A practical topographic correction method for improving Moderate Resolution Imaging Spectroradiometer gross primary productivity estimation over mountainous areas
•A topographic correction method was proposed to improve mountainous MODIS GPP.•Radiation, temperature, and water heterogeneity are useful for GPP correction.•Improvement of MODIS GPP after correction was proved at 11 mountainous sites.•It is essential to incorporate topography into coarse resolutio...
Gespeichert in:
Veröffentlicht in: | International journal of applied earth observation and geoinformation 2021-12, Vol.103, p.102522, Article 102522 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | 102522 |
container_title | International journal of applied earth observation and geoinformation |
container_volume | 103 |
creator | Xie, Xinyao Li, Ainong Guan, Xiaobin Tan, Jianbo Jin, Huaan Bian, Jinhu |
description | •A topographic correction method was proposed to improve mountainous MODIS GPP.•Radiation, temperature, and water heterogeneity are useful for GPP correction.•Improvement of MODIS GPP after correction was proved at 11 mountainous sites.•It is essential to incorporate topography into coarse resolution GPP products.
Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) has been served as an effective tool to assess the terrestrial carbon budget for the entire globe since 2000. However, the current MODIS GPP product neglects the surface heterogeneity in the modeling process and is always generated at 500 m or 1 km resolution, which could cause errors to these estimates over mountainous areas. In this work, the MODIS GPP model (MOD17) was applied to obtain 1 km GPP estimates at eleven mountainous sites. Then, a topographic correction method based on three indexes associated with the spatial heterogeneity of received radiation (TCIAPAR), temperature (TCITMIN), and water (TCIVPD) stresses was developed to reduce GPP errors in these MOD17-simulated estimates. Results showed that a closer relationship between tower-based GPP and MOD17-simulated GPP was achieved after applying the proposed topographic correction method, with the determination coefficient (R2) increased from 0.61 to 0.74 and root mean square error (RMSE) reduced from 24.24 to 14.56 gC m−2 8d−1 at all the eleven mountainous sites. As for the effectiveness of each topographic correction index, an obvious improvement of MOD17-simulated GPP was observed after TCIAPAR correction (increasing R2 by 0.09 and decreasing RMSE by 8.75 gC m−2 8d−1), TCITMIN correction (increasing R2 by 0.05 and decreasing RMSE by 7.80 gC m−2 8d−1), and TCIVPD correction (increasing R2 by 0.06 and decreasing RMSE by 7.89 gC m−2 8d−1), indicating that the spatial heterogeneity information of radiation, temperature, and water within coarse pixels is necessary for improving the MODIS GPP over mountainous areas. It is notable that the combination of the TCIAPAR, TCITMIN, and TCIVPD corrections was found to have the largest improvement for MOD17-simulated GPP (increasing R2 by 0.13 and decreasing RMSE by 9.68 gC m−2 8d−1), indicating that the combined consideration of topographic factors in the correction process might achieve a larger improvement. This study highlights the feasibility of incorporating surface topographic characteristics into current coarse resolution GPP products in obtai |
doi_str_mv | 10.1016/j.jag.2021.102522 |
format | Article |
fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_proquest_miscellaneous_2718234761</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0303243421002294</els_id><doaj_id>oai_doaj_org_article_25cf05690e0c4e43872a2da8b7285014</doaj_id><sourcerecordid>2718234761</sourcerecordid><originalsourceid>FETCH-LOGICAL-c439t-a0f8f6035afc31bad455553fdaeb44cc6080cc81c160a50d13ba16514d5cb7843</originalsourceid><addsrcrecordid>eNp9Uctu1TAQjRBIlMIHsPOSTS624ySuWFUVlCsVVeIhsbMm9iR1lMTBTq7U3-FLmdxULOvN2OM5Z2bOybL3gh8EF9XH_tBDd5BcCnrLUsoX2YXQtcy1rH6_pHtZXeVaFfJ19ialnnNR15W-yP5eszmCXbyFgS1hDl2E-cFbZkOMSPkwsRGXh-BYGyLz4xzDyU8d-xYcRliQfccUhvVceByh2_5-zISMIYLzgcAYWRdDStTJjxAfKQa3EvfJL48M00LZMz6cqHQM67SAn8KaGESE9DZ71cKQ8N1TvMx-ffn88-Zrfnd_e7y5vsutKq6WHHir24oXJbS2EA04VdIpWgfYKGVtxTW3VgsrKg4ld6JoQFSlUK60TU3SXGbHndcF6M3TrCaAN-dEiJ2BSEINaGRpW06KcuRWoSpIaJAOdFNLXXKxcX3YuWjVPyutaEafLA4DTEiLGVkLLQtVV4JKxV5qN40itv9bC242b01vyFuzeWt2bwnzaccg6XHyGE2yHieLzm-m0cD-GfQ_8NyxOg</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2718234761</pqid></control><display><type>article</type><title>A practical topographic correction method for improving Moderate Resolution Imaging Spectroradiometer gross primary productivity estimation over mountainous areas</title><source>DOAJ Directory of Open Access Journals</source><source>ScienceDirect Journals (5 years ago - present)</source><creator>Xie, Xinyao ; Li, Ainong ; Guan, Xiaobin ; Tan, Jianbo ; Jin, Huaan ; Bian, Jinhu</creator><creatorcontrib>Xie, Xinyao ; Li, Ainong ; Guan, Xiaobin ; Tan, Jianbo ; Jin, Huaan ; Bian, Jinhu</creatorcontrib><description>•A topographic correction method was proposed to improve mountainous MODIS GPP.•Radiation, temperature, and water heterogeneity are useful for GPP correction.•Improvement of MODIS GPP after correction was proved at 11 mountainous sites.•It is essential to incorporate topography into coarse resolution GPP products.
Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) has been served as an effective tool to assess the terrestrial carbon budget for the entire globe since 2000. However, the current MODIS GPP product neglects the surface heterogeneity in the modeling process and is always generated at 500 m or 1 km resolution, which could cause errors to these estimates over mountainous areas. In this work, the MODIS GPP model (MOD17) was applied to obtain 1 km GPP estimates at eleven mountainous sites. Then, a topographic correction method based on three indexes associated with the spatial heterogeneity of received radiation (TCIAPAR), temperature (TCITMIN), and water (TCIVPD) stresses was developed to reduce GPP errors in these MOD17-simulated estimates. Results showed that a closer relationship between tower-based GPP and MOD17-simulated GPP was achieved after applying the proposed topographic correction method, with the determination coefficient (R2) increased from 0.61 to 0.74 and root mean square error (RMSE) reduced from 24.24 to 14.56 gC m−2 8d−1 at all the eleven mountainous sites. As for the effectiveness of each topographic correction index, an obvious improvement of MOD17-simulated GPP was observed after TCIAPAR correction (increasing R2 by 0.09 and decreasing RMSE by 8.75 gC m−2 8d−1), TCITMIN correction (increasing R2 by 0.05 and decreasing RMSE by 7.80 gC m−2 8d−1), and TCIVPD correction (increasing R2 by 0.06 and decreasing RMSE by 7.89 gC m−2 8d−1), indicating that the spatial heterogeneity information of radiation, temperature, and water within coarse pixels is necessary for improving the MODIS GPP over mountainous areas. It is notable that the combination of the TCIAPAR, TCITMIN, and TCIVPD corrections was found to have the largest improvement for MOD17-simulated GPP (increasing R2 by 0.13 and decreasing RMSE by 9.68 gC m−2 8d−1), indicating that the combined consideration of topographic factors in the correction process might achieve a larger improvement. This study highlights the feasibility of incorporating surface topographic characteristics into current coarse resolution GPP products in obtaining large-scale mountain GPP estimates.