Black Soil Organic Matter Content Prediction Based on Reflectance Simulation Models
LIU Huan-jun1,2,ZHANG Bai1,LIU Dian-wei1,SONG Kai-shan1,WANG Zong-ming1*,YANG Fei1,2
1. Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences, Changchun 130012, China 2. Graduate School of Chinese Academy of Sciences, Beijing 100039, China
Abstract：The hyperspectral reflectance characteristics of black soil in Heilongjiang province were analyzed quantitatively, and then the main characteristic controlling points of reflectance were determined and used to build soil reflectance prediction models; the relationship between organic matter content and reflectance and the coefficients of simulating models were studied, Black soil organic matter content spectral prediction models were built, and the feasibility of hyperspectral reflectance simulatiib method was discussed. The results are as follows: (1) Organic matter content is the determining factor of black soil reflectance characteristics in the range less than 1 000 nm. When the content is low, the covering effect of organic matter on the black soil parent matrix reflectance characteristics is very weak, there are two absorption vales at 500 and 640 nm; when the content reaches a certain content (about 5%), the reflectance characteristics of black soil parent matrix are totally covered by organic matter, and there is only one large absorption vale in the region caused by organic matter. (2) The spectral characteristic controlling points of black soil hyperspectral reflectance in the range of 450-930 nm are located at 450, 500, 590, 660 and 930 nm, and divide the black soil reflectance into four parts. (3) Simulation models (linear, quadratic) rightly describe the characteristics of black soil hyperspectral reflectance, and the linear piecewise model shows a better performance. (4) The organic matter content prediction models with the coefficients of reflectance simulation models as independent variables are more precise than that based on soil reflectance and its derivate, which indicates that the characteristic controlling points for reflectance simulation models are selected reasonably and representatively, and the simulation models partly solve the data redundancy problem of soil hyperspectral reflectance, and improve the precision of black soil organic matter content prediction models with remote sensing methods. Reflectance simulating method can be used for data simplification and compression, data redundancy removal, organic matter and other soil parameters’ remote sensing studies.
[1] Kaleita A L, Tian L F, Hirschi M C. Transactions of American Society of Agrcultural Engineers(ASAE), 2005, 48(5): 1979. [2] Schultz G A, Engman E T. Remote Sensing in Hydrology and Water Management. New York: Springer, 2000. 483. [3] Ben-dor E, Banin A. Soil Sci. Soc. Am. J., 1995, 59: 364. [4] Chang C W, Laird D A. Soil Sci., 2002, 167(2): 110. [5] Galvao L S, Pizarro M A, Epiphanio J C N. Remote Sensing of Environment, 2001, 75: 245. [6] SUN Jian-ying, LI Min-zan, ZHENG Li-hua, et al(孙建英, 李民赞, 郑立华, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(3): 426. [7] Farifteh J, Van der Meer F, Atzberger C, et al. Remote Sensing of Environment, 2007, 110(1): 59. [8] Viscarra Rossel R A, McGlynn R N, McBratney A B. Geoderma, 2006, 137: 70. [9] Chappell A, Strong C, McTainsh G, et al. Remote Sensing of Environment, 2007, 106: 508. [10] LIN Cheng-gu(林成谷). Agrology(North China)(土壤学·北方本). Beijing: Agricultural Press(北京: 农业出版社), 1980. 34. [11] LIU Huan-jun, ZHANG Bai, ZHAO Jun, et al(刘焕军, 张 柏, 赵 军, 等). Acta Pedologica Sinica(土壤学报), 2007, 44: 27. [12] Krishnan P, Alexander J D, Butler B J. Soil Sci. Soc. Am. J., 1980, 44: 1282. [13] Galvao L S, Vitorello I. Remote Sensing of Environment, 1998, 63: 166. [14] XU Bin-bin, JI Geng-shan, ZHU Yong-hao(徐彬彬, 季耿善, 朱永豪). Remote Sensing of Environment China(环境遥感), 1991, 6(2): 142. [15] LIU Huan-jun, ZHANG Bai, ZHANG Yuan-zhi, et al(刘焕军, 张 柏, 张渊智, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(3): 624. [16] Marie-Madeleine C, Bjorn B, Pere R. Soil Biology and Biochemistry, 2003, 35: 1587. [17] Cloutis E A. Int. J. Remote Sensing, 1996, 17(12): 2215. [18] Goodin D G, Han L, Fraser R N, et al. Photogrammetric Engineering and Remote Sensing, 1993, 59: 505. [19] Udelhoven T, Emmerling C, Jarmer T. Plant and Soil, 2003, 251: 319.
