Enhancing soil profile analysis with soil spectral libraries and laboratory hyperspectral imaging
文献类型： 外文期刊
作者： Zhou, Yuwei 1 ; Biswas, Asim 2 ; Hong, Yongsheng 3 ; Chen, Songchao 4 ; Hu, Bifeng 6 ; Shi, Zhou 4 ; Guo, Yan 7 ; Li, Shuo 1 ;
作者机构： 1.Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China
2.Univ Guelph, Sch Environm Sci, Guelph, ON N1G 2W1, Canada
3.Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Peoples R China
4.Zhejiang Univ, Inst Appl Remote Sensing & Informat Technol, Coll Environm & Resource Sci, Hangzhou 310058, Peoples R China
5.ZJU Hangzhou Global Sci & Technol Innovat Ctr, Hangzhou 311200, Peoples R China
6.Jiangxi Univ Finance & Econ, Sch Tourism & Urban Management, Dept Land Resource Management, Nanchang 330013, Peoples R China
7.Henan Acad Agr Sci, Inst Agr Informat Technol, Zhengzhou 450002, Peoples R China
8.Minist Agr & Rural Affairs, Key Lab Huang Huai Hai Smart Agr Technol, Zhengzhou 450002, Peoples R China
关键词： Soil organic carbon; Soil spectral library; Local; Hyperspectral imaging; Profile mapping
期刊名称：GEODERMA （ 影响因子：6.6； 五年影响因子：7.3 ）
ISSN： 0016-7061
年卷期： 2024 年 450 卷
页码：
收录情况： SCI
摘要： Soil visible-near-infrared (vis-NIR) spectroscopy offers a rapid, uncontaminated, and cost-efficient method for estimating physicochemical properties such as soil organic carbon (SOC). The development of soil spectral libraries (SSLs) and localized modeling methods has significantly improved the selection of appropriate modeling sets from SSLs for soil analysis. Nevertheless, most studies assume that the SSLs sufficiently cover the target samples for prediction. This study challenges this assumption by investigating the feasibility of using an SSL to predict SOC accurately in a local area when the dataset to be predicted (156,800 samples) vastly exceeds the SSL capacity (3755 samples). We utilized 1-meter-deep whole-soil profile and employed spectral similarity and continuum-removal (SS-CR) calculation to construct a Local dataset from the SSL, with a Global subset serving as a baseline for comparison. The effectiveness of partial least-squares regression (PLSR) and random forest (RF) algorithms in establishing quantitative relationships between spectra and SOC content was evaluated. Our results demonstrated that the Local model, with significantly fewer samples (1116), achieved higher predictive accuracy than the Global model. Both Global (R-2 = 0.80, RMSE = 0.74 %) and Local (R-2 = 0.83, RMSE = 0.75 %) models, developed using the RF algorithm, not only exhibited excellent accuracy but also enabled detailed and cost-effective characterization of the spatial distribution of SOC. Thus, leveraging SSLs enhances the cost-efficiency and predictive capacity of vis-NIR spectral analysis, particularly in handling large datasets at a local scale, underscoring the value of localized approaches in soil spectroscopy.
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