Operational in-season rice area estimation through Earth observation data in Nepal - working paper

In an effort to adopt emerging technologies in food security assessment through a codevelopment approach, the Government of Nepal’s Ministry of Agriculture and Livestock Development (MoALD) and the International Centre for Integrated Mountain Development’s (ICIMOD) SERVIR-HKH Initiative undertook a pilot study in Chitwan District in 2019 to jointly develop methods for satellite remote sensing and machine learning-based in-season crop assessment. MoALD experts and relevant stakeholders thoroughly reviewed the approach before the honourable minister approved it for formal use in the national-level assessment for 2020 and onwards. For wider adoption of the advanced data science methods established in the pilot study, we customised the technology by developing a digital suite of software, including GeoFairy (a mobile app to facilitate field data collection by field extension professionals at the district level) and RiceMapEngine (a simplified platform for machine learning-based crop classification to facilitate crop area map production by MoALD’s GIS Section). In the current federal governance structure of Nepal, high-quality crop maps and yield estimates will not only bridge information needs among the federal and subnational institutions but also provide a means for consistent cross-country crop status assessments and communication.