基于分数阶微分预处理高光谱数据的荒漠土壤有机碳含量估算
对光谱数据进行预处理是提升高光谱建模精度十分必要和有效的途径。为了研究分数阶微分预处理方法在高光谱数据估算荒漠土壤有机碳含量中的应用,该研究以艾比湖流域为研究靶区,利用2015年5月采集的103个表层土壤样本的实测有机碳数据和室内测定的高光谱数据,以0.2阶为步长对原始光谱反射率及对应的倒数变换、对数变换、对数倒数变换、均方根变换的0-2阶微分进行分数阶运算预处理并研究其与土壤有机碳含量相关性,基于通过0.01显著性检验的特征波段对土壤有机碳含量进行偏最小二乘回归建模并进行精度分析。结果表明:1)分数阶微分预处理可以细化土壤有机碳及其光谱反射率相关性的变化趋势;2)各阶微分预处理后的相关系数通...
Saved in:
| Published in | 农业工程学报 Vol. 32; no. 21; pp. 161 - 169 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
新疆大学绿洲生态教育部重点实验室，乌鲁木齐 830046
2016
新疆大学资源与环境科学学院，乌鲁木齐 830046 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.2016.21.021 |
Cover
| Abstract | 对光谱数据进行预处理是提升高光谱建模精度十分必要和有效的途径。为了研究分数阶微分预处理方法在高光谱数据估算荒漠土壤有机碳含量中的应用,该研究以艾比湖流域为研究靶区,利用2015年5月采集的103个表层土壤样本的实测有机碳数据和室内测定的高光谱数据,以0.2阶为步长对原始光谱反射率及对应的倒数变换、对数变换、对数倒数变换、均方根变换的0-2阶微分进行分数阶运算预处理并研究其与土壤有机碳含量相关性,基于通过0.01显著性检验的特征波段对土壤有机碳含量进行偏最小二乘回归建模并进行精度分析。结果表明:1)分数阶微分预处理可以细化土壤有机碳及其光谱反射率相关性的变化趋势;2)各阶微分预处理后的相关系数通过显著性检验波段的数量均呈现先增后减的趋势,但波段数量最多的对应阶数并不统一;3)对数变换的1.6阶微分所建立的模型为最优模型,该模型的RMSEC=2.433 g/kg,R2c=0.786,RMSEP=2.263 g/kg,R2p=0.825,RPD=2.392。说明了分数阶预处理过后的模型精度和稳健性较整数阶微分有了大幅提升,并且运用在高光谱反演土壤有机碳含量上是可行的。 |
|---|---|
| AbstractList | TP79%S127; 对光谱数据进行预处理是提升高光谱建模精度十分必要和有效的途径。为了研究分数阶微分预处理方法在高光谱数据估算荒漠土壤有机碳含量中的应用，该研究以艾比湖流域为研究靶区，利用2015年5月采集的103个表层土壤样本的实测有机碳数据和室内测定的高光谱数据，以0.2阶为步长对原始光谱反射率及对应的倒数变换、对数变换、对数倒数变换、均方根变换的0-2阶微分进行分数阶运算预处理并研究其与土壤有机碳含量相关性，基于通过0.01显著性检验的特征波段对土壤有机碳含量进行偏最小二乘回归建模并进行精度分析。结果表明：1）分数阶微分预处理可以细化土壤有机碳及其光谱反射率相关性的变化趋势；2）各阶微分预处理后的相关系数通过显著性检验波段的数量均呈现先增后减的趋势，但波段数量最多的对应阶数并不统一；3）对数变换的1.6阶微分所建立的模型为最优模型，该模型的RMSEC=2.433 g/kg，R2c=0.786，RMSEP=2.263 g/kg，R2p=0.825，RPD=2.392。说明了分数阶预处理过后的模型精度和稳健性较整数阶微分有了大幅提升，并且运用在高光谱反演土壤有机碳含量上是可行的。 对光谱数据进行预处理是提升高光谱建模精度十分必要和有效的途径。为了研究分数阶微分预处理方法在高光谱数据估算荒漠土壤有机碳含量中的应用,该研究以艾比湖流域为研究靶区,利用2015年5月采集的103个表层土壤样本的实测有机碳数据和室内测定的高光谱数据,以0.2阶为步长对原始光谱反射率及对应的倒数变换、对数变换、对数倒数变换、均方根变换的0-2阶微分进行分数阶运算预处理并研究其与土壤有机碳含量相关性,基于通过0.01显著性检验的特征波段对土壤有机碳含量进行偏最小二乘回归建模并进行精度分析。结果表明:1)分数阶微分预处理可以细化土壤有机碳及其光谱反射率相关性的变化趋势;2)各阶微分预处理后的相关系数通过显著性检验波段的数量均呈现先增后减的趋势,但波段数量最多的对应阶数并不统一;3)对数变换的1.6阶微分所建立的模型为最优模型,该模型的RMSEC=2.433 g/kg,R2c=0.786,RMSEP=2.263 g/kg,R2p=0.825,RPD=2.392。说明了分数阶预处理过后的模型精度和稳健性较整数阶微分有了大幅提升,并且运用在高光谱反演土壤有机碳含量上是可行的。 |
| Abstract_FL | Soil organic carbon (SOC) is a crucial soil property which has attracted wide attention in the field of global change. This is especially true in the arid and semi-arid regions. In recent years, it is a hot topic to estimate SOC content by hyperspectral remote sensing technology, however, it is hard to estimate SOC content in desert area precisely when it is less than 2%. Existing work, including related research history and current status, has mostly focused on integer differential, which yet might influence the effective information detection and cause the loss of spectral information to some extent. Therefore, this study aimed to bring fractional order differential algorithm into the preprocessing of hyperspectral data. With 103 surface soil