Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data.
Identifieur interne : 000034 ( PubMed/Corpus ); précédent : 000033; suivant : 000035Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data.
Auteurs : Tianxiang Cui ; Yujie Wang ; Rui Sun ; Chen Qiao ; Wenjie Fan ; Guoqing Jiang ; Lvyuan Hao ; Lei ZhangSource :
- PloS one [ 1932-6203 ] ; 2016.
English descriptors
- KwdEn :
- MESH :
- geographic : China.
- physiology : Crops, Agricultural.
- Algorithms, Ecosystem, Environmental Monitoring, Models, Theoretical, Photosynthesis, Plant Development, Remote Sensing Technology, Rivers, Seasons.
Abstract
Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m(-2) d(-1) and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m(-2) d(-1) and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution.
DOI: 10.1371/journal.pone.0153971
PubMed: 27088356
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Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</nlm:affiliation></affiliation></author><author><name sortKey="Qiao, Chen" sort="Qiao, Chen" uniqKey="Qiao C" first="Chen" last="Qiao">Chen Qiao</name><affiliation><nlm:affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</nlm:affiliation></affiliation></author><author><name sortKey="Fan, Wenjie" sort="Fan, Wenjie" uniqKey="Fan W" first="Wenjie" last="Fan">Wenjie Fan</name><affiliation><nlm:affiliation>Institute of Remote Sensing and Geographical Information System, Peking University, Beijing, China.</nlm:affiliation></affiliation></author><author><name sortKey="Jiang, Guoqing" sort="Jiang, Guoqing" uniqKey="Jiang G" first="Guoqing" last="Jiang">Guoqing Jiang</name><affiliation><nlm:affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</nlm:affiliation></affiliation></author><author><name sortKey="Hao, Lvyuan" sort="Hao, Lvyuan" uniqKey="Hao L" first="Lvyuan" last="Hao">Lvyuan Hao</name><affiliation><nlm:affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhang, Lei" sort="Zhang, Lei" uniqKey="Zhang L" first="Lei" last="Zhang">Lei Zhang</name><affiliation><nlm:affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2016">2016</date><idno type="RBID">pubmed:27088356</idno><idno type="pmid">27088356</idno><idno type="doi">10.1371/journal.pone.0153971</idno><idno type="wicri:Area/PubMed/Corpus">000034</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000034</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data.</title><author><name sortKey="Cui, Tianxiang" sort="Cui, Tianxiang" uniqKey="Cui T" first="Tianxiang" last="Cui">Tianxiang Cui</name><affiliation><nlm:affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</nlm:affiliation></affiliation></author><author><name sortKey="Wang, Yujie" sort="Wang, Yujie" uniqKey="Wang Y" first="Yujie" last="Wang">Yujie Wang</name><affiliation><nlm:affiliation>Northwest Regional Climate Center, Lanzhou, China.</nlm:affiliation></affiliation></author><author><name sortKey="Sun, Rui" sort="Sun, Rui" uniqKey="Sun R" first="Rui" last="Sun">Rui Sun</name><affiliation><nlm:affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</nlm:affiliation></affiliation></author><author><name sortKey="Qiao, Chen" sort="Qiao, Chen" uniqKey="Qiao C" first="Chen" last="Qiao">Chen Qiao</name><affiliation><nlm:affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</nlm:affiliation></affiliation></author><author><name sortKey="Fan, Wenjie" sort="Fan, Wenjie" uniqKey="Fan W" first="Wenjie" last="Fan">Wenjie Fan</name><affiliation><nlm:affiliation>Institute of Remote Sensing and Geographical Information System, Peking University, Beijing, China.</nlm:affiliation></affiliation></author><author><name sortKey="Jiang, Guoqing" sort="Jiang, Guoqing" uniqKey="Jiang G" first="Guoqing" last="Jiang">Guoqing Jiang</name><affiliation><nlm:affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</nlm:affiliation></affiliation></author><author><name sortKey="Hao, Lvyuan" sort="Hao, Lvyuan" uniqKey="Hao L" first="Lvyuan" last="Hao">Lvyuan Hao</name><affiliation><nlm:affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhang, Lei" sort="Zhang, Lei" uniqKey="Zhang L" first="Lei" last="Zhang">Lei Zhang</name><affiliation><nlm:affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</nlm:affiliation></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>China</term><term>Crops, Agricultural (physiology)</term><term>Ecosystem</term><term>Environmental Monitoring</term><term>Models, Theoretical</term><term>Photosynthesis</term><term>Plant Development</term><term>Remote Sensing Technology</term><term>Rivers</term><term>Seasons</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>China</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Crops, Agricultural</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Ecosystem</term><term>Environmental Monitoring</term><term>Models, Theoretical</term><term>Photosynthesis</term><term>Plant Development</term><term>Remote Sensing Technology</term><term>Rivers</term><term>Seasons</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m(-2) d(-1) and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m(-2) d(-1) and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">27088356</PMID><DateCreated><Year>2016</Year><Month>04</Month><Day>19</Day></DateCreated><DateCompleted><Year>2016</Year><Month>09</Month><Day>13</Day></DateCompleted><DateRevised><Year>2016</Year><Month>04</Month><Day>30</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>11</Volume><Issue>4</Issue><PubDate><Year>2016</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data.