</description><identifier>ISSN: 1569-8432</identifier><identifier>EISSN: 1872-826X</identifier><identifier>DOI: 10.1016/j.jag.2021.102522</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>global carbon budget ; gross primary productivity ; Gross primary productivity (GPP) ; MODIS ; Mountainous areas ; mountains ; Remote sensing ; spatial data ; spatial variation ; Surface topography ; temperature ; time series analysis ; topography</subject><ispartof>International journal of applied earth observation and geoinformation, 2021-12, Vol.103, p.102522, Article 102522</ispartof><rights>2021 The Author(s)</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c439t-a0f8f6035afc31bad455553fdaeb44cc6080cc81c160a50d13ba16514d5cb7843</citedby><cites>FETCH-LOGICAL-c439t-a0f8f6035afc31bad455553fdaeb44cc6080cc81c160a50d13ba16514d5cb7843</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jag.2021.102522$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Xie, Xinyao</creatorcontrib><creatorcontrib>Li, Ainong</creatorcontrib><creatorcontrib>Guan, Xiaobin</creatorcontrib><creatorcontrib>Tan, Jianbo</creatorcontrib><creatorcontrib>Jin, Huaan</creatorcontrib><creatorcontrib>Bian, Jinhu</creatorcontrib><title>A practical topographic correction method for improving Moderate Resolution Imaging Spectroradiometer gross primary productivity estimation over mountainous areas</title><title>International journal of applied earth observation and geoinformation</title><description>•A topographic correction method was proposed to improve mountainous MODIS GPP.•Radiation, temperature, and water heterogeneity are useful for GPP correction.•Improvement of MODIS GPP after correction was proved at 11 mountainous sites.•It is essential to incorporate topography into coarse resolution GPP products.
Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) has been served as an effective tool to assess the terrestrial carbon budget for the entire globe since 2000. However, the current MODIS GPP product neglects the surface heterogeneity in the modeling process and is always generated at 500 m or 1 km resolution, which could cause errors to these estimates over mountainous areas. In this work, the MODIS GPP model (MOD17) was applied to obtain 1 km GPP estimates at eleven mountainous sites. Then, a topographic correction method based on three indexes associated with the spatial heterogeneity of received radiation (TCIAPAR), temperature (TCITMIN), and water (TCIVPD) stresses was developed to reduce GPP errors in these MOD17-simulated estimates. Results showed that a closer relationship between tower-based GPP and MOD17-simulated GPP was achieved after applying the proposed topographic correction method, with the determination coefficient (R2) increased from 0.61 to 0.74 and root mean square error (RMSE) reduced from 24.24 to 14.56 gC m−2 8d−1 at all the eleven mountainous sites. As for the effectiveness of each topographic correction index, an obvious improvement of MOD17-simulated GPP was observed after TCIAPAR correction (increasing R2 by 0.09 and decreasing RMSE by 8.75 gC m−2 8d−1), TCITMIN correction (increasing R2 by 0.05 and decreasing RMSE by 7.80 gC m−2 8d−1), and TCIVPD correction (increasing R2 by 0.06 and decreasing RMSE by 7.89 gC m−2 8d−1), indicating that the spatial heterogeneity information of radiation, temperature, and water within coarse pixels is necessary for improving the MODIS GPP over mountainous areas. It is notable that the combination of the TCIAPAR, TCITMIN, and TCIVPD corrections was found to have the largest improvement for MOD17-simulated GPP (increasing R2 by 0.13 and decreasing RMSE by 9.68 gC m−2 8d−1), indicating that the combined consideration of topographic factors in the correction process might achieve a larger improvement. This study highlights the feasibility of incorporating surface topographic characteristics into current coarse resolution GPP products in obtaining large-scale mountain GPP estimates.