WorldCover

Product User Manual | V1.0

Product User Manual
Document Ref:
Version:Contract No:
Creation Date:
Last Modified:WorldCover_PUM_v1.0
1.04000128231/19/I-LG
2020-09-01
2020-10-15

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DOCUMENT RELEASE SHEET
Name

Signature

Book Captain

Ruben Van De Kerchove (VITO)

Contributing
Authors

Daniele Zanaga (VITO)
Wanda De Keersmaecker(VITO)
Linlin Li (WU)
Nandika Tsendbazar (WU)
Myroslava Lesiv (IIASA)
Ruben Van De Kerchove (VITO)
Olivier Arino
PublicApproval (internal)
Approval (ESA)
DistributionDate
15-10-2021

15-10-2021
19-10-2021

DOCUMENT CHANGE LOG
Issue

Issue datePages affected

Relevant information

0.9
1.0

2020-09-01
2021-10-15

All
All

First version, 10% prototype
Updated for the global productWorldCover

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Table of Content
TABLE OF CONTENT...................................................................................................................... 3
LIST OF FIGURES........................................................................................................................... 5
LIST OF TABLES ............................................................................................................................ 5
ACRONYMS AND ABBREVIATIONS ................................................................................................ 6
1

2

INTRODUCTION .................................................................................................................... 7
1.1

Project objectives and approach ............................................................................................ 7

1.2

Purpose of the document ....................................................................................................... 7

1.3

Overview ................................................................................................................................. 8

ALGORITHM ......................................................................................................................... 9
2.1

3

Summary ................................................................................................................................. 9

PRODUCT DESCRIPTION ...................................................................................................... 11
3.1

File naming ............................................................................................................................ 11

3.2

File format ............................................................................................................................. 12

3.3

Product content .................................................................................................................... 123.3.1

Map: Land cover classification ...................................................................................... 12

3.3.2

InputQuality: Quality layer ............................................................................................ 14

3.4

Product characteristics ......................................................................................................... 14

3.4.1

Projection and grid Information ................................................................................... 14

3.4.2

Spatial Information ....................................................................................................... 14

3.4.3

Temporal information ................................................................................................... 144

KNOWN LIMITATIONS......................................................................................................... 15

5

DATA ACCESS AND CITATION .............................................................................................. 17
5.1

License................................................................................................................................... 17

5.2

Citation .................................................................................................................................. 17

5.3

Delivery Mechanisms ............................................................................................................ 17

5.3.1

Explore .......................................................................................................................... 17

5.3.2

Data Download ............................................................................................................. 18

5.3.3

Google Earth Engine...................................................................................................... 20

5.4

Supporting files ..................................................................................................................... 20

5.4.1
5.5

Symbology ..................................................................................................................... 20

Contact .................................................................................................................................. 20

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6

VALIDATION ....................................................................................................................... 21

7

REFERENCES ....................................................................................................................... 23

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List of Figures
Figure 1: The WorldCover 2020 Map V100............................................................................................. 8
Figure 2: Combined workflow to generate the WorldCover land cover product ................................... 9
Figure 3: Scheme of the 3 x 3 degree tiles ............................................................................................ 11
Figure 4: Screenshot of the WorldCover viewer ................................................................................... 18
Figure 5: Spatial distribution of all validation sample sites for different (sub) continents .................. 21

List of Tables
Table 1: Land Cover layer names in the filename ................................................................................. 12
Table 2: Coding of the Map and definition of the classes..................................................................... 13
Table 3: Overall accuracy for the WorldCover product at the continental level and sample sites used
(SSU- secondary sample units, PSU – primary sample units) ............................................................... 21

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Acronyms and Abbreviations
CCI
CEOS
COG
CGLS
CRS
DEM
EO
ESA
FAO
GIS
GLC
GRD
LC
LCCS
UN
PSU
PUM
SAR
SCL
SSU
UTM
VI

Climate Change Initiative
Committee on Earth Observation Satellites
Cloud Optimized GeoTIFF
Copernicus Global Land Service
Coordinate Reference System
Digital Elevation Model
Earth Observation
European Space Agency
Food and Agriculture Organization of the United Nations
Geographic Information System
Global Land CoverGround Range Detected
Land CoverLand Cover Classification System
United Nations
Primary Sampling Unit
Product User Manual
Synthetic Aperture Radar
Scene Classification Layer
Secondary Sampling Unit
Universal Transverse Mercator
Vegetation Indices

