Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands

Two spatially-explicit estimates of gross primary production (GPP) are available for the Northern Great Plains. An empirical piecewise regression (PWR) GPP model was developed from flux tower measurements to map carbon flux across the region. The Moderate Resolution Imaging Spectrometer (MODIS) GPP...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Remote sensing of environment 2007-01, Vol.106 (2), p.173-189
Hauptverfasser: Zhang, Li, Wylie, Bruce, Loveland, Thomas, Fosnight, Eugene, Tieszen, Larry L., Ji, Lei, Gilmanov, Tagir
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 189
container_issue 2
container_start_page 173
container_title Remote sensing of environment
container_volume 106
creator Zhang, Li
Wylie, Bruce
Loveland, Thomas
Fosnight, Eugene
Tieszen, Larry L.
Ji, Lei
Gilmanov, Tagir
description Two spatially-explicit estimates of gross primary production (GPP) are available for the Northern Great Plains. An empirical piecewise regression (PWR) GPP model was developed from flux tower measurements to map carbon flux across the region. The Moderate Resolution Imaging Spectrometer (MODIS) GPP model is a process-based model that uses flux tower data to calibrate its parameters. Verification and comparison of the regional PWR GPP and the global MODIS GPP are important for the modeling of grassland carbon flux. This study compared GPP estimates from PWR and MODIS models with five towers in the grasslands. Among them, PWR GPP and MODIS GPP showed a good agreement with tower-based GPP at three towers. The global MODIS GPP, however, did not agree well with tower-based GPP at two other towers, probably because of the insensitivity of MODIS model to regional ecosystem and climate change and extreme soil moisture conditions. Cross-validation indicated that the PWR model is relatively robust for predicting regional grassland GPP. However, the PWR model should include a wide variety of flux tower data as the training data sets to obtain more accurate results. In addition, GPP maps based on the PWR and MODIS models were compared for the entire region. In the northwest and south, PWR GPP was much higher than MODIS GPP. These areas were characterized by the higher water holding capacity with a lower proportion of C 4 grasses in the northwest and a higher proportion of C 4 grasses in the south. In the central and southeastern regions, PWR GPP was much lower than MODIS GPP under complicated conditions with generally mixed C 3/C 4 grasses. The analysis indicated that the global MODIS GPP model has some limitations on detecting moisture stress, which may have been caused by the facts that C 3 and C 4 grasses are not distinguished, water stress is driven by vapor pressure deficit (VPD) from coarse meteorological data, and MODIS land cover data are unable to differentiate the sub-pixel cropland components.
doi_str_mv 10.1016/j.rse.2006.08.012
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_19727389</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0034425706003178</els_id><sourcerecordid>19727389</sourcerecordid><originalsourceid>FETCH-LOGICAL-c424t-a5086feebe1da139dce0d925dd036c075dfcad7b67a1ab0796f58c6ffd60f7213</originalsourceid><addsrcrecordid>eNp9UE1rGzEQFaGBukl-QG-6tLfdjvZLu_RUgusUQtpDchZjaVTLrFepZm3Iv68cG3LL6THDmzfvPSE-KygVqO7btkxMZQXQldCXoKoLsVC9HgrQ0HwQC4C6KZqq1R_FJ-YtgGp7rRZiszzguMc5xEni5KSNu2dMgfMYvfybIrN8TmGH6SVjdHv7SiWe824mlj4mOW9IPsSUIU1ylQhn-WfEMHEWQOYxC_O1uPQ4Mt2c8Uo8_Vw-3t4V979Xv25_3Be2qZq5wBb6zhOtSTlU9eAsgRuq1jmoOwu6dd6i0-tOo8I16KHzbW87710HXleqvhJfT7rZ7b999ml2gS2N2QTFPRs16ErX_ZCJ6kS0x5CJvDnnNArMsVOzNblTc-zUQG9yp_nmy1kc2eLoE0428Nth31S1bo7a3088ykkPgZJhG2iy5EIiOxsXwztf_gO_H49E</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>19727389</pqid></control><display><type>article</type><title>Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands</title><source>ScienceDirect Journals (5 years ago - present)</source><creator>Zhang, Li ; Wylie, Bruce ; Loveland, Thomas ; Fosnight, Eugene ; Tieszen, Larry L. ; Ji, Lei ; Gilmanov, Tagir</creator><creatorcontrib>Zhang, Li ; Wylie, Bruce ; Loveland, Thomas ; Fosnight, Eugene ; Tieszen, Larry L. ; Ji, Lei ; Gilmanov, Tagir</creatorcontrib><description>Two spatially-explicit estimates of gross primary production (GPP) are available for the Northern Great Plains. An empirical piecewise regression (PWR) GPP model was developed from flux tower measurements to map carbon flux across the region. The Moderate Resolution Imaging Spectrometer (MODIS) GPP model is a process-based model that uses flux tower data to calibrate its parameters. Verification and comparison of the regional PWR GPP and the global MODIS GPP are important for the modeling of grassland carbon flux. This study compared GPP estimates from PWR and MODIS models with five towers in the grasslands. Among them, PWR GPP and MODIS GPP showed a good agreement with tower-based GPP at three towers. The global MODIS GPP, however, did not agree well with tower-based GPP at two other towers, probably because of the insensitivity of MODIS model to regional ecosystem and climate change and extreme soil moisture conditions. Cross-validation indicated that the PWR model is relatively robust for predicting regional grassland GPP. However, the PWR model should include a wide variety of flux tower data as the training data sets to obtain more accurate results. In addition, GPP maps based on the PWR and MODIS models were compared for the entire region. In the northwest and south, PWR GPP was much higher than MODIS GPP. These areas were characterized by the higher water holding capacity with a lower proportion of C 4 grasses in the northwest and a higher proportion of C 4 grasses in the south. In the central and southeastern regions, PWR GPP was much lower than MODIS GPP under complicated conditions with generally mixed C 3/C 4 grasses. The analysis indicated that the global MODIS GPP model has some limitations on detecting moisture stress, which may have been caused by the facts that C 3 and C 4 grasses are not distinguished, water stress is driven by vapor pressure deficit (VPD) from coarse meteorological data, and MODIS land cover data are unable to differentiate the sub-pixel cropland components.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2006.08.012</identifier><identifier>CODEN: RSEEA7</identifier><language>eng</language><publisher>New York, NY: Elsevier Inc</publisher><subject>Animal, plant and microbial ecology ; Applied geophysics ; Biological and medical sciences ; Carbon flux ; Decision tree ; Earth sciences ; Earth, ocean, space ; Exact sciences and technology ; Fundamental and applied biological sciences. Psychology ; General aspects. Techniques ; Grassland ; Gross primary production (GPP) ; Internal geophysics ; Model comparison ; MODIS GPP ; Northern Great Plains ; Teledetection and vegetation maps</subject><ispartof>Remote sensing of environment, 2007-01, Vol.106 (2), p.173-189</ispartof><rights>2006 Elsevier Inc.</rights><rights>2007 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c424t-a5086feebe1da139dce0d925dd036c075dfcad7b67a1ab0796f58c6ffd60f7213</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.rse.2006.08.012$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,45974</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=18423749$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Li</creatorcontrib><creatorcontrib>Wylie, Bruce</creatorcontrib><creatorcontrib>Loveland, Thomas</creatorcontrib><creatorcontrib>Fosnight, Eugene</creatorcontrib><creatorcontrib>Tieszen, Larry L.</creatorcontrib><creatorcontrib>Ji, Lei</creatorcontrib><creatorcontrib>Gilmanov, Tagir</creatorcontrib><title>Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands</title><title>Remote sensing of environment</title><description>Two spatially-explicit estimates of gross primary production (GPP) are available for the Northern Great Plains. An empirical piecewise regression (PWR) GPP model was developed from flux tower measurements to map carbon flux across the region. The Moderate Resolution Imaging Spectrometer (MODIS) GPP model is a process-based model that uses flux tower data to calibrate its parameters. Verification and comparison of the regional PWR GPP and the global MODIS GPP are important for the modeling of grassland carbon flux. This study compared GPP estimates from PWR