太阳成8722(中国)有限公司-GREEN NO.1

标题：Transferability of Vis-NIR models for Soil Organic Carbon Estimation between Two Study Areas by using Spiking

作者： Hong, Yongsheng; Chen, Yiyun; Zhang, Yong; Liu, Yanfang; Liu, Yaolin;Yu, Lei; Liu, Yi; Cheng, Hang

来源出版物：SOIL SCIENCE SOCIETY OF AMERICA JOURNAL  卷：82 期：5 页码：1231-1242  DOI：10.2136/sssaj2018.03.0099  出版年：SEP-OCT 2018  

摘要：Visible and near infrared (Vis-NIR) spectroscopy technique has been shown to be a cost-effective alternative for rapidly analyzing soil organic carbon (SOC). However, great challenges remain when applying a Vis-NIR model for SOC estimation developed in one study area to other study areas without further calibration. The scope of this study was to use spiking strategy to improve the transferability of Vis-NIR models between two study areas. Specifically, we explored the optimal spiking subset by adding different quantities of spiking samples to construct different-sized models, and the strategy of spiking with extra-weighting was used for comparison. Soil data was acquired in two independent study areas (WH area and HH area) in Hubei Province, Central China. The reflectance spectra and SOC contents were measured in the laboratory. Partial least squares regression (PLSR) was used for model calibration. The representativeness of the spiking samples was assessed through the absolute difference between the selected sample variance (s(2)) and the original variance (sigma(2)) in the principal component space derived from soil spectra. Results showed that the initial models yielded successful SOC predictions for the soil samples from the same area as the calibration samples, but failed in those samples from the other area. Spiking improved the model transferability between these two study areas. Approximately 33%/48% of the HH/WH calibration set was required as spiking samples in model calibrations and applications in the other area. Spiking with extra-weighting was of limited use in small-sized spectral libraries. The use of vertical bar s(2)-sigma(2)vertical bar is potentially effective in identifying the optimal spiking samples to improve model transferability between different small-sized study areas in the Vis-NIR assessments of SOC.

入藏号：WOS:000446667000022

文献类型：Article

语种：English

扩展关键词：DIFFUSE-REFLECTANCE SPECTROSCOPY; NEAR-INFRARED SPECTROSCOPY; SPECTRAL LIBRARY; LOCAL SCALE; EUROPEAN FARMS; LAND-USE; PREDICTION; CALIBRATION; MATTER; REGRESSION

通讯作者地址：Chen, YY; Liu, YF (reprint author), Wuhan Univ, Sch Resource & Environ Sci, Wuhan 430079, Hubei, Peoples R China.
Chen, YY (reprint author), Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Jiangsu, Peoples R China.

电子邮件地址：chenyy@whu.edu.cn; yfliu610@sina.com

地址：

[Hong, Yongsheng; Chen, Yiyun; Liu, Yanfang; Liu, Yaolin; Liu, Yi; Cheng, Hang] Wuhan Univ, Sch Resource & Environ Sci, Wuhan 430079, Hubei, Peoples R China.
[Hong, Yongsheng; Chen, Yiyun; Cheng, Hang] Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Jiangsu, Peoples R China.
[Zhang, Yong] Anhui Univ Finance & Econ, Sch Publ Finance & Admin, Bengbu 233030, Peoples R China.
[Yu, Lei] Cent China Normal Univ, Sch Urban & Environ Sci, Wuhan 430079, Hubei, Peoples R China.
[Yu, Lei] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Hubei, Peoples R China.

影响因子：1.92