samples collected from the Ebinur Lake basin in Xinjiang Uighur Autonomous Region, China, the SOC contents and reflectance spectra were measured in the laboratory. After removing the marginal bands (350-400 and 2401-2500 nm) and smoothed by Savitzky-Golay filter, the raw hyperspectral reflectance (R) data were transformed by 4 mathematical methods, i.e., the reciprocal, logarithm, logarithm-reciprocal and root mean square method, respectively. Secondly, their 0-2 order differentials (taking 0.2-order as step) were calculated by Grünwald-Letnikov fractional differential equation. And then, we computed the correlation coefficients between each fractional order differential value ofR, its 4 mathematical transformation forms and SOC content. After choosing the feature bands whose correlation coefficient passed the significance test at 0.01 level, 103 samples were divided into 2 parts: 69 for model calibration and 34 for validation. Subsequently, partial least squares regression (PLSR) was employed to build the hyperspectral estimation models of SOC content. And then, root mean square error of calibration (RMSEC), determination coefficient of calibration (R2c), root mean square error of prediction (RMSEP), determination coefficient of predicting (R2p) and relative prediction deviation (RPD) were used for accuracy assessment. The results showed that: 1) Fractional order differential algorithm could refine the correlation coefficient curves between the SOC content and the raw hyperspectral reflectance in the wavelength ranges of 450-600, 640-700 and 1400-1500 nm, and also reduce the information loss to some extent; 2) With the order increasing, the number of bands whose correlation coefficient passed the significance test at 0.01 level firstly increased and then decreased, but the number of bands did not reach the maximum at the same order, and some differences occurred; 3) Comparing the predictive effects of 55 SOC estimating models calibrated by PLSR, the model based on 1.6-order differential of logarithm transform was much better than others, and had better performance in predicting SOC content in the study area (RMSEC=2.433 g/kg,R2c=0.786, RMSEP=2.263 g/kg, R2p=0.825, RPD=2.392). And indeed, the models based on fractional order differentials were more robust and accurate than the conventional integer differential ones. Over all, it is a fairly satisfactory preprocessing method of hyperspectral data in the quantitative study on soil nutrients by means of remote sensing. In order to achieve more universal and stable inversion model, the next step is to enlarge the sampling area and the number of soil samples as much as possible to improve and perfect the soil hyperspectral database. |
| Author | 王敬哲 塔西甫拉提·特依拜 丁建丽 张东 刘巍 |
| AuthorAffiliation | 新疆大学资源与环境科学学院;新疆大学绿洲生态教育部重点实验室 |
| AuthorAffiliation_xml | – name: 新疆大学资源与环境科学学院，乌鲁木齐 830046; 新疆大学绿洲生态教育部重点实验室，乌鲁木齐 830046 |