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1 Introduction
1.1 Project objectives and approach
In an ever-changing environment, the need for accurate, timely and high-resolution information onland use/land cover and its changes has increased tremendously. However, until now, regional orcontinental land cover maps either used low-resolution images (>100 m) as input or were based
exclusively on high-resolution optical Earth observation data, such as Sentinel-2 or Landsat. The useof SAR data such as Sentinel-1 to produce large area land cover maps is still in its infancy.
For this purpose and inspired by the 2017 WorldCover conference (http://worldcover2017.esa.int,
attended by more than 400 participants) the European Space Agency (ESA) initiated the WorldCoverproject. The key outcome of this project was the release in October 2021 of a freely accessible global
land cover product at 10 m resolution for 2020, based on both Sentinel-1 and Sentinel-2 data,containing 11 land cover classes and independently validated with a global overall accuracy of 74.4%.
A crucial aspect for WorldCover was the involvement of several end users active in different domains
who provided primary input for all engineering aspects and followed the whole project workflow fromdesign up to validation and uptake. Consequently, WorldCover intends to provide a substantial benefit
to various user communities and expands the established global land cover base of users and thedevelopment of novel services.

1.2 Purpose of the document
This Product User Manual (PUM) describes the first version (V100) of the WorldCover Land Coverproducts, generated for 2020 and contains the required information to use these products. The PUM
is the primary document that users have to read before handling the product. It gives an overview ofthe product characteristics, in terms of algorithm and technical characteristics and describes the
known limitations and a summary of the independent validation results.

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1.3 Overview

Figure 1: The WorldCover 2020 Map V100
In the following sections first the retrieval methodology of the WorldCover product is summarized(section 2) followed by a description of the technical properties of the product (section 3). Next, welist some identified known limitations of the product (section 4) as well as more information on how
to access the product (section 5). Finally also a summary of the independent validation results areprovided (section 6).

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2 Algorithm
The ESA WorldCover global land cover product builds further on the GlobCover and CCI Land Coverexperiences from the European Space Agency (Arino et al. 2008; ESA 2017). The algorithm used togenerate the ESA WorldCover product is based on the algorithm to produce the dynamic yearly
Copernicus Global Land Service Land Cover (CGLS-LC) map at 100 m resolution (Buchhorn et al.,2020a). The CGLS-LC workflow uses 100 m, 5-day, Proba-V data as an input which were re-processedto the Sentinel-2 UTM grid together with training data obtained at 10 m resolution. For the generationof the WorldCover product however both Sentinel-2 multi-spectral image data and Sentinel-1 C-band
Synthetic Aperture Radar (SAR) data are used instead of Proba-V data.

2.1 Summary
The methodology for producing the WorldCover land cover product can be split in 3 separate
workflows which together generate the WorldCover product (Figure 2): Data pre-processing, ii)
Classification and iii) Map generation. The classification workflow is further subdivided in a training& prediction phase:

Figure 2: Combined workflow to generate the WorldCover land cover productWorldCover

