Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery
Identifieur interne : 000128 ( Pmc/Corpus ); précédent : 000127; suivant : 000129Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery
Auteurs : Yuanwei Qin ; Xiangming Xiao ; Jinwei Dong ; Yuting Zhou ; Zhe Zhu ; Geli Zhang ; Guoming Du ; Cui Jin ; Weili Kou ; Jie Wang ; Xiangping LiSource :
- ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) [ 0924-2716 ] ; 2015.
Abstract
Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases emissions, food and water security, and human health. Rice paddy field maps were developed using optical images with high temporal resolution and coarse spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) or low temporal resolution and high spatial resolution (e.g., Landsat TM/ETM+). In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. In this paper, time series MODIS and Landsat ETM+/OLI images, and the pixel- and phenology-based algorithm are used to map paddy rice planting area. The unique physical features of rice paddy fields during the flooding/open-canopy period are captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. The algorithm is tested in the Sanjiang Plain (path/row 114/27) in China in 2013. The overall accuracy of the resulted map of paddy rice planting area generated by both Landsat ETM+ and OLI is 97.3%, when evaluated with areas of interest (AOIs) derived from geo-referenced field photos. The paddy rice planting area map also agrees reasonably well with the official statistics at the level of state farms (
Url:
DOI: 10.1016/j.isprsjprs.2015.04.008
PubMed: 27695195
PubMed Central: 5042353
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Jinwei" sort="Dong, Jinwei" uniqKey="Dong J" first="Jinwei" last="Dong">Jinwei Dong</name><affiliation><nlm:aff id="A1">Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA</nlm:aff></affiliation></author><author><name sortKey="Zhou, Yuting" sort="Zhou, Yuting" uniqKey="Zhou Y" first="Yuting" last="Zhou">Yuting Zhou</name><affiliation><nlm:aff id="A1">Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA</nlm:aff></affiliation></author><author><name sortKey="Zhu, Zhe" sort="Zhu, Zhe" uniqKey="Zhu Z" first="Zhe" last="Zhu">Zhe Zhu</name><affiliation><nlm:aff id="A3">Center for Remote Sensing, Department of Geography and Environment, Boston University, Boston, MA 02215, USA</nlm:aff></affiliation></author><author><name sortKey="Zhang, Geli" sort="Zhang, Geli" uniqKey="Zhang G" first="Geli" last="Zhang">Geli Zhang</name><affiliation><nlm:aff 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type="RBID">PMC:5042353</idno><idno type="doi">10.1016/j.isprsjprs.2015.04.008</idno><date when="2015">2015</date><idno type="wicri:Area/Pmc/Corpus">000128</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000128</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery</title><author><name sortKey="Qin, Yuanwei" sort="Qin, Yuanwei" uniqKey="Qin Y" first="Yuanwei" last="Qin">Yuanwei Qin</name><affiliation><nlm:aff id="A1">Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA</nlm:aff></affiliation></author><author><name sortKey="Xiao, Xiangming" sort="Xiao, Xiangming" uniqKey="Xiao X" first="Xiangming" last="Xiao">Xiangming Xiao</name><affiliation><nlm:aff id="A1">Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA</nlm:aff></affiliation><affiliation><nlm:aff id="A2">Institute of Biodiversity Science, Fudan University, Shanghai 200433, China</nlm:aff></affiliation></author><author><name sortKey="Dong, Jinwei" sort="Dong, Jinwei" uniqKey="Dong J" first="Jinwei" last="Dong">Jinwei Dong</name><affiliation><nlm:aff id="A1">Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA</nlm:aff></affiliation></author><author><name sortKey="Zhou, Yuting" sort="Zhou, Yuting" uniqKey="Zhou Y" first="Yuting" last="Zhou">Yuting Zhou</name><affiliation><nlm:aff id="A1">Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA</nlm:aff></affiliation></author><author><name sortKey="Zhu, Zhe" sort="Zhu, Zhe" uniqKey="Zhu Z" first="Zhe" last="Zhu">Zhe Zhu</name><affiliation><nlm:aff id="A3">Center for Remote Sensing, Department of Geography and Environment, Boston University, Boston, MA 02215, USA</nlm:aff></affiliation></author><author><name sortKey="Zhang, Geli" sort="Zhang, Geli" uniqKey="Zhang G" first="Geli" last="Zhang">Geli