Abstract:Taking Feng-qiu County as a case of soil salinization widely existing in the semiarid region, the spatial variability of soil salinity was investigated by using remote sensing and EM (electromagnetic induction) technologies in the present study. Descriptive statistics was applied to soil salinity data interpreted from EM38 measurements using field sampling method. Spectral indices (soil index and plant index) were derived from 25-resolution Landsat TM image taken in April 2005, and proved to be significantly correlated with soil salinity interpreted by EM38 readings. Regression models were further established between the interpreted soil electrical conductivity and spectral indices (soil index and plant index), and spatial distribution patterns across the study area were finally mapped based on the above regression models. Results indicated that soil salinity at each soil layer is from 0.259 to 0.572 and exhibits the moderate spatial variability owing to compound impact of intrinsic and extrinsic factors. Spatial distribution maps of soil salinity were obtained with the application of plant index, soil index and EM38 measurements. It was shown that soil salinization, mainly located in the north and south of the study area, exhibited obvious trend effect. Salinity at surface soil was the greatest and showed the trend of a decrease at subsoil layer and then an increase at deep layer in the whole soil profile. The accuracy of the predictions was tested using 40 soil sampled points. The root mean square error (RMSE) of calibration for soil salinity in each layer was 0.094, 0.052, 0.071 and 0.067 ds·m-1 respectively, showing that the precision is ideal. The change trends of RMSE were the same as soil salinity in soil profile. The trends indicated that soil salinity had effect on the salinity prediction by spectral indices, and showed better accuracy at low soil salinity.
吴亚坤1,2,杨劲松1*,李晓明1 . 基于光谱指数与EM38的土壤盐分空间变异性研究[J]. 光谱学与光谱分析, 2009, 29(04): 1023-1027.
WU Ya-kun1,2,YANG Jin-song1*,LI Xiao-ming1 . Study on Spatial Variability of Soil Salinity Based on Spectral Indices and EM38 Readings . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2009, 29(04): 1023-1027.
[1] Rao B R M, RaviSankar T, Dwivedi R S, et al. International Journal of Remote Sensing, 1995, 16(12): 2125. [2] GUAN Yuan-xiu, LIU Gao-huan, LIU Qing-sheng, et al. Journal of Remote Sensing(遥感学报), 2001, 5(1): 46. [3] GUAN Yuan-xiu, LIU Gao-huan, WANG Jing-feng(关元秀,刘高焕,王劲峰). Acta Geographica Sinica(地理学报), 2001, 56(2): 198. [4] LUO Yu-xia, CHEN Huan-wei(骆玉霞,陈焕伟). Remote Sensing for Land & Resources(国土资源遥感), 2002, (2): 46. [5] Mettemieht G I, Zinck J A. Remote Sensing of Environment, 2003, 85: 1. [6] Farfteh J, Farshad A, George R J. Geodema, 2006, 130: 191. [7] YAO Rong-jiang, YANG Jing-song, JIANG Long(姚荣江,杨劲松, 姜 龙). Journal of Zhejiang University·Agriculture and Life Science(浙江大学学报·农业与生命科学版), 2007, 33(2): 207. [8] YAO Rong-jiang, YANG Jing-song, LIU Guang-ming, et al(姚荣江,杨劲松, 刘广明,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2006, 22(6): 61. [9] LIU Guang-ming, YANG Jing-song, JU Mao-sen, et al(刘广明, 杨劲松, 鞠茂森, 等). Soils(土壤), 2003, 35(1): 27. [10] Corwin D L, Lesch S M. Computers and Electronics in Agriculture, 2005, 46: 103. [11] Triantafilis J, Lesch S M. Computers and Electronics in Agriculture, 2005, 46: 203. [12] FENG Lei, FANG Hui, ZHOU Wei-jun, et al(冯 雷,方 慧,周伟军,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(9): 1749. [13] LI Zhi-wei, PAN Jian-jun, ZHANG Jia-bao(李志伟,潘剑君,张佳宝). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(10): 1813. [14] LU Ru-kun(鲁如坤). Analysis Methods of Soil and Agricultural Chemistry(土壤农业化学分析方法). Beijing: Chinese Agricultural Science and Technology Press(北京:中国农业科技出版社), 1999. [15] Abd El Kader Douaoui, Hervé Nicolas. Geoderma, 2006, 134: 217. [16] LI Ha-bin, WANG Zheng-quan, WANG Qing-cheng(李哈滨,王政权,王庆成). Chinese Journal of Applied Ecology(应用生态学报), 1998, 9(6): 651. [17] WANG Zheng-quan(王政权). Geostatistics and Application in Ecology(地统计学及其在生态学中的应用). Beijing: Science Press(北京:科学出版社), 1999. 162.