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Abstract：
Leaf chlorophyll content (LCC) is an important physiological index to evaluate the photosynthetic capacity and growth health of crops. In this investigation, the focus was placed on the chlorophyll content per unit of leaf area (LCCA) and the chlorophyll content per unit of fresh weight (LCCW) during the tuber formation phase of potatoes in Northern Shaanxi. Ground-based hyperspectral data were acquired for this purpose to formulate the vegetation index. The correlation coefficient method was used to obtain the \"trilateral\" parameters with the best correlation between potato LCCA and LCCW, empirical vegetation index, any two-band vegetation index constructed after 0-2 fractional differential transformation (step size 0.5), and the parameters with the highest correlation among the three spectral parameters, which were divided into four combinations as model inputs. The prediction models of potato LCCA and LCCW were constructed using the support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN) algorithms. The results showed that, compared with the \"trilateral\" parameter and the empirical vegetation index, the spectral index constructed by the hyperspectral reflectance after differential transformation had a stronger correlation with potato LCCA and LCCW. Compared with no treatment, the correlation between spectral index and potato LCC and the prediction accuracy of the model showed a trend of decreasing after initial growth with the increase in differential order. The highest correlation index after 0-2 order differential treatment is DI, and the maximum correlation coefficients are 0.787, 0.798, 0.792, 0.788 and 0.756, respectively. The maximum value of the spectral index correlation coefficient after each order differential treatment corresponds to the red edge or near-infrared band. A comprehensive comparison shows that in the LCCA and LCCW estimation models, the RF model has the highest accuracy when combination 3 is used as the input variable. Therefore, it is more recommended to use the LCCA to estimate the chlorophyll content of crop leaves in the agricultural practices of the potato industry. The results of this study can enhance the scientific understanding and accurate simulation of potato canopy spectral information, provide a theoretical basis for the remote sensing inversion of crop growth, and promote the development of modern precision agriculture.
摘要：
叶片叶绿素含量是评价作物光合能力和生长健康的重要生理指标。在这次调查中,重点研究了陕北马铃薯块茎形成阶段单位叶面积叶绿素含量(LCCA)和单位鲜重叶绿素含量(LCCW)。为此，获取了地面高光谱数据以制定植被指数。采用相关系数法得到马铃薯LCCA与LCCW相关性最好的“三边”参数,经验植被指数，0-2分数阶微分变换(步长0.5)后构建的任意两波段植被指数，和三个光谱参数中相关性最高的参数，将其分为四个组合作为模型输入。利用支持向量机(SVM)构建了马铃薯LCCA和LCCW的预测模型,随机森林(RF)和反向传播神经网络(BPNN)算法。结果表明,与“三边”参数和经验植被指数相比，差分变换后的高光谱反射率构建的光谱指数与马铃薯LCCA和LCCW有较强的相关性。与不治疗相比,光谱指数与马铃薯LCC的相关性和模型的预测精度在初始生长后随微分阶数的增加呈下降趋势。经过0-2阶差分处理后的最高相关指数为DI，最大相关系数分别为0.787、0.798、0.792、0.788和0.756。各阶差分处理后的光谱指数相关系数的最大值对应于红色边缘或近红外波段。综合比较表明，在LCCA和LCCW估计模型中，当组合3用作输入变量时,RF模型具有最高的精度。因此,在马铃薯行业的农业实践中，更建议使用LCCA来估算作物叶片的叶绿素含量。本研究结果可增强对马铃薯冠层光谱信息的科学认识和准确模拟,为作物生长遥感反演提供理论依据,促进现代精准农业的发展。