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As a first step in the processing, Level 2A (L2A) and Ground Range Detected (GRD) products for
Sentinel-2 (S2) and Sentinel-1 (S1) respectively, are selected and either filtered for cloud cover(Sentinel-2) or terrain corrected to Gamma0 backscatter time series (for Sentinel-1) which are
geocoded to the Sentinel-2 grid. To reduce data redundancy, a maximum of one observation per day
per tile is processed. In case of multiple observations per day, the first observation with the highest
number of valid data pixels is kept.
Next, clouds, cloud shadows, cirrus and saturated pixels are removed in the Sentinel-2 reflectancebands using the scene classification layer (SCL) of the L2A product. Ten days median composites (using
a 20 days moving window) are computed from the band time series, eliminating further noise, or pixels
not correctly masked by the SCL layer. The remaining missing values are then filled by estimating theirvalue from a linear interpolation between the closest available values in the time series. After the timeseries are pre-processed, additional vegetation indices (VI) for each time series step are calculated.
Starting from those cleaned Sentinel-2 surface reflectance and VI time series, descriptive statistics arecomputed for each band and VI. These includes the 10th, 50th, 90th percentiles and the interquartile
range (75th percentile – 25th percentile). In addition to these statistics, long range averaged
timestamps are used as features together with the quantiles to introduce time dynamics informationin the classification. For this purpose, the NDVI timeseries is downsampled to 6 timestamps using
Fourier methods. The same features are calculated on the S1 VV and VH backscatter time series aswell. For S1 bands, an additional multitemporal speckle filter is used before compositing thetimeseries. Altitude and slope are extracted from the Copernicus Global 30 m Digital Elevation Model(DEM) (Fahrland et al., 2019) and used as features as well. A series of spatial localizing features arealso included: the position of each pixel (latitude & longitude) and the biome and realm featuresderived from the Ecoregions2017 layer (Dinerstein et al., 2017). Overall, 90 features are extracted
from Sentinel-2, 16 features from Sentinel-1, 2 features from the DEMs and 23 positional features fora total of 131 features.
In the training phase, these features are extracted for each training location, from which training
points are randomly sampled, with a maximum of 20 points per class per location. The training points
selected are randomly splitted by location in training and test sets and used for training different
models (scenarios) with a gradient boosting decision tree algorithm (CatBoost) (Prokhorenkova et al.,2018).
In the classification phase, exactly the same steps are carried out until the features are extracted, atthat point the models/scenarios trained during the training phase are loaded and used to predict thelabels of the input data together with the class probabilities.
Finally, the different scenarios are combined into a final land cover map through the application ofdifferent expert rules and subsequently tiled into 3 x 3 degree tiles in geographic projection(EPSG:4326). Some of these expert rules use the following auxiliary datasets (OpenStreetMap(OpenStreetMap Contributors, 2020), Global Surface Water Explorer (Pekel et al., 2016), Global
Mangrove Watch (Bunting et al., 2018) , Global Human Settlement Layer (Corbane et al., 2020a,
2020b), AgERA5 historic and near real time forcing data, CGLOPS permanent Ice mask (Buchhorn etal., 2020b) in order to modulate the probabilities provided by the classifier in order to produce a better
final prediction. It is important to note that these auxiliary datasets are not imprinted in the final land
cover map but only support the decision of the final class attribution.

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3 Product description
The ESA WorldCover products are provided per 3 x 3 degree tile (Figure 3), 2651 in total. Each tile
contains a set of 2 Cloud Optimized GeoTIFF (COG) files corresponding to the following data layers:•

Map: Land cover map with 11 classes•

InputQuality: Three band GeoTIFF providing three per pixel quality indicators of the Sentinel1 and Sentinel-2 input data

Tiles are provided in EPSG:4326, geographic latitude/longitude CRS.

3.1 File naming
The GeoTIFF files follow this naming convention:
ESA_WorldCover_10m_<YEAR>_<VERSION>_<TILE>_<LAYER>.tif e.g.
ESA_WorldCover_10m_2020_v100_S48E036_Map.tif

where• “ESA_WorldCover_10m” indicates this is the 10 m resolution ESA WorldCover product•<YEAR> indicates the reference year (observation period) in four digits (e.g. 2020)•<VERSION> shows the product version. The version denoted as vMmr (e.g. v100), with ‘M’representing the major version (e.g. v1), ‘m’ the minor version (starting from 0) and ‘r’ theproduction run number (starting from 1)•<TILE> the designation of the 3 x 3 degree tile, composed of the 2-digit latitude and 3-digit
longitude of the lower-left corner (Figure 3)
Example: S48E036 for the tile covering the area from 36E to 39E and 48S to 45S.

•<LAYER> gives the name of the data layer (see Table 1; e.g. Map)

Figure 3: Scheme of the 3 x 3 degree tiles

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Table 1: Land Cover layer names in the filename
Layer in filename

Description

Map

Main discrete classification according to FAO LCCS scheme, see Table 2
Per pixel quality indicator showing the quality of the EO input data
3 band GeoTIFF, see 3.3.2

InputQuality

3.2 File format
The data layers of the WorldCover product are provided as Cloud Optimized GeoTIFF (COG) files thatare internally compressed (DEFLATE), have an internal tiling of size 1024, include overviews on levels
2, 4, 8, 16, 32 and 64 for faster loading in Geographic Information System (GIS) and include standard
metadata attributes.
All land cover layers are stored as single bytes (Map) or UINT16 (InputQuality) per pixel, without
scaling or offset.

