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星机协同的冬小麦长势遥感监测研究——以陕西省咸阳市为例
Remote Sensing Monitoring of Winter Wheat Growth Based on Satellite and Aircraft Cooperation:A Case Study of Xianyang City,Shaanxi Province
投稿时间:2022-11-24  修订日期:2022-12-23
DOI:
中文关键词:  冬小麦  多光谱  卫星  无人机  作物分类  长势监测
英文关键词:Winter Wheat  Multispectral  Satellite  UAV  Crop Classification  Growth Monitoring
基金项目:
作者单位地址
彭梓励[] 西北农林科技大学 农学院
西北农林科技大学 农学院 
陕西省杨凌区西北农林科技大学南校区
马丽娟  
郭曾辉  
李军  
王瑞[] 西北农林科技大学 农学院 陕西省杨凌区西北农林科技大学南校区
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中文摘要:
      作物长势遥感监测在高效量化评价作物分布概况、生长状况,为管理措施与宏观政策制定提供决策支持等方面具有重要应用价值。传统卫星遥感监测空间和时间分辨率低、精度较差,近年来快速发展的无人机遥感能够很好地弥补卫星遥感的不足,但星机协同的遥感监测方法有待进一步完善。本研究在田块尺度上,通过设置小麦氮素和灌溉梯度试验,在各关键生育期测定SPAD、LAI等长势指标并获取无人机遥感数据,构建小麦长势多光谱监测模型;将获取模型通过优化的波段比值修正法结合Sentinel-2A影像进行升尺度应用。结果表明:在拔节期、孕穗期、开花期和灌浆期,分别利用Clgreen、Clrededge、OSAVI和OSAVI构建三次函数、指数函数、指数函数和幂函数对SPAD拟合效果最佳,升尺度应用至孕穗期、开花期和灌浆期卫星遥感监测后验证精度均较好;上述四个生育期分别利用Clgreen、Clrededge、DATT和OSAVI构建幂函数、二次函数、指数函数和指数函数对LAI拟合效果最佳,升尺度应用验证精度均较好。基于该星机协同方法对咸阳市冬小麦长势进行监测发现,2021年,武功县,兴平市以及三原县等区域小麦各生育期长势较优,永寿县、淳化县、彬州市等地的小麦长势较差。本研究完善了无人机和卫星遥感影像的融合方法,构建了星机协同监测冬小麦长势的技术流程。结果可为咸阳市冬小麦不同生育期长势精准监测与宏观生产政策制定等提供技术与数据支撑。
英文摘要:
      Crop growth monitoring by remote sensing has important application value in the efficient quantitative evaluation of crop distribution and growth, and in providing decision support for management measures and macro policy formulation. The traditional satellite remote sensing monitoring has low spatial and temporal resolution and poor accuracy. The rapid development of UAV remote sensing in recent years can make up for the shortcomings of satellite remote sensing, but the satellite aircraft coordinated remote sensing monitoring method needs to be further improved. At the field scale, this study set up wheat nitrogen and irrigation gradient tests, measured SPAD, LAI and other growth indicators at each key growth period, obtained UAV remote sensing data, and built a wheat growth multi spectral monitoring model; The acquisition model is scaled up using the optimized band ratio correction method combined with Sentinel-2A image. The results showed that at jointing stage, booting stage, flowering stage and filling stage, the cubic function, exponential function, exponential function and power function constructed by Clgreen, Clrededge, OSAVI and OSAVI respectively were the best for SPAD fitting, and the accuracy of verification was better when scale up was applied to booting stage, flowering stage and filling stage; The power function, quadratic function, exponential function and exponential function constructed by Clgreen, Clrededge, DATT and OSAVI in the above four growth periods are the best for LAI fitting, and the accuracy of scaling up application verification is good. According to the monitoring of the growth of winter wheat in Xianyang City based on the satellite computer coordination method, in 2021, the growth of wheat in Wugong County, Xingping City and Sanyuan County will be better, while that in Yongshou County, Chunhua County and Binzhou City will be worse. This study improved the fusion method of UAV and satellite remote sensing images, and constructed the technical process of satellite aircraft cooperative monitoring of winter wheat growth. The results can provide technical and data support for the accurate monitoring of the growth of winter wheat in different growth periods and the formulation of macro production policies in Xianyang City.
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