Estimation of Soil Organic Matter Content Using Hyperspectral Techniques Combined with Normalized Difference Spectral Index
HONG Yong-sheng1,2, ZHU Ya-xing1,2, SU Xue-ping1,2, ZHU Qiang1,2, ZHOU Yong1,2, YU Lei1,2*
1. Hubei Provincial Key Laboratory for the Analysis and Simulation of Geographical Process, Central China Normal University, Wuhan 430079, China
2. College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China
Abstract：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 wavelengths. 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 prepared for three spectral transformations, i.e. inverse-log reflectance (LR), first order differential reflectance (FDR) and continuum removal reflectance (CR). 2-D correlograms of the determination coefficients (R2) were constructed using all two-band combinations of 4 spectral transformations in NDSI against SOM in the range of 400～2 400 nm. Then, the determination coefficients (R2) 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 regression (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 coefficients 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 R2, 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 potential in the field of sensor design for portable proximal sensing researching.
Key words：Soil oragnic matter; Hyperspectral; Normalized difference spectral index (NDSI); Partial least squares regression
[1] Shi Z，Ji W，Viscarra Rossel R A，et al. European Journal of Soil Science，2015，66(4)：679.
[2] Peng X T，Shi T Z，Song A H，et al. Remote Sensing，2014，6(4)：2699.
[3] LIU Yan-de，XIONG Song-sheng，LIU De-li(刘燕德，熊松盛，刘德力). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2014，34(10)：2639.
[4] Shi T Z，Chen Y Y，Liu Y L，et al. Journal of Hazardous Materials，2014，265：166.
[5] Tian Y C，Zhang J J，Yao X，et al. Geoderma，2013，202：161.
[6] ZHANG Juan-juan，TIAN Yong-chao，YAO Xia，et al(张娟娟，田永超，姚 霞，等). Acta Pedological Sinica(土壤学报)，2012，49(1)：50.
[7] HONG Yong-sheng，YU Lei，GENG Lei，et al(洪永胜，于 雷，耿 雷，等). Journal of Central China Normal University·Nat. Sci.(华中师范大学学报·自然科学版)，2016，50(2)：303.
[8] Savitzky A，Golay M J E. Analytical Chemistry，1964，36：1627.
[9] BAO Shi-dan(鲍士旦). Soil Agrichemical Analysis(土壤农化分析). Beijing: China Agriculture Press(北京：中国农业出版社)，1999. 30.
[10] Wold S，Sjostrom M，Eriksson L. Chemometrics and Intelligent Laboratory Systems，2001，58(2)：109.
[11] YU Lei，HONG Yong-sheng，GENG Lei，et al(于 雷，洪永胜，耿 雷，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2015，31(14)：103.
[12] Knox N M，Grunwald S，McDowell M L，et al. Geoderma，2015，239：229.
[13] Viscarra Rossel R A，Behrens T. Geoderma，2010，158(1-2)：46.
