BAO Leer,LIU Qingyan,LIANG Yunjiang.Research Progress on UAV Remote Sensing Monitoring of Orchard Water and Nutrient Status Under Complex Canopy Backgrounds[J].Northern Horticulture,2026,(15):129-136.[doi:10.11937/bfyy.20260276]
复杂冠层背景下果园水肥状况无人机遥感监测研究进展
- Title:
- Research Progress on UAV Remote Sensing Monitoring of Orchard Water and Nutrient Status Under Complex Canopy Backgrounds
- 文章编号:
- 1001-0009(2026)15-0129-08
- Keywords:
- precision agriculture; UAV remote sensing; orchard water and fertilizer monitoring; multi-source data fusion; machine learning
- 分类号:
- S365
- 文献标志码:
- A
- 摘要:
- 针对复杂冠层背景下果园水肥状况监测中存在的混合像元干扰、阴影影响明显及尺度匹配困难等问题，对无人机遥感在果园水分胁迫诊断与养分监测中的研究进展进行了综述。从树冠目标提取与背景剔除、监测特征变量选择、反演模型构建、多源数据融合及精准管理应用等方面进行了归纳分析。结果表明：无人机遥感能够较好地弥补地面采样效率低和卫星遥感空间分辨率不足，在果园精细化监测中具有较好的应用前景；阴影校正、混合像元分解及纯净冠层光谱提取是提高监测精度的重要环节，融合红边、热红外、纹理及三维结构等多维信息有助于提升对果园水分与养分状况的表征能力，机器学习方法在复杂非线性关系处理中总体优于传统经验模型。今后应进一步加强模型跨区域、跨时相适用性研究，提升数据实时处理与协同感知能力，推进“星-机-地”一体化监测与变量作业决策的衔接应用，为果园水肥精准管理提供参考。
- Abstract:
- To address the problems of mixed-pixel interference,significant shadow effects,and scale mismatch in monitoring orchard water and nutrient status under complex canopy backgrounds, recent advances in UAV remote sensing for orchard water stress diagnosis and nutrient monitoring was reviewed.Existing studies from the aspects of canopy target extraction and background removal,feature variable selection,inversion model development,multi-source data fusion,and precision management applications were summarized.The results showed that UAV remote sensing could effectively compensate for the low efficiency of ground sampling and the limited spatial resolution of satellite remote sensing,and had good potential for fine-scale orchard monitoring.Shadow correction,mixed-pixel decomposition,and pure canopy spectral extraction are important steps for improving monitoring accuracy.In addition,the integration of red-edge,thermal infrared,texture,and three-dimensional structural information helps improve the characterization of orchard water and nutrient status,while machine learning methods generally outperform traditional empirical models in dealing with complex nonlinear relationships.Future studies should further strengthen model applicability across regions and time periods,improve real-time data processing and collaborative sensing capability,and promote the coordinated application of integrated ‘satellite-UAV-ground’ monitoring and variable-rate management decision-making,in order to provide references for precision orchard water and nutrient management.
相似文献/References:
[1]张烨.基于ARM控制器和公共物联网云平台的农业信息采集系统研究[J].北方园艺,2017,41(15):185.[doi:10.11937/bfyy.20170391]
ZHANG Ye.Design of Agriculture Information Acquisition System Based on ARM and Common Internet of Things Cloud Platform[J].Northern Horticulture,2017,41(15):185.[doi:10.11937/bfyy.20170391]
[2]李瑞,敖雁,孙启洵,等.大田农业物联网应用现状与展望[J].北方园艺,2018,42(14):148.[doi:10.11937/bfyy.20180081]
LI Rui,AO Yan,SUN Qixun,et al.Application and Prospect of Agricultural Internet of Things in Crop Field[J].Northern Horticulture,2018,42(15):148.[doi:10.11937/bfyy.20180081]
[3]詹帅,霍红.供应链创新驱动的精准农业实现路径及保障措施[J].北方园艺,2021,(02):165.[doi:10.11937/bfyy.20201295]
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备注/Memo
第一作者简介：宝勒尔（1999- ），男，硕士研究生，研究方向为土壤学。E-mail：2534332177@qq.com.责任作者：梁运江（1972-），男，博士，教授，现主要从事土壤学与植物营养等研究工作。E-mail：lyjluo@ybu.edu.cn.基金项目：吉林省科学基金资助项目（20220101189JC）；国家自然科学基金资助项目（31460117）。收稿日期：2026-01-21