RiceExtent v1.0: a global rice-specific land cover product from multi-source data integration
Year: 2026
Type: article
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.
Source: Scientific Reports
Authors: Akansha Patel, Arun Bawa, Seonggyu Park, Celray James Chawanda, Sang-Min Jun, Soon-Kun Choi, Sushil Kumar Himanshu, Raghavan Srinivasan, Jaehak Jeong
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7104382264