光谱净化提高了覆膜冬小麦长势综合评价指数的监测精度

doi:10.1016/j.jia.2023.05.036

摘要/Abstract

摘要：

为提高无人机遥感快速准确监测膜下冬小麦长势状况的精度，本研究利用垄覆膜、垄沟全覆膜和平作全覆膜冬小麦样区，基于模糊综合评价法（FCE），采用四种农艺参数（叶面积指数、地上生物量、株高、叶片叶绿素含量）计算冬小麦的综合长势评价指数（CGEI），使用光谱净化技术处理无人机多光谱遥感图像，并计算14种可见光和近红外光谱指数。采用偏最小二乘法、支持向量机、随机森林和人工神经网络（ANN）四种机器学习算法，构建了地膜覆盖冬小麦的长势监测模型，并进行精度评价，绘制冬小麦长势状况的时空分布图。结果表明，基于FCE方法构建的地膜覆盖冬小麦CGEI能够客观、全面地评价作物长势状况，ANN模型对CGEI的反演精度高于单一农艺参数，决定系数为0.75，均方根误差为8.40，平均绝对值误差为6.53。光谱净化可以消除地膜和土壤造成的背景效应干扰，有效提高地膜覆盖冬小麦长势的遥感反演精度，在光谱净化后的垄沟全覆膜区域反演效果最佳。该成果为无人机遥感监测地膜覆盖冬小麦长势状况提供了理论依据。

Abstract:

In order to further improve the utility of unmanned aerial vehicle (UAV) remote-sensing for quickly and accurately monitoring the growth of winter wheat under film mulching, this study examined the treatments of ridge mulching, ridge–furrow full mulching, and flat cropping full mulching in winter wheat. Based on the fuzzy comprehensive evaluation (FCE) method, four agronomic parameters (leaf area index, above-ground biomass, plant height, and leaf chlorophyll content) were used to calculate the comprehensive growth evaluation index (CGEI) of the winter wheat, and 14 visible and near-infrared spectral indices were calculated using spectral purification technology to process the remote-sensing image data of winter wheat obtained by multispectral UAV. Four machine learning algorithms, partial least squares, support vector machines, random forests, and artificial neural network networks (ANN), were used to build the winter wheat growth monitoring model under film mulching, and accuracy evaluation and mapping of the spatial and temporal distribution of winter wheat growth status were carried out. The results showed that the CGEI of winter wheat under film mulching constructed using the FCE method could objectively and comprehensively evaluate the crop growth status. The accuracy of remote-sensing inversion of the CGEI based on the ANN model was higher than for the individual agronomic parameters, with a coefficient of determination of 0.75, a root mean square error of 8.40, and a mean absolute value error of 6.53. Spectral purification could eliminate the interference of background effects caused by mulching and soil, effectively improving the accuracy of the remote-sensing inversion of winter wheat under film mulching, with the best inversion effect achieved on the ridge–furrow full mulching area after spectral purification. The results of this study provide a theoretical reference for the use of UAV remote-sensing to monitor the growth status of winter wheat with film mulching.

Key words: mulched winter wheat , machine learning , fuzzy comprehensive evaluation , comprehensive growth evaluation index , unmanned aerial vehicle

. 光谱净化提高了覆膜冬小麦长势综合评价指数的监测精度[J]. Journal of Integrative Agriculture, 2024, 23(5): 1523-1540.

Zhikai Cheng, Xiaobo Gu, Yadan Du, Zhihui Zhou, Wenlong Li, Xiaobo Zheng, Wenjing Cai, Tian Chang.

Spectral purification improves monitoring accuracy of the comprehensive growth evaluation index for film-mulched winter wheat [J]. Journal of Integrative Agriculture, 2024, 23(5): 1523-1540.

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