光学学报, 2019, 39 (9): 0930002, 网络出版: 2019-09-09   

特征变量选择和回归方法相结合的土壤有机质含量估算 下载: 1177次

Estimation of Soil Organic Matter Content Based on Characteristic Variable Selection and Regression Methods
作者单位
1 青海师范大学地理科学学院,青海省自然地理与环境过程重点实验室, 青海 西宁 810008
2 中国环境科学研究院, 北京 100012
引用该论文

李冠稳, 高小红, 肖能文, 肖云飞. 特征变量选择和回归方法相结合的土壤有机质含量估算[J]. 光学学报, 2019, 39(9): 0930002.

Guanwen Li, Xiaohong Gao, Nengwen Xiao, Yunfei Xiao. Estimation of Soil Organic Matter Content Based on Characteristic Variable Selection and Regression Methods[J]. Acta Optica Sinica, 2019, 39(9): 0930002.

参考文献

[1] 南锋, 朱洪芬, 毕如田. 黄土高原煤矿区复垦农田土壤有机质含量的高光谱预测[J]. 中国农业科学, 2016, 49(11): 2126-2135.

    Nan F, Zhu H F, Bi R T. Hyperspectral prediction of soil organic matter content in the reclamation cropland of coal mining areas in the Loess Plateau[J]. Scientia Agricultura Sinica, 2016, 49(11): 2126-2135.

[2] MishraU, Torn MS, MasanetE, et al. Improving regional soil carbon inventories: combining the IPCC carbon inventory method with regression kriging[J]. Geoderma, 2012, 189/190: 288- 295.

[3] St LuceM, ZiadiN, Zebarth BJ, et al. Rapid determination of soil organic matter quality indicators using visible near infrared reflectance spectroscopy[J]. Geoderma, 2014, 232/233/234: 449- 458.

[4] 刘亚秋, 陈红艳, 王瑞燕, 等. 基于可见/近红外光谱的黄河口区土壤盐分及其主要离子的定量分析[J]. 中国农业科学, 2016, 49(10): 1925-1935.

    Liu Y Q, Chen H Y, Wang R Y, et al. Quantitative analysis of soil salt and its main ions based on visible/near infrared spectroscopy in estuary area of Yellow River[J]. Scientia Agricultura Sinica, 2016, 49(10): 1925-1935.

[5] 刘焕军, 张柏, 刘殿伟, 等. 松嫩平原典型土壤高光谱定量遥感研究[J]. 遥感学报, 2008, 12(4): 647-654.

    Liu H J, Zhang B, Liu D W, et al. Study on quantitatively remote sensing typical soils in Songnen plain, northeast China[J]. Journal of Remote Sensing, 2008, 12(4): 647-654.

[6] 卢艳丽, 白由路, 杨俐苹, 等. 基于高光谱的土壤有机质含量预测模型的建立与评价[J]. 中国农业科学, 2007, 40(9): 1989-1995.

    Lu Y L, Bai Y L, Yang L P, et al. Prediction and validation of soil organic matter content based on hyperspectrum[J]. Scientia Agricultura Sinica, 2007, 40(9): 1989-1995.

[7] 汪六三, 鲁翠萍, 王儒敬, 等. 土壤碱解氮含量可见/近红外光谱预测模型优化[J]. 发光学报, 2018, 39(7): 1016-1023.

    Wang L S, Lu C P, Wang R J, et al. Optimization for vis/NIRS prediction model of soil available nitrogen content[J]. Chinese Journal of Luminescence, 2018, 39(7): 1016-1023.

[8] 朱亚星, 于雷, 洪永胜, 等. 土壤有机质高光谱特征与波长变量优选方法[J]. 中国农业科学, 2017, 50(22): 4325-4337.

    Zhu Y X, Yu L, Hong Y S, et al. Hyperspectral features and wavelength variables selection methods of soil organic matter[J]. Scientia Agricultura Sinica, 2017, 50(22): 4325-4337.

[9] Vohland M, Ludwig M, Harbich M, et al. Using variable selection and wavelets to exploit the full potential of visible-near infrared spectra for predicting soil properties[J]. Journal of Near Infrared Spectroscopy, 2016, 24(3): 255-269.

[10] 林志丹, 汪玉冰, 王儒敬, 等. 波长优选对土壤有机质含量可见光/近红外光谱模型的优化[J]. 发光学报, 2017, 84(3): 529-534.

    Lin Z D, Wang Y B, Wang R J, et al. Improvements of the vis-NIRS model in the prediction of soil organic matter content using spectral pretreatments, sample selection, and wavelength optimization[J]. Journal of Applied Spectroscopy, 2017, 84(3): 529-534.

[11] Nawar S, Buddenbaum H, Hill J, et al. Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy[J]. Soil and Tillage Research, 2016, 155: 510-522.

[12] Viscarra Rossel R A, Rizzo R, Demattê J A M, et al. . Spatial modeling of a soil fertility index using visible-near-infrared spectra and terrain attributes[J]. Soil Science Society of America Journal, 2010, 74(4): 1293-1300.

[13] 李梦洁, 张曼胤, 崔丽娟, 等. 基于连续小波变换和随机森林的芦苇叶片汞含量反演[J]. 中国生态农业学报, 2018, 26(11): 1730-1738.

    Li M J, Zhang M Y, Cui L J, et al. Inversion of Hg content in reed leaf using continuous wavelet transformation and random forest[J]. Chinese Journal of Eco-Agriculture, 2018, 26(11): 1730-1738.

[14] 葛翔宇, 丁建丽, 王敬哲, 等. 基于竞争适应重加权采样算法耦合机器学习的土壤含水量估算[J]. 光学学报, 2018, 38(10): 1030001.

