Cloud-Powered Agricultural Mapping: A Revolution Toward 10m Resolution Cropland Data Layers

The transition from 30m to 10m resolution in the annual Cropland Data Layers (CDL) is imperative for enhanced national-scale assessments. This paper addresses the computational challenges posed by this transition, emphasizing the need for increased efficiency in data preparation, image processing and improved crop classification accuracy. Leveraging Google Earth Engine (GEE) cloud-based platform and advanced machine learning algorithms, this paper introduces a novel operational workflow to swiftly generate a 10-meter resolution CDL for 2022 across the conterminous United States (CONUS). The workflow includes the development and use of Sentinel-2 and Landsat 8/9 derived multi-sensor gap-filled 10-day image composites, additional ancillary variables, tile-based localized analysis, and stratified random sampling strategy, leading to improved accuracy for major and specialty crops. This workflow not only reduces labor requirements but also enhances decision-making processes for agriculture and policymaking stakeholders.