基于高光谱的水稻土有机质含量估算研究

doi:10.11924/j.issn.1000-6850.2013-2912

摘要/Abstract

摘要： 利用高光谱技术可实现土壤有机质含量的快速、精确反演。然而运用不同的光谱预处理算法及建模方法获取的模型预测精度及稳定性不同。为了选取最佳土壤有机质估算模型，本研究应用ASD波谱仪测定河南省潢川县水稻土的光谱数据，比较使用2种建模方法组合18种光谱预处理转换算法建立模型的反演效果。对于多元逐步回归模型和偏最小二乘模型，使用SGF3-2预处理算法均获得了最佳的预测效果，所建模型具有较小的误差和较高的精度。相比使用多元逐步回归法，使用偏最小二乘回归法可以获取更稳定的预测模型。运用偏最小二乘模型结合SGF3-2预处理算法得到了最佳的水稻土有机质含量估算模型，模型预测均方根误差RMSEv=0.036，决定系数Rv2=0.89。选择最佳的建模方法结合预处理算法，可以改进模型反演精度。本研究对比的不同方法也可以应用到类似的土壤模型选取中。

关键词: 谷子, 谷子, 萌芽期, 抗旱性鉴定, 萌发抗旱指数

Abstract: The content of soil organic matter (SOM) could be obtained rapidly and accurately by using hyperspectral technology. But the model accuracy and stability are different by using different pre-processing transformations and multivariate techniques. In order to identify the best model to predict SOM, in this experiment, the spectral data of paddy soil in Huangchuan County of Henan Province was measured using ASD FieldSpec 3 Hi- Res hyperspectral meter, two multivariate techniques (Multiple Stepwise linear Regression (MSLR), Partial Least Squares Regression (PLSR)) and eighteen pre-processing transformations of spectra data were compared with the aim of identifying the predictive effects of SOM. The results showed that the SavitzkyGolay first derivative using binomial polynomial and three smooth points (SGF3- 2) was the best preprocessing transformation method，either using the MSLR model or PLSR model the predictive model had both low errors and high accuracy. Compared to using MSLR method, the use of PLSR method can obtain more robust prediction model. The combination of PLSR multivariate technique and SGF3- 2 pre- processing transformation provided the best prediction model with the root mean square error for the validation set RMSEv=0.036 and coefficient determination for the validation set Rv² =0.89. To choose the suit multivariate technique combined with pre-processing transformation can improve model accuracy. These different methods used in this study can also be applied to similar soil models for selection.

卢岩孙成明. 基于高光谱的水稻土有机质含量估算研究[J]. 中国农学通报, 2014, 30(18): 49-54.

参考文献

[1] 张勇,庞学勇,包维楷,等.土壤有机质及其研究方法综述[J].世界科技研究与发展,2005,27(5):72-78.
[2] Dalal R C. Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry [J]. Soil Science Society of America Journal, 1986, 50:120-123.
[3] 徐彬彬,戴昌达.南疆土壤光谱反射特性与有机质含量的相关分析[J].科学通报,1980(6):282-284.
[4] Henderson T L, Baumgardner M F, Franzmeier D P, et al. High dimensional reflectance analysis of soil organic matter[J]. Soil Science Society of America Journal, 1992,56:865-872.
[5] 沙晋明,陈鹏程,陈松林,等.土壤有机质光谱响应特性研究[J].水土保持研究,2003,10(2):21-24.
[6] 何挺,王静,林宗坚,等.土壤有机质光谱特征研究[J].武汉大学学报·信息科学版,2006,31:975-979.
[7] Sudduth K A, Hummel J W. Evaluation of reflectance methods for soil organic matter sensing[J] .Transaction of the ASAE,1991,34(4): 1900-1909.
[8] 卢艳丽,白由路,杨俐苹,等.东北平原不同类型土壤有机质含量高光谱反演模型同质性研究[J].植物营养与肥料学报,2011,17(2): 456-463.
[9] 刘焕军,张柏,赵军,等. 黑土有机质含量高光谱模型研究[J].土壤学报,2007,44(1):27-32
[10] Stark E W. Calibration methods for NIRS analysis. In: Creaser, C. S., Davies, A.M.C.(Eds.), Analytical applications of spectroscopy [M]. Royal Society of Chemistry, London, United Kingdom,1988: 21-34.
[11] Dunn B W, Beecher H G, Batten G D, et al. The potential of nearinfrared reflectance spectroscopy for soil analysis - a case study from the Riverine Plain of south-eastern Australia[J]. Aust. J. Exp. Agric., 2002,42:607-614.
[12] McCarty G W, Reeves III J B, Reeves V B, et al. Mid-infrared and near- infrared diffuse reflectance spectroscopy for soil carbon measurement[J]. Soil Science Society of America Journal, 2002,66: 640-646.
[13] Kooistra L, Wanders J, Epema G F, et al. The potential of field spectroscopy for the assessment of sediment properties in river floodplains[J]. Analytica Chimica Acta, 2003, 484:189-200.
[14] Reeves III J B, McCarty G W, Reeves V B. Mid- infrared diffuse reflectance spectroscopy for the quantitative analysis of agricultural soils[J]. Food Chemistry, 2001,49(2):766-772.
[15] Vasques G M, Grunwald S, Sickman J O. Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra[J].Geoderma, 2008,146:14-25.
[16] Dalal R C, Henry R J. Simultaneous determination of moisture, organic carbon, and total nitrogen by near- infrared reflectance spectrophotometry[J]. Soil Science Society of America Journal, 1986,50:120-123.
[17] Chang C W, Laird D A, Mausbach M J, et al. Near- infrared reflectance spectroscopy principal components regression analysis of soil properties[J]. Soil Science Society of America Journal, 2001, 65(2):480-490.
[18] Brunet D, Bernoux M, Barthes B G. Comparison between predictions of C and N contents in tropical soils using a VIS-NIR spectrometer including a fibre- optic probe versus a NIR spectrometer including a sample transport module[J].Biosystems Engineering, 2008,100:448-452.
[19] Chang C, Laird D A. Near- infrared reflectance spectroscopic analysis of soil C and N[J]. Soil Science, 2002,167:110-116.
[20] He Y, Huang M, Garcia A, et al. Prediction of soil macronutrients content using near- infrared spectroscopy[J]. Computer and Electronics in Agriculture, 2007,58:144-153.
[21] 沈润平,丁国香,魏国栓,等.基于人工神经网络的土壤有机质含量高光谱反演[J].土壤学报,2009,46(3):391-397.
[22] 郑立华,李民赞,潘娈,等. 基于近红外光谱技术的土壤参数 BP神经网络预测[J].光谱学与光谱分析, 2008,28(5):1160-1164.
[23] 刘磊,沈润平,丁国香.基于高光谱的土壤有机质含量估算研究[J].光谱学与光谱分析,2011,31(3):762-766