[1]周鱼,薛晓斌,宁琳,等.基于无人机多光谱遥感的赤霞珠葡萄黎明前叶片水势反演与验证[J].江苏农业学报,2026,42(04):745-755.[doi:doi:10.3969/j.issn.1000-4440.2026.04.011]
　ZHOU Yu,XUE Xiaobin,NING Lin,et al.Inversion and validation of pre-dawn leaf water potential for Cabernet Sauvignon grapes based on multispectral remote sensing by unmanned aerial vehicle[J].,2026,42(04):745-755.[doi:doi:10.3969/j.issn.1000-4440.2026.04.011]
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基于无人机多光谱遥感的赤霞珠葡萄黎明前叶片水势反演与验证()

江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
42
期数:
2026年04期
页码:
745-755
栏目:
农业信息工程
出版日期:
2026-04-30

文章信息/Info

Title:
Inversion and validation of pre-dawn leaf water potential for Cabernet Sauvignon grapes based on multispectral remote sensing by unmanned aerial vehicle
作者:
周鱼1; 薛晓斌2; 宁琳1; 刘虹君1; 胡宏远2; 郑明鑫1; 王振平1; 李栋梅1
(1.宁夏大学葡萄酒与园艺学院,宁夏银川750021；2.宁夏大学生命科学学院,宁夏银川750021)
Author(s):
ZHOU Yu1; XUE Xiaobin2; NING Lin1; LIU Hongjun1; HU Hongyuan2; ZHENG Mingxin1; WANG Zhenping1; LI Dongmei1
(1.School of Enology and Horticulture, Ningxia University, Yinchuan 750021, China；2.School of Life Sciences, Ningxia University, Yinchuan 750021, China)
关键词:
赤霞珠; 无人机; 多光谱; 黎明前叶片水势; 偏最小二乘回归（PLSR）模型
Keywords:
Cabernet Sauvignon; unmanned aerial vehicle (UAV); multispectral; pre-dawn leaf water potential; partial least squares regression (PLSR) model
分类号:
S663.1
DOI:
doi:10.3969/j.issn.1000-4440.2026.04.011
文献标志码:
A
摘要:
本研究旨在利用无人机多光谱技术构建高精度反演模型,实现葡萄树的水分动态、无损和大尺度监测。以酿酒葡萄赤霞珠为试验材料,分析冠层单波段反射率、多光谱指数、可见光谱指数与葡萄不同发育时期(花后20～40 d、花后41～60 d、花后61～80 d)黎明前叶片水势(Ψpre)的相关性,以筛选与不同时期Ψpre密切相关的遥感参数。采用一元线性回归、多元线性回归及偏最小二乘回归(PLSR)方法,构建各生育期Ψpre的估测模型。结果表明,基于3种模型构建的黎明前叶片水势预测模型均具有一定的预测能力,花后20～40 d、花后41～60 d、花后61～80 d的一元线性模型的决定系数(R2)的最高值分别为0.560、0.448、0.641；多元线性模型的R2的最高值分别为0.622、0.680、0.713；PLSR模型的R2的最高值分别为0.695、0.777、0.763,对水势的预测精度较一元线性回归模型和多元线性模型提升了7.01%～73.44%。综上,基于无人机多光谱遥感的机器学习模型能够对黎明前叶片水势进行良好的预测,PLSR模型可通过融合多光谱特征与非线性关系显著提升葡萄黎明前叶片水势的预测精准度,且在花后41～60 d的表现最佳,可为葡萄园的大面积精准灌溉决策提供依据。
Abstract:
This study aimed to utilize unmanned aerial vehicle (UAV) multispectral technology to construct a high-precision inversion model for dynamic, non-destructive, and large-scale monitoring of grapevine water status. Cabernet Sauvignon wine grapes were used as the experimental material to analyze the correlations between canopy single-band reflectance, multispectral vegetation indices, visible light indices, and pre-dawn leaf water potential (Ψpre) at different developmental stages (20-40 days after anthesis, 41-60 days after anthesis, and 61-80 days after anthesis), thereby screening remote sensing parameters closely related to Ψpre at each stage. Univariate linear regression, multiple linear regression, and partial least squares regression (PLSR) were employed to construct Ψpre estimation models for each growth stage. The results showed that the pre-dawn leaf water potential prediction models constructed using the three methods all exhibited certain predictive capabilities. The coefficients of determination (R2) for the univariate linear models were 0.560, 0.448, and 0.641 for 20-40 days after anthesis (DAA), 41-60 DAA, and 61-80 DAA, respectively. For the multiple linear models, the R2 values were 0.622, 0.680, and 0.713, and the PLSR models achieved R2 values of 0.695, 0.777, and 0.763, improving prediction accuracy by 7.01%-73.44% compared with univariate and multiple linear regression models. In summary, machine learning models based on UAV multispectral remote sensing can effectively predict pre-dawn leaf water potential. The PLSR model significantly improved prediction accuracy by integrating multispectral features and nonlinear relationships, with the best performance observed at 41-60 DAA, providing a basis for precision irrigation decision-making in large-scale vineyards.

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备注/Memo

备注/Memo:
收稿日期:2025-06-19基金项目:宁夏重点研发项目（2023BSB03032）；国家自然科学基金项目（U20A2042）；财政部和农业农村部国家葡萄产业技术体系水分生理与节水栽培岗位项目（CARS-29-zp-3）作者简介:周鱼（2001-），女，宁夏银川人，硕士研究生，研究方向为葡萄逆境生理与分子生物学。(E-mail)13209511934@163.com通讯作者:王振平，（E-mail）wangzhp@nxu.edu.cn；李栋梅，（E-mail）ldm2022068@nxu.edu.c
更新日期/Last Update: 2026-05-11