and MODIS models with five towers in the grasslands. Among them, PWR GPP and MODIS GPP showed a good agreement with tower-based GPP at three towers. The global MODIS GPP, however, did not agree well with tower-based GPP at two other towers, probably because of the insensitivity of MODIS model to regional ecosystem and climate change and extreme soil moisture conditions. Cross-validation indicated that the PWR model is relatively robust for predicting regional grassland GPP. However, the PWR model should include a wide variety of flux tower data as the training data sets to obtain more accurate results. In addition, GPP maps based on the PWR and MODIS models were compared for the entire region. In the northwest and south, PWR GPP was much higher than MODIS GPP. These areas were characterized by the higher water holding capacity with a lower proportion of C 4 grasses in the northwest and a higher proportion of C 4 grasses in the south. In the central and southeastern regions, PWR GPP was much lower than MODIS GPP under complicated conditions with generally mixed C 3/C 4 grasses. The analysis indicated that the global MODIS GPP model has some limitations on detecting moisture stress, which may have been caused by the facts that C 3 and C 4 grasses are not distinguished, water stress is driven by vapor pressure deficit (VPD) from coarse meteorological data, and MODIS land cover data are unable to differentiate the sub-pixel cropland components.</description><subject>Animal, plant and microbial ecology</subject><subject>Applied geophysics</subject><subject>Biological and medical sciences</subject><subject>Carbon flux</subject><subject>Decision tree</subject><subject>Earth sciences</subject><subject>Earth, ocean, space</subject><subject>Exact sciences and technology</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects. Techniques</subject><subject>Grassland</subject><subject>Gross primary production (GPP)</subject><subject>Internal geophysics</subject><subject>Model comparison</subject><subject>MODIS GPP</subject><subject>Northern Great Plains</subject><subject>Teledetection and vegetation maps</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><recordid>eNp9UE1rGzEQFaGBukl-QG-6tLfdjvZLu_RUgusUQtpDchZjaVTLrFepZm3Iv68cG3LL6THDmzfvPSE-KygVqO7btkxMZQXQldCXoKoLsVC9HgrQ0HwQC4C6KZqq1R_FJ-YtgGp7rRZiszzguMc5xEni5KSNu2dMgfMYvfybIrN8TmGH6SVjdHv7SiWe824mlj4mOW9IPsSUIU1ylQhn-WfEMHEWQOYxC_O1uPQ4Mt2c8Uo8_Vw-3t4V979Xv25_3Be2qZq5wBb6zhOtSTlU9eAsgRuq1jmoOwu6dd6i0-tOo8I16KHzbW87710HXleqvhJfT7rZ7b999ml2gS2N2QTFPRs16ErX_ZCJ6kS0x5CJvDnnNArMsVOzNblTc-zUQG9yp_nmy1kc2eLoE0428Nth31S1bo7a3088ykkPgZJhG2iy5EIiOxsXwztf_gO_H49E</recordid><startdate>20070130</startdate><enddate>20070130</enddate><creator>Zhang, Li</creator><creator>Wylie, Bruce</creator><creator>Loveland, Thomas</creator><creator>Fosnight, Eugene</creator><creator>Tieszen, Larry L.</creator><creator>Ji, Lei</creator><creator>Gilmanov, Tagir</creator><general>Elsevier Inc</general><general>Elsevier Science</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7ST</scope><scope>7U6</scope><scope>C1K</scope></search><sort><creationdate>20070130</creationdate><title>Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands</title><author>Zhang, Li ; Wylie, Bruce ; Loveland, Thomas ; Fosnight, Eugene ; Tieszen, Larry L. ; Ji, Lei ; Gilmanov, Tagir</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c424t-a5086feebe1da139dce0d925dd036c075dfcad7b67a1ab0796f58c6ffd60f7213</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Animal, plant and microbial ecology</topic><topic>Applied geophysics</topic><topic>Biological and medical sciences</topic><topic>Carbon flux</topic><topic>Decision tree</topic><topic>Earth sciences</topic><topic>Earth, ocean, space</topic><topic>Exact sciences and technology</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General aspects. Techniques</topic><topic>Grassland</topic><topic>Gross primary production (GPP)</topic><topic>Internal geophysics</topic><topic>Model comparison</topic><topic>MODIS GPP</topic><topic>Northern Great Plains</topic><topic>Teledetection and vegetation maps</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Li</creatorcontrib><creatorcontrib>Wylie, Bruce</creatorcontrib><creatorcontrib>Loveland, Thomas</creatorcontrib><creatorcontrib>Fosnight, Eugene</creatorcontrib><creatorcontrib>Tieszen, Larry L.