 20 days of stress induction and 13 days of rewatering. Water stress caused Pn, Cond, and Trmmol rapid and continuous decline throughout the entire drought period. The upper layer was more sensitive to drought than middle and lower layers. Water stress could also bring continuous and dynamic changes of the mean spectral reflectance and absorptance over time. After trees were rewatered, these differences were not obvious. The original reflectance spectra of the four water stresses were surprisingly of low diversity and could not track drought responses, whereas specific hyperspectral spectral vegetation indices (SVIs) and absorption features or wavelength position variables presented great potential. The following machine-learning algorithms: random forest (RF), support vector machine (SVM), gradient boost (GDboost), and adaptive boosting (Adaboost) were used to develop a measure of photosynthesis from leaf reflectance spectra. The performance of four machine-learning algorithms were assessed, and RF algorithm yielded the highest predictive power for predicting photosynthetic parameters (R2 was 0.92, 0.89, and 0.88 for Pn, Cond, and Trmmol, respectively). Our results indicated that leaf hyperspectral reflectance is a reliable and stable method for monitoring water stress and yield increase, with great potential to be applied in large-scale orchards.
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影响因子：
5
作者：
Gerhards, Max;Schlerf, Martin;Inoue, Yoshio
通讯作者：
Inoue, Yoshio
DOI：
--
发表时间：
--
期刊：
--
影响因子：
--
作者：
通讯作者：
--
影响因子：
13.5
作者：
Ceccato, P;Flasse, S;Grégoire, JM
通讯作者：
Grégoire, JM
影响因子：
6.2
作者：
M. Zovko;U. Žibrat;M. Knapic;M. Kovačić;D. Romić
通讯作者：
M. Zovko;U. Žibrat;M. Knapic;M. Kovačić;D. Romić
影响因子：
13.5
作者：
A. S. Streher;R. Torres;L. Morellato;T. S. F. Silva
通讯作者：
A. S. Streher;R. Torres;L. Morellato;T. S. F. Silva