摘要
利用高光谱遥感技术反演土壤性质已经成为土壤学和遥感科学研究领域的新手段，特别对土壤化学元素含量的高光谱反演，已成为土壤元素快速监测方法的的研究热点。以往研究往往关注不同类型土壤的化学元素光谱响应特征模型，以试图找到普适性的元素一光谱反演模型。由于成土因素的复杂性，土壤类型及其化学元素分布具有明显的空间异质性特征，宏观尺度上的土壤一光谱统计反演模型客观上具有较大的不确定性。若范围缩小到同一个气候带，土壤生物地球化学反应过程较相似，土壤化学元素一光谱反演模型的不确定性相对较小。以福州市为研究区，采集福州市典型红壤样品135个，研究土壤全氮含量的高光谱响应特征，对土壤样品在350～2500nm的光谱反射率分别进行倒数对数、微分等五种变换，分析变换后的光谱信息与土壤总氮含量的相关性，筛选出强相关敏感波段，通过设计不同的建模和验证样品比例，用逐步多元线性回归获得福州土壤的氮元素高光谱反演优化模型。结果表明：亚热带红壤全氮的敏感光谱波段为：可见光634～688nm和红外872，873，1414和1415nm；亚热带沿海地区土壤全氮一高光谱反演的优化模型为：Y-5．384X664-1．039（决定系数彤为0．616，均方根误差为0．422nag·g^-1，检验R^2为0．608，均方根误差为0．546mg·g。），该模型可以用于福州地区土壤全氮的光谱快速监测。
The present paper studied the hyperspectral response cnaractensncs oi rea soil, with ,135,soil samples in Fuzhou city After monitoring the hypersectral reflection of soil samples with ASD （analytical spectral device） and total nitrogen contents with Vario MAX（for nitrogen and carbon analysis）, the paper gained the spectral reflection data between 350～2 500 nm （resolution is 1 nm） and soil total nitrogen contents. Then the paper treated the hyperspectral reflection data with 5 mathematic conversions such as first derivative and second derivative conversions of original reflection, reciprocal logarithmic conversion and its first de rivative and second derivative conversion in advance. The next step was to calculate the correlation coefficient of soil nitrogen and the above spectral information, and select the sensitive spectral bands according to the highest correlation coefficient. Finally, by designing different proportions of modeling and validation sample data sets, the paper established the quantitative linear models between soil total nitrogen contents and hyperspectral reflection and its 5 converted information, the final optimal mathematic model between soil nitrogen and hyperspectral information was significantly determined. Results showed that 634～688, 872, 873, 1 414 and 1 415 nm were the main sensitive bands for soil total nitrogen, and Y=5. 384X664 -1. 039 （Y represents soil ni trogen content, X664 is the soil spectral absorbance value at 664 nm） was the optimal soil total nitrogen predicting model （in the model, the determination coefficients R2 and the RMSE of total nitrogen were 0. 616 and 0. 422 mg. g^-1 , the inspection coeffi cient R2 and the RMSE were 0. 608 and 0. 546 mg. g^-1 respectively）. The model can be used to rapidly monitor soil total nitro- gen with hyperspectral reflection in Fuzhou area.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2013年第11期3111-3115,共5页
Spectroscopy and Spectral Analysis
基金
欧盟第七框架项目(IGIT:247608)
科技部专项(247608)
福建省外专局重点项目
福建2012年高等学校优秀学科带头人赴海外访学研修项目资助~~
关键词
土壤
总氮
高光谱
多元线性回归
Soil
Total nitrogem Hyperspectral Multivariate linear regression
