世界银行-利用光学遥感和有限训练数据绘制斯里兰卡田间水稻面积和产量图（英）

Policy Research Working Paper11194Field-Scale Rice Area and Yield Mapping in Sri Lanka with Optical Remote Sensing and Limited Training DataMutlu ÖzdoğanSherrie WangDevaki GhoseEduardo FragaAna FernandesGonzalo VarelaDevelopment Research Group &Prosperity VerticalAugust 2025 Public Disclosure AuthorizedPublic Disclosure AuthorizedPublic Disclosure AuthorizedPublic Disclosure AuthorizedProduced by the Research Support TeamAbstractThe Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.Policy Research Working Paper 11194Rice 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 classifica-tion algorithm that high-lights rice presence by observing water occurrence during transplanting and vegetation activ-ity 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 cor-relation with government statistics. They also demonstrate the ad-vantages of a rule-based, phenology-driven classifi-cation over purely statistical machine learning models for long-term consistency in dynamic agricultural environ-ments. 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.This paper is a product of the