Mekong River Delta crop mapping using a machine learning approach

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cg.contributor.affiliationInternational Water Management Instituteen_US
cg.contributor.affiliationAustralian National Universityen_US
cg.contributor.donorCGIAR Trust Funden_US
cg.contributor.initiativeLow-Emission Food Systemsen_US
cg.coverage.countryVietnamen_US
cg.coverage.iso3166-alpha2VNen_US
cg.coverage.regionSouth-eastern Asiaen_US
cg.coverage.subregionMekong River Deltaen_US
cg.creator.identifierSurajit Ghosh: 0000-0002-3928-2135en_US
cg.creator.identifierBunyod Holmatov: 0000-0001-9267-7008en_US
cg.identifier.iwmilibraryH051629en_US
cg.identifier.urlhttps://www.iwmi.cgiar.org/Publications/Other/PDF/mekong_river_delta_crop_mapping_using_a_machine_learning_approach.pdfen_US
cg.placeColombo, Sri Lankaen_US
cg.river.basinMEKONGen_US
dc.contributor.authorGhosh, Surajiten_US
dc.contributor.authorWellington, Michaelen_US
dc.contributor.authorHolmatov, Bunyoden_US
dc.date.accessioned2023-01-23T06:57:08Zen_US
dc.date.available2023-01-23T06:57:08Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/127825en_US
dc.titleMekong River Delta crop mapping using a machine learning approachen_US
dcterms.abstractAgricultural land use and practices have important implications for climate change mitigation and adaptation. It is, therefore, important to develop methods of monitoring and quantifying the extent of crop types and cropping practices. A machine learning approach using random forest classification was applied to Sentinel-1 and 2 satellite imagery and satellite-derived phenological statistics to map crop types in the Mekong River Delta, enabling levels of rice intensification to be identified. This initial classification differentiated between broad and prevalent crop types, including perennial tree crops, rice, other vegetation, oil palm and other crops. A two-step classification was used to classify rice seasonality, whereby the areas identified as rice in the initial classification were further classified into single, double, or triple-cropped rice in a subsequent classification with the same input data but different training polygons. Both classifications had an overall accuracy of approximately 96% when cross-validated on test data. Radar bands from Sentinel-1 and Sentinel-2 reflectance bands were important predictors of crop type, perhaps due to their capacity to differentiate between periodically flooded rice fields and perennial tree cover, which were the predominant classes in the Delta. On the other hand, the Start of Season (SoS) and End of Season (EoS) dates were the most important predictors of single, double, or triple-cropped rice, demonstrating the efficacy of the phenological predictors. The accuracy and detail are limited by the availability of reliable training data, especially for tree crops in small-scale orchards. A preliminary result is presented here, and, in the future, efficient collection of ground images may enable cost-effective training data collection for similar mapping exercises.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.bibliographicCitationGhosh, Surajit; Wellington, Michael; Holmatov, Bunyod. 2022. Mekong River Delta crop mapping using a machine learning approach. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Low-Emission Food Systems (Mitigate+). 11p.en_US
dcterms.extent11p.en_US
dcterms.issued2022-12-30en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-4.0en_US
dcterms.publisherInternational Water Management Instituteen_US
dcterms.subjectcropsen_US
dcterms.subjectmappingen_US
dcterms.subjectdeltasen_US
dcterms.subjectmachine learningen_US
dcterms.subjectsatellite imageryen_US
dcterms.subjectland useen_US
dcterms.subjectland coveren_US
dcterms.subjectfarmlanden_US
dcterms.typeReporten_US

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