[1] [1] A V Bilgili, H M V Es, F Akbas, et al.. Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semi-arid area of Turkey [J]. Journal of Arid Environments, 2010, 74(2): 229-238.

[2] [2] Lu Wanzhen. The Modern Analysis Technique of Near-Infrared Spectrum (2nd edition) [M]. Beijing: China Petrochemical Press, 2006. 10-13.

[3] [3] Chen Cong, Lu Qipeng, Peng Zhongqi. Preprocessing methods of near-infrared spectrum based on NLMS adaptive filtering [J]. Acta Optica Sinica, 2012, 32(5): 0530001.

[4] [4] Gao Hongzhi, Lu Qipeng, Ding Haiquan, et al.. Robust calibration methods of near-infrared spectrum based on random sample consensus algorithm [J]. Acta Optica Sinica, 2013, 33(s2): s230001.

[5] [5] B Stenberg, R A V Rossel, A M Mouazen, et al.. Visible and near infrared spectroscopy in soil science [J]. Advances in Agronomy, 2010, 107: 163-215.

[6] [6] B Y Kuang, H S Mahmood, M Z Quraishi, et al.. Sensing soil properties in the laboratory, in situ, and on-line: a review [J]. Advances in Agronomy, 2012, 114: 155-223.

[7] [7] Y X Song, F L Li, Z F Yang, et al.. Diffuse reflectance spectroscopy for monitoring potentially toxic elements in the agricultural soils of Changjiang River Delta, China [J]. Applied Clay Science, 2012, 64: 75-83.

[8] [8] M Nocita, A Stevens, G Toth, et al.. Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach [J]. Soil Biology and Biochemistry, 2014, 68: 337-347.

[9] [9] D J Brown, K D Shepherd, M G Walse, et al.. Global soil characterization with VNIR diffuse reflectance spectroscopy [J]. Geoderma, 2006, 132(3-4): 273-290.

[10] [10] R A V Rossel, R Webster. Predicting soil properties from the Australian soil visible-near infrared spectroscopic database [J]. European Journal of Soil Science, 2012, 63(6): 848-860.

[11] [11] Z Shi, Q L Wang, J Peng, et al.. Development of national VNIR soil-spectral library for soil classification and the predictions of organic matter [J]. Science China Earth Sciences, 2014, 57(7): 1671-1680.

[12] [12] D J Brow. Using a global VNIR soil-spectral library for local soil characterization and landscape modeling in a 2nd-order Uganda watershed [J]. Geoderma, 2007, 140(4): 444-453.

[13] [13] J Wetterlind, B Stenberg. Near-infrared spectroscopy for within-field soil characterization: small local calibrations compared with national libraries spiked with local samples [J]. European Journal of Soil Science, 2010, 61(6): 823-843.

[14] [14] F Goge, R Joffre, C Jolivet, et al.. Optimization criteria in sample selection step of local regression for quantitative analysis of large soil NIRS database [J]. Chemometr Intell Lab, 2012, 110(1): 168-176.

[15] [15] A Stevens, M Nocita, G Toth, et al.. Prediction of soil organic carbon at the european scale by visible and near infrared reflectance spectroscopy [J]. Plos One, 2013, 8(6): 1-13.

[16] [16] Y Peng, M Knadel, R Gislum, et al.. Predicting soil organic carbon at field scale using a national soil spectral library [J]. J Near Infrared Spectroscopy, 2013, 21(3): 213-222.

[17] [17] Xu Yongming, Lin Qizhong, Huang Xiuhua, et al.. Experimental study on total nitrogen concentration in soil by VNIR reflectance spectrum [J]. Geography and Geo-Information Science, 2005, 21(1): 19-22.

[18] [18] Lu Yanli, Bai Youlu, Wang Lei, et al.. Determination for total nitrogen content in black soil using hyperspectral data [J]. Transactions of the CSAE, 2010, 26(1): 256-261.

[19] [19] Li Shuo, Wang Shanqin, Zhang Meiqin. Comparison among principal component regression, partial least squares regression and back propagation neural network for prediction of soil nitrogen with visible-near infrared spectroscopy [J]. Acta Optica Sinica, 2012, 32(8): 0830001.

[20] [20] Zhang Juanjuan, Tian Yongchao, Yao Xia, et al.. Estimating soil total nitrogen content based on hyperspectral analysis technology [J]. J Natural Resources, 2011, 26(5): 881-890.

[21] [21] Zhang Juanjuan, Tian Yongchao, Yao Xia, et al.. Estimating model of soil total nitrogen content based on near-infrared spectroscopy analysis [J]. Transactions of the CSAE, 2012, 28(12): 183-188.

[22] [22] Ji Wenjun, Shi Zhou, Zhou Qing, et al.. VIS-NIR reflectance spectroscopy of the organic matter in several types of soils [J]. Journal of Infrared and Millimeter Waves, 2012, 31(3): 277-282.

[23] [23] Ji Wenjun, Li Xi, Li Chengxue, et al.. Using different data mining algorithms to predict soil organic matter based on visible-near infrared spetroscopy [J]. Spectroscopy and Spectral Analysis, 2012, 32(9): 2393-2398.

[24] [24] T Naes, T Isaksson, B Kowalski. Locally weighted regression and scatter correction for near infrared reflectance data [J]. Analytical Chemistry, 1990, 62(7): 664-673.

[25] [25] Y Guo, Z Shi, H Y Li, et al.. Application of digital soil mapping methods to identify salinity management classes in coastal lands of central China [J]. Soil Use and Management, 2013, 29(3): 445-456.

[26] [26] Y Li, Z Shi, F Li, et al.. Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land [J]. Computers and Electronics in Agriculture, 2007, 56(2): 174-186.

[27] [27] V Bellon-Maurel, E Fernandez-Ahumada, B Palagos, et al.. Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy [J]. TrAC Trends in Analytical Chemistry, 2010, 29(9): 1073-1081.

[28] [28] L Ramirez-Lopez, T Behrens, K Schmidt, et al.. Distance and similarity-search metrics for use with soil vis-NIR spectra [J]. Geoderma, 2013, 199: 43-53.