| 基于多维高光谱植被指数的冬小麦叶面积指数估算 |
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| 引用本文： | 吾木提·艾山江,尼加提·卡斯木,陈晨,买买提·沙吾提.基于多维高光谱植被指数的冬小麦叶面积指数估算[J].农业机械学报,2022,53(5):181-190. |
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| 作者姓名： | 吾木提·艾山江 尼加提·卡斯木 陈晨 买买提·沙吾提 |
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| 作者单位： | 伊犁师范大学;新疆大学 |
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| 基金项目： | 新疆维吾尔自治区高校科研计划项目（XJEDU2020Y037）、伊犁师范大学博士引进人才科研项目（2020YSBSYJ001）和伊犁师范大学资源与生态研究所开放课题重点项目（YLNURE202206） |
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| 摘 要： | 为提高干旱区冬小麦叶面积指数（Leaf area index, LAI）遥感估算精度，以拔节期冬小麦LAI为研究对象，在对冠层高光谱数据进行一阶（First derivative, FD）、二阶（Second derivative, SD）微分预处理的基础上，计算了任意波段组合的二维植被指数（Two-dimensional vegetation index, 2DVI）和三维植被指数（Three-dimensional vegetation index, 3DVI）,通过进行与LAI之间相关性分析，寻求最佳波段组合的植被指数；利用人工神经网络（Artificial neural network, ANN）、K近邻（K-nearest neighbors, KNN）和支持向量回归（Support vector regression, SVR）算法分别建立LAI估算模型，并进行精度验证。结果表明：任意波段组合的植被指数与LAI相关性均显著提高，尤其是基于一阶微分预处理光谱的FD-3DVI-4（714 nm, 400 nm, 1 001 nm）相关系数达到0.93（P<0.01）,且最...
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| 关 键 词： | 冬小麦 任意波段组合 植被指数 叶面积指数 高光谱 |
| 收稿时间： | 2022/1/27 0:00:00 |
Estimation of Winter Wheat LAI Based on Multi-dimensional Hyperspectral Vegetation Indices |
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| UMUT Hasan,NIJAT Kasim,CHEN Chen,MAMAT Sawut.Estimation of Winter Wheat LAI Based on Multi-dimensional Hyperspectral Vegetation Indices[J].Transactions of the Chinese Society of Agricultural Machinery,2022,53(5):181-190. |
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| Authors: | UMUT Hasan NIJAT Kasim CHEN Chen MAMAT Sawut |
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| Institution: | Yili Normal University; Xinjiang University |
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| Abstract: | Winter wheat is one of the important food crops in China, and its planting area and output are the second only to rice. In order to improve the accuracy of remote sensing estimation of winter wheat leaf area index (LAI) in arid regions, taking the LAI of winter wheat at the jointing stage as research object, based on the first derivative (FD) and second derivative (SD) differential preprocessing of the canopy hyperspectral data, the two-dimensional vegetation index (2DVI) and three-dimensional vegetation index (3DVI) of any band combination was calculated, and the correlation with LAI was carried out. To find the vegetation index of the best band combination;the artificial neural network (ANN), K-nearest neighbors (KNN) and support vector regression (SVR) were used to establish LAI estimation respectively model and verify the accuracy. The results showed that the correlation between vegetation index and LAI in any combination of wavelength bands was significantly improved, especially the correlation coefficient of FD-3DVI-4(714nm, 400nm, 1001nm) based on the FD preprocessing spectrum reached 0.93 (P<0.01), and the optimal combination band was mainly located in the red edge position. The estimation model based on the optimal FD-3DVI index and K-nearest neighbors algorithm performed outstanding, its R2=0.89, the root mean square error (RMSE) was the lowest (0.31), and the relative analysis error (RPD) was 2.41. It was conclused that the K-nearest neighbor algorithm was more suitable for solving the nonlinear problem and improve the estimation accuracy, and it can provide a theoretical basis for the later crop growth evaluation and reasonable fertilization. |
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| Keywords: | winter wheat any band combination vegetation index leaf area index hyperspectral |
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