土壤有机质含量可见-近红外光谱反演模型校正集优选方法
土壤有机质含量可见-近红外光谱反演过程中校正集的构建策略对模型的预测精度有重要影响。以江汉平原洪湖地区水稻土为研究对象,采用Kennard-Stone（KS）法,Rank-KS（RKS）和Sample set Partitioning based on joint X-Y distance（SPXY）法,构建样本数占总校正集不同比例的子校正集,通过偏最小二乘回归,建立土壤有机质含量的可见—近红外光谱反演模型。结果表明：KS法无法提高模型预测精度,但可以在保证标准差与预测均方根误差比（ratio of performance to standard deviation,RPD）〉2.0的前提下减...
Saved in:
| Published in | 农业工程学报 Vol. 33; no. 6; pp. 107 - 114 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
武汉大学资源与环境科学学院,武汉 430079
2017
武汉大学教育部地理信息系统重点实验室,武汉430079%武汉大学资源与环境科学学院,武汉 430079 浙江大学农业遥感与信息技术应用研究所,杭州 310058%武汉大学资源与环境科学学院,武汉 430079 土壤与农业可持续发展国家重点实验室,南京 210008 武汉大学苏州研究院,苏州 215123 中国科学院地理科学与资源研究所,北京 100101%武汉大学资源与环境科学学院,武汉 430079 武汉大学地球空间信息技术协同创新中心,武汉 430079 湖泊与环境国家重点实验室(中国科学院南京地理与湖泊研究所),南京 210008%武汉大学资源与环境科学学院,武汉,430079%湖北师范大学,城市与环境学院,黄石 435002%武汉大学资源与环境科学学院,武汉 430079 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.2017.06.014 |
Cover
| Abstract | 土壤有机质含量可见-近红外光谱反演过程中校正集的构建策略对模型的预测精度有重要影响。以江汉平原洪湖地区水稻土为研究对象,采用Kennard-Stone（KS）法,Rank-KS（RKS）和Sample set Partitioning based on joint X-Y distance（SPXY）法,构建样本数占总校正集不同比例的子校正集,通过偏最小二乘回归,建立土壤有机质含量的可见—近红外光谱反演模型。结果表明：KS法无法提高模型预测精度,但可以在保证标准差与预测均方根误差比（ratio of performance to standard deviation,RPD）〉2.0的前提下减少30%的校正样本;基于SPXY法的模型,当子校正集样本比例为总校正集的50%时达到最佳的模型预测精度,RPD为2.557;RKS法能够在保证预测精度的情况下（RPD〉2.0）,最多减少总校正集70%的样本,对应模型RPD为2.212。当校正集与验证集的有机质含量分布相近时,能够以较少的建模样本达到与总校正集相近甚至更高的模型预测精度,提升土壤有机质光谱反演模型的实用性。 |
|---|---|
| AbstractList | S151.9; 土壤有机质含量可见-近红外光谱反演过程中校正集的构建策略对模型的预测精度有重要影响.以江汉平原洪湖地区水稻土为研究对象,采用Kennard-Stone(KS)法,Rank-KS(RKS)和Sample set Partitioning based on joint X-Y distance(SPXY)法,构建样本数占总校正集不同比例的子校正集,通过偏最小二乘回归,建立土壤有机质含量的可见—近红外光谱反演模型.结果表明:KS法无法提高模型预测精度,但可以在保证标准差与预测均方根误差比(ratio of performance to standard deviation,RPD)>2.0的前提下减少30%的校正样本;基于SPXY法的模型,当子校正集样本比例为总校正集的50%时达到最佳的模型预测精度,RPD为2.557;RKS法能够在保证预测精度的情况下(RPD>2.0),最多减少总校正集70%的样本,对应模型RPD为2.212.当校正集与验证集的有机质含量分布相近时,能够以较少的建模样本达到与总校正集相近甚至更高的模型预测精度,提升土壤有机质光谱反演模型的实用性. 土壤有机质含量可见-近红外光谱反演过程中校正集的构建策略对模型的预测精度有重要影响。以江汉平原洪湖地区水稻土为研究对象,采用Kennard-Stone（KS）法,Rank-KS（RKS）和Sample set Partitioning based on joint X-Y distance（SPXY）法,构建样本数占总校正集不同比例的子校正集,通过偏最小二乘回归,建立土壤有机质含量的可见—近红外光谱反演模型。结果表明：KS法无法提高模型预测精度,但可以在保证标准差与预测均方根误差比（ratio of performance to standard deviation,RPD）〉2.0的前提下减少30%的校正样本;基于SPXY法的模型,当子校正集样本比例为总校正集的50%时达到最佳的模型预测精度,RPD为2.557;RKS法能够在保证预测精度的情况下（RPD〉2.0）,最多减少总校正集70%的样本,对应模型RPD为2.212。当校正集与验证集的有机质含量分布相近时,能够以较少的建模样本达到与总校正集相近甚至更高的模型预测精度,提升土壤有机质光谱反演模型的实用性。 |
