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dc.contributorXiao, Xiangmingen_US
dc.contributorKou, Weilien_US
dc.contributorQin, Yuanweien_US
dc.contributorZhang, Gelien_US
dc.contributorLi, Lien_US
dc.contributorJin, Cuien_US
dc.contributorZhou, Yutingen_US
dc.contributorWang, Jieen_US
dc.contributorBiradar, Chandrashekharen_US
dc.contributorLiu, Jiyuanen_US
dc.contributorMoore III, Berrienen_US
dc.creatorDong, Jinweien_US
dc.date.accessioned2016-05-15T09:42:30Z
dc.date.available2016-05-15T09:42:30Z
dc.identifierhttps://mel.cgiar.org/dspace/limiteden_US
dc.identifierhttp://www.sciencedirect.com/science/journal/00344257/160/supp/Cen_US
dc.identifier.citationJinwei Dong, Xiangming Xiao, Weili Kou, Yuanwei Qin, Geli Zhang, Li Li, Cui Jin, Yuting Zhou, Jie Wang, Chandrashekhar Biradar, Jiyuan Liu, Berrien Moore III. (30/4/2015). Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms. Remote Sensing of Environment, 160, pp. 99-113.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/4807
dc.description.abstractAgricultural land use change substantially affects climate,water, ecosystems, biodiversity, and humanwelfare. In recent decades, due to increasing population and food demand and the backdrop of global warming, croplands have been expanding into higher latitude regions. One such hotspot is paddy rice expansion in northeast China. However, there are no maps available for documenting the spatial and temporal patterns of continuous paddy rice expansion. In this study, we developed an automated, Landsat-based paddy rice mapping (Landsat- RICE) systemthat uses time series Landsat images and a phenology-based algorithmbased on the unique spectral characteristics of paddy rice during the flooding/transplanting phase. As a pilot study, we analyzed all the available Landsat images from 1986 to 2010 (498 scenes) in one tile (path/row 113/27) of northeast China, which tracked paddy rice expansion in epochs with five-year increments (1986–1990, 1991–1995, 1996–2000, 2001–2005, and 2006–2010). Several maps of land cover types (barren land and built-up land; evergreen, deciduous and sparse vegetation types; and water-related land cover types such as permanent water body, mixed pixels of water and vegetation, spring flooded wetlands and summer flooded land) were generated as masks. Air temperature was used to define phenology timing and crop calendar, which were then used to select Landsat images in the phenology-based algorithms for paddy rice and masks. The resultant maps of paddy rice in the five epochs were evaluated using validation samples from multiple sources, and the overall accuracies and Kappa coefficients ranged from84 to 95% and 0.6–0.9, respectively. The paddy rice area in the study area substantially increased from 1986 to 2010, particularly after the 1990s. This study demonstrates the potential of the Landsat-RICE systemand time series Landsat images for tracking agricultural land use changes at 30-mresolution in the temperate zone with single crop cultivationen_US
dc.formatPDFen_US
dc.languageenen_US
dc.publisherElsevieren_US
dc.sourceRemote Sensing of Environment;160,(2015) Pagination 99-113en_US
dc.subjectlandsat-rice phenologyen_US
dc.subjectnortheast chinaen_US
dc.subjectpaddy riceen_US
dc.titleTracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithmsen_US
dc.typeJournal Articleen_US
dcterms.available2015-01-28en_US
dcterms.extent99-113en_US
dcterms.issued2015-04-30en_US
cg.creator.idBiradar, Chandrashekhar: 0000-0002-9532-9452en_US
cg.subject.agrovocland useen_US
cg.subject.agrovocland use changeen_US
cg.subject.agrovocRiceen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerUniversity of Oklahoma, Center for Spatial Analysis - OU - CSAen_US
cg.contributor.centerUniversity of Oklahoma, School of Meteorology - OU - SoMen_US
cg.contributor.centerUniversity of Oklahoma, College of Arts and Sciences - OU - CASen_US
cg.contributor.centerUniversity of Oklahoma, College of Atmospheric and Geographic Science - OU - AGSen_US
cg.contributor.centerChinese Academy of Sciences, Institute of Geographic Sciences and Natural Resources Research - cas-igsnrren_US
cg.contributor.crpCGIAR Research Program on Dryland Systems - DSen_US
cg.contributor.funderIndian Council of Agricultural Research - ICARen_US
cg.contributor.projectIndia Collaborative Program: Restricted funding for breeding for resistance to abiotic stresses in pulses & for 2017/2018 - 2017/2020 - 2020/2021en_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.date.embargo-end-date2115-01-28en_US
cg.coverage.regionEastern Asiaen_US
cg.coverage.countryCNen_US
cg.contactxiangming.xiao@ou.eduen_US
cg.identifier.doihttps://dx.doi.org/10.1016/j.rse.2015.01.004en_US
cg.isijournalISI Journalen_US
dc.identifier.statusLimited accessen_US
mel.project.openhttp://geoagro.icarda.org/india/en_US
mel.impact-factor6.265en_US
cg.issn0034-4257en_US
cg.journalRemote Sensing of Environmenten_US
cg.volume160en_US


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