3.3 Product content
3.3.1 Map: Land cover classification
The discrete classification map provides 11 classes and is defined using the Land Cover Classification
System (LCCS) developed by the United Nations (UN) Food and Agriculture Organization (FAO). The
UN-LCCS system was designed as a hierarchical classification, which allows adjusting the thematic
detail of the legend to the amount of information available

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Table 2: Coding of the Map and definition of the classes
Map
code

Land CoverClass

LCCS code

Definition

Color code
(RGB)

10

Tree cover

A12A3 // A11A1
A24A3C1(C2)R1(R2)0,100,020

Shrubland

A12A4 // A11A2

30

Grassland

A12A2

40

CroplandA11A3(A4)(A5) //A23

50

Built-up

B15A1

60

Bare /
sparsevegetation
Snow andIce

B16A1(A2) //B15A2

This class includes any geographic area dominated by trees with acover of 10% or more. Other land cover classes (shrubs and/or herbs
in the understorey, built-up, permanent water bodies, …) can bepresent below the canopy, even with a density higher than trees.
Areas planted with trees for afforestation purposes and plantations(e.g. oil palm, olive trees) are included in this class. This class also
includes tree covered areas seasonally or permanently flooded withfresh water except for mangroves.
This class includes any geographic area dominated by natural shrubs
having a cover of 10% or more. Shrubs are defined as woody
perennial plants with persistent and woody stems and without any
defined main stem being less than 5 m tall. Trees can be present inscattered form if their cover is less than 10%. Herbaceous plants canalso be present at any density. The shrub foliage can be either
evergreen or deciduous.
This class includes any geographic area dominated by natural
herbaceous plants (Plants without persistent stem or shoots above
ground and lacking definite firm structure): (grasslands, prairies,
steppes, savannahs, pastures) with a cover of 10% or more,irrespective of different human and/or animal activities, such as:
grazing, selective fire management etc. Woody plants (trees and/or
shrubs) can be present assuming their cover is less than 10%. It may
also contain uncultivated cropland areas (without harvest/ bare soil
period) in the reference year
Land covered with annual cropland that is sowed/planted andharvestable at least once within the 12 months after thesowing/planting date. The annual cropland produces an herbaceous
cover and is sometimes combined with some tree or woody
vegetation. Note that perennial woody crops will be classified as theappropriate tree cover or shrub land cover type. Greenhouses are
considered as built-up.
Land covered by buildings, roads and other man-made structures
such as railroads. Buildings include both residential and industrial
building. Urban green (parks,
sport facilities) is not included in this class. Waste dump deposits
and extraction sites are considered as bare.
Lands with exposed soil, sand, or rocks and never has more than 10% vegetated cover during any time of the year

B28A2(A3)This class includes any geographic area covered by snow or glacierspersistently

240, 240, 240

80

Permanentwaterbodies

B28A1(B1) //B27A1(B1)0, 100, 200

90

Herbaceous
wetland

A24A2

95

Mangroves

A24A3C5-R3

100

Moss andlichenA12A7This class includes any geographic area covered for most of the year
(more than 9 months) by water bodies: lakes, reservoirs, and rivers.
Can be either fresh or salt-water bodies. In some cases the water
can be frozen for part of the year (less than 9 months).
Land dominated by natural herbaceous vegetation (cover of 10% ormore) that is permanently or regularly flooded by fresh, brackish or
salt water. It excludes unvegetated sediment (see 60), swamp
forests (classified as tree cover) and mangroves see 95)
Taxonomically diverse, salt-tolerant tree and other plant species
which thrive in intertidal zones of sheltered tropical shores,"overwash" islands, and estuaries.
Land covered with lichens and/or mosses. Lichens are composite
organisms formed from the symbiotic association of fungi andalgae. Mosses contain photo-autotrophic land plants without true
leaves, stems, roots but with leaf-and stemlike organs.

70

255, 187, 34

255, 255, 76

240, 150, 255

250, 0, 0

180, 180, 180

0, 150, 160

0, 207, 117

250, 230, 160

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3.3.2 InputQuality: Quality layer
Per pixel quality indicator showing the quality of the Earth Observation (EO) input data. The layer is a
3 band GeoTIFF with
• Band 1: Number of Sentinel-1 GAMMA0 observations used in the classification workflow• Band 2: Number of Sentinel-2 L2A observations used in the classification workflow• Band 3 Percentage (0-100) of invalid S2 observations discarded in the classification workflow(after cloud and cloud shadow filtering).
Combining Band 2 and Band 3 yields the total absolute number of valid Sentinel-2 L2A observations
used in the classification workflow.

