Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Renmin 5268, Changchun 130024, China.
Jilin Provincial Key Laboratory for Wetland Ecological Processes and Environmental Change in the Changbai Mountains, Institute for Peat and Mire Research, Northeast Normal University, Renmin 5268, Changchun 130024, China.
Sensors (Basel). 2019 Sep 20;19(19):4059. doi: 10.3390/s19194059.
As a primary pigment of leafy green vegetables, chlorophyll plays a major role in indicating vegetable growth status. The application of hyperspectral remote sensing reflectance offers a quick and nondestructive method to estimate the chlorophyll content of vegetables. Reflectance of adaxial and abaxial leaf surfaces from three common leafy green vegetables: Pakchoi var. Shanghai Qing ( L. var. Shanghai Qing), Chinese white cabbage (), and Romaine lettuce ( ) were measured to estimate the leaf chlorophyll content. Modeling based on spectral indices and the partial least squares regression (PLS) was tested using the reflectance data from the two surfaces (adaxial and abaxial) of leaves in the datasets of each individual vegetable and the three vegetables combined. The PLS regression model showed the highest accuracy in estimating leaf chlorophyll content of pakchoi var. Shanghai Qing (R = 0.809, RMSE = 62.44 mg m), Chinese white cabbage (R = 0.891, RMSE = 45.18 mg m) and Romaine lettuce (R = 0.834, RMSE = 38.58 mg m) individually as well as of the three vegetables combined (R = 0.811, RMSE = 55.59 mg m). The good predictability of the PLS regression model is considered to be due to the contribution of more spectral bands applied in it than that in the spectral indices. In addition, both the uninformative variable elimination PLS (UVE-PLS) technique and the best performed spectral index: MDATT, showed that the red-edge region (680-750 nm) was effective in estimating the chlorophyll content of vegetables with reflectance from two leaf surfaces. The combination of the PLS regression model and the red-edge region are insensitive to the difference between the adaxial and abaxial leaf structure and can be used for estimating the chlorophyll content of leafy green vegetables accurately.
叶绿素作为叶菜类蔬菜的主要色素，在指示蔬菜生长状况方面起着重要作用。高光谱遥感反射率的应用为快速无损地估计蔬菜的叶绿素含量提供了一种方法。本研究测量了三种常见绿叶蔬菜：上海青小白菜（ Pakchoi var. Shanghai Qing）、大白菜（ Chinese white cabbage）和生菜（ Romaine lettuce）的叶面和叶背的反射率，以估算叶片的叶绿素含量。基于光谱指数和偏最小二乘回归（PLS）的模型，分别使用三种蔬菜叶片正反两面的反射率数据集以及三种蔬菜的混合数据集进行了测试。PLS 回归模型在单独估计上海青小白菜（ R = 0.809，RMSE = 62.44 mg m）、大白菜（ R = 0.891，RMSE = 45.18 mg m）和生菜（ R = 0.834，RMSE = 38.58 mg m）叶片叶绿素含量以及三种蔬菜混合时（ R = 0.811，RMSE = 55.59 mg m）的精度最高。PLS 回归模型的良好预测能力被认为是由于它所应用的光谱波段比光谱指数更多。此外，无信息变量消除偏最小二乘（UVE-PLS）技术和表现最好的光谱指数：MDATT 都表明，在使用正反两面的反射率时，红边区域（680-750nm）对估计蔬菜叶绿素含量是有效的。PLS 回归模型和红边区域的结合对叶片正反两面的结构差异不敏感，可以准确地估计叶菜类蔬菜的叶绿素含量。
