摘要：Space-based crop identifcation and acreage estimation have played a signifcant role in agricultural studies in recent years, due to the development of Remote Sensing technology. The Cropland Data Layer (CDL), which was developed by the U.S . Department of Agriculture (USDA), has been widely used in agricultural studies and achieved massive success in recent years . Although the CDL’s accuracy assessments report high overall accuracy on various crops classifcations, misclassifcation is still common and easy to discern from visual inspection . This study is aimed to identify and resolve inaccurate crop classifcation in CDL . A decision tree method was employed to fnd questionable pixels and refne them with spatial and temporal crop information . The refned data was then evaluated with high-resolution satellite images and ofcial acreage estimates from USDA . Two validation experiments were also developed to examine the data at both the pixel and county level . Data generated from this research was published online in two repositories, while both applications allow users to download the entire dataset at no cost .
