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为进一步深化作物长势遥感监测机理与方法,给大田管理及时提供信息与技术,结合2011-2013年定点观测试验,以HJ-1A/1B数据为遥感影像源,研究了返青期冬小麦主要生长指标、籽粒品质参数和产量间及其与遥感变量间的定量关系,分别构建及评价基于HJ-1A/1B影像遥感变量的返青期叶面积指数、生物量、SPAD值和叶片含氮量监测模型。结果表明,返青期,归一化植被指数(NDVI)、比值植被指数(RVI)、蓝光波段反射率(B1)和RVI可分别作为监测冬小麦叶面积指数、生物量、SPAD和叶片含氮量的敏感遥感变量,所构建的遥感监测模型可靠且精度较高,模型的决定系数(R2)分别为0.62、0.56、0.46和0.58,均方根误差(RMSE)分别为0.42、452.3kg·hm-2、4.39和0.54%。同时,对冬小麦不同等级主要生长指标进行遥感监测并制图,量化表达了主要生长指标区域空间分布。
In order to further improve the mechanism and method of remote sensing monitoring of crop growth and provide timely information and technology to Daejeon Management, combined with the fixed point observation test from 2011 to 2013 and the HJ-1A / 1B data as remote sensing image source, the main growth indices of winter wheat during turning- The quantitative relationship between grain quality parameters and yield and its relationship with remote sensing variables was used to construct and evaluate the monitoring model of leaf area index, biomass, SPAD and leaf nitrogen content based on HJ-1A / 1B remote sensing variables. The results showed that normalized NDVI, RVI, B1 and RVI could be used as indicators for monitoring leaf area index, biomass, SPAD and leaf nitrogen content of winter wheat Sensitive remote sensing variables, the constructed remote sensing monitoring model is reliable and has high precision. The determination coefficients (R2) of the model are 0.62, 0.56, 0.46 and 0.58, respectively, and the root mean square error (RMSE) are 0.42, 452.3 kg · hm-2 , 4.39 and 0.54% respectively. At the same time, the main growth indexes of different grades of winter wheat were monitored remotely and mapped, and the spatial distribution of the main growth indexes was quantitatively expressed.