Global Rice Mapping Driven by Intelligent Models and Big Earth Data Supporting Progress Assessment of SDG 2

Global food security is facing increasing pressure from population growth and climate change. Rice, the staple food for over half of the world’s population, is essential to nutritional supply and social stability, especially in developing regions. Highly dependent on water resources, rice production is highly sensitive to climate change and extreme events, and its changes affect global carbon emissions backward. Therefore, timely, accurate, and high-resolution global rice distribution information is indispensable for agricultural management and hunger elimination to achieve Sustainable Development Goal 2 (Zero Hunger) of the United Nations.

Overcoming the uncertainty of optical remote sensing data acquisition with all-weather, all-time, and stable revisits, Synthetic aperture radar (SAR) provides a highly promising solution for the timely acquisition of global rice cultivation. Deep learning models provide strong interpretability of rice scattering patterns and superior generalization capabilities among different agricultural scenarios. Combining the most advanced computation technology with the big remote sensing data, we developed a Time-Series-to-Vision Rice Classification Model (T2VRCM). Instead of learning from the remote sensing image stacks, T2VRCM learns the intrinsic feature variations during rice growth with standardized 2D visual representations, so that problems such as irregular sampling and insufficient modalities can be avoided. A novel global rice dataset, GlobalRice20, was achieved, providing comprehensive and consistent global rice cultivation data in 2015 and 2024 at 20 m resolution for the first time. An overall accuracy of 92.33% was achieved with rigorous validation against over 160,000 reference samples, enabling promising spatiotemporal analysis over the first decade of the SDGs.

Our team has been dedicated to large-scale rice mapping using intelligent computation methods, advancing from national and regional to global scales. Starting with the classic U-Net model, we produced the first 20 m interannual rice maps for Southeast Asia (2019–2021) using time-series Sentinel-1 data. We then proposed an optical–SAR fusion strategy using stacked random forests to generate EARice10, a 10 m rice distribution product in 2023 with comprehensive coverage of four East Asia countries. To overcome global spatial heterogeneity, we further upgraded the framework to the Explainable Mamba U-Net (XM-UNet). With strong generalization, the model provides a physically explainable interpretation of multi-temporal Sentinel-1 SAR data and possesses robust generalization capabilities in countries with diverse cultivation patterns. In addition, we constructed the world's first plot-level rice dataset, Plot-Rice v1.0, with the SAM-2 model and Sentinel-1/2 features. Covering various climatic zones, the dataset supports multiple mainstream deep learning models and demonstrates strong transferability among cross-regional and cross-annual scenarios.

As a result of the achievements outlined above, we provided the 20 m global rice product in 2023 to support the assessments of the UN’s SDG2 indicators, as detailed in the Reports on Big Earth Data in Support of the Sustainable Development Goals in 2024 and 2025. This study reveals the up-to-date progress of our research to address advanced intelligent models in global rice mapping. Meanwhile, we are developing an in-season rice mapping methodology to enhance the timeliness of rice distribution information, in comparison to the current mainstream post-season rice mapping methods.