Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance
Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance
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DOI：
10.3390/rs12193265
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发表时间：
2020-10-01
期刊：
影响因子：
5
通讯作者：
Ikka, Takashi
中科院分区：
文献类型：
--
作者：
Sonobe, Rei;Yamashita, Hiroto;Ikka, Takashi
日本辣根(山葵)生长在非常特殊的条件下，最近的环境气候变化损害了山葵的生产。此外，最优的栽培方法还不为人所知，初出茅庐的农民越来越难培养它。叶绿素a、b和类胡萝卜素的含量及其分配可以作为评估其产量和环境胁迫的一个适当的指标；因此，发展一种基于反射率的光合色素原位监测方法可能对农业管理有用。除了原始反射率(OR)外，还比较了一阶导数反射率(FDR)、连续统去除(CR)、去趋势(DT)、乘性散射校正(MSC)和标准正态变量变换(SNV)等五种预处理技术的准确性。在此基础上，研究了五种机器学习算法--随机森林算法、支持向量机算法、基于核的极限学习机算法、立方学习算法和随机梯度提升算法。对于不同pH或硫离子浓度条件下的样品，红边带的末端对OR、FDR、DT、MSC和SNV是有效的，而绿色峰带对CR是有效的。总体而言，Kelm和Cubist表现出高性能，并结合预处理技术有效地获得高精度的估计值。最优组合为DT-KELM(RPD=1.511-5.17，RMSE=1.23-3.62MU·g·cm~(-2))和Chla：B(RPD=0.73~3.17，RMSE=0.13~0.60)，CR-KELM对Chl_b(RPD=1.92~5.06，RMSE=0.41~1.03MU·cm~(-2))，Chla：Car(RPD=1.31~3.23，RMSE=0.26~0.50)；Car的SNV-Cubist(RPD=1.63-3.32，RMSE=0.31-1.89µg cm(-2))和Chl：Car的DT-Cubist(RPD=1.53-3.96，RMSE=0.27-0.74)。
Japanese horseradish (wasabi) grows in very specific conditions, and recent environmental climate changes have damaged wasabi production. In addition, the optimal culture methods are not well known, and it is becoming increasingly difficult for incipient farmers to cultivate it. Chlorophyll a, b and carotenoid contents, as well as their allocation, could be an adequate indicator in evaluating its production and environmental stress; thus, developing an in situ method to monitor photosynthetic pigments based on reflectance could be useful for agricultural management. Besides original reflectance (OR), five pre-processing techniques, namely, first derivative reflectance (FDR), continuum-removed (CR), de-trending (DT), multiplicative scatter correction (MSC), and standard normal variate transformation (SNV), were compared to assess the accuracy of the estimation. Furthermore, five machine learning algorithms-random forest (RF), support vector machine (SVM), kernel-based extreme learning machine (KELM), Cubist, and Stochastic Gradient Boosting (SGB)-were considered. To classify the samples under different pH or sulphur ion concentration conditions, the end of the red edge bands was effective for OR, FDR, DT, MSC, and SNV, while a green-peak band was effective for CR. Overall, KELM and Cubist showed high performance and incorporating pre-processing techniques was effective for obtaining estimated values with high accuracy. The best combinations were found to be DT-KELM for chl a (RPD = 1.511-5.17, RMSE = 1.23-3.62 mu g cm(-2)) and chl a:b (RPD = 0.73-3.17, RMSE = 0.13-0.60); CR-KELM for chl b (RPD = 1.92-5.06, RMSE = 0.41-1.03 mu g cm(-2)) and chl a:car (RPD = 1.31-3.23, RMSE = 0.26-0.50); SNV-Cubist for car (RPD = 1.63-3.32, RMSE = 0.31-1.89 mu g cm(-2)); and DT-Cubist for chl:car (RPD = 1.53-3.96, RMSE = 0.27-0.74).