</creatorcontrib><creatorcontrib>Ji, Lei</creatorcontrib><creatorcontrib>Gilmanov, Tagir</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Li</au><au>Wylie, Bruce</au><au>Loveland, Thomas</au><au>Fosnight, Eugene</au><au>Tieszen, Larry L.</au><au>Ji, Lei</au><au>Gilmanov, Tagir</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands</atitle><jtitle>Remote sensing of environment</jtitle><date>2007-01-30</date><risdate>2007</risdate><volume>106</volume><issue>2</issue><spage>173</spage><epage>189</epage><pages>173-189</pages><issn>0034-4257</issn><eissn>1879-0704</eissn><coden>RSEEA7</coden><abstract>Two spatially-explicit estimates of gross primary production (GPP) are available for the Northern Great Plains. An empirical piecewise regression (PWR) GPP model was developed from flux tower measurements to map carbon flux across the region. The Moderate Resolution Imaging Spectrometer (MODIS) GPP model is a process-based model that uses flux tower data to calibrate its parameters. Verification and comparison of the regional PWR GPP and the global MODIS GPP are important for the modeling of grassland carbon flux. This study compared GPP estimates from PWR and MODIS models with five towers in the grasslands. Among them, PWR GPP and MODIS GPP showed a good agreement with tower-based GPP at three towers. The global MODIS GPP, however, did not agree well with tower-based GPP at two other towers, probably because of the insensitivity of MODIS model to regional ecosystem and climate change and extreme soil moisture conditions. Cross-validation indicated that the PWR model is relatively robust for predicting regional grassland GPP. However, the PWR model should include a wide variety of flux tower data as the training data sets to obtain more accurate results. In addition, GPP maps based on the PWR and MODIS models were compared for the entire region. In the northwest and south, PWR GPP was much higher than MODIS GPP. These areas were characterized by the higher water holding capacity with a lower proportion of C 4 grasses in the northwest and a higher proportion of C 4 grasses in the south. In the central and southeastern regions, PWR GPP was much lower than MODIS GPP under complicated conditions with generally mixed C 3/C 4 grasses. The analysis indicated that the global MODIS GPP model has some limitations on detecting moisture stress, which may have been caused by the facts that C 3 and C 4 grasses are not distinguished, water stress is driven by vapor pressure deficit (VPD) from coarse meteorological data, and MODIS land cover data are unable to differentiate the sub-pixel cropland components.</abstract><cop>New York, NY</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2006.08.012</doi><tpages>17</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0034-4257
ispartof Remote sensing of environment, 2007-01, Vol.106 (2), p.173-189
issn 0034-4257
1879-0704
language eng
recordid cdi_proquest_miscellaneous_19727389
source ScienceDirect Journals (5 years ago - present)
subjects Animal, plant and microbial ecology
Applied geophysics
Biological and medical sciences
Carbon flux
Decision tree
Earth sciences
Earth, ocean, space
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Grassland
Gross primary production (GPP)
Internal geophysics
Model comparison
MODIS GPP
Northern Great Plains
Teledetection and vegetation maps
title Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T16%3A46%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Evaluation%20and%20comparison%20of%20gross%20primary%20production%20estimates%20for%20the%20Northern%20Great%20Plains%20grasslands&rft.jtitle=Remote%20sensing%20of%20environment&rft.au=Zhang,%20Li&rft.date=2007-01-30&rft.volume=106&rft.issue=2&rft.spage=173&rft.epage=189&rft.pages=173-189&rft.issn=0034-4257&rft.eissn=1879-0704&rft.coden=RSEEA7&rft_id=info:doi/10.1016/j.rse.2006.08.012&rft_dat=%3Cproquest_cross%3E19727389%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=19727389&rft_id=info:pmid/&rft_els_id=S0034425706003178&rfr_iscdi=true