Information from the abstract
Accurate land cover information is fundamental for monitoring land-use dynamics, supporting sustainable agriculture, and informing environmental and climate-related decision-making; however, detailed and globally consistent crop-specific datasets, particularly for rice, remain limited. The study presents RiceExtent v1.0, a harmonized global rice-specific land cover dataset athigh (10 m) and moderate (250 m) resolution, developed by integrating verified local, national, and regional rice maps with ESA WorldCover v200 using a hierarchical multi-criteria dataset ranking and spatial merging approach that prioritizes source accuracy and spatial detail. Using an extent-based merging strategy that aggregates multi-source rice data, RiceExtent derives a maximum observed rice cultivation extent rather than annual mean estimates, thereby capturing double and triple cropping patterns, fragmented smallholder fields, and inter-annual variability often underrepresented in other products. The final product was evaluated using area-based statistical comparison metrics against Spatial Production Allocation Model (SPAM), RiceAtlas, and GloRice, along with USDA rice production statistics. Validation using independent ground-truth samples was performed using True Positive Rate (TPR). The developed dataset captures an estimated global rice area of ~ 1.67 million km² and shows broad agreement with official statistics and improving representation in Southeast Asia, South Asia, Africa and the Americas. RiceExtent v1.0, provided through an open repository ( https://dataverse.tdl.org/dataset.xhtml?persistentId=doi:10.18738/T8/JYAEHV ) with accompanying Python script, offers a robust resource for agricultural monitoring, crop and hydrological modeling, and climate impact assessments, while enabling collaborative refinement of global rice mapping.
Why this record is monitored
This record has an Impact Signal of 74/100 based on recency, source, collaboration, and bibliographic signals. It prioritizes monitoring and is not a judgment of research quality.
Related topics: Remote Sensing in Agriculture · Rice Cultivation and Yield Improvement · Land Use and Ecosystem Services
Thai researcher and institutional participation
Sushil Kumar Himanshu · Asian Institute of Technology
Data limitations
This page is a bibliographic record based on abstract-level information, not a full analysis or quality assessment. Verify the DOI and original article before citation.