土壤全氮的无人机高光谱响应特征及估测模型构建  

UAV hyperspectral response characteristics and estimation model construction of soil total nitrogen

作　　者：彭涛 赵丽 张爱军 杨晓楠 周智 常新汉 PENG Tao;ZHAO Li;ZHANG Aijun;YANG Xiaonan;ZHOU Zhi;CHANG Xinhan(College of Resources and Environment Sciences,Hebei Agricultural University,Baoding 071000,China;College of Land and Resources,Hebei Agricultural University,Baoding 071000,China;National Engineering Research Center for Agriculture in Northern Mountainous Areas,Baoding 071000,China;Hebei Mountain Research Institute,Baoding 071000,China)

机构地区：[1]河北农业大学资源与环境科学学院,保定071000 [2]河北农业大学国土资源学院,保定071000 [3]国家北方山区农业工程技术研究中心,保定071000 [4]河北省山区研究所,保定071000

出　　处：《农业工程学报》2023年第4期92-101,共10页Transactions of the Chinese Society of Agricultural Engineering

基　　金：河北省重点研发计划项目(19226421D)。

摘　　要：为更好地体现出光谱与土壤全氮(soil totalnitrogen,STN)含量之间的响应关系,实现以高光谱快速估测土壤全氮含量,该研究以无人机搭载高光谱传感器获取农田土壤高光谱影像,提取光谱反射率并进行数学变换,基于灰色关联度和皮尔逊相关系数提取各光谱中土壤全氮含量的敏感波段,基于敏感波段采用偏最小二乘回归(partialleastsquares regression,PLSR)、岭回归(ridge regression,RR)和随机森林(random forest,RF)构建土壤全氮的高光谱反演模型,筛选出最优模型并对研究区土壤全氮含量进行反演制图。结果表明:1)反射率的倒数光谱中的敏感波段(996~1003 nm)集中在近红外长波范围内,反射率的一阶微分(first derivative of reflectance,FDR)光谱中的敏感波段(398~459、469和472~1003 nm)和反射率对数的一阶微分光谱中的敏感波段(398~459、463~973和978~1003 nm)在可见光和近红外范围内都有分布,反射率的一阶微分光谱中的敏感波段(615~625、632和666~670 nm)主要集中在可见光的红光范围内。2)与基于灰色关联度提取敏感波段构建模型相比,基于皮尔森相关系数提取敏感波段所构建的土壤全氮估测模型精度更高。3)RF-FDR模型精度最高,其验证集R^(2)为0.859,均方根误差为0.143 g/kg,平均绝对误差为0.114 g/kg。基于RF-FDR模型对研究区土壤全氮含量进行反演制图,发现研究区大部分面积土壤全氮含量处于1.50~2.00g/kg范围内,与实际情况相符。研究可为农田土壤全氮含量快速估测提供技术参考和支撑。Soil total nitrogen(STN)content can be accurately and rapidly estimated to better reflect the response relationship between spectrum and STN content.In this study,an unmanned aerial vehicle(UAV)equipped with a hyperspectral sensor was used to obtain the soil hyperspectral images in farmland.The original spectral reflectance(R)was then transformed into the reciprocal of reflectance(RR),logarithm of reflectance(LR),the first derivative of reflectance(FDR),the first derivative of reciprocal reflectance(FRR),and the first derivative of logarithm of reflectance(FLR).Grey correlation degree and Pearson correlation coefficient were also selected to extract the sensitive band of STN content in each spectrum.The hyperspectral inversion model of STN was finally constructed using the sensitive band,the partial least squares regression(PLSR),ridge regression(RR),and random forest regression(RF).The determination coefficient R^(2),root mean square error RMSE,and mean absolute error MAE were used to evaluate the accuracy of the model.After that,the model with the highest accuracy was selected for the inversion mapping of STN content in the study area.The availability of the model was tested,according to the distribution of STN content.The optimal model was selected to invert and map the STN content.The results showed that:1)The sensitive band(996-1003 nm)of the RR spectrum was concentrated in the near-infrared long wave range,according to the gray correlation degree and Pearson correlation coefficient.The sensitive bands in the FRR spectrum(398-459,469,and 472-1003 nm),and FLR spectrum(398-459,463-973,and 978-1003 nm)were distributed in the visible and near-infrared range.The sensitive bands(615-625,632,and 666-670 nm)in the FDR spectrum were concentrated mainly in the red range of visible light.2)Pearson correlation coefficient was used to better reflect the response relationship between spectrum and STN content.The STN inversion model R^(2),RMSE,and MAE were in the range of 0.058-0.693,0.226-0.477 g/kg,and 0.171-0.416 g/kg,r

关 键 词：无人机 氮 土壤 高光谱 灰色关联度 相关系数 反演模型 

分 类 号：S153.6[农业科学—土壤学]