Field-Scale Rice Area and Yield Mapping in Sri Lanka with Optical Remote Sensing and Limited Training Data
Name
remotesensing-17-03065.pdf
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4.6 MB
Format
Adobe PDF
Checksum (MD5)
724f80123b6ad615ab223c7ac9e83be6
Author(s)
Özdoğan, Mutlu
Wang, Sherrie
Ghose, Devaki
Fraga, Eduardo
Fernandes, Ana
Varela, Gonzalo
Date Issued
September 2, 2025
Journal
Remote Sensing
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Özdoğan, M.; Wang, S.; Ghose, D.; Fraga, E.; Fernandes, A.; Varela, G. Field-Scale Rice Area and Yield Mapping in Sri Lanka with Optical Remote Sensing and Limited Training Data. Remote Sens. 2025, 17, 3065.
Version
Final published version
Abstract
Rice 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 smallholder 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 highlights 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 estimates with less than 20% error. These highly granular results, which are not available through traditional surveys, show a strong correlation with government statistics. They also demonstrate the advantages 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.
MIT Department
MIT Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/rs17173065
