Publication:
Field-Scale Rice Area and Yield Mapping in Sri Lanka with Optical Remote Sensing and Limited Training Data

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dc.contributor.authorÖzdoğan, Mutlu
dc.contributor.authorWang, Sherrie
dc.contributor.authorGhose, Devaki
dc.contributor.authorFraga, Eduardo
dc.contributor.authorFernandes, Ana
dc.contributor.authorVarela, Gonzalo
dc.date.accessioned2025-08-21T21:31:58Z
dc.date.available2025-08-21T21:31:58Z
dc.date.issued2025-08-21
dc.description.abstractRice is a staple crop for over half the world’s population, and accurate, timely information on its planted area and production is crucial for food security and agricultural policy, particularly in developing nations like Sri Lanka. However, reliable rice monitoring in regions like Sri Lanka faces significant challenges due to frequent cloud cover and the fragmented nature of small-holder farms. This research introduces a novel, cost-effective method for mapping rice planted area and yield at field scales in Sri Lanka using optical satellite data. The rice planted fields were identified and mapped using a phenologically-tuned image classification algorithm that high-lights rice presence by observing water occurrence during transplanting and vegetation activity during subsequent crop growth. To estimate yields, a random forest regression model was trained at the district level by incorporating a satellite-derived chlorophyll index and environmental variables and subsequently applied at the field level. The approach has enabled the creation of two decades (2000–2022) of reliable, field-scale rice area and yield estimates, achieving map accuracies between 70% and over 90% and yield estimations with less than 20% RMSE. These highly granular results, which were previously unattainable through traditional surveys, show strong correlation with government statistics. They also demonstrate the ad-vantages of a rule-based, phenology-driven classification over purely statistical machine learning models for long-term consistency in dynamic agricultural environments. This work highlights the significant potential of remote sensing to provide accurate and detailed insights into rice cultivation, supporting policy decisions and enhancing food security in Sri Lanka and other cloud-prone regions.en
dc.identifierhttps://openknowledge.worldbank.org/entities/publication/4b584045-7d17-4c76-bd20-e628a695e6dd
dc.identifier.doihttps://doi.org/10.1596/1813-9450-11194
dc.identifier.urihttps://hdl.handle.net/10986/43631
dc.languageEnglish
dc.language.isoen_US
dc.publisherWashington, DC: World Bank
dc.relation.ispartofseriesPolicy Research Working Paper; 11194
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/igo/
dc.subjectRICE
dc.subjectSRI LANKA
dc.subjectLANDSAT
dc.subjectSENTINEL2
dc.subjectIRRIGATION
dc.subjectPHENOLOGY
dc.titleField-Scale Rice Area and Yield Mapping in Sri Lanka with Optical Remote Sensing and Limited Training Dataen
dc.typeWorking Paper
dspace.entity.typePublication
okr.date.disclosure2025-08-21
okr.date.doiregistration2025-08-23T02:12:09.544516Z
okr.date.lastmodified2025-08-21T20:05:00Zen
okr.doctypePolicy Research Working Paper
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/099930408202574637
okr.guid099930408202574637
okr.identifier.docmidIDU-5ec0c075-9abb-48f4-be61-14c566553092
okr.identifier.doi10.1596/1813-9450-11194
okr.identifier.externaldocumentum40039029
okr.identifier.internaldocumentum40039029
okr.identifier.reportWPS11194
okr.import.id8362
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttps://documents.worldbank.org/curated/en/099930408202574637/pdf/IDU-5ec0c075-9abb-48f4-be61-14c566553092.pdfen
okr.region.administrativeSouth Asia
okr.region.countrySri Lanka
okr.topicAgriculture::Crops & Crop Management Systems
okr.topicWater Resources::Irrigation and Drainage
okr.topicAgriculture::Agricultural Irrigation and Drainage
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relation.isAuthorOfPublication.latestForDiscoverycfd6599c-a57b-5ad9-9130-46963c7fef80
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