摘要
快速、准确地估算农田土壤有机碳含量并对其进行空间分布制图,有利于土壤精细化管理和智慧农业的发展。该文以青海湟水流域3个典型农田区为例,在研究区内同步采集296个土壤样品和相应的野外原位光谱,使用无人机搭载高光谱相机进行影像获取,并对土壤样品进行室内光谱采集和有机碳含量测定。对光谱反射率进行7种不同形式的变换,通过相关性分析从中筛选出主要特征波段,利用多元线性回归、偏最小二乘回归和随机森林3种方法分别对室内光谱、野外原位光谱和无人机光谱进行建模,对比各模型的精度。用光谱直接转换法对无人机光谱进行校正,使用校正后的无人机光谱最优模型进行建模,模型代入无人机高光谱影像进行有机碳含量制图,最后对满足制图精度要求的农田区进行分析和讨论。结果表明:①除对无人机高光谱进行对数变换后的多元线性回归不能估算有机碳外(相对分析误差为1.375),实验室光谱、野外原位光谱及无人机高光谱的原始光谱及所有转换方法均能对有机碳进行估算,决定系数R 2为0.562~0.942,均方根误差为1.713~5.211,相对分析误差为1.445~4.182;②在所有光谱变换方法中,多元散射校正+一阶微分变换与有机碳含量的相关性最高,特征波段分别为429~449 nm,498~527 nm,830~861 nm和869 nm;③在所有建模结果中,随机森林模型精度最高,其次为偏最小二乘模型,多元线性回归模型精度最低,校正后的无人机光谱建模精度均有所提高;④3个农田区的反演精度均满足制图要求,R 2均在0.88以上。其中,A农田区有机碳含量均值最高,为28.88 g·kg^(-1),整体空间分布均匀;B农田区均值为13.52 g·kg^(-1),整体分布呈现出较强的空间差异性;C农田区有机碳含量均值最低,为8.54 g·kg^(-1),高值和低值的分化明显。本研究可为无人机高光谱遥感技术应用于田间尺度的土壤有机碳含
Rapid and accurate estimation and spatial distribution mapping of soil organic carbon content in farmland facilitate the refined management of soil and the development of smart agriculture.This study investigated three typical farmland areas in the Huangshui River basin of Qinghai Province using 296 soil samples and corresponding field in situ spectra collected synchronously.The unmanned aerial vehicle(UAV)with a hyperspectral camera was employed for image acquisition,and the soil samples were tested for spectral acquisition and organic carbon content in the laboratory.The spectral reflectance was transformed into seven different forms,and the main characteristic bands were screened out through correlation analysis.Using multiple linear regression,partial least squares regression,and random forest,the experimental spectra,field in situ spectra,and UAV spectra were modeled,with the accuracy of the models compared.The UAV spectra were corrected using the direct spectral conversion method,and the optimal model of corrected UAV spectra was used for modeling.The model was substituted into the UAV hyperspectral images for the organic carbon content mapping.Finally,the farmland areas meeting the mapping accuracy requirements were analyzed and discussed.The results show that:①The multiple linear regression after logarithmic transformation of UAV hyperspectra failed to estimate the organic carbon content,with a relative percent deviation(RPD)of 1.375.Except for it,the experimental spectra,field in situ spectra,and original spectra of UAV hyperspectra as well as all conversion methods could estimate the organic carbon content,with coefficients of determination(R 2)ranging from 0.562 to 0.942,root mean square errors(RMSEs)ranging from 1.713 to 5.211.and RPDs between 1.445 and 4.182;②Among all spectral transformation methods,multiple scatter correction and first-order differential transformation exhibited the highest correlation with the organic carbon content,presenting characteristic bands of 429~449 nm,498~527 nm,830~
作者
宋奇
高小红
宋玉婷
黎巧丽
陈真
李润祥
张昊
才桑洁
SONG Qi;GAO Xiaohong;SONG Yuting;LI Qiaoli;CHEN Zhen;LI Runxiang;ZHANG Hao;CAI Sangjie(School of Geographical Sciences,Qinghai Normal University,Xining 810008,China;Qinghai Province Key Laboratory of Physical Geography and Environmental Process,Xining 810008,China;MOE Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological,Xining 810008,China;Academy of Plateau Science and Sustainability,Xining 810008,China)
出处
《自然资源遥感》
CSCD
北大核心
2024年第2期160-172,共13页
Remote Sensing for Natural Resources
基金
国家自然科学基金项目“近地传感、无人机及卫星遥感相结合的土壤表层关键属性多尺度估算”(编号:42161061)资助。
关键词
无人机
高光谱遥感
土壤有机碳
光谱特征选择
光谱校正
unmanned aerial vehicle(UAV)
hyperspectral remote sensing
soil organic carbon
spectral feature selection
spectrum correction