| Abstract_FL | Soil organic matter (SOM) is not only an important indicator of soil fertility but also an important source and sink of the global carbon cycle. Therefore, it is essential to acquire the information of SOM for soil management. Visible and near-infrared (VIS-NIR) reflectance spectroscopy, known as a novel, rapid, accurate, environment-friendly and efficient approach compared with conventional laboratory analyses, has proven to be promising in the acquisition of various soil properties. Construction of a calibration set is key to the VIS-NIR quantitative analysis in building up a prediction model of high quality. The aim of this paper was to explore how the sample selection method and the number of samples may affect the accuracy of VIS-NIR models for SOM estimation. A total of 100 paddy soil samples (0-15 cm) were collected from the Honghu City, which is located in the Jianghan Plain, China. After air drying, grinding and sieving (0.25 mm), reflectance of these pretreated samples was measured with FieldSpec3 (Analytical Spectral Devices Inc., America). Three samples were neglected after outlier detections of spectra and SOM content. Out of the remaining 97 samples, 20 samples were selected by means of concentration gradient, which then formed the validation sample set. The remaining 77 samples formed the total calibration set. With SOM content or soil spectral information as inputs, 3 sample selection methods, namely Kennard-Stone (KS), sample set partitioning based on joint X-Y distance (SPXY) and Rank-KS, were used in the construction of calibration subsets with different proportions of the samples in total calibration set, such as 10% and 20%. Based on the different calibration subsets, partial least squares regression (PLSR) was used for model calibrations. Results showed that the calibration set selected by KS approach could not improve model predictive capability compared with the total calibration set. The KS approach, however, could reduce as many as 30% samples of the total calibration set while the ratio of performance to standard deviation (RPD) was retained above 2.0. The SPXY approach performed the best when 50% samples of the total calibration set were selected in the model calibration. The determination coefficient for calibration (Rc2) reached 0.922, the determination coefficient for prediction (Rp2) was 0.848, and the RPD reached 2.557. This was because the SPXY approach took into account both SOM content and soil spectra in the sample selection process. With only 30% samples of the total calibration set selected by the Rank-KS method, it had the lowest cost of calibration with satisfactory performance (Rc2=0.872,Rp2=0.802 and RPD=2.212). Overall, such results indicate that it is possible to reduce the number of calibration samples while retaining or even improving the predictive capacity of VIS-NIR models for SOM estimation. All the 3 calibration selection approaches have been proven to be useful for the improvement of model practicability. |