</ArticleTitle><Pagination><MedlinePgn>e0153971</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0153971</ELocationID><Abstract><AbstractText>Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m(-2) d(-1) and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m(-2) d(-1) and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Cui</LastName><ForeName>Tianxiang</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>School of geography and Remote Sensing Sciences, Beijing Normal University, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Beijing Key Lab for Remote Sensing of Environment and Digital Cities, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Yujie</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Northwest Regional Climate Center, Lanzhou, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>School of Atmospheric Sciences, Nanjing University, Nanjing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sun</LastName><ForeName>Rui</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>School of geography and Remote Sensing Sciences, Beijing Normal University, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Beijing Key Lab for Remote Sensing of Environment and Digital Cities, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Qiao</LastName><ForeName>Chen</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>School of geography and Remote Sensing Sciences, Beijing Normal University, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Beijing Key Lab for Remote Sensing of Environment and Digital Cities, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fan</LastName><ForeName>Wenjie</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>Institute of Remote Sensing and Geographical Information System, Peking University, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jiang</LastName><ForeName>Guoqing</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>School of geography and Remote Sensing Sciences, Beijing Normal University, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Beijing Key Lab for Remote Sensing of Environment and Digital Cities, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hao</LastName><ForeName>Lvyuan</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>School of geography and Remote Sensing Sciences, Beijing Normal University, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Beijing Key Lab for Remote Sensing of Environment and Digital Cities, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Lei</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, CAS, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>School of geography and Remote Sensing Sciences, Beijing Normal University, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Beijing Key Lab for Remote Sensing of Environment and Digital Cities, Beijing, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>04</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>J Environ Manage. 2007 Nov;85(3):574-84</RefSource><PMID Version="1">17129660</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Environ Manage. 2007 Nov;85(3):563-73</RefSource><PMID Version="1">17234327</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>PLoS One. 2015;10(3):e0120660</RefSource><PMID Version="1">25803840</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Symp Soc Exp Biol. 1965;19:205-34</RefSource><PMID Version="1">5321565</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Environ Manage. 2007 Nov;85(3):607-15</RefSource><PMID Version="1">17166651</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002681" MajorTopicYN="N" Type="Geographic">China</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018556" MajorTopicYN="N">Crops, Agricultural</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017753" MajorTopicYN="Y">Ecosystem</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004784" MajorTopicYN="N">Environmental Monitoring</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008962" MajorTopicYN="Y">Models, Theoretical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010788" MajorTopicYN="N">Photosynthesis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D063245" MajorTopicYN="N">Plant Development</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058998" MajorTopicYN="Y">Remote Sensing Technology</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D045483" MajorTopicYN="N">Rivers</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012621" MajorTopicYN="N">Seasons</DescriptorName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC4835106</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2015</Year><Month>09</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2016</Year><Month>04</Month><Day>06</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2016</Year><Month>4</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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