摘要
【目的】叶片氮素状况是小麦生产中精确施氮管理与调控的前提,实时无损监测叶片氮素状况对小麦生产管理具有重要意义。本文旨在综合分析不同环境下小麦冠层光谱响应差异,进而构建其估测模型,为小麦氮肥合理运筹提供技术支持。【方法】本研究基于3种不同土壤质地(砂土、壤土和黏土)、5种不同施氮水平(0、120、225、330和435 kg.hm-2)及3种河南省主栽小麦品种(矮抗58、周麦22和郑麦366)连续2年的大田试验,于小麦主要生育时期同步测定冠层光谱反射率和叶片氮含量,对3种不同土壤质地条件下小麦冠层叶片氮含量的高光谱响应差异进行比较,系统分析350—1 050 nm波段范围内任意两波段组合而成的差值(DSI)、比值(RSI)及归一化差值(NDSI)光谱指数与叶片氮含量的量化关系,并建立估算模型。【结果】冠层光谱反射率在不同施氮水平和不同生育时期下存在明显差异,但趋势基本一致;比较3种土壤质地小麦冠层光谱反射率大小表现为:黏土>壤土>砂土,可以反映小麦实时田间长势。通过系统分析3种土壤质地小麦冠层反射光谱与对应叶片氮含量间的定量关系,表明在可见光和近红外区域均有较好的相关性,但敏感波段区域有所不同。对3种质地获取的样本进行系统分析表明,砂土、壤土和黏土质地小麦叶片氮含量分别以光谱指数NDSI(FD710,FD690)、DSI(R515,R460)和RSI(R535,R715)建模结果表现最好,决定系数分别达到0.88、0.87和0.87。经不同年份独立资料检验结果显示,基于上述光谱指数估测小麦叶片氮含量的预测决定系数分别为0.87、0.85和0.77,预测均方根误差分别为0.31、0.32和0.26。【结论】利用光谱参数NDSI(FD710,FD690)、DSI(R515,R460)和RSI(R535,R715)为自变量建立的估测模型分别可以较好地预测砂土、壤土和黏土3种质地小麦叶片氮含量。
[ Objective] Leaf nitrogen status is a premise for management and control of precise-using nitrogen in wheat production. Non-destructive and real-time assessment of leaf nitrogen content （LNC） has an important significance for production and management of wheat. [Method] Two field experiments were conducted with three different soil textures （sand, loam and clay）, five different nitrogen levels （0, 120, 225, 330 and 435 kg.hm2） and 3 main wheat cultivars in Henan （Aikang58, Zhoumai22 and Zhengmai366） across growing seasons. High spectral reflectance and LNC of canopy were taken by synchronous measurement during main growth stages of wheat. Compared with the high spectral response differences of canopy LNC in wheat under the three different soil textures, several kinds of hyperspectral indices including difference spectral indices （DSI）, ratio spectral indices （RSI） and normalized difference spectral indices （NDSI） with all combinations of two wavebands between 350 and 1 050 nm were calculated, their relationships with LNC were analyzed, and the estimation models were established. [Result] The experimental results showed that there was an obvious difference in the spectra of canopy reflectance under different nitrogen levels and different growth periods, but the trend was almost consistent. Compared with the spectra of canopy reflectance in the three different soil textures, the performance was clay〉loam〉sand, it could reflect the real-time field growing in wheat. The quantitative relationships between the spectra of canopy reflectance and the associated LNC under the three soil textures were systematicaly analyzed, and the calculated results showed that there was a better correlation between the visible and near-infrared area with the different sensitive band intervals. NDSI （FD710, FD690）, DSI （R515, R460） and RSI （R535, R715） were the best indicators to the integrated modeling of LNC in sand, loam and clay, with the predictive determination coefficient （R2） of 0.88, 0.87 and 0.87. Testing the above better spectral parameters of monitoring models with independent sample in 2010-2011, the results reconfirmed that they were the best indicators, with the predictive determination coefficient （R2） of 0.87, 0.85 and 0.77 respectively, and the root mean square error （RMSE） of 0.31, 0.32 and 0.26, respectively. [ Conclusion ] The monitoring model which used the high spectral parameter of NDSI （FD710, FD690）, DSI （R515, R460） and RSI （R535, R715） as independent variables, could be used for better estimation of the LNC of wheat in sand, loam and clay soils.
出处
《中国农业科学》
CAS
CSCD
北大核心
2013年第13期2655-2667,共13页
Scientia Agricultura Sinica
基金
河南省小麦产业体系项目(S2012-01-G04)
河南省科技攻关项目(112102110030)
公益性行业(农业)科研专项经费(201303109-6)
关键词
小麦
土壤质地
叶片氮含量
高光谱遥感
监测模型
wheat
soil texatre
leaf nitrogen content
hyperspectral remote sensing
monitoring model
作者简介
联系方式：翟清云，E—mail：jyzhaizhai1987@163．com。
通信作者马新明，E-mail：xinmingma@126．com
