1. 武汉大学资源与环境科学学院，湖北 武汉 430079
2. 自然资源部数字制图与国土信息应用重点实验室，湖北 武汉 430079
3. 土壤与农业可持续发展国家重点实验室，江苏 南京 210008
4. 华中农业大学资源与环境学院，湖北 武汉 430070
5. Geographical and Sustainability Sciences, The University of Iowa, Iowa City, IA 52246, USA
Estimation of Soil Organic Carbon Content by Imaging Spectroscopy With Soil Roughness
XU Lu1, CHEN Yi-yun1, 2, 3*, HONG Yong-sheng1, WEI Yu1, GUO Long4, Marc Linderman5
1. School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
2. Key Laboratory of Digital Mapping and Land Information Application, Ministry of Natural Resources, Wuhan 430079, China
3. State Key Laboratory of Soil and Sustainable Agriculture, Nanjing 210008, China
4. College of Resource and Environment, Huazhong Agricultural University, Wuhan 430070, China
5. Geographical and Sustainability Sciences, The University of Iowa, Iowa City, IA 52246, USA
Abstract：Visible and near-infrared (VIS-NIR) non-imaging spectroscopy has been widely applied to estimate soil organic carbon (SOC) content. Due to the high demand for soil sample pretreatments, VIS-NIR non-imaging spectroscopy easily suffers from soil roughness in practical application. This study explored the potential of imaging spectroscopy to estimate SOC content with high soil roughness. With soil samples collected in Iowa State, United States, imaging spectra were utilized to measure the VIS-NIR spectra of soil samples with and without ground. With five spectral pre-processing including continuum removed (CR), absorbance transformation (AB), S-G smoothing (SG), standard normal variate (SNV), and multiplicative scatter correction (MSC), partial least squares regression (PLSR) and support vector regression (SVR) were used to build estimation models to analyze the potential of imaging spectra. Non-imaging spectra were also applied to build PLSR and SVR models as a comparison. Results demonstrated that imaging spectra could achieve SOC content estimation for soil samples with high roughness, but non-imaging spectra could not successfully estimate that. The best PLSR and SVR model developed by imaging spectra could reach 0.739 and 0.712 of R2 for SOC content estimation of soil samples with high roughness, while that established by non-imaging spectra could achieve 0.344 and 0.311 of R2. Based on the imaging spectra after the four pre-processing methods of AB, SG, SNV, and MSC, the performance of the PLSR model established before soil sample grinding was better than that of the PLSR model established after soil sample grinding, while the performance of the SVR model was just the opposite. For non-imaging spectra, the accuracies of PLSR and SVR models established after soil samples grinding were always better than that of models established before soil samples grinding. For these two spectral data and the two estimation models, different spectral pre-processing methods had different abilities to improve the estimation accuracy of the model. The performance of imaging spectroscopy outperformed non-imaging spectra before or after being ground soil samples. Imaging spectra could enhance the correlation coefficient between VIS-NIR spectra and SOC for soil samples with high roughness, there by improving PLSR model’s performance. Our findings provide a new way to estimate SOC content on large-scale yield because imaging spectra could overcome the influence of soil roughness.
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