Accurate and scalable crop type maps are vital for supporting food security, as they provide critical information on the specific crops cultivated in a given area to inform agricultural decision-making and enhance crop productivity. The generation of these maps depends on high-quality crop type ground truth data, which are essential for developing remote sensing–based crop type classification models applicable across varying spatial and temporal contexts. Yet existing crop type ground truth datasets often focus on specific crop types of limited spatial and temporal ranges, constrained by the high cost and labor intensity of traditional field surveys. This limitation hinders their applicability to large-scale and multi-year applications, such as nationwide crop monitoring and long-term yield forecasting. Additionally, most existing crop type ground truth datasets contain only pixel-level labels without explicit field boundaries, impeding the extraction of field-level texture and structure information needed for accurate crop type mapping in heterogeneous agricultural landscapes. Collectively, these limitations hinder the development of scalable crop type mapping workflows and reduce the precision and reliability of resulting crop type maps for agricultural monitoring and decision support. In this study, we introduce CropSight-US, the first national scale, object-based crop type ground truth dataset for the contiguous United States (CONUS). This dataset spans the years 2013 to 2023 and includes over 100,000 crop type ground truth objects across 17 major crops and 294 Agricultural Statistics Districts, offering broad spatial and temporal coverage and high representativeness at field level. Each crop type ground truth object is accompanied by an uncertainty score that quantifies the confidence in its crop type identification, enabling users to filter or weight samples according to their specific reliability requirements. The crop type ground truthing framework of CropSight-US innovatively integrates crop labels derived from Google Street View imagery with field boundaries delineated from Sentinel-2 imagery to produce object-based crop type ground truth data. This scalable framework offers a valuable alternative to traditional field surveys by replacing in-person observations with virtual audits, significantly improving the efficiency, scalability, and cost-effectiveness of ground truth data collection. This framework achieves 97.2 % overall accuracy in crop type identification and 98.0 % F1 score in cropland field boundary delineation using the reference dataset. By delivering high-resolution, standardized, and reproducible reference data, CropSight-US establishes a new benchmark for crop type ground truthing and supports more informed agricultural research, monitoring, and decision-making. CropSight-US is available at <a href="https://doi.org/10.5281/zenodo.15702415">https://doi.org/10.5281/zenodo.15702415</a> (Zhou et al., 2025).