长江流域资源与环境 >> 2013, Vol. 22 >> Issue (03): 307-.

• 农业发展 • 上一篇    下一篇

基于高光谱遥感和HJ1卫星的冬小麦SPAD反演研究

夏 天| 周 勇| 周清波| 吴文斌   

  1. (1.华中师范大学城市与环境科学学院|湖北 武汉 430079; 2.中国农业科学院农业资源与农业区划研究所|北京 100081;3.农业部资源遥感与数字农业重点开放实验室|北京 100081
  • 出版日期:2013-03-20

MONITORING WINTER WHEAT SPAD BASED ON HYPERSPECTRAL REMOTE SENSING AND HJ1

XIA Tian1,2,3, ZHOU Yong1, ZHOU Qingbo2,3, WU Wenbin2,3   

  1. (1.College of Urban and Environment Sciences,Huazhong Normal University,Wuhan 430079,China; 2.Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing 100081,China|3.Key Laboratory of Agricultural Information Technology,Ministry of Agriculture,Beijing 100081,China
  • Online:2013-03-20

摘要:

冬小麦SPAD(Soil and Plant Analyzer Development)是评价其健康状况的重要农学参数,传统监测方法效率较低,旨在将田间监测和遥感技术相结合,探讨我国江汉平原地区冬小麦SPAD的遥感监测方法。研究选取湖北省潜江市后湖管理区为研究区域,利用ASD Fieldspec 3地物光谱仪和SPAD502叶绿素仪在田间采集冬小麦冠层光谱和叶片SPAD,选取4种植被指数与叶片SPAD进行回归分析并构建预测模型。经模型精度检验,NDVI较适合对该地区冬小麦SPAD反演。然后将NDVISPAD反演模型与HJ1卫星影像相结合,进行研究区域的冬小麦SPAD反演,通过比较HJ1卫星影像反演SPAD与田间实测值,经分析均方根误差(RMSE)为632。结果表明,利用NDVI植被指数模型能够较好进行研究区内冬小麦SPAD反演。实现了从地面监测到卫星遥感不同尺度的冬小麦SPAD反演,为大面积冬小麦SPAD监测提供技术和方法

Abstract:

SPAD (Soil and Plant Analyzer Development) is known as the green leaves degrees,which means the parameters of leaf in relative chlorophyll content.SPAD is the important parameters of agronomy for assessing the winter wheat health condition.Therefore,crops in the farmland can be diagnosed and managed based on crop leaf chlorophyll content or SPAD.However,the traditional monitoring method is low efficient in monitoring the SPAD.This paper aims to integrate the field monitoring and remote sensing techniques in order to monitor the growth of winter wheat at Jianghan plain,China.The study was carried out at Houhu Management District of Qianjiang city,South China’s Jianghan Plain.At a winter wheat growth stage,the winter wheat canopy spectral reflectance and SPAD were monitored in field by using the ASD FieldSpec 3 and SPAD502.The Winter wheat spectroscopic monitoring were on a clear day or the breeze weather at time during 10〖DK〗∶00~14〖DK〗∶00.The monitor fiber was from the soil surface 120 cm.The ASD system cans automatically recorded 10 sampling information with its average as the spectral reflectance unobserved.This study randomly selected winter wheat leaves for monitoring SPAD in each period of winter wheat.This research combined the winter wheat growth period spectrum reflectance characteristics change,selected the four Hyperspectral Vegetation Index(GRVI,RVI,NDVI and DVI) with the winter wheat SPAD to build estimate model. Then the correlation between the Hyperspectral Vegetation Index (HVI) and SPAD was analyzed by SPSS.Four inversion models were constructed for estimating SPAD by using correlation analysis,regression analysis and other methods.The model tested NDVI accuracy,and found that NDVI was suitable for winter wheat SPAD inversion to the region.And then,it combined NDVISPAD inversion model and HJ1 remote sensing to monitoring winter wheat SPAD.By comparing HJ1 remote sensing with the field measured SPAD,the rootmeansquare error (RMSE) was 632.It is concluded that the research used differed scales from ground monitoring to remote sensing monitoring to the SPAD of winter wheat,and this is proved to be an effective method and technology

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