GLCFCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery

Abstract. Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m land-cover classification with a fine classification system for the year 2015 (GLCFCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the CCILC (Climate Change Initiative Global Land Cover) land-cover and MCD43A4 NBAR products (MODIS Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance). Secondly, a local adaptive random forest model was built for each 5∘×5∘ geographical tile by using the multi-temporal Landsat spectral and texture features and the corresponding training data, and the GLCFCS30-2015 land-cover product containing 30 land-cover types was generated for each tile. Lastly, the GLCFCS30-2015 was validated using three different validation systems (containing different land-cover details) using 44 043 validation samples. The validation results indicated that the GLCFCS30-2015 achieved an overall accuracy of 82. 5 % and a kappa coefficient of 0. 784 for the level-0 validation system (9 basic land-cover types), an overall accuracy of 71. 4 % and kappa coefficient of 0. 686 for the UN-LCCS (United Nations Land Cover Classification System) level-1 system (16 LCCS land-cover types), and an overall accuracy of 68. 7 % and kappa coefficient of 0. 662 for the UN-LCCS level-2 system (24 fine land-cover types). The comparisons against other land-cover products (CCILC, MCD12Q1, FROMGLC, and GlobeLand30) indicated that GLCFCS30-2015 provides more spatial details than CCILC-2015 and MCD12Q1-2015 and a greater diversity of land-cover types than FROMGLC-2015 and GlobeLand30-2010. They also showed that GLCFCS30-2015 achieved the best overall accuracy of 82. 5 % against FROMGLC-2015 of 59. 1 % and GlobeLand30-2010 of 75. 9 %. Therefore, it is concluded that the GLCFCS30-2015 product is the first global land-cover dataset that provides a fine classification system (containing 16 global LCCS land-cover types as well as 14 detailed and regional land-cover types) with high classification accuracy at 30 m. The GLCFCS30-2015 global land-cover products produced in this paper are free access at https: //doi. org/10. 5281/zenodo. 3986872 (Liu et al. , 2020).