Validation and refinement of cropland data layer using a spatial-temporal decision tree algorithm.

ABSTRACT: Space-based crop identification and acreage estimation have played a significant 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 classifications, misclassification is still common and easy to discern from visual inspection. This study is aimed to identify and resolve inaccurate crop classification in CDL. A decision tree method was employed to find questionable pixels and refine them with spatial and temporal crop information. The refined data was then evaluated with hig

SUBMITTER: Lin L 

PROVIDER: S-EPMC8891360 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature