Improved Soil Organic Carbon Prediction in a Forest Area by Near-Infrared Spectroscopy: Spiking of a Soil Spectral Library
文献类型：期刊论文
| 作者 | Long, Miao2,3; Yue, Tianxiang1,2,4,5,6; Xu, Zhe4; Guo, Jiaxin2,3; Luo, Jie6; Guo, Xi2,3; Zhao, Xiaomin2,3 |
| 刊名 | FORESTS
 |
| 出版日期 | 2023 |
| 卷号 | 14期号:1页码:16 |
| 关键词 | near-infrared spectroscopy soil organic carbon soil spectral library spiking forest assessment |
| DOI | 10.3390/f14010118 |
| 通讯作者 | Zhao, Xiaomin(zhaoxm889@126.com) |
| 英文摘要 | The rapid quantitative assessment of soil organic carbon (SOC) is essential for understanding SOC dynamics and developing management strategies in forest ecosystems. Compared with traditional laboratory methods, visible and near-infrared spectroscopy is an efficient and inexpensive technique widely used to predict SOC content. Herein, we compared three different spiking strategies. That is, a large-scale global soil spectral library (global-SSL; 3122 samples) was used as the basis for predicting SOC content in a small-scale local soil spectral library (local-SSL; 89 samples) in Wugong Mountain, Jiangxi Province, China. Partial least squares regression models using global-SSL 'spiking' with local samples did not necessarily achieve more accurate predictions than models using local-SSL. Using the developed strategy, a calibration set can be established by selecting the top N spectral samples from global-SSL with high similarity to each local sample, together with the 'spiking' set from local-SSL. It is possible to individually improve the prediction results based on local samples (R-2 = 0.90, RMSE = 7.19, RPD = 3.38) and still allow for quantitative prediction from fewer local calibration samples (R-2 = 0.83, RMSE = 8.71, RPD = 2.68). The developed method is cost-effective and accurate for local-scale SOC assessment in target forest areas using a large soil spectral library. |
| WOS关键词 | PARTIAL LEAST-SQUARES ; LOCAL SCALE ; REGRESSION ; SAMPLES ; CLASSIFICATION ; REFLECTANCE ; SELECTION ; MODELS |
| WOS研究方向 | Forestry |
| 语种 | 英语 |
| WOS记录号 | WOS:000915140400001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/189794]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhao, Xiaomin |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China 2.Jiangxi Agr Univ, Coll Land Resources & Environm, Nanchang 330045, Peoples R China 3.Key Lab Poyang Lake Watershed Agr Resources & Ecol, Nanchang 330045, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 5.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China 6.Jiangxi Coll Appl Technol, Sch Resource Environm & Jewelry, Ganzhou 341000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Long, Miao,Yue, Tianxiang,Xu, Zhe,et al. Improved Soil Organic Carbon Prediction in a Forest Area by Near-Infrared Spectroscopy: Spiking of a Soil Spectral Library[J]. FORESTS,2023,14(1):16. |
| APA | Long, Miao.,Yue, Tianxiang.,Xu, Zhe.,Guo, Jiaxin.,Luo, Jie.,...&Zhao, Xiaomin.(2023).Improved Soil Organic Carbon Prediction in a Forest Area by Near-Infrared Spectroscopy: Spiking of a Soil Spectral Library.FORESTS,14(1),16. |
| MLA | Long, Miao,et al."Improved Soil Organic Carbon Prediction in a Forest Area by Near-Infrared Spectroscopy: Spiking of a Soil Spectral Library".FORESTS 14.1(2023):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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