Map Of Land Cover Agreement - MOLCA

Map Of Land Cover Agreement (MOLCA) is generated by reusing existing datasets for High-Resolution Land Cover (HRLC), but only select portions with unanimous agreement among multiple datasets. We combined multiple HRLCs using intersection methods, retaining only the areas where all datasets agree on the land cover classes and disregarding areas of disagreement. The following HRLCs were used:

FROM-GLC 2017 ([1][2][3][4]),

GL30 2020 ([5][6]),

GHS BU S1NODSM 2016 ([7][8]),

WSF 2019 ([9][10][11]),

GSW 2019 ([12][13]),

FNF 2018 ([14][15]),

MapBiomas 2019 ([16][17]),

CCI Africa Prototype 2016 ([18][19]),

ESA DUE GlobPermafrost 2016 ([20][21]

 MOLCA contains around 117 billion 10-meter pixels (covering about 11.7 million square kilometers) distributed across a total area of 19 million square kilometers.  It is available for 3 macro-regions in Siberia, Africa, and Amazon. The land cover classes represented in MOLCA are Bareland, Built-up, Cropland, Forest, Grassland, Shrubland, Water, Wetland, and Permanent ice and snow, covering the period between 2016 and 2020  The accuracy estimate for MOLCA indicates an Overall Accuracy (OA) of 96%.  

The repository contains:

MOLCA_nnxxx_v1.tif x 2075:  2075 MOLCA tiles (893 in Africa, 658 in Amazon, 524 in Siberia). Tiles of MOLCA follow the Sentinel-2 Level-1C product tiling grid, and consequently, the identifier of MOLCA tiles (nnxxx) is the same as the corresponding Sentinel-2 Level-1C tile identifier.  

MOLCA_tiles_with_statistics.gpkg: Vector of MOLCA tile extents with statistics (number of pixels per class, total number of pixels, proportion of valid values) of each tile in the attribute table.

MOLCA_legend.csv:  Class code and labels of MOLCA

The creation of the MOLCA dataset was done in the project Climate Change Initiative Extension (CCI+) Phase 1 New Essential Climate Variables (NEW ECVS) High Resolution Land Cover ECV (HR_LandCover_cci)  funded by the European Space Agency (ESA) known under the abbreviation CCI HRLC or CCI+ HRLC [22].