文献类型：期刊文献

英文题名：Estimating Soil Organic Carbon Content with Visible-Near-Infrared (Vis-NIR) Spectroscopy

作者：Gao, Yin[1,2,3] Cui, Lijuan[4] Lei, Bing[5] Zhai, Yanfang[6] Shi, Tiezhu[2,3] Wang, Junjie[2,3] Chen, Yiyun[2,3] He, Hui[7] Wu, Guofeng[8,9,10]

第一作者：Gao, Yin

通信作者：Wu, GF[1]

机构：[1]Natl Geomat Ctr China, Beijing 100830, Peoples R China;[2]Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China;[3]Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Peoples R China;[4]Chinese Acad Forestry, Inst Wetland Res, Beijing 100091, Peoples R China;[5]Natl Adm Surveying Mapping & Geoinformat China, Satellite Surveying & Mapping Applicat Ctr, Beijing 100830, Peoples R China;[6]Chongqing Inst Surveying & Mapping, Chongqing 400014, Peoples R China;[7]Star Map Press, Beijing 100088, Peoples R China;[8]Shenzhen Univ, Key Lab Geoenvironm Monitoring Coastal Zone, Natl Adm Surveying Mapping & Geoinformat, Shenzhen 518060, Peoples R China;[9]Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China;[10]Shenzhen Univ, Coll Life Sci, Shenzhen 518060, Peoples R China

年份：2014

卷号：68

期号：7

起止页码：712-722

外文期刊名：APPLIED SPECTROSCOPY

收录：;EI(收录号：20151800795953);Scopus(收录号：2-s2.0-84928398570);WOS:【SCI-EXPANDED(收录号:WOS:000338823300002)】；

基金：This study was supported by grants from the Special Foundation of the Ministry of Finance of China for Nonprofit Research of Forestry Industry (200904001) and the National Natural Science Foundation of China (41171290).

语种：英文

外文关键词：Soil organic carbon; Visible-near-infrared, Vis-NIR; Support vector machine regression; Partial least squares regression; Spectral preprocessing

摘要：The selection of a calibration method is one of the main factors influencing measurement accuracy with visible-near-infrared (Vis-NIR, 350-2500 nm) spectroscopy. This study, based on both air-dried unground (DU) and air-dried ground (DG) soil samples, used nine spectral preprocessing methods and their combinations, with the aim to compare the commonly used partial least squares regression (PLSR) method with the new machine learning method of support vector machine regression (SVMR) to find a robust method for soil organic carbon (SOC) content estimation, and to further explore an effective Vis-NIR spectral preprocessing strategy. In total, 100 heterogeneous soil samples collected from Southeast China were used as the dataset for the model calibration and independent validation. The determination coefficient (R-2), root mean square error (RMSE), residual prediction deviation (RPD), and ratio of performance to interquartile range were used for the model evaluation. The results of this study show that both the PLSR and SVMR models were significantly improved by the absorbance transformation (LOG), standard normal variate with wavelet de-trending (SW), first derivative (FD), and mean centering (MC) spectral preprocessing methods and their combinations. SVMR obtained optimal models for both the DU and DG soil, with R-2, RMSE, and RPD values of 0.72, 2.48 g/kg, and 1.83 for DU soil and 0.86, 1.84 g/kg, and 2.60 for DG soil, respectively. Among all the PLSR and SVMR models, SVMR showed a more stable performance than PLSR, and it also outperformed PLSR, with a smaller mean RMSE of 0.69 g/kg for DU soil and 0.50 g/kg for DG soil. This study concludes that PLSR is an effective linear algorithm, but it might not be sufficient when dealing with a nonlinear relationship, and SVMR turned out to be a more suitable nonlinear regression method for SOC estimation. Effective SOC estimation was obtained based on the DG soil samples, but the accurate estimation of SOC with DU soil samples needs to be further explored. In addition, LOG, SW, FD, and MC are valuable spectral preprocessing methods for Vis-NIR optimization, and choosing two of them (except for FD + SW and LOG + FD) in a simple combination is a good way to get acceptable results.