Altunel, A.O.Çelik, D.A.2024-11-252024-11-252024.01.011735-1472https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=dspace_ku&SrcAuth=WosAPI&KeyUT=WOS:001355280800001&DestLinkType=FullRecord&DestApp=WOS_CPLhttps://hdl.handle.net/20.500.12597/33799Swiftly and reliably establishing a spatially and geometrically correct land-cover map of any region is rather important in natural resource planning for conservation and utilization. JAXA's PALSAR2/PALSAR/JERS-1 Mosaic and Forest / Non-forest maps, which as the name suggested, have specifically focused on global forest cover since 2007, benefiting from L-band SAR imagery. ESRI Land-cover, on the other hand, owing to exceptional Sentinel-2 imagery, has produced rather detailed land-cover maps including a distinct forest class. In this particular study, coverages of 2017-2020 readied by both institutions, utilizing the aforementioned imageries, were questioned on yearly basis against a rather detailed geodatabase which is still-in-effective use by two of the current regional directorates of forestry, Kastamonu and Sinop in T & uuml;rkiye, utilizing long adopted accuracy metrics (user, producer and overall accuracies). When all year coverages were concerned, the best overall accuracies were held with 82% in 2017 ESRI land-cover and 83% in 2017 PALSAR-FNF. Both datasets yielded relatively good results in the forest class when user accuracies were investigated. ESRI land-covers managed more than 87% across all four years, while PALSAR-FNFs produced 84.33% in 2020 as the highest scoring year. As for producer accuracies, PALSAR-FNFs produced over 89% across all year coverages, while ESRI produced 84% in 2017 as the highest scoring year. It is worth noting that the ESRI land-covers had better compliance with the compartment boundaries of the reference geodatabase.eninfo:eu-repo/semantics/openAccessESRI Land-coverSentinel-2L-band SARPALSAR2/PALSAR/JERS-1 MosaicFNF MapsArticle10.1007/s13762-024-06164-90013552808000011735-2630