Study on the Soil Salinization Monitoring Based on Measured Hyperspectral and HSI Data
LEI Lei1,2, TIYIP Tashpolat1,2, DING Jian-li1,2*, JIANG Hong-nan1,2, KELIMU Ardak1,2
1. College of Resource and Environment Sciences，Xinjiang University, Urumqi 830046, China 2. Key Laboratory of Oasis Ecology of Ministry of Education, Urumqi 830046, China
Abstract：The present paper selects the Kuqa Oasis as the study area, studies spectrum characteristics of soil salinity, and establishes soil spectrum library. Through transforming and analyzing varying degrees of soil salinization reflectance spectra data in the typical study area, and selecting the most sensitive spectral bands in response to salinization, we established the measured hyperspectral soil salinity monitoring model, and by correcting the soil salinity monitoring model established by HIS image through scale effect conversion improved the model accuracy under the conditions of a regional-scale monitoring of soil salinization. The results show that both measured hyperspectral soil salinity monitoring model and HSI image soil salinity inversion model have good accuracy, model determination coefficient (R2) is higher than 0.57 and the model stability is better. Compared with the corrected HSI image soil salinity inversion model and uncorrected HSI image soil salinity inversion model, the coefficient of determination has been greatly improved, which increased from 0.571 to 0.681, and through the 0.01 significance level, the root mean square error (RMSE) value is 0.277. The correction HIS image soil salinization monitoring model can better improve the model accuracy under the condition of regional scale soil salinization monitoring, and using this method to carry out the soil salinization quantitative remote sensing monitoring is feasible, and also can provide scientific reference for future research.
Key words：Hyperspectral;HSI;Soil salinization;Multiple linear regression
[1] Dehaan R L, TayLor G R. Remote Sensing of Environment, 2002, 80(3): 406. [2] Mettmicht G I, Zinck J A. Remote Sensingof Environment, 2003, 85(1): 1. [3] Graciela Metternicht, Alfred Zinck. Remote Sensing of Soil Salinization-Impact on Land Management. CRC Press，2009. 63. [4] TAN Jun-li, KANG Yue-hu, JIAO Yan-ping, et al(谭军利, 康跃虎, 焦艳平, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2008, 24(6): 59. [5] DONG Xin-guang, ZHOU Jin-long, CHEN Yue-bin(董新光, 周金龙, 陈跃滨). Study on Moisture and Salinity Monitoring and Its Application in Arid Inlard Region(干旱内陆区水盐监测与模型研究及其应用). Beijing: Science Press(北京: 科学出版社), 2007. 1. [6] Farifteh J, Farshad A, George R J. Geoderma, 2006, 130(3-4): 191. [7] LI Xiao-song, LI Zeng-yuan, GAO Zhi-hai, et al(李晓松, 李增元, 高志海, 等). J. of Desert Research(中国沙漠), 2011, 31(1): 162. [8] LIU Dong-xing, GONG Wei-guang(刘东兴, 宫伟光). Science of Soil and Water Conservation(中国水土保持科学), 2009, 3(3): 98. [9] GU Xiao-he, WANG Kun, PAN Yu-chun, et al(顾晓鹤，王 堃，潘瑜春，等). Geography and Geo-Information Science(地理与地理信息科学)，2011，27(6)：69. [10] YAO Yuan, DING Jian-li, ZHANG Fang, et al(姚 远, 丁建丽, 张 芳, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(6): 1658. [11] TANG Yan(唐 彦). Engineering of Surveying and Mapping(测绘工程), 2010, 19(6): 65. [12] ZHAO Zhen-liang, TASHPOLAT Tiyip, ZHANG Fei, et al(赵振亮, 塔西甫拉提·特依拜, 张 飞, 等). Journal of Natural Disasters(自然灾害学报), 2012, 21(5): 72. [13] Ben-Dor E, Patin K, Banin A, et al. International Journal of Remote Sensing, 2002, 23(6): 1043. [14] Lu Ning, Zhang Zhi, Gao Yang. Geoscience and Remote Sensing Symposium, IEEE，2005, 6: 4520. [15] Inakwu O A，Odeh·Alex O. Environmental Management, 2008, 42: 265.