3.4 Product characteristics
3.4.1 Projection and grid Information
The WorldCover prototype products are delivered in a regular latitude/longitude grid (EPSG:4326)(Figure 3) with the ellipsoid WGS 1984 (Terrestrial radius=6378 km). The resolution of the grid is
1°/12000 or approximately 10 m at equator for the Map product. The InputQuality layer is provided
at 1/2000 or approximately 60 m at equator

3.4.2 Spatial Information
The WorldCover product is constrained to all land areas observed by Sentinel-2 in 2020 except forareas in Antarctica (see Figure 3). In the north, the WorldCover product is limited to 82.75°N.

3.4.3 Temporal information
The products represent the land cover for 2020 (from 01 January to 31 December). Sentinel-1 andSentinel-2 data covering the reference year is used in its processing.

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4 Known limitations
We identified a non-exhaustive list of limitations in the WorldCover map layer:•

Cloud artefacts in areas with a high cloud cover % (see e.g. 7.38°E; 1.58°N): Despite the use of
Sentinel-1, cloud artefacts might still appear in areas with persistent cloud cover.

•Hard borders (see e.g. 63°W, 23°S): In areas with a high confusion between land cover classes,
Sentinel-2 orbit borders or block borders might be visible due to sensitivity of the model
inference to the different amount of observations/input signal between neighbouring areas.

•

Water on glaciers/ice sheets (e.g. 73,28°W , 49.89°S): Some areas in the ablation zone ofglaciers are sometimes misclassified as water bodies.

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Irrigated agriculture vs wetland confusion (e.g. 116.30°C, 29.05°N): Due to the high spectral
similarity between irrigated agriculture and herbaceous wetland, sometimes
misclassifications happen between those classes•

Lichen artefact (e.g. 94.65°E; 50.20°N): A moss/lichen artefact is visible in west-mongolia

In order to improve the map, users are encouraged to give feedback on artefacts/misclassifications
via a dedicated feedback tool embedded in WorldCover viewer (see 5.3.2)

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5 Data Access and citation
5.1 License
The WorldCover product is provided free of charge, without restriction of use. For the full license
information see the Creative Commons Attribution 4.0 International License
Publications, models and data products that make use of these datasets must include proper
acknowledgement, including citing the datasets and the journal article as in the following citation.

5.2 Citation
A reference publication on the product will be available in the next month.
If you are using the data as a layer in a published map, please include the following attribution text:'© ESA WorldCover project 2020 / Contains modified Copernicus Sentinel data (2020) processed by
ESA WorldCover consortium’
To cite these maps as data source in your publication, please add:
Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R.,
Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv,
M., Carter, S., Herold, M., Li, Linlin, Tsendbazar, N.E., Ramoino, F., Arino, O., 2021. ESA WorldCover 10
m 2020 v100. https://doi.org/10.5281/zenodo.5571936

5.3 Delivery Mechanisms
The ESA WorldCover 10 m 2020 V100 product is delivered in 3x3 degree tiles as Cloud
Optimized GeoTIFFs (COGs) in EPSG:4326 projection (geographic latitude/longitude CRS). Thereare 2651 tiles
Each tile contains 2 data layers:• Map: Land cover map with 11 classes, a total of approximately 117 GB.
• InputQuality: Three band GeoTIFF providing three per pixel quality indicators of the Sentinel1 and Sentinel-2 input data
The dataset can be accessed and/or downloaded in different ways:

5.3.1 Explore
5.3.1.1 WorldCover viewer
The WorldCover viewer is a simple web-mapping tool that allows users to navigate the globe
visualizing the ESA WorldCover product without installing any software. Furthermore, it also allows
users to compare the map with Sentinel-1 and Sentinel-2 median composites for 2020, view land
cover statistics per administrative area and provides the possibility to give feedback on the product.
Finally, users can also use the viewer to download the WorldCover product either by administrative
area, or customized region of interest.

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Figure 4: Screenshot of the WorldCover viewer
5.3.1.2 Google Earth Engine App
A dedicated Earth Engine App has been made which allows users to visualize the ESA WorldCoverproduct and compare with Sentinel-1 and Sentinel-2 composites for 2020.
5.3.1.3 Web Map Services
The ESA WorldCover product can also be used within other websites or GIS clients by 'Web Map
Services'. These services provide a direct link to the cached images and are the best option if you
simply want to map the data and produce cartographic products. They are not suitable for analysis as
the data are represented only as RGB images.
WMTS: https://services.terrascope.be/wmts/v2
WMS: https://services.terrascope.be/wms/v2
Layer: WORLDCOVER_2020_MAP