Zhang</name><affiliation><nlm:aff id="A1">Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA</nlm:aff></affiliation></author><author><name sortKey="Du, Guoming" sort="Du, Guoming" uniqKey="Du G" first="Guoming" last="Du">Guoming Du</name><affiliation><nlm:aff id="A4">College of Resources and Environment, Northeast Agricultural University, Harbin, Heilongjiang 150030, China</nlm:aff></affiliation></author><author><name sortKey="Jin, Cui" sort="Jin, Cui" uniqKey="Jin C" first="Cui" last="Jin">Cui Jin</name><affiliation><nlm:aff id="A1">Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA</nlm:aff></affiliation></author><author><name sortKey="Kou, Weili" sort="Kou, Weili" uniqKey="Kou W" first="Weili" last="Kou">Weili Kou</name><affiliation><nlm:aff id="A5">Department of Computer and Information Science, Southwest Forestry University, Kunming, Yunnan 650224, China</nlm:aff></affiliation><affiliation><nlm:aff id="A1">Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA</nlm:aff></affiliation></author><author><name sortKey="Wang, Jie" sort="Wang, Jie" uniqKey="Wang J" first="Jie" last="Wang">Jie Wang</name><affiliation><nlm:aff id="A1">Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA</nlm:aff></affiliation></author><author><name sortKey="Li, Xiangping" sort="Li, Xiangping" uniqKey="Li X" first="Xiangping" last="Li">Xiangping Li</name><affiliation><nlm:aff id="A2">Institute of Biodiversity Science, Fudan University, Shanghai 200433, China</nlm:aff></affiliation></author></analytic><series><title level="j">ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS)</title><idno type="ISSN">0924-2716</idno><idno type="eISSN">1872-8235</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P1">Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases emissions, food and water security, and human health. Rice paddy field maps were developed using optical images with high temporal resolution and coarse spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) or low temporal resolution and high spatial resolution (e.g., Landsat TM/ETM+). In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. In this paper, time series MODIS and Landsat ETM+/OLI images, and the pixel- and phenology-based algorithm are used to map paddy rice planting area. The unique physical features of rice paddy fields during the flooding/open-canopy period are captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. The algorithm is tested in the Sanjiang Plain (path/row 114/27) in China in 2013. The overall accuracy of the resulted map of paddy rice planting area generated by both Landsat ETM+ and OLI is 97.3%, when evaluated with areas of interest (AOIs) derived from geo-referenced field photos. The paddy rice planting area map also agrees reasonably well with the official statistics at the level of state farms (<italic>R</italic><sup>2</sup> = 0.94). These results demonstrate that the combination of fine spatial resolution images and the phenology-based algorithm can provide a simple, robust, and automated approach to map the distribution of paddy rice agriculture in a year.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">101551484</journal-id><journal-id journal-id-type="pubmed-jr-id">39007</journal-id><journal-id journal-id-type="nlm-ta">ISPRS J Photogramm Remote Sens</journal-id><journal-id journal-id-type="iso-abbrev">ISPRS J Photogramm Remote Sens</journal-id><journal-title-group><journal-title>ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS)</journal-title></journal-title-group><issn pub-type="ppub">0924-2716</issn><issn pub-type="epub">1872-8235</issn></journal-meta><article-meta><article-id pub-id-type="pmid">27695195</article-id><article-id pub-id-type="pmc">5042353</article-id><article-id pub-id-type="doi">10.1016/j.isprsjprs.2015.04.008</article-id><article-id pub-id-type="manuscript">NIHMS784847</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Qin</surname><given-names>Yuanwei</given-names></name><xref ref-type="aff" rid="A1">a</xref></contrib><contrib contrib-type="author"><name><surname>Xiao</surname><given-names>Xiangming</given-names></name><xref ref-type="aff" rid="A1">a</xref><xref ref-type="aff" rid="A2">b</xref><xref ref-type="corresp" rid="cor1">*</xref></contrib><contrib contrib-type="author"><name><surname>Dong</surname><given-names>Jinwei</given-names></name><xref