Black Soil Organic Matter Predicting Model Based on Field Hyperspectral Reflectance
LIU Huan-jun1, 2, ZHANG Xin-le2, ZHENG Shu-feng3*, TANG Na2, HU Yan-liang2
1. Key Laboratory of Resources Remote Sensing & Digital Agriculture, Ministry of Agriculture, Beijing 100081, China 2. College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China 3. College of Agricuture Resources and Environment, Heilongjiang University, Harbin 150080, China
Abstract：To develop soil organic matter (OM) quick measuring methods, deepen the application of remote sensing in agriculture, improve agricultural production and management way, and promote the development of quantitative remote sensing studies relating to terrestrial ecosystem, field hyperspectral reflectance in the visible/near infrared bands of black soil in Hailun city, northeast China, was collected and analyzed with spectral analysis methods to discover the spectral characteristics of field reflectance and its influencing factors, and the spectral indices were derived, then black soil organic matter predicting model based on the correlation between OM content and spectral indices was built. Root mean squared error (RMSE) was introduced to validate the predictability and precision of the models, and coefficient of the determination (R2) was used to evaluate stability of the models. The results are as follows: the main spectral region of remarkable differences between field black soil reflectance curves is less than 1 250 nm, especially less than 1 000 nm; OM is the main factor determining the curve shape of field black soil reflectance, anc there are single or double spectral wave troughs for different soil samples because of varying OM content at the spectral region less 1 100 nm; correlation between OM and differential coefficient of logarithmic reflectance reciprocal (DCLRR) is much more significant than that between OM and other reflectance or its transforms, and the maximum coefficient of correlation is at 1 260 nm; the predicting model for black soil OM content is built with DCLRR at 1 260 nm as independent varialble and OM as dependent variable, and the coefficients of determination R2 of the model is 0.71, RMSE is 0.42, so the model is quite good in stability and predictability, and can be used in fast testing of organic matter in black soil.
[1] Malley D F, Yesmin L, Wray D, et al. Commun. Soil Sci. Plant Anal., 1999, 30: 999. [2] Cozzolino D, Moro′n A. J. Agric. Sci., 2003, 140: 65. [3] Maleki M R, Van Holm L, Ramon H, et al. Biosystems Engineering, 2006, 95(3): 425. [4] He Y, Huang M, García A, et al. Computers and Electronics in Agriculture, 2007, 58(2): 144. [5] Liu Huanjun, Zhang Yuanzhi, Zhang Xinle, et al. Pedosphere, 2009, 19(4): 532. [6] Martin P D, Malley D F, Manning G, et al. Can. J. Soil Sci., 2002, 82: 413. [7] Fox G A, Sabbagh G J. Soil Sci. Soc. Am. J., 2002, 66: 1922. [8] He Y, Song H Y, Pereira A G, et al. Lecture Notes in Computer Science, 2005, 3644: 859. [9] Aichi H, Fouad Y, C Walter, R A et al. Biosystems Engineering, 2009, 104(3): 442. [10] Jacquemoud S, Baret E, Hanocq J F. Remote Sensing of the Environment, 1992, 41: 123. [11] Zimmermann M, Leifeld J, Fuhrer J. Soil Biology and Biochemistry, 2007, 39(1): 224. [12] LIU Jing, ZHOU Qing, TANG Kun, et al(刘 静, 周 清, 唐 琨, 等). Hunan Agricultural Sciences(湖南农业科学), 2009, (2): 67. [13] Summers D, Lewis M, Ostendorf B, et al. Ecol. Indicat. (2009), doi: 10.1016/j.ecolind.2009.05.001. [14] ZHANG Juan-juan, TIAN Yong-chao, ZHU Yan, et al (张娟娟, 田永超, 朱 艳, 等). Scientia Agricultura Sinica(中国农业科学), 2009, 42(9): 3154. [15] HE Ting, WANG Jing, CHENG Ye, et al(何 挺, 王 静, 程 烨, 等). Geography and Geo-Information Science(地理与地理信息科学), 2006, 22(2): 30. [16] Stoner E R, Baumgardner M F. Soil Sci. Soc. Am. J., 1981, 45: 1161. [17] 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(1): 27.
