학술논문
基于光谱指数波段优化算法的小麦玉米冠层含氮量估测 / Study on the Estimation of Nitrogen Content in Wheat and Maize Canopy Based on Band Optimization of Spectral Parameters
Document Type
Academic Journal
Author
Source
光谱学与光谱分析. 36(4):1150-1157
Subject
Language
Chinese
ISSN
1000-0593
Abstract
作物关键生育时期冠层氮素含量的实时监测对于优化氮肥用量和减少环境风险具有重要的意义。为了寻求预测不同作物氮素含量的最佳光谱参数，实现作物氮素无损营养诊断。本研究通过2008年—2011年在德国慕尼黑弗莱辛和河北曲周的不同氮量的小麦玉米田间试验，采用高光谱仪获取小麦玉米冠层的反射光谱，利用光谱理论模型进行光谱指数波段的优化，从而抽取不同冠层结构条件下的小麦玉米氮素营养敏感波段。结果表明与传统的基于红光的光谱指数相比，优化光谱指数显著提高了小麦玉米冠层氮素含量的预测能力，克服了传统的基于红光光谱指数的饱和问题。优化光谱指数的波段结合随着作物品种及其冠层结构的变化而变化，其优化波段范围主要集中在红边（730～760 nm ）和红边向近红外的过渡区域（760～880 nm ）。优化结果显示玉米最佳光谱指数为 Rλ766／Rλ738－1，小麦最佳光谱指数为 Rλ796／Rλ760－1，玉米小麦相结合优化后的最佳光谱指数为 Rλ876／Rλ730－1。结果进一步验证了优化光谱指数估测的不同作物含氮量的预测值与实测值相关性最高，且验证偏差最小，证实了优化后的光谱特征参数可对不同作物氮素丰缺状况进行快速、准确、无损估测。试验结果也为设计作物冠层氮素传感器和更好的利用现有基于卫星的传感器实施区域上的作物氮素营养监测提供了理论基础。
Nitrogen fertilizer plays a crucial role in keeping food production in pace with population grow th .However ,the excee‐ding application of nitrogen fertilizer causes environmental risks .Timely and accurate quantification of canopy nitrogen content in crops is important for the rational application of nitrogen fertilizer and reduction environmental risks .The current research aimed to remotely estimate canopy nitrogen content in winter wheat and summer maize .Experiments with different N rates for different cultivars of wheat and maize were conducted in southeast Germany and in the North China Plain from 2008 to 2011 .The results showed that ,compared with traditional red light based spectral parameters ,optimized spectral parameters significantly improved the prediction capacity ,overcoming saturation problem in deriving canopy nitrogen content of wheat and maize .Band combina‐tion of optimized parameters varied with the difference in species and canopy structure of crops .The optimized bands concentrat‐ed on 730~760 and 760~800 nm .The best performing spectral parameter was Rλ766/Rλ738 -1 for maize ,Rλ796/Rλ760 -1 for wheat and Rλ876/Rλ730 -1 for wheat and maize combination .The validation results further confirmed that the optimized spectral parameters had the lowest predictive error ,indicating that optimized spectral indices could estimate canopy nitrogen content of crops .In conclusion ,the band optimization of spectral parameters is a promising approach to derive canopy N content in wheat and maize .The findings from this study may be useful for designing improved nitrogen diagnosis systems and for enhancing the applications of satellite‐based sensors .
Nitrogen fertilizer plays a crucial role in keeping food production in pace with population grow th .However ,the excee‐ding application of nitrogen fertilizer causes environmental risks .Timely and accurate quantification of canopy nitrogen content in crops is important for the rational application of nitrogen fertilizer and reduction environmental risks .The current research aimed to remotely estimate canopy nitrogen content in winter wheat and summer maize .Experiments with different N rates for different cultivars of wheat and maize were conducted in southeast Germany and in the North China Plain from 2008 to 2011 .The results showed that ,compared with traditional red light based spectral parameters ,optimized spectral parameters significantly improved the prediction capacity ,overcoming saturation problem in deriving canopy nitrogen content of wheat and maize .Band combina‐tion of optimized parameters varied with the difference in species and canopy structure of crops .The optimized bands concentrat‐ed on 730~760 and 760~800 nm .The best performing spectral parameter was Rλ766/Rλ738 -1 for maize ,Rλ796/Rλ760 -1 for wheat and Rλ876/Rλ730 -1 for wheat and maize combination .The validation results further confirmed that the optimized spectral parameters had the lowest predictive error ,indicating that optimized spectral indices could estimate canopy nitrogen content of crops .In conclusion ,the band optimization of spectral parameters is a promising approach to derive canopy N content in wheat and maize .The findings from this study may be useful for designing improved nitrogen diagnosis systems and for enhancing the applications of satellite‐based sensors .