Spectroscopy Letters ( IF 1.7 ) Pub Date : 2021-03-10 , DOI: 10.1080/00387010.2021.1894177 Chao Liu 1 , Zhenghua Hu 1 , Rui Kong 1 , Lingfei Yu 2 , Yuanyuan Wang 1 , Shutao Chen 1 , Xuesong Zhang 1
Abstract
Leaf area index is a vital biological parameter, which is widely used to monitor plant growth, evaluate health status, and predict yield. Remote sensing techniques are known to be nondestructive and effective methods for estimating leaf area index of plants, but little attention has been paid to predicting leaf area index under carbon dioxide enrichment. Field experiments were conducted to estimate the rice leaf area index under elevated carbon dioxide using hyperspectral remote sensing. The results showed that various spectral parameters, including the first derivative reflectance at 467 nm, the yellow edge amplitude, the normalized value of the green peak and red valley reflectance, the ratio of red edge and blue edge area, and the normalized value of the red edge and blue edge area, have a significantly correlated with the leaf area index. Further comparing the results of models, the normalized value of the green peak and red valley reflectance exhibited the optimal performance for estimating leaf area index. The best-fitted inversion model was y=6.89x0.46 with the coefficient of determination was 0.75 and root mean square error was 0.31. This finding is helpful in providing guidance for monitoring rice leaf area index under carbon dioxide enrichment.
中文翻译:
二氧化碳富集下水稻(Oryza sativa L.)的高光谱特征和叶面积指数监测。
摘要
叶面积指数是至关重要的生物学参数,广泛用于监视植物生长,评估健康状况和预测产量。遥感技术是估计植物叶面积指数的非破坏性和有效方法,但很少有人关注二氧化碳富集下叶面积指数的预测。利用高光谱遥感进行了田间试验,以估计二氧化碳浓度升高时的稻叶面积指数。结果表明,各种光谱参数包括467 nm处的一阶导数反射率,黄色边缘幅度,绿色峰和红色谷反射率的归一化值,红色边缘与蓝色边缘面积之比以及归一化值红色边缘和蓝色边缘区域与叶面积指数有显着相关。进一步比较模型结果,绿峰和红谷反射率的归一化值表现出最佳的叶面积指数估计性能。最适合的反演模型是y = 6.89 x 0.46,测定系数为0.75,均方根误差为0.31。这一发现有助于为监测二氧化碳富集下的稻叶面积指数提供指导。