</description><subject>global carbon budget</subject><subject>gross primary productivity</subject><subject>Gross primary productivity (GPP)</subject><subject>MODIS</subject><subject>Mountainous areas</subject><subject>mountains</subject><subject>Remote sensing</subject><subject>spatial data</subject><subject>spatial variation</subject><subject>Surface topography</subject><subject>temperature</subject><subject>time series analysis</subject><subject>topography</subject><issn>1569-8432</issn><issn>1872-826X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9Uctu1TAQjRBIlMIHsPOSTS624ySuWFUVlCsVVeIhsbMm9iR1lMTBTq7U3-FLmdxULOvN2OM5Z2bOybL3gh8EF9XH_tBDd5BcCnrLUsoX2YXQtcy1rH6_pHtZXeVaFfJ19ialnnNR15W-yP5eszmCXbyFgS1hDl2E-cFbZkOMSPkwsRGXh-BYGyLz4xzDyU8d-xYcRliQfccUhvVceByh2_5-zISMIYLzgcAYWRdDStTJjxAfKQa3EvfJL48M00LZMz6cqHQM67SAn8KaGESE9DZ71cKQ8N1TvMx-ffn88-Zrfnd_e7y5vsutKq6WHHir24oXJbS2EA04VdIpWgfYKGVtxTW3VgsrKg4ld6JoQFSlUK60TU3SXGbHndcF6M3TrCaAN-dEiJ2BSEINaGRpW06KcuRWoSpIaJAOdFNLXXKxcX3YuWjVPyutaEafLA4DTEiLGVkLLQtVV4JKxV5qN40itv9bC242b01vyFuzeWt2bwnzaccg6XHyGE2yHieLzm-m0cD-GfQ_8NyxOg</recordid><startdate>20211201</startdate><enddate>20211201</enddate><creator>Xie, Xinyao</creator><creator>Li, Ainong</creator><creator>Guan, Xiaobin</creator><creator>Tan, Jianbo</creator><creator>Jin, Huaan</creator><creator>Bian, Jinhu</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7S9</scope><scope>L.6</scope><scope>DOA</scope></search><sort><creationdate>20211201</creationdate><title>A practical topographic correction method for improving Moderate Resolution Imaging Spectroradiometer gross primary productivity estimation over mountainous areas</title><author>Xie, Xinyao ; Li, Ainong ; Guan, Xiaobin ; Tan, Jianbo ; Jin, Huaan ; Bian, Jinhu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c439t-a0f8f6035afc31bad455553fdaeb44cc6080cc81c160a50d13ba16514d5cb7843</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>global carbon budget</topic><topic>gross primary productivity</topic><topic>Gross primary productivity (GPP)</topic><topic>MODIS</topic><topic>Mountainous areas</topic><topic>mountains</topic><topic>Remote sensing</topic><topic>spatial data</topic><topic>spatial variation</topic><topic>Surface topography</topic><topic>temperature</topic><topic>time series analysis</topic><topic>topography</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xie, Xinyao</creatorcontrib><creatorcontrib>Li, Ainong</creatorcontrib><creatorcontrib>Guan, Xiaobin</creatorcontrib><creatorcontrib>Tan, Jianbo</creatorcontrib><creatorcontrib>Jin, Huaan</creatorcontrib><creatorcontrib>Bian, Jinhu</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>International journal of applied earth observation and geoinformation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xie, Xinyao</au><au>Li, Ainong</au><au>Guan, Xiaobin</au><au>Tan, Jianbo</au><au>Jin, Huaan</au><au>Bian, Jinhu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A practical topographic correction method for improving Moderate Resolution Imaging Spectroradiometer gross primary productivity estimation over mountainous areas</atitle><jtitle>International journal of applied earth observation and geoinformation</jtitle><date>2021-12-01</date><risdate>2021</risdate><volume>103</volume><spage>102522</spage><pages>102522-</pages><artnum>102522</artnum><issn>1569-8432</issn><eissn>1872-826X</eissn><abstract>•A topographic correction method was proposed to improve mountainous MODIS GPP.•Radiation, temperature, and water heterogeneity are useful for GPP correction.•Improvement of MODIS GPP after correction was proved at 11 mountainous sites.•It is essential to incorporate topography into coarse resolution GPP products.
Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) has been served as an effective tool to assess the terrestrial carbon budget for the entire globe since 2000. However, the current MODIS GPP product neglects the surface heterogeneity in the modeling process and is always generated at 500 m or 1 km resolution, which could cause errors to these estimates over mountainous areas. In this work, the MODIS GPP model (MOD17) was applied to obtain 1 km GPP estimates at eleven mountainous sites. Then, a topographic correction method based on three indexes associated with the spatial heterogeneity of received radiation (TCIAPAR), temperature (TCITMIN), and water (TCIVPD) stresses was developed to reduce GPP errors in these MOD17-simulated estimates. Results showed that a closer relationship between tower-based GPP and MOD17-simulated GPP was achieved after applying the proposed topographic correction method, with the determination coefficient (R2) increased from 0.61 to 0.74 and root mean square error (RMSE) reduced from 24.24 to 14.56 gC m−2 8d−1 at all the eleven mountainous sites. As for the effectiveness of each topographic correction index, an obvious improvement of MOD17-simulated GPP was observed after TCIAPAR correction (increasing R2 by 0.09 and decreasing RMSE by 8.75 gC m−2 8d−1), TCITMIN correction (increasing R2 by 0.05 and decreasing RMSE by 7.80 gC m−2 8d−1), and TCIVPD correction (increasing R2 by 0.06 and decreasing RMSE by 7.89 gC m−2 8d−1), indicating that the spatial heterogeneity information of radiation, temperature, and water within coarse pixels is necessary for improving the MODIS GPP over mountainous areas. It is notable that the combination of the TCIAPAR, TCITMIN, and TCIVPD corrections was found to have the largest improvement for MOD17-simulated GPP (increasing R2 by 0.13 and decreasing RMSE by 9.68 gC m−2 8d−1), indicating that the combined consideration of topographic factors in the correction process might achieve a larger improvement. This study highlights the feasibility of incorporating surface topographic characteristics into current coarse resolution GPP products in obtaining large-scale mountain GPP estimates.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.jag.2021.102522</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1569-8432 |
ispartof | International journal of applied earth observation and geoinformation, 2021-12, Vol.103, p.102522, Article 102522 |
issn | 1569-8432 1872-826X |
language | eng |
recordid | cdi_proquest_miscellaneous_2718234761 |
source | DOAJ Directory of Open Access Journals; ScienceDirect Journals (5 years ago - present) |
subjects | global carbon budget gross primary productivity Gross primary productivity (GPP) MODIS Mountainous areas mountains Remote sensing spatial data spatial variation Surface topography temperature time series analysis topography |
title | A practical topographic correction method for improving Moderate Resolution Imaging Spectroradiometer gross primary productivity estimation over mountainous areas |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T00%3A11%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20practical%20topographic%20correction%20method%20for%20improving%20Moderate%20Resolution%20Imaging%20Spectroradiometer%20gross%20primary%20productivity%20estimation%20over%20mountainous%20areas&rft.jtitle=International%20journal%20of%20applied%20earth%20observation%20and%20geoinformation&rft.au=Xie,%20Xinyao&rft.date=2021-12-01&rft.volume=103&rft.spage=102522&rft.pages=102522-&rft.artnum=102522&rft.issn=1569-8432&rft.eissn=1872-826X&rft_id=info:doi/10.1016/j.jag.2021.102522&rft_dat=%3Cproquest_doaj_%3E2718234761%3C/proquest_doaj_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2718234761&rft_id=info:pmid/&rft_els_id=S0303243421002294&rft_doaj_id=oai_doaj_org_article_25cf05690e0c4e43872a2da8b7285014&rfr_iscdi=true |