| Author | 陈奕云 齐天赐 黄颖菁 万远 赵瑞瑛 亓林 张超 费腾 |
| AuthorAffiliation | 武汉大学资源与环境科学学院,武汉430079 土壤与农业可持续发展国家重点实验室,南京210008 武汉大学苏州研究院,苏州215123 武汉大学地球空间信息技术协同创新中心,武汉430079 武汉大学教育部地理信息系统重点实验室,武汉430079 湖泊与环境国家重点实验室中国科学院南京地理与湖泊研究所,南京210008 湖北师范大学、城市与环境学院,黄石435002 浙江大学农业遥感与信息技术应用研究所,杭州310058 中国科学院地理科学与资源研究所,北京100101 |
| AuthorAffiliation_xml | – name: 武汉大学资源与环境科学学院,武汉 430079;土壤与农业可持续发展国家重点实验室,南京 210008;武汉大学苏州研究院,苏州 215123;武汉大学地球空间信息技术协同创新中心,武汉 430079;武汉大学教育部地理信息系统重点实验室,武汉430079%武汉大学资源与环境科学学院,武汉 430079;湖泊与环境国家重点实验室(中国科学院南京地理与湖泊研究所),南京 210008%武汉大学资源与环境科学学院,武汉,430079%湖北师范大学,城市与环境学院,黄石 435002%武汉大学资源与环境科学学院,武汉 430079;浙江大学农业遥感与信息技术应用研究所,杭州 310058%武汉大学资源与环境科学学院,武汉 430079;中国科学院地理科学与资源研究所,北京 100101%武汉大学资源与环境科学学院,武汉 430079;武汉大学苏州研究院,苏州 215123 |
| Author_xml | – sequence: 1 fullname: 陈奕云 齐天赐 黄颖菁 万远 赵瑞瑛 亓林 张超 费腾 |
| BookMark | eNo9j01LAkEAhudgkJl_IohOu82487FzDOkLhC7eZd3ZsZUayyXKWx0qJMKCkiCpTCEhoqIuidCfaab1X2QYnV54eXhfnimQUBUVADCLoI0QZ2S-bIdRpGwEYcaiLuJ2BiJmQ2pDhBMg-d9PgnQUhUVIkMMgxCgJcrp1qztd06qbVj9-7-mzx-FxQzee44cDK_48_-7f625TH9bjl1fdODWDC9Nr65sTc9c2T53h9dHX4Gq4XzfND_N2OQ0mpLcRBem_TIH80mI-u2Ll1pZXsws5yyccWw6XrqAUUi6EoAQLFLicCepxD3kBD5BLKJa-xIwIn0nJGBZSOtxxocudwHdSYG48u-sp6alSoVzZqarRYUHVSv5e8Vce0pH6iJwZk_56RZW2wxG7VQ03vWqtQBlihBNMnB-22Hnw |
| ClassificationCodes | S151.9 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2RA 92L CQIGP W95 ~WA 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.11975/j.issn.1002-6819.2017.06.014 |
| DatabaseName | 中文期刊服务平台 中文科技期刊数据库-CALIS站点 维普中文期刊数据库 中文科技期刊数据库-农业科学 中文科技期刊数据库- 镜像站点 Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Agriculture |
| DocumentTitleAlternate | Optimization method of calibration dataset for VIS-NIR spectral inversion model of soil organ ic matter content |
| DocumentTitle_FL | Optimization method of calibration dataset for VIS-NIR spectral inversion model of soil organic matter content |
| EndPage | 114 |
| ExternalDocumentID | nygcxb201706014 671759545 |
| GrantInformation_xml | – fundername: 国家自然科学基金项目; 苏州市应用基础农业项目 funderid: (41501444); (SYN201422,SYN201309) |
| GroupedDBID | -04 2B. 2B~ 2RA 5XA 5XE 92G 92I 92L ABDBF ABJNI ACGFO ACGFS AEGXH AIAGR ALMA_UNASSIGNED_HOLDINGS CCEZO CHDYS CQIGP CW9 EOJEC FIJ IPNFZ OBODZ RIG TCJ TGD TUS U1G U5N W95 ~WA 4A8 93N ACUHS PSX |
| ID | FETCH-LOGICAL-c594-39f8d66069ddd654d1e897d6a9a1ae9e18564fcf475dc7ff774dff39380893ec3 |
| ISSN | 1002-6819 |
| IngestDate | Thu May 29 04:04:20 EDT 2025 Wed Feb 14 10:04:18 EST 2024 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 6 |
| Keywords | models partial least squares regression 模型 可见-近红外反射光谱 有机质 organic matter 校正集优选 visible and near-infrared reflectance spectrum soils 偏最小二乘回归 optimization of calibration set 土壤 |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c594-39f8d66069ddd654d1e897d6a9a1ae9e18564fcf475dc7ff774dff39380893ec3 |
