基于无人机多光谱影像的冬小麦返青期变量施氮决策模型研究

Decision Model of Variable Nitrogen Fertilizer in Winter Wheat Returning Green Stage Based on UAV Multi-Spectral Images

氮素是影响冬小麦生长的重要元素,如何根据冬小麦需求适时变量施用氮肥是现代农业精准施肥研究需要解决的关键问题之一。无人机遥感技术在冬小麦生长情况监测中具有高分辨率、高时效性、低成本等优势,为解决施肥需求监测问题提供了重要数据源。因此研究无人机多光谱影像数据,构建其与冬小麦产量与施肥量之间的关系模型对于精准施肥研究十分重要。选择冬小麦典型生产区山东省桓台县为实验区,布置4种不同施氮水平的田间实验。利用无人机搭载Sequoia多光谱传感器,采集实验区不同氮素施肥水平的冬小麦返青初期多光谱影像,同时测得冬小麦冠层叶绿素含量(soil and plant analyzer development,SPAD)数据及产量数据。通过多光谱影像数据计算获得归一化植被指数(normalized difference vegetation index,NDVI)、叶绿素吸收指数(modified chlorophyll absorption ratio index,MCARI2)等6种形式植被指数,建立无人机多光谱影像植被指数与小麦冠层SPAD值的线性、二阶多项式、对数、指数和幂函数模型,优选地面氮素状况最优植被指数模型,反演冬小麦不同施氮水平的状况,进而根据不同施氮水平与敏感植被指数和冬小麦产量的关系,构建了基于植被指数指标的氮肥变量施肥模型,并将模型应用于同时期小麦多光谱影像。结果如下:(1)地面实测的SPAD值能较好的反映冬小麦施氮水平及生长状况。无人机多光谱数据分区统计结果表明不同施氮水平冬小麦冠层反射率有较大差异性。(2)结构性植被指数与SPAD拟合效果优于其他类型指数。MCARI2的二阶多项式模型精度最优(R2=0.790,RMSE=0.22),其能较好的移除冬小麦返青初期土壤背景等因素的影响,为氮肥敏感植被指数。(3)基于产量-施氮量模型和产量-敏感植被指数模型,构建敏感植被指数的氮肥变量施肥模型为Nr=10 707.63×MCARI22-5 992.36×MCARI2+715.27。通过模型应用生成了实验区冬小麦氮肥变量施肥图,与实际情况具有较高一致性。该研究提出了利用无人机多光谱数据进行冬小麦施氮决策的模型及方法,为冬小麦精准施肥的进一步研究提供了依据。

Nitrogen is an important element affecting the growth of winterwheat.The real-time application of nitrogen fertilizer based on the demand of winterwheat is one of the key problems to be solved in modern agricultural precision fertilization. Unmanned Aerial Vehicles(UAV)remote sensing technology has the advantages of high resolution,high timeliness and low cost in the monitoring of winterwheat growth,which provides an important data source for solving the problem ofwinter wheat fertilizer demand monitoring.Therefore,studying the multi-spectral image data of UAV and constructing its relationship model with winter wheat yield and fertilization is very important for precision fertilization research.This study carried out field trials with four different kinds of nitrogen levels in a typical production area of winter wheat in Huantai,Shandong.The multispectral images of winter wheat at the returning green stage were collected from experimental area with different nitrogen fertilization levels using Sequoia multispectral sensor equipped with UAV.Meanwhile,winter wheat canopySoil and Plant Analyzer Development( SPAD)and yield were measured.Six vegetation index such as NDVI,SAVI and MCARI2were obtained after calculation, and established UAV multispectral images vegetation indexes and the winter wheat canopy SPAD of linear function,quadratic polynomial function,logarithm function,exponential function and power function,to screen out the sensitivity index of winter wheat canopy reflecting different nitrogen levels.Further,according to the relationships of different nitrogen fertilization levels with sensitive vegetation indexes and winter wheat yield,a variable nitrogen fertilization model based on vegetation indexes was constructed and applied to simultaneous images.The results are as follows:(1)SPAD could reflect the nitrogen fertilization level and growth of winter wheat,and the canopy reflectance of winter wheat with different nitrogen fertilization levels varied greatly.( 2)The structural vegetationindex and SPAD fit better than other types of index.and the optimal vegetation index of the estimation model established based on SPAD was MCARI2(R2=0.790,RMSE=0.22),which was considered as the sensitive vegetation index of nitrogen fertilizer.(3)Based on the yield-nitrogen fertilizer model and yield-sensitive vegetation index model,the variable rate fertilization model of nitrogen fertilizer was Nr=10 707.63×MCARI22-5 992.36×MCARI^2+715.27.Based on the model,a variable nitrogen fertilization map for winter wheat was produced in the experimental area,which was highly consistent with actual fertilization.In this study,the model and method of nitrogen fertilization for winter wheat based on UAV multispectral data was proposed,which provides areference for the precise fertilization of winter wheat.