| Author_FL | Ding Jianli Tashpolat·Tiyip Liu Wei Zhang Dong Wang Jingzhe |
| Author_FL_xml | – sequence: 1 fullname: Wang Jingzhe – sequence: 2 fullname: Tashpolat·Tiyip – sequence: 3 fullname: Ding Jianli – sequence: 4 fullname: Zhang Dong – sequence: 5 fullname: Liu Wei |
| Author_xml | – sequence: 1 fullname: 王敬哲 塔西甫拉提·特依拜 丁建丽 张东 刘巍 |
| BookMark | eNo9UF1LAkEUnQeDrPwZ0ZPb3NnZ3dnHkL5AiMB3mZ11baXGcony3SwfLAkiCcEw3wQxgh5E-jXurvsvGjF6uedw7-Gec-8GSsmqLCG0DVgDsC1jt6L5QSA1wJhkTQa2RjCYGgENE0ih9H9_HWWCwHewAbqFMYU0Og370_n0MXxoRi-TpPsd_owVTwaNcNiIO81k1A3vWovJp5pG7XH81li0n6PZe9jrhx_DqNeKetN48BV2Rsn903w2icevW2jN4-dBKfOHm6hwsF_IHWXzJ4fHub18VlgmZIFzlUIw7oFhlgjGglmeKQin1KbMUBR0Vziu6xATM3CxxYVLbUx03aG6i_VNtLNae8Olx2W5WKle16QyLMp6Wdw6yw8QUPcrpb5SirOqLF_5SntZ8y94rV60GLMtYIaBKaO2QRUu3VXVfwEwroDf |
| ClassificationCodes | TP79%S127 |
| 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 ~WA 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.11975/j.issn.1002-6819.2016.21.021 |
| DatabaseName | 维普期刊资源整合服务平台 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- 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 |
| DocumentTitle_FL | Estimation of desert soil organic carbon content based on hyperspectral data preprocessing with fractional differential |
| EndPage | 169 |
| ExternalDocumentID | nygcxb201621021 78897185504849545049485049 |
| GrantInformation_xml | – fundername: 国家自然科学基金; 国家科技支撑计划项目 funderid: (41130531、U1503302、41561089); (2014BAC15B01) |
| 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 ~WA 4A8 93N ACUHS PSX |
| ID | FETCH-LOGICAL-c761-1aa051c8af156e200c87f6c2a449485f6c13dcbddb26081d07acd490233b43d03 |
| ISSN | 1002-6819 |
| IngestDate | Thu May 29 04:04:20 EDT 2025 Wed Feb 14 10:07:46 EST 2024 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 21 |
| Keywords | 预处理 spectrum analysis pretreatment hyperspectra 光谱分析 分数阶微分 soils 有机碳 fractional differential organic carbon 土壤 高光谱 |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c761-1aa051c8af156e200c87f6c2a449485f6c13dcbddb26081d07acd490233b43d03 |
| Notes | 11-2047/S Wang Jingzhe;Tashpolat·Tiyip;Ding Jianli;Zhang Dong;Liu Wei;College of Resource and Environment Sciences, Xinjiang University;Key Laboratory of Oasis Ecology under Ministry of Education, Xinjiang University |
| PageCount | 9 |
| ParticipantIDs | wanfang_journals_nygcxb201621021 chongqing_primary_78897185504849545049485049 |
| PublicationCentury | 2000 |
| PublicationDate | 2016 |
| PublicationDateYYYYMMDD | 2016-01-01 |
| PublicationDate_xml | – year: 2016 text: 2016 |
| PublicationDecade | 2010 |
| PublicationTitle | 农业工程学报 |
| PublicationTitleAlternate | Transactions of the Chinese Society of Agricultural Engineering |
| PublicationYear | 2016 |
| Publisher | 新疆大学绿洲生态教育部重点实验室，乌鲁木齐 830046 新疆大学资源与环境科学学院，乌鲁木齐 830046 |
| Publisher_xml | – name: 新疆大学绿洲生态教育部重点实验室，乌鲁木齐 830046 – name: 新疆大学资源与环境科学学院，乌鲁木齐 830046 |
