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作者	单位
魏青，张宝忠，魏征，韩信，段晨斐	（1.中国水利水电科学研究院流域水循环模拟与调控国家重点实验室,北京 100038；2.国家节水灌溉北京工程技术研究中心，北京 100048；3.山西农业大学林学院，山西晋中 030801）

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中文摘要:

为探讨利用无人机多光谱影像监测冬小麦叶绿素含量的可行性，基于北京市大兴区中国水科院试验基地的2019年冬小麦无人机多光谱影像和田间实测冠层叶绿素含量数据，选取16种光谱植被指数，确定对冬小麦冠层叶绿素含量显著相关的植被指数，采用一元二次线性回归和逐步回归分析方法建立各生育时期及全生育期的SPAD值估测模型，通过精度检验确定对冬小麦冠层叶绿素含量监测的最优模型。结果表明，两种分析方法中逐步回归建模效果最佳。拔节期选取4个植被指数（MSR 、CARI、NGBDI、TVI）建模效果最好，模型率定的决定系数（r）为0.73，模型验证的r、相对误差（RE）和均方根误差（RMSE）分别为0.63、2.83%、1.68；抽穗期选取3个植被指数（GNDVI、GOSAVI、CARI）建模效果最好，模型率定的r为0.81，模型验证的r、RE、RMSE分别为0.63、2.83%、1.68；灌浆期选取2个植被指数（MSR、NGBDI）建模效果最好，模型率定的r为0.67，模型验证的r、RE、RMSE分别为0.65、2.83%、1.88。因此，无人机多光谱影像结合逐步回归模型可以很好地监测冬小麦SPAD值动态变化。

英文摘要:

Unmanned aerial vehicle(UAV) multispectral remote sensing has the advantages of high efficiency and flexibility in monitoring crop nutritional status. However, there are few attempts to monitor winter wheat chlorophyll using UAV multispectral images under different nitrogen treatments.In this paper, the chlorophyll content in winter wheat canopy was estimated by using the multispectral image of winter wheat UAV in 2019 and the field measured data of chlorophyll content in the canopy at the experimental base of Chinese Academy of Water Sciences in Daxing district,Beijing.Firstly, 16 spectral vegetation indices are selected to determine the vegetation indices that are significantly related to the chlorophyll content in winter wheat canopy. The SPAD value estimation model for each growth period and the whole growth period is established by the method of single quadratic linear regression and stepwise regression analysis. The optimal model for monitoring the chlorophyll content in winter wheat canopy is determined by precision test.The results showed that the stepwise regression model was the best in the process of modeling based on the two analytical methods. Four vegetation indices(MSR, CARI, NGBDI, TVI) were selected at jointing stage, and r of the calibrated model was 0.73. r, RE and RMSE of the validated model were 0.63, 2.83% and 1.68, respectively. Three vegetation indices(GNDVI, GOSAVI and CARI) were selected at heading stage. GOSAVI and CARI had the best modeling effect, in which r of calibrated model was 0.81.r, RE and RMSE of validated model were 0.63, 2.83% and 1.68, respectively. Two vegetation indices(MSR and NGBDI) were selected for the best modeling effect during grain filling period, in which r of calibrated model was 0.67.r, RE and RMSE of validated model were 0.65, 2.83% and 1.88, respectively.Therefore, UAV multi-spectral imagery combined with stepwise regression model can monitor the dynamic changes of SPAD value of winter wheat well, which can provide some guidance for precise nitrogen fertilizer management.

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