高光谱技术联合归一化光谱指数估算土壤有机质含量

随着近地高光谱遥感技术的发展，为快速、有效、非破坏性地获取土壤有机质(SOM)信息提供了可能。土壤高光谱波段数据众多，光谱数据变量之间存在较为严重的多重共线性，影响模型复杂结构，而构建归一化光谱指数(NDSI)可以有效去除冗余信息变量，放大光谱特征信息。以江汉平原公安县为研究区，采集56份耕层土样，在室内获取土壤光谱数据，采用“重铬酸钾-外加热法”测定SOM含量，对实测土壤光谱数据(Raw)进行倒数之对数(LR)、一阶微分(FDR)和连续统去除(CR)三种变换，计算四种变换的NDSI数值，分析SOM与NDSI的二维相关性，并对一维、二维相关系数进行全波段范围内的p=0.001水平上显著性检验，提取敏感波段和敏感光谱指数，结合偏最小二乘回归(PLSR)建立SOM的估算模型，探讨二维光谱指数用于建模的可行性。研究表明，二维相关系数相比一维相关系数有不同程度的提升，以LR最为显著，相关系数数值提升约0.26；基于二维相关性分析提取的敏感光谱指数的PLSR建模效果整体优于一维相关性分析提取的敏感波段，其中，NDSI_LR-PLSR模型的稳健性最优，验证集R2为0.82，模型验证RPD值为2.46，模型稳定可靠，可以满足SOM的精确监测需要，适合推广到区域范围内低分辨率的航空航天遥感(如ASTER，Landsat TM等)，应用潜力较大。

Estimation of Soil Organic Matter Content Using Hyperspectral Techniques Combined with Normalized Difference Spectral Index

In recent years,proximal hyperspectral technology provides a new approach in timely,effectively and nondestructive way to detect soil organic matter (SOM).However,the hyperspectral dataset contains too many wavelengths which could lead to the collinearity,redundancy and noise to models.The Normalized Difference Spectral Index (NDSI)derived from soil spectral reflectance could enhance the relationship between spectral features and SOM,and also could eliminate the irrelevant wave-lengths.In this paper,56 topsoil samples at 0～20 cm depth were collected as research objects from Gong'an County in Jianghan Plain,the spectral reflectance was measured using the ASD FieldSpec3 spectrum analyzer,and the SOM was determined using potassium dichromate external heating method in the laboratory.In the next stage,the raw spectral reflectance (Raw)was pre-pared for three spectral transformations,i.e.inverse-log reflectance (LR),first order differential reflectance (FDR)and contin-uum removal reflectance (CR).2-D correlograms of the determination coefficients (R 2 )were constructed using all two-band combinations of 4 spectral transformations in NDSI against SOM in the range of 400～2400 nm.Then,the determination coeffi-cients (R 2 )of the 4 spectral transformations for 1-D determination coefficients and 2-D determination coefficients by F significant test were got (p <0.001),which could be used to extract sensitive bands and spectral index.At last,partial least squares re-gression (PLSR)method were used to build quantitative inversion model of SOM based on sensitive bands and spectral index for this study area,respectively.Feasibility of 2-D spectral index for building model was this study aimed to explore.The results showed that,the 2-D determination coefficients were better than 1-D determination coefficients,especially the determination co-efficients of LR was improved by about 0.26.Compared to the sensitive bands derived from 1-D determination coefficients,on the whole,the sensitive spectral index derived from 2-D determination coefficients using PLSR method could obtain more robust prediction accuracies.The prediction accuracy of NDSILR-PLSR was the best,and its values of R 2 ,RPD for the predicted model were 0.82,2.46,which could estimate SOM comprehensively and stably.In the future,this method could be applied to air-or space-borne images with a lower spectral resolution (e.g.ASTER,Landsat TM),and the results could also provide great po-tential in the field of sensor design for portable proximal sensing researching.