| SSID | ssib051370041 ssib017478172 ssj0041925 ssib001101065 ssib023167668 |
| Score | 2.136153 |
| Snippet | 对光谱数据进行预处理是提升高光谱建模精度十分必要和有效的途径。为了研究分数阶微分预处理方法在高光谱数据估算荒漠土壤有机碳含量中的应用,该研究以艾比湖流域为研究靶... TP79%S127; 对光谱数据进行预处理是提升高光谱建模精度十分必要和有效的途径。为了研究分数阶微分预处理方法在高光谱数据估算荒漠土壤有机碳含量中的应用，该研究以艾比湖... |
| SourceID | wanfang chongqing |
| SourceType | Aggregation Database Publisher |
| StartPage | 161 |
| SubjectTerms | 土壤;有机碳;光谱分析;分数阶微分;高光谱;预处理 |
| Title | 基于分数阶微分预处理高光谱数据的荒漠土壤有机碳含量估算 |
| URI | http://lib.cqvip.com/qk/90712X/201621/78897185504849545049485049.html https://d.wanfangdata.com.cn/periodical/nygcxb201621021 |
| Volume | 32 |
| 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/eLvHCXMwnV1LaxRBEB7iBkQP4hPjixzsk27cefTrODM7SxAUhAi5LTszu-tpozEBzTlGc4gGQQwSiMTcAiEieAjBX5PZzf4Lq2p6dwcVXxCGpru66quqSVd3b3eNZd2UifKaXKQwc0vcspe0cBz0ZNmTsYc_A6Wc8szeuy-mH3p3Z_nsWEkXTi0tLsRTydIv75X8j1ehDvyKt2T_wbNDplABZfAvPMHD8PwrH7OIM11jgc8iD58qwhqlmBIsEkxzFlRYpJlWLBDYFETML9Jo5jtMeVjje1SQTFcGTQF2RGL40yxSyC2wC5wFU1ViCL186q6wRjvYFITMrxDCEEGiCBelYPeQGFIBwUuEEbhEXEG5IF1JpmqkV0iyJArKD9kOJtOEjZggmUIMqCNQcqIHMAHJBZBkEOVD0-AVQxIwGJKQQn5I8l0WOCMSqLaZJsWAaVAjVT2CKLBrrgYABdyhg7KRq2aBJkyAuGoodTjiSmiVTWiDgfvAvNWiYGO_XLOwuDuTXxul_yTCLoyBoKCE8aUvC5pLElMjrwA_x2ihqUZVCArZQJOblUM04AMXjWicUSXVHFKNkzMiekvA8aGpQe_WELjKXaKpoJGbEREahgG8JZVbChOxiUJcxMAplIluJnCONqYXB_fcTRi08wT7ZkZl5x_j-TlYa8kpWqOIqaEIPG8pphzMpWuPZinDs6Od5-3kWYw0uFthn7DGHdyKK1njflANaqO1gI3bHcNg5WDKBzFaW3PbxS87DM-D4WkITkcjDIyTFhuAvPM7iJiU5dFcp_0Eppp086_TanTahUnqzFnrjFldTvr5UHHOGlt6dN467bfnTYad5gXrQbZ1cHTwOnu10n2339_4mn3bg3J_eznbWe6tr_R3N7IXq8f7n6G1u7bX-7B8vPa2e_gx29zKPu10N1e7mwe97S_Z-m7_5Zujw_3e3vuL1kwtmgmny-a7KuUELFW2Gw3QPlGNls1FE8bTRMmWSJyGR6mioGi7aRKnaewIWDCkFdlIUk-DBd3Yc9OKe8kqdeY6zcvWZCzdtKGEbjW59LgNUckR3I2TFqzjZBI7E9btoW3qj_P0OXWplIYZMecwefA0rODyFFXwnLAmjfnqZpR9Wv_B3Vf-THLVOoXlfJ_0mlVamF9sXoeVw0J8w7wj3wFJd9U2 |
| 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%9F%BA%E4%BA%8E%E5%88%86%E6%95%B0%E9%98%B6%E5%BE%AE%E5%88%86%E9%A2%84%E5%A4%84%E7%90%86%E9%AB%98%E5%85%89%E8%B0%B1%E6%95%B0%E6%8D%AE%E7%9A%84%E8%8D%92%E6%BC%A0%E5%9C%9F%E5%A3%A4%E6%9C%89%E6%9C%BA%E7%A2%B3%E5%90%AB%E9%87%8F%E4%BC%B0%E7%AE%97&rft.jtitle=%E5%86%9C%E4%B8%9A%E5%B7%A5%E7%A8%8B%E5%AD%A6%E6%8A%A5&rft.au=%E7%8E%8B%E6%95%AC%E5%93%B2&rft.au=%E5%A1%94%E8%A5%BF%E7%94%AB%E6%8B%89%E6%8F%90%C2%B7%E7%89%B9%E4%BE%9D%E6%8B%9C&rft.au=%E4%B8%81%E5%BB%BA%E4%B8%BD&rft.au=%E5%BC%A0%E4%B8%9C&rft.date=2016&rft.pub=%E6%96%B0%E7%96%86%E5%A4%A7%E5%AD%A6%E7%BB%BF%E6%B4%B2%E7%94%9F%E6%80%81%E6%95%99%E8%82%B2%E9%83%A8%E9%87%8D%E7%82%B9%E5%AE%9E%E9%AA%8C%E5%AE%A4%EF%BC%8C%E4%B9%8C%E9%B2%81%E6%9C%A8%E9%BD%90+830046&rft.issn=1002-6819&rft.volume=32&rft.issue=21&rft.spage=161&rft.epage=169&rft_id=info:doi/10.11975%2Fj.issn.1002-6819.2016.21.021&rft.externalDocID=nygcxb201621021 |
| 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 |