| Notes | 11-2047/S Chen Yiyun1,2,3,4,5, Qi Tianci1,6, Huang Yingjing1, Wan Yuan7, Zhao Ruiying1,8, Qi Lin1,9, Zhang Chao1, Fei Teng1,3 （1. School of Resource and Environment Science, Wuhan University, Wuhan 430079, China; 2. State Key Laboratory of Soil and Sustainable Agriculture, Nanjing 210008, China; 3. Suzhou Institute of Wuhan University, Suzhou, Jiangsu 215123, China; 4. Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China; 5. Key Laboratory of Geographic Information System of Ministry of Education, Wuhan University, Wuhan 430079, China; 6. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; 7. College of Urban and Environmental Sciences, Hubei Normal University, Huangshi 435002, China; 8. Institute of AgriculturaI Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310058, China; 9. Institute of Geographic Sciences and Natural Resources R |
| PageCount | 8 |
| ParticipantIDs | wanfang_journals_nygcxb201706014 chongqing_primary_671759545 |
| PublicationCentury | 2000 |
| PublicationDate | 2017 |
| PublicationDateYYYYMMDD | 2017-01-01 |
| PublicationDate_xml | – year: 2017 text: 2017 |
| PublicationDecade | 2010 |
| PublicationTitle | 农业工程学报 |
| PublicationTitleAlternate | Transactions of the Chinese Society of Agricultural Engineering |
| PublicationTitle_FL | Transactions of the Chinese Society of Agricultural Engineering |
| PublicationYear | 2017 |
| Publisher | 武汉大学资源与环境科学学院,武汉 430079 武汉大学教育部地理信息系统重点实验室,武汉430079%武汉大学资源与环境科学学院,武汉 430079 浙江大学农业遥感与信息技术应用研究所,杭州 310058%武汉大学资源与环境科学学院,武汉 430079 土壤与农业可持续发展国家重点实验室,南京 210008 武汉大学苏州研究院,苏州 215123 中国科学院地理科学与资源研究所,北京 100101%武汉大学资源与环境科学学院,武汉 430079 武汉大学地球空间信息技术协同创新中心,武汉 430079 湖泊与环境国家重点实验室(中国科学院南京地理与湖泊研究所),南京 210008%武汉大学资源与环境科学学院,武汉,430079%湖北师范大学,城市与环境学院,黄石 435002%武汉大学资源与环境科学学院,武汉 430079 |
| Publisher_xml | – name: 浙江大学农业遥感与信息技术应用研究所,杭州 310058%武汉大学资源与环境科学学院,武汉 430079 – name: 武汉大学教育部地理信息系统重点实验室,武汉430079%武汉大学资源与环境科学学院,武汉 430079 – name: 武汉大学资源与环境科学学院,武汉 430079 – name: 中国科学院地理科学与资源研究所,北京 100101%武汉大学资源与环境科学学院,武汉 430079 – name: 武汉大学地球空间信息技术协同创新中心,武汉 430079 – name: 武汉大学苏州研究院,苏州 215123 – name: 土壤与农业可持续发展国家重点实验室,南京 210008 – name: 湖泊与环境国家重点实验室(中国科学院南京地理与湖泊研究所),南京 210008%武汉大学资源与环境科学学院,武汉,430079%湖北师范大学,城市与环境学院,黄石 435002%武汉大学资源与环境科学学院,武汉 430079 |
| SSID | ssib051370041 ssib017478172 ssj0041925 ssib001101065 ssib023167668 |
| Score | 2.177294 |
| Snippet | 土壤有机质含量可见-近红外光谱反演过程中校正集的构建策略对模型的预测精度有重要影响。以江汉平原洪湖地区水稻土为研究对象,采用Kennard-Stone（KS）法,Rank-KS（RKS）... S151.9; 土壤有机质含量可见-近红外光谱反演过程中校正集的构建策略对模型的预测精度有重要影响.以江汉平原洪湖地区水稻土为研究对象,采用Kennard-Stone(KS)... |
| SourceID | wanfang chongqing |
| SourceType | Aggregation Database Publisher |
