Optimizing satellite-based cropland area estimation through integrated map accuracy assessment and stratified sampling design across six African countries

Accurate cropland area estimation is essential for food security, yet conventional surveys are costly. Satellite-derived area estimates offer a scalable alternative; however, “pixel-counting” from satellite products introduces bias, while probability-based sampling with design-based inference provides unbiased area estimates, but its efficiency depends on map quality. In this study, we introduce an integrated framework linking map accuracy with relative efficiency to optimize stratified sampling designs. We evaluated seven land cover products across six African countries using independent reference data. Our results demonstrate that map selection is a critical determinant of survey costs: the most efficient products (predominantly GLAD and Digital Earth Africa) reduced required sample sizes by 20%–40% compared to less efficient alternatives while maintaining a target 10% coefficient of variation. We found that for rare cropland classes, gains in producers’ accuracy improved sampling efficiency more than equivalent gains in users’ accuracy. Our sample-based estimates ranged from 1.41 Mha (Rwanda) to 12.66 Mha (Tanzania) and correlated strongly with FAOSTAT arable land statistics ( R 2 = 0 . 89 ). Our framework minimizes the sample size required to achieve a target precision, providing an operational-ready, cost-effective, and statistically rigorous guide for national agricultural monitoring in resource- and data-limited regions. • We introduce an integrated framework linking map accuracy assessment with efficiency analysis to minimize the sample size required to achieve target precision. • Efficient maps reduce sample size requirements by 20%–40%, directly lowering reference data collection costs. • Producer’s accuracy gains improve efficiency more than user’s accuracy gains for rare cropland classes. • Sample-based area estimates achieved 8%–15% precision, outperforming conventional methods (27%–36%) across six African countries. • No single global product is optimal; country-specific evaluation is essential for cost-effective area estimation.