冬小麦生物量高光谱遥感监测模型研究

发布时间：2018-03-16 08:57

  本文选题：农作物　切入点：冬小麦　出处：《植物营养与肥料学报》2017年02期 　论文类型：期刊论文

【摘要】：【目的】高光谱遥感能快速、实时、无损监测作物长势。研究不同氮磷水平下冬小麦不同生育时期地上部生物量高光谱遥感监测模型,可提高地上部生物量高光谱监测精度。【方法】在西北农林科技大学连续进行了5年田间定位试验,设置5个施氮水平(N,0,75,150,225和300 kg/hm~2)和4个磷施用水平(P2O5,0,60,120和180 kg/hm~2),选用不同抗旱类型冬小麦品种,测定了从拔节期至成熟期生物量与冠层光谱反射率,通过相关分析、回归分析等统计方法,建立并筛选基于不同植被指数的冬小麦不同生育时期生物量分段遥感监测模型。【结果】冬小麦生物量与光谱反射率在670 nm和930 nm附近具有较高相关性,在可见光和近红外波段处均有敏感波段;在拔节期、孕穗期、抽穗期、灌浆期、成熟期,生物量与归一化绿波段差值植被指数(GNDVI)、比值植被指数(RVI)、修正土壤调节植被指数(MSAVI)、红边三角植被指数(RTVI)和修正三角植被指数Ⅱ(MTVIⅡ)均达极显著相关性(P0.01),相关系数(r)范围为0.923~0.979;在不同生育时期,分别基于GNDVI、RVI、MSAVI、RTVI和MTVIⅡ能建立较好的生物量分段监测模型,决定系数(R2)分别为0.987、0.982、0.981、0.985、0.976;估计标准误差SE分别为0.157、0.153、0.163、0.133、0.132;预测值与实测值间相对误差(RE)分别为8.47%、7.12%、7.56%、8.21%、8.65%;均方根误差(RMSE),分别为0.141 kg/m~2、0.113kg/m~2、0.137 kg/m~2、0.176 kg/m~2、0.187 kg/m~2。【结论】在拔节期、孕穗期、抽穗期、灌浆期、成熟期可以用GNDVI、RVI、MSAVI、RTVI和MTVIⅡ监测冬小麦生物量,具有较好的年度间重演性和品种间适用性。同时,分段监测模型较统一监测模型具有较好的监测效果及验证效果,能有效改善高光谱遥感监测模型精度。
[Abstract]:[objective] Hyperspectral remote sensing can be used to monitor crop growth quickly, in real time and without damage. The hyperspectral remote sensing monitoring model of aboveground biomass of winter wheat at different growth and growth stages was studied under different nitrogen and phosphorus levels. The accuracy of hyperspectral monitoring of aboveground biomass could be improved. [methods] the field positioning experiment was carried out in Northwest University of Agriculture and Forestry Science and Technology for 5 years. The biomass and canopy spectral reflectance of winter wheat varieties with different drought resistance types were measured from jointing stage to maturity stage. Regression analysis and other statistical methods were used to establish and screen segmental remote sensing monitoring models of winter wheat biomass at different growth stages based on different vegetation indices. [results] there was a high correlation between biomass and spectral reflectance near 670 nm and 930 nm. There were sensitive bands in both visible and near infrared bands, at jointing stage, booting stage, heading stage, grain filling stage, mature stage, Biomass and normalized green band difference vegetation index (GNDVI), ratio vegetation index (RVI), modified soil adjustment vegetation index (MSAVI), red triangulation vegetation index (RTVI) and modified triangular vegetation index 鈪，

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