| StartPage | 107 |
| SubjectTerms | 偏最小二乘回归 可见-近红外反射光谱 土壤 有机质 校正集优选 模型 |
| Title | 土壤有机质含量可见-近红外光谱反演模型校正集优选方法 |
| URI | http://lib.cqvip.com/qk/90712X/201706/671759545.html https://d.wanfangdata.com.cn/periodical/nygcxb201706014 |
| Volume | 33 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate issn: 1002-6819 databaseCode: ABDBF dateStart: 20140101 customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn isFulltext: true dateEnd: 99991231 titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn omitProxy: true ssIdentifier: ssj0041925 providerName: EBSCOhost |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR1Na9RAdKgtiB7ET6xV6cE5SWo-JsnMcbKbpYh6qtDbskk27WmrtQXtSQ8qRaQKWgSLWluwIKKiF0tB_C8mbv-F771k19iKX5fhMfu-5r3MvjfDzBvGzpipD4EpNg2ViNQQIrYMaaWuoWSUiCiNTeXjbeSLl7zxy-L8pDs5sOdL5dTS_Fw0Fi_88l7J_3gV-sCveEv2HzzbZwodAIN_oQUPQ_tXPuahy1WNqwYC2uFa8NDDHqlKINA8lDwQXEtCNrkOeKi49LkkKmh1A3E09FgGYTe4snjoI7G2ibXgyiNsl1gDjskDq6SXdRQWgB4kHiRp-kmFXAbUY1IPAHXUEsSrgEtgKIhKkkJmT2lgRUDg8OJhzF7uTOJoVEgoudLYE_hcu6gtyEVxLknxkIME_d3eF0ViQQ7ZASiUS2w0V1YVJaijkYox62KoaLafUEB5gQAYB80iyQgVLqQdWLi0Za26s1JcIaVZ0LMI6QrWxPEXcv3KMEgBSZZFZc0e-xDHDK0unG9z6ZAVfHJen7wP0OgBza7tlntWOJDEqUqAwgjmyTLMlBGsKCVSztRqOCpfFC4zG6u4rrs7aCrfpaiJEsb6EvDco0_FbQu6HXXJOzem4uuRXdRfwqfkh2zcEhtkQzqoB40fObmF2w79oGFj6QXvxxrXtRx8YaF_LgtPJbh0RKFUYy_jPSXP_U5FLI4yPdOZugopH93A66StzlQlWZw4yA6Uq7xRXUzZQ2xgYfow26-nZstKN-0j7EK28jxbW89XFvOVze7HjezB6-27S9nS2-6rW0b388Nvmy-z9eXs9mL33fts6X6-9SjfWM2e3ctfrOZv1raf3vm69WT75mK-_Cn_8Pgom2iEE7Vxo3zaxIhdJQxHpTLxPNNTSZJ4rkistlR-4rVUy2q1VRuSaE-kcSp8N4n9NIU1WpKmjnKkCeuLduwcY4OdmU77OBuF9Yhjm3HkmzIRcdRqQQ4vo9gDRN-WUTTMRvpmaV4pKtg0PR9WDQoWT8NstDRUs_xfu9bc4dgTf0YZYfsQLnYmT7LBudn59inI1eei0-XX8B13ZbZQ |
| linkProvider | EBSCOhost |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E5%9C%9F%E5%A3%A4%E6%9C%89%E6%9C%BA%E8%B4%A8%E5%90%AB%E9%87%8F%E5%8F%AF%E8%A7%81-%E8%BF%91%E7%BA%A2%E5%A4%96%E5%85%89%E8%B0%B1%E5%8F%8D%E6%BC%94%E6%A8%A1%E5%9E%8B%E6%A0%A1%E6%AD%A3%E9%9B%86%E4%BC%98%E9%80%89%E6%96%B9%E6%B3%95&rft.jtitle=%E5%86%9C%E4%B8%9A%E5%B7%A5%E7%A8%8B%E5%AD%A6%E6%8A%A5&rft.au=%E9%99%88%E5%A5%95%E4%BA%91&rft.au=%E9%BD%90%E5%A4%A9%E8%B5%90&rft.au=%E9%BB%84%E9%A2%96%E8%8F%81&rft.au=%E4%B8%87%E8%BF%9C&rft.date=2017&rft.pub=%E6%AD%A6%E6%B1%89%E5%A4%A7%E5%AD%A6%E8%B5%84%E6%BA%90%E4%B8%8E%E7%8E%AF%E5%A2%83%E7%A7%91%E5%AD%A6%E5%AD%A6%E9%99%A2%2C%E6%AD%A6%E6%B1%89+430079&rft.issn=1002-6819&rft.volume=33&rft.issue=6&rft.spage=107&rft.epage=114&rft_id=info:doi/10.11975%2Fj.issn.1002-6819.2017.06.014&rft.externalDocID=nygcxb201706014 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fimage.cqvip.com%2Fvip1000%2Fqk%2F90712X%2F90712X.jpg http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fnygcxb%2Fnygcxb.jpg |