Comparative validation of recent 10 m-resolution global land cover maps

作者：Panpan Xu, Nandin-Erdene Tsendbazar, Martin Herold, Sytze de Bruin, Myke Koopmans, Tanya Birch, Sarah Carter, Steffen Fritz, Myroslava Lesiv, Elise Mazur, Amy Pickens, Peter Potapov, Fred Stolle, Alexandra Tyukavina, Ruben Van De Kerchove, Daniele Zanaga · 发表于：Remote Sensing of Environment · 年份：2024 · DOI：10.1016/j.rse.2024.114316 · 被引用次数：127 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Land Use and Ecosystem Services

Accurate and high-resolution land cover (LC) information is vital for addressing contemporary environmental challenges. With the advancement of satellite data acquisition, cloud-based processing, and deep learning technology, high-resolution Global Land Cover (GLC) map production has become increasingly feasible. With a growing number of available GLC maps, a comprehensive evaluation and comparison is necessary to assess their accuracy and suitability for diverse uses. This particularly applies to maps lacking statistically robust accuracy assessment or sufficient reported detail on the validation procedures. This study conducts a comparative independent validation of recent 10 m GLC maps, namely ESRI Land Use/Land Cover (LULC), ESA WorldCover, and Google and World Resources Institute (WRI)’s Dynamic World, examining their spatial detail representation and thematic accuracy at global, continental, and national (for 47 larger countries) levels. Since high-resolution map validation is impacted by reference data uncertainty owing to geolocation and labelling errors, five validation approaches dealing with reference data uncertainty were evaluated. Of the considered approaches, validation using the sample label supplemented by majority label within the neighborhood is found to produce more reasonable accuracy estimates compared to the overly optimistic approach of using any label within the neighborhood and the overly pessimistic approach of direct comparison between the map and ...