| 光谱学与光谱分析 |
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| Estimation and Mapping of Soil Organic Matter Based on Vis-NIR Reflectance Spectroscopy |
| GUO Yan1, JI Wen-jun1, WU Hong-hai2*, SHI Zhou1, 3 |
1. Institute of Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China
2. Department of Earth Sciences, Zhejiang University, Hangzhou 310027, China
3. Cyrus Tang Center for Sensor Materials and Applications, Zhejiang University, Hangzhou 310058, China |
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Abstract Visible-near infrared (Vis-NIR) reflectance spectroscopy, which is rapid, cost-effective, in-situ, nondestructive and without hazardous chemicals, is increasingly being used for prediction and digital soil mapping of soil organic matter (SOM). This method is the inevitable demand for precision agriculture and soil remote sensing mapping. In the present study, the Vis-NIR (350~2 500 nm) diffuse reflectance spectral collected by ASD FieldSpec Pro FR spectrometer was truncated by removing the noisy edge values below 400 nm and above 2 450 nm and then was transformed into apparent absorbance spectral using log(1/R). Based on the relationship analysis between absorbance spectral, spectral indices and SOM, partial least squares regression (PLSR) model was applied to predict SOM, and finally the spatial variability of SOM was characterized by geostatistics method. The results indicated that good model was modeling from the characteristic bands (CB, R2=0.91，RPD=3.28) of correlation coefficient more than 0.5, the spectral index (SI) of normalized difference index (NDI, R2=0.90，RPD=3.08), CB integrating SI with which a correlation coefficient was more than 0.5 (R2=0.87，RPD=2.67), and total bands (TA, 400~2 450 nm, R2=0.95，RPD=4.36). While the digital mapping of SOM produced by kriging and cokriging interpolation methods implied a better prediction result, showing similar spatial distribution with the measured SOM, indicating that it is feasible and reliable to use these spectral indices to predict and map the spatial variability.
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Received: 2012-07-30
Accepted: 2012-10-30
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Corresponding Authors:
WU Hong-hai
E-mail: honghaiw@163.com
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[1] JI Wen-jun, SHI Zhou, ZHOU Qing, et al(纪文君, 史 舟, 周 清, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2012, 31(3): 277.
[2] XIE Bo-cheng, XUE Xu-zhang, WANG Ji-hua, et al(谢伯承, 薛绪掌, 王纪华, 等). Chinese Journal of Soil Science(土壤通报), 2004, 35(3): 391.
[3] LIU Huan-jun, ZHANG Bai, ZHAO Jun, et al(刘焕军, 张 柏, 赵 军, 等). Acta Pedological Sinica(土壤学报), 2007, 44(1): 27.
[4] Christy C D. Computers and Electronics in Agriculture, 2008, 61: 10.
[5] Morgan C L S, Waiser T H, Brown D J, et al. Geoderma, 2009, 151: 249.
[6] Wang B W, Zhou W J, Ma S, et al. Agricultural Sci. & Tech., 2012, 13(4): 838.
[7] Viscarra Rossel R A, Chen C. Remote Sensing of Environment, 2011, 115: 1443.
[8] Viscarra Rossel R A, Behrens T. Geoderma, 2010, 158: 46.
[9] Burgess T M, Webster R. Journal of Soil Science，1980，31(2): 333.
[10] Sherman D M, Waite T D. American Mineralogist, 1985, 70: 1262.
[11] Hunt G R, Salisbury J W. Modern Geology, 1970, 1(4): 283.
[12] Clark R N, King T V V, Klejwa M, et al. Journal of Geophysical Research, 1990, 95: 12653.
[13] Clark R N. Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy. In: Rencz, A N. (Ed. ), Remote Sensing for the Earth Sciences: Manual of Remote Sensing. John Wiley & Sons, New York, 1999, 3. |
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