    Ge X Y, Ding J L. W J Z, et al. Estimation of soil moisture content based on competitive adaptive reweighted sampling algorithm combined with machine learning[J]. Acta Optica Sinica, 2018, 38(10): 1030001.

[15] 李冠稳, 高小红, 杨灵玉, 等. 不同粒径土壤有机质含量可见光-近红外光谱估算研究-以湟水流域为例[J]. 土壤通报, 2017, 48(6): 1360-1370.

    Li G W, Gao X H, Yang L Y, et al. Estimating soil organic matter contents from different soil particle size using visible and near-infrared reflectance spectrum-a case study of the Huangshui basin[J]. Chinese Journal of Soil Science, 2017, 48(6): 1360-1370.

[16] Conforti M, Castrignanò A, Robustelli G, et al. Laboratory-based vis-NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content[J]. Catena, 2015, 124: 60-67.

[17] 陈丛, 卢启鹏, 彭忠琦. 基于NLMS自适应滤波的近红外光谱去噪处理方法研究[J]. 光学学报, 2012, 32(5): 0530001.

    Chen C, Lu Q P, Peng Z Q. Preprocessing methods of near-infrared spectrum based on NLMS adaptive filtering[J]. Acta Optica Sinica, 2012, 32(5): 0530001.

[18] 姜雪芹, 叶勤, 林怡, 等. 基于谐波分析和高光谱遥感的土壤含水量反演研究[J]. 光学学报, 2017, 37(10): 1028001.

    Jiang X Q, Ye Q, Lin Y, et al. Inverting study on soil water content based on harmonic analysis and hyperspectral remote sensing[J]. Acta Optica Sinica, 2017, 37(10): 1028001.

[19] 张晓羽, 李庆波, 张广军. 基于稳定竞争自适应重加权采样的光谱分析无标模型传递方法[J]. 光谱学与光谱分析, 2014, 34(5): 1429-1433.

    Zhang X Y, Li Q B, Zhang G J. Calibration transfer without standards for spectral analysis based on stable competitive adaptive re-weighted sampling[J]. Spectroscopy and Spectral Analysis, 2014, 34(5): 1429-1433.

[20] 宋相中. 近红外光谱定量分析中三种新型波长选择方法研究[D]. 北京: 中国农业大学, 2017.

    Song XZ. Research of three new wavelength selection methods in near infrared spectroscopy quantitative analysis area[D]. Beijing: China Agricultural University, 2017.

[21] 陈红艳, 赵庚星, 张晓辉, 等. 基于遗传算法结合偏最小二乘的潮土碱解氮高光谱特征及含量估测[J]. 中国农学通报, 2015, 31(2): 209-214.

    Chen H Y, Zhao G X, Zhang X H, et al. Hyperspectral characteristic and estimation modeling of fluvo-aquic soil alkali hydrolysable nitrogen content based on genetic algorithm in combination with partial least squares[J]. Chinese Agricultural Science Bulletin, 2015, 31(2): 209-214.

[22] Yun Y H, Wang W T, Tan M L, et al. A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration[J]. Analytica Chimica Acta, 2014, 807: 36-43.

[23] 于雷, 洪永胜, 周勇, 等. 高光谱估算土壤有机质含量的波长变量筛选方法[J]. 农业工程学报, 2016, 32(13): 95-102.

    Yu L, Hong Y S, Zhou Y, et al. Wavelength variable selection methods for estimation of soil organic matter content using hyperspectral technique[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(13): 95-102.

[24] 张娟娟, 田永超, 朱艳, 等. 一种估测土壤有机质含量的近红外光谱参数[J]. 应用生态学报, 2009, 20(8): 1896-1904.

    Zhang J J, Tian Y C, Zhu Y, et al. A near-infrared spectral index for estimating soil organic matter content[J]. Chinese Journal of Applied Ecology, 2009, 20(8): 1896-1904.

[25] Krishnan P, Alexander J D, Butler B J, et al. Reflectance technique for predicting soil organic matter 1[J]. Soil Science Society of America Journal, 1980, 44(6): 1282-1285.

[26] Ben-Dor E, Banin A. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties[J]. Soil Science Society of America Journal, 1995, 59(2): 364-372.

[27] AbdelRahman A M, Pawling J, Ryczko M, et al. . Targeted metabolomics in cultured cells and tissues by mass spectrometry: method development and validation[J]. Analytica Chimica Acta, 2014, 845: 53-61.

[28] Nawar S, Mouazen A M. Predictive performance of mobile vis-near infrared spectroscopy for key soil properties at different geographical scales by using spiking and data mining techniques[J]. Catena, 2017, 151: 118-129.

[29] Rossel R A V, Behrens T. Using data mining to model and interpret soil diffuse reflectance spectra[J]. Geoderma, 2010, 158(1/2): 46-54.

[30] Douglas RK, NawarS, Alamar MC, et al. Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques[J]. Science of the Total Environment, 2018, 616/617: 147- 155.

[31] 高洪智, 卢启鹏, 丁海泉, 等. 基于随机抽样一致性算法的近红外光谱稳健模型研究[J]. 光学学报, 2013, 33(s2): s230001.

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

李冠稳, 高小红, 肖能文, 肖云飞. 特征变量选择和回归方法相结合的土壤有机质含量估算[J]. 光学学报, 2019, 39(9): 0930002. Guanwen Li, Xiaohong Gao, Nengwen Xiao, Yunfei Xiao. Estimation of Soil Organic Matter Content Based on Characteristic Variable Selection and Regression Methods[J]. Acta Optica Sinica, 2019, 39(9): 0930002.

本文已被 9 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!