ref-type="aff" rid="A1">a</xref></contrib><contrib contrib-type="author"><name><surname>Zhou</surname><given-names>Yuting</given-names></name><xref ref-type="aff" rid="A1">a</xref></contrib><contrib contrib-type="author"><name><surname>Zhu</surname><given-names>Zhe</given-names></name><xref ref-type="aff" rid="A3">c</xref></contrib><contrib contrib-type="author"><name><surname>Zhang</surname><given-names>Geli</given-names></name><xref ref-type="aff" rid="A1">a</xref></contrib><contrib contrib-type="author"><name><surname>Du</surname><given-names>Guoming</given-names></name><xref ref-type="aff" rid="A4">d</xref></contrib><contrib contrib-type="author"><name><surname>Jin</surname><given-names>Cui</given-names></name><xref ref-type="aff" rid="A1">a</xref></contrib><contrib contrib-type="author"><name><surname>Kou</surname><given-names>Weili</given-names></name><xref ref-type="aff" rid="A5">e</xref><xref ref-type="aff" rid="A1">a</xref></contrib><contrib contrib-type="author"><name><surname>Wang</surname><given-names>Jie</given-names></name><xref ref-type="aff" rid="A1">a</xref></contrib><contrib contrib-type="author"><name><surname>Li</surname><given-names>Xiangping</given-names></name><xref ref-type="aff" rid="A2">b</xref></contrib></contrib-group><aff id="A1"><label>a</label>Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA</aff><aff id="A2"><label>b</label>Institute of Biodiversity Science, Fudan University, Shanghai 200433, China</aff><aff id="A3"><label>c</label>Center for Remote Sensing, Department of Geography and Environment, Boston University, Boston, MA 02215, USA</aff><aff id="A4"><label>d</label>College of Resources and Environment, Northeast Agricultural University, Harbin, Heilongjiang 150030, China</aff><aff id="A5"><label>e</label>Department of Computer and Information Science, Southwest Forestry University, Kunming, Yunnan 650224, China</aff><author-notes><corresp id="cor1"><label>*</label>Corresponding author at: Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA. <email>xiangming.xiao@ou.edu</email> (X. Xiao)</corresp></author-notes><pub-date pub-type="nihms-submitted"><day>10</day><month>5</month><year>2016</year></pub-date><pub-date pub-type="epub"><day>4</day><month>5</month><year>2015</year></pub-date><pub-date pub-type="ppub"><month>7</month><year>2015</year></pub-date><pub-date pub-type="pmc-release"><day>29</day><month>9</month><year>2016</year></pub-date><volume>105</volume><fpage>220</fpage><lpage>233</lpage><pmc-comment>elocation-id from pubmed: 10.1016/j.isprsjprs.2015.04.008</pmc-comment>
<self-uri xlink:href="http://www.sciencedirect.com/science/article/pii/S0924271615001185"></self-uri><abstract><p id="P1">Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases emissions, food and water security, and human health. Rice paddy field maps were developed using optical images with high temporal resolution and coarse spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) or low temporal resolution and high spatial resolution (e.g., Landsat TM/ETM+). In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. In this paper, time series MODIS and Landsat ETM+/OLI images, and the pixel- and phenology-based algorithm are used to map paddy rice planting area. The unique physical features of rice paddy fields during the flooding/open-canopy period are captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. The algorithm is tested in the Sanjiang Plain (path/row 114/27) in China in 2013. The overall accuracy of the resulted map of paddy rice planting area generated by both Landsat ETM+ and OLI is 97.3%, when evaluated with areas of interest (AOIs) derived from geo-referenced field photos. The paddy rice planting area map also agrees reasonably well with the official statistics at the level of state farms (<italic>R</italic><sup>2</sup> = 0.94). These results demonstrate that the combination of fine spatial resolution images and the phenology-based algorithm can provide a simple, robust, and automated approach to map the distribution of paddy rice agriculture in a year.</p></abstract><kwd-group><kwd>Rice paddy</kwd><kwd>Cropland</kwd><kwd>Observation frequency</kwd><kwd>Data availability</kwd><kwd>Vegetation indices</kwd><kwd>Sanjiang Plain</kwd></kwd-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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