农业机械学报2025，Vol.56Issue(8)：74-85,12.DOI:10.6041/j.issn.1000-1298.2025.08.007
基于无人机多光谱数据的夏玉米综合水分指标估算模型
Estimation Model of Comprehensive Moisture Index for Summer Maize Based on UAV Multispectral Data
摘要
Abstract
Accurate farmland moisture monitoring is vital for agricultural water conservation and yield protection.However,existing technologies mainly focus on single indicators like soil or leaf/plant water content,lacking a systematic characterization of soil-plant water collaborative mechanisms.Taking summer maize in the Guanzhong Plain as the research object,seven indicators were integrated,including multi-depth soil water content,leaf water content,and plant water content through ground sampling.Two comprehensive moisture indices,CMI1(using the entropy weight method)and CMI2(using principal component analysis),were constructed to reflect the overall soil-plant moisture status.Sensitive vegetation indices were calculated and screened based on UAV multispectral data,and machine learning algorithms such as random forest(RF)and support vector machine(SVM)were applied to develop data-driven models for moisture estimation.The results showed that both CMI1 and CMI2 effectively reflected the comprehensive moisture status of summer maize farmland soil-plant systems,while CMI2 showed better characterization accuracy of soil-plant water coupling features than CMI 1 in most growth stages(e.g.,jointing and silking stages).The response relationships between vegetation indices and comprehensive moisture indices varied dynamically with growth stages,and the highest correlation coefficients between optimal vegetation indices and CMI reached 0.761,0.795,0.769,and 0.771 in the jointing,silking,grain-filling,and milky stages,respectively.The RF model exhibited more stable performance in both modeling and validation sets,with estimation accuracy superior to other models,enabling robust estimation of comprehensive moisture indices for summer maize.The research result presented a"multi-index integration-UAV remote sensing-dynamic modeling"framework through dual performance comparisons of moisture indices and machine learning models,offering precise field-scale monitoring solutions for smart irrigation decisions.关键词
综合水分指标/无人机多光谱/机器学习/土壤含水率/作物含水率Key words
comprehensive moisture index/UAV multispectrum/machine learning/soil water content/crop moisture status分类
农业科技引用本文复制引用
王亚昆,马宇欣,范晓懂,陈洪,胡笑涛..基于无人机多光谱数据的夏玉米综合水分指标估算模型[J].农业机械学报,2025,56(8):74-85,12.基金项目
国家自然科学基金项目(U2243235)、陕西省秦创原引用高层次创新创业人才项目(QCYRCXM-2023-060)和陕西省科协青年人才托举计划项目(20240439) （U2243235）
