基于无人机高光谱影像的引黄灌区水稻叶片全氮含量估测  

Rice leaf nitrogen content estimation based on hysperspectral imagery of UAV in Yellow River diversion irrigation district

作　　者：秦占飞[1,2] 常庆瑞[1] 谢宝妮 申健[1] Qin Zhanfei Chang Qingrui Xie Baoni Shen Jian(College of Natural Resources and Environment, Northwest A ＆_F University, Yangling 712100, China Institute of Land Resource and Urban and Rural Planning, Hebei GEO University, Shijiazhuang 050031, China)

机构地区：[1]西北农林科技大学资源环境学院,杨凌712100 [2]河北地质大学土地资源与城乡规划学院,石家庄050031

出　　处：《农业工程学报》2016年第23期77-85,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家(863计划)项目(2013AA102401-2);高等学校博士学科点专项科研基金(20120204110013)

摘　　要：实时监测水稻氮素状况对于评估水稻长势及精准田间管理意义重大。为确定宁夏引黄灌区水稻叶片全氮含量的最优高光谱估测方法,该文依托不同氮素水平水稻试验,基于成像高光谱数据和无人机高光谱影像,综合运用统计分析及遥感参数成图技术,对比分析光谱指数与偏最小二乘回归方法预测水稻叶片全氮含量的精确度和稳健性。结果表明,以组合波段73-8和522 nm光谱反射率的一阶导数构成的比值光谱指数（ratio spectral index,RSI）构建的线性模型为水稻叶片全氮含量的最优估测模型（检验R^2为0.673,均方根误差为0.329,相对分析误差为2.02）;无人机高光谱影像反演的水稻叶片全氮含量分布范围（1.28%-2.56%）与地面实际情况较相符（1.34%-2.49%）。研究结果可为区域尺度水稻氮素含量的空间反演及精准农业的高效实施提供科学和技术依据。Nitrogen is essential for the improvement of photosynthesis and productivity of plants. However, nitrogen fertilizer is also a significant non-point source of water and atmospheric pollution. Therefore, a timely and accurate assessment of leaf nitrogen content（LNC） in crops is critical for crop growth diagnosis and precision management, eventually promoting crop yield and quality while minimizing environmental costs. The aim of this study was to determine the most suitable algorithm, based on hyperspectral reflectance data, for the regional assessment of LNC at critical growth stages of paddy rice. In this study rice experiments with different nitrogen levels and growth stages were conducted at different sites of Ningxia irrigation zone. Ground-based hyperspectral datasets were obtained from the stem elongation stage to the dough grain stage at plot and field scales. The plot and field datasets were used for model calibration and validation, respectively. A hyperspectral imagery was obtained over the field region at milk grain stage using UHD 185 carried by an unmanned serial vehicle（UAV）. On the basis of a comprehensive analysis of the hyperspectral data, significant spectral indices（SIs） such as the normalized difference spectral index（NDSI） and ratio spectral index（RSI） were derived for an accurate and robust assessment of the LNC. Spectral indices representing a complete combination of the spectral bands between 450 nm to 950 nm were calculated using the NDSI and RSI formulations. The contour map of coefficient of determination（R^2） between LNC and the combinations of 2 separate wavelengths in the hyperspectrum was used to evaluate the new SIs through comparing the predictions with plot-experiment measurements and determine which one produce the higher prediction accuracy over the others. Then the predictions of the SIs were validated by independent datasets collected at field experiments. The capability of multivariable regression approaches such as partial least-squares regression（

关 键 词：氮素 无人机 作物 水稻 高光谱 波段选择 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置] S127[自动化与计算机技术—控制科学与工程]