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dc.contributorFriedl, Marken_US
dc.contributorFrolking, Steveen_US
dc.contributorRamankutty, Navinen_US
dc.contributorNelson, Andrewen_US
dc.contributorGumma, Murali Krishnaen_US
dc.creatorGray, Joshuaen_US
dc.identifier.citationJoshua Gray, Mark Friedl, Steve Frolking, Navin Ramankutty, Andrew Nelson, Murali Krishna Gumma. (31/8/2014). Mapping Asian Cropping Intensity With MODIS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8), pp. 3373-3379.en_US
dc.description.abstractAgricultural systems are geographically extensive, have profound significance to society, and affect regional energy, climate, and water cycles. Since most suitable lands worldwide have been cultivated, there is a growing pressure to increase yields on existing agricultural lands. In tropical and subtropical regions, multicropping is widely used to increase food production, but regional-to-global information related to multicropping practices is poor. The high temporal resolution and moderate spatial resolution of the MODIS sensors provide an ideal source of information for characterizing cropping practices over large areas. Relative to studies that document agricultural extensification, however, systematic assessment of agricultural intensification via multicropping has received relatively little attention. The goal of this work was to help close this information gap by developing methods that use multitemporal remote sensing to map multicropping systems in Asia. Image time-series analysis is especially challenging in this part of the world because atmospheric conditions including clouds and aerosols lead to high frequencies of missing or low-quality observations, especially during the Asian Monsoon. The methodology that we developed builds upon the algorithm used to produce the MODIS Land Cover Dynamics product (MCD12Q2), but uses an improved methodology optimized for crops. We assessed our results at the aggregate scale using state, district, and provincial level inventory statistics reporting total cropped and harvested areas, and at the field scale using survey results for 191 field sites in Bangladesh. While the algorithm highlighted the dominant continental-scale patterns in agricultural practices throughout Asia, and produced reasonable estimates of state and provincial level total harvested areas, fieldscale assessment revealed significant challenges in mapping high cropping intensity due to abundant missing data.en_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.sourceIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;7,(2014) Pagination 3373,3379en_US
dc.subjecttime seriesen_US
dc.subjectcropping intensityen_US
dc.titleMapping Asian Cropping Intensity With MODISen_US
dc.typeJournal Articleen_US
cg.creator.idRamankutty, Navin: 0000-0002-3737-5717en_US
cg.subject.agrovocremote sensingen_US
cg.subject.agrovocagricultural systemsen_US
cg.contributor.centerNorth Carolina State University - NC Stateen_US
cg.contributor.centerBoston University, Earth & Environment - BU - E&Een_US
cg.contributor.centerUniversity of New Hampshire, Institute for the Study of Earth, Oceans, and Space - UNH-EOSen_US
cg.contributor.centerThe University of British Columbia, Institute for Resources, Environment and Sustainability - UBC-IRESen_US
cg.contributor.centerInternational Rice Research Institute - IRRIen_US
cg.contributor.centerInternational Crops Research Institute for the Semi-Arid Tropics - ICRISATen_US
cg.contributor.crpCRP on Dryland Systems - DSen_US
cg.contributor.funderNot Applicableen_US
cg.coverage.regionSouthern Asiaen_US
dc.identifier.statusLimited accessen_US

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