基于高光谱植被指数的冬小麦估产模型  

Development of winter wheat yield estimation models based on hyperspectral vegetation indices

作　　者：肖璐洁[1] 杨武德[1] 冯美臣[1] 孙慧 王超[1] XIAO Lu-jie;YANG Wu-de;FENG Mei-chen;SUN Hui;WANG Chao(College of Agronomy,Shanxi Agriculural University,Taigu 030801,Shanxi,China)

机构地区：[1]山西农业大学农学院,山西太谷030801

出　　处：《生态学杂志》2022年第7期1433-1440,共8页Chinese Journal of Ecology

基　　金：国家自然科学基金项目(31371572,31201168,31871571);山西省应用基础研究项目(201801D221299);山西农业大学科技创新基金项目(2020BQ32)资助。

摘　　要：粮食遥感估产是农情遥感业务中重要的组成部分。及时准确进行粮食产量早期预测预报,对于国家相关部门制定粮食供需政策、进行粮食安全宏观调控和粮食贸易决策具有重要意义。本研究以不同水分处理试验为基础,利用ASD FieldSpec 3野外光谱仪测定冬小麦关键生育时期冠层光谱反射率,计算29种高光谱植被指数,筛选出与产量相关性较高的植被指数,分别建立基于单植被指数与多植被指数组合的冬小麦估产模型。结果表明:利用冬小麦孕穗期和抽穗期光谱反射率计算植被指数,预测冬小麦产量最为可靠有效;对于单植被指数,以抽穗期-DVI-3所建估产模型精度最高(R^(2)=0.59,RMSE=977.60 kg·hm^(-2));基于多植被指数组合所构建的冬小麦产量预测模型明显优于单植被指数,有效提高了产量预测精度,以抽穗期-VIs所建产量预测模型预测效果最好(R^(2)=0.69,RMSE=889.55 kg·hm^(-2))。本研究结果可为黄土高原旱区冬小麦高光谱遥感估产提供科学依据。The estimation of grain yield by remote sensing is an important component of agricultural remote sensing.Timely and accurate early prediction of grain yield is of great significance for relevant national sectors to make grain marketing polices,to conduct macroeconomic regulation of food security,and to make grain trade decisions.Under different moisture treatments in arid regions of the Loess Plateau,we used ASD FieldSpec-3 spectrometer to determine the spectral reflectance of winter wheat during key growth stages.A total of 29 vegetation indices were calculated.The vegetation indices highly correlated with grain yield were screened out,and the winter wheat yield estimation models were constructed based on either the single vegetation index or the combination of multiple vegetation indices.The results showed that the most reliable and effective index for yield prediction was the spectral reflectance data collected during the booting and heading stages.The highest accuracy of yield estimation model constructed with single vegetation index was determined based on heading stage data(DVI-3)(R^(2)=0.59,RMSE=977.60 kg·hm^(-2)).The models constructed with the combination of multiple vegetation indices were better than those with a single vegetation index,and generated greater accuracy of yield prediction,with the model built with heading stage data-VIs showing the best prediction result(R^(2)=0.69,RMSE=889.55 kg·hm^(-2)).Our results can provide scientific basis for yield estimation of winter wheat by hyperspectral remote sensing in arid regions of the Loess Pla-teau.

关 键 词：冬小麦 干旱胁迫 冠层光谱 植被指数 估产 

分 类 号：S512.11[农业科学—作物学] S127