Mekong River Delta crop mapping using a machine learning approach

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cg.contributor.affiliationInternational Water Management Instituteen
cg.contributor.affiliationAustralian National Universityen
cg.contributor.donorCGIAR Trust Funden
cg.contributor.initiativeLow-Emission Food Systems
cg.coverage.countryVietnam
cg.coverage.iso3166-alpha2VN
cg.coverage.regionSouth-eastern Asia
cg.coverage.subregionMekong River Delta
cg.creator.identifierSurajit Ghosh: 0000-0002-3928-2135en
cg.creator.identifierBunyod Holmatov: 0000-0001-9267-7008en
cg.identifier.iwmilibraryH051629en
cg.placeColombo, Sri Lankaen
cg.river.basinMEKONGen
dc.contributor.authorGhosh, Surajiten
dc.contributor.authorWellington, Michaelen
dc.contributor.authorHolmatov, Bunyoden
dc.date.accessioned2023-01-23T06:57:08Zen
dc.date.available2023-01-23T06:57:08Zen
dc.identifier.urihttps://hdl.handle.net/10568/127825
dc.titleMekong River Delta crop mapping using a machine learning approachen
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
dcterms.accessRightsOpen Access
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
dcterms.extent11p.en
dcterms.issued2022-12-30en
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherInternational Water Management Instituteen
dcterms.subjectcropsen
dcterms.subjectmappingen
dcterms.subjectdeltasen
dcterms.subjectmachine learningen
dcterms.subjectsatellite imageryen
dcterms.subjectland useen
dcterms.subjectland coveren
dcterms.subjectfarmlanden
dcterms.typeReport

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