Inversion of Organic Matter Content of the North Fluvo-Aquic Soil Based on Hyperspectral and Multi-Spectra
WANG Yan-cang1, 2, 3, GU Xiao-he2, 3*, ZHU Jin-shan1, LONG Hui-ling2, XU Peng2, LIAO Qin-hong2
1. College of Geometrics, Shandong University of Science and Technology, Qindao 266590, China 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China 3. Key Laboratory of Information Technology in Agriculture，Ministry of Agriculture, Beijing 100097, China
Abstract：The present study aims to assess the feasibility of multi-spectral data in monitoring soil organic matter content. The data source comes from hyperspectral measured under laboratory condition, and simulated multi-spectral data from the hyperspectral. According to the reflectance response functions of Landsat TM and HJ-CCD (the Environment and Disaster Reduction Small Satellites, HJ), the hyperspectra were resampled for the corresponding bands of multi-spectral sensors. The correlation between hyperspectral, simulated reflectance spectra and organic matter content was calculated, and used to extract the sensitive bands of the organic matter in the north fluvo-aquic soil. The partial least square regression (PLSR) method was used to establish experiential models to estimate soil organic matter content. Both root mean squared error (RMSE) and coefficient of the determination (R2) were introduced to test the precision and stability of the modes. Results demonstrate that compared with the hyperspectral data, the best model established by simulated multi-spectral data gives a good result for organic matter content, with R2=0.586, and RMSE=0.280. Therefore, using multi-spectral data to predict tide soil organic matter content is feasible.
Key words：Organic matter;Fluvo-aquic soil;Multi-spectra;Hyperspectral;Partial least squares regression
[1] XU Yong-ming, LIN Qin-zhong, WANG Lu, et al(徐永明, 蔺启忠, 王 璐, 等). Acta Pedologica Sinica(土壤学报), 2006, 43(5): 709. [2] Stephan Gmur Daniel Vogt, Darlene Zabowski，L Monika Moskal. Sensors, 2012, 12：10639. [3] Garey A Fox, George J Sabbagh. Soil Science Society of American Journal, 2002, 66: 1922. [4] Dalal R C, Henry R J. Soil Science Society of American Journal, 1986, 50: 120. [5] Henderson T L, Baumgardner M F, Franzmeier D P, et al. Soil Science Society of American Journal, 1992, 56: 856. [6] LU Yan-lin, BAI You-lu, YANG Li-ping, et al(卢艳丽, 白由路, 杨俐苹, 等). Scientia Agricultura Sinica(中国农业科学), 2007, 40(9): 1989. [7] HE Jun-liang, JIANG Jian-jun, ZHOU Sheng-lu, et al(贺军亮, 蒋建军, 周生路, 等). Scientia Agricultura Sinica(中国农业科学), 2007, 40(3): 638. [8] WANG Jing, HE Ting, LI Yu-huan(王 静, 何 挺, 李玉环). Journal of Remote Sensing(遥感学报), 2005, 9(4): 438. [9] LU Yan-lin, BAI You-lu, YANG Li-ping, et al(卢艳丽, 白由路, 杨俐苹, 等). Plant Nutrition and Fertilizer Science(植物营养与肥料科学), 2008, 14(6): 1076. [10] ZHANG Fa-sheng, QU Wei, YIN Guang-hua, et al(张法升, 曲 威, 尹光华, 等). Chinese Journal of Applied Ecology(应用生态学报), 2010, 21(4): 883. [11] Touré S，Tychon B. Carbon Sequestration in Terrestrial, 2003: 26. [12] XU Bin-bin, DAI Chang-da(徐彬彬, 戴昌达). Chinese Science Bulletin(科学通报), 1980, 6: 282. [13] Rao C R, Toutenburg H. Linear Models. New York: Springer Verlag Press, 1995. 67. [14] Wold S, Ruhe A, Wold H, et al. J. Stat. Comp., 1984, 5: 735. [15] Clark R N, Roush T L. Journal of Geophysical Research, 1984, 89: 6329. [16] JI Wen-jun, SHI Zhou, ZHOU Qing, et al(纪文君, 史 舟, 周 清, 等). Journal of Infrared and Millimeter Waves(近红外与毫米波学报)，2012, 31(3): 277.
