摘要
叶绿素含量是评价植物生长状况的重要参数.为建立不同生育期玉米(Zea Mays L.)叶绿素含量估算模型,考虑土壤、大气的影响以及卫星遥感影像的适用性.以夏玉米为研究对象,建立拔节期、灌浆期、乳熟期和完熟期玉米叶绿素含量估算模型,采用决定系数(R^(2))、均方根误差(RMSE)作为模型的评价和检验指标,基于Planet Labs遥感卫星(简称PL)数据和野外踏查数据对模型进行验证.结果表明,GARI植被指数建立的玉米叶绿素含量估算模型在拔节期和乳熟期运算值最优,在灌浆期GNDVI植被指数构建的模型运算效果最好,完熟期的最佳模型由NDVI植被指数建立.4个生育期模型的R^(2)分别为:0.76、0.85、0.84、0.88,模型精度分别为:80%、90%、90%和90%.该模型可以用于田间尺度快速、高效地估算玉米叶绿素含量,对农作物长势、品质评价和产量估算提供理论依据.
Chlorophyll is an important parameter to evaluate plant growth status.In order to establish the model for estimating the chlorophyll content of maize at different growth stages,the influence of soil and atmosphere and the applicability of satellite remote sensing images were considered.Taking summer maize as the research object,the estimation model of chlorophyll content in elongation stage,filling stage,milk stage and maturity stage was established.The coefficient of determination(R^(2))and root mean square error(RMSE)were used as evaluation and test indexes.The model was validated based on Planet Labs remote sensing satellite(PL)data and field trip data.It showed that:the Green atmospherically resistant vegetation index(GARI)established the best model for estimating the chlorophyll content of maize at elongation stage and milk stage.In the filling stage,the model based on Green Normalized Differential Vegetation Index(GNDVI)has the best result.The optimal model of the maturity stage was based on the Normalized Differential Vegetation Index(NDVI).Value of R^(2) of these models in the four growth stages were 0.76,0.85,0.84 and 0.88,respectively,and the models accuracy was 80%,90%,90%and 90%,respectively.Therefore,this model can be used to estimate rapidly and efficiently the chlorophyll content of maize in field scaleand provide theoretical basis for crop growth,quality evaluation and yield estimation.
作者
陈圣波
陈彦冰
任枫荻
郑煜
CHEN Shengbo;CHEN Yanbing;REN Fengdi;ZHENG Yu(College of Earth Exploration Science and Technology,Jilin University,Changchun 130026,China)
出处
《信阳师范学院学报（自然科学版）》
CAS
北大核心
2021年第2期225-229,共5页
Journal of Xinyang Normal University(Natural Science Edition)
基金
国家高分辨率对地观测系统重大科技专项省(自治区、市)域产业化应用(71-Y40G04-9001-15/18)
吉林省省校共建计划专项(71-Y40G04-9001-15/18)。
关键词
植被指数
玉米
叶绿素估算
高光谱数据
vegetation index
maize
chlorophyll estimation
hyperspectral data