5.3.2 Data Download
The dataset can be downloaded from:
1. WorldCover viewer (Login required)• Navigate to the WorldCover viewer• Register for VITO’s Terrascope platform (see the link in the top right)• Log in the WorldCover viewer with your Terrascope credentials• From the 'Download' tab in the WorldCover viewer, zoom to an area of interest or
draw a polygon to retrieve the list of tiles covering that area. The platform will zip thetiles and initiate the download.
2. Terrascope with Python (Login required)
Tiles
can
be
downloaded
programmaticallyusing
Pythonandthe terracatalogueclient package. Please use your Terrascope credentials to download(see 1)• Install the terracatalogueclient, see
https://vitobelgium.github.io/terracatalogueclient/installation.html or run:
pip install --extra-index-url
https://artifactory.vgt.vito.be/api/pypi/python-packages/simple
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Customize and run the following python script:

from terracatalogueclient import Catalogue# create catalogue object and authenticate
catalogue = Catalogue().authenticate()# search for products in the WorldCover collection
products =catalogue.get_products("urn:eop:VITO:ESA_WorldCover_10m_2020_V1")# download the products to the given directory
catalogue.download_products(products, "downloads")from shapely.geometry import Polygon
from terracatalogueclient import Catalogue### Authenticate to the Terrascope platform (registration required)# create catalogue object and authenticate interactively with a browser
catalogue = Catalogue().authenticate()# or authenticate with username and password# catalogue = catalogue.authenticate_non_interactive(username, password)### Filter catalogue# search for all products in the WorldCover collection# products =catalogue.get_products("urn:eop:VITO:ESA_WorldCover_10m_2020_V1")# or filter to a desired geometry, by providing it as an argument to`get_products`
bounds = (3, 33, 15, 45)geometry = Polygon.from_bounds(*bounds)
products =catalogue.get_products("urn:eop:VITO:ESA_WorldCover_10m_2020_V1",
geometry=geometry)### Download# download the products to the given directory
catalogue.download_products(products, "downloads")

3. AWS S3 bucket
The products are hosted on the esa-worldcover S3 bucket in eu-central-1 region. To
download all the products:••

install the AWS command line interface, see
https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-install.html or run pip
install awscli in a python environment.
run aws s3 sync –no-sign-request s3://esaworldcover/v100/2020/map/ /local/path (please modify /local/path to thedesired download location)

4. Zenodo
The dataset can be downloaded as a single tar.gz file from Zenodo.

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Please note that the Map data layer can be downloaded from all 4 options. The InputQuality layer isso far only available from options 1 or 2.

5.3.3 Google Earth Engine
The WorldCover data can also be accessed and used in the Google Earth Engine platform - for more
information see here. The WorldCover data is available with Asset ID ESA/WorldCover/v100

5.4 Supporting files
5.4.1 Symbology
The WorldCover map layer contains a colormap which will display the files in desktop GIS tools (suchas QGIS or ArcGIS) using the symbology that has been used in the WorldCover viewer. However, thesecolormaps do not contain the labels for the values. These can be added to the files by using thesymbology files for either QGIS or ArcGIS.
••

To use the *.qml file in QGIS: Double click on the layer to open the Layer properties dialog. At
the bottom of the dialog, click Style | Load Style… Navigate to the location of the *.qml fileand click Open.
To use the *.lyr file in ArcGIS: Double click on the layer to open the Layer properties dialog
Click on the Symbology tab and change the rendered to Unique Values. Click ‘Yes’ when
prompted to build an attribute table. Click on the Import Symbology button in the top right
Navigate to the location of the *.lyr file and click Add

Besides the standard WorldCover colormap also colormap files with a symbology used in the CCI land
cover maps is provided. QGIS or ArcGis
There are no symbology files for the InputQuality layer.

5.5 Contact
More information and documentation about the product is available from the WorldCover web site
at https://esa-worldcover.org
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6 Validation
The WorldCover prototype product was statistically validated using an independent validation
dataset. The validation followed the CEOS (the Committee on Earth Observation Satellites) Validation
guidelines (Stage 3).
The statistical validation used the multi-purpose Global Land Cover Validation dataset developed for
Copernicus Global Land Service- Land Cover 100 product (the CGLS-LC validation dataset) which issuitable for validation of 10m resolution global land cover maps. This dataset was updated for thereference year 2020 by revisiting random and targeted (for land cover change) subsets.
The validation dataset includes more than 21 000 primary sampling units (PSUs) spread over seven(sub)continents selected based on a stratified random sampling (Figure 5). Each PSU consists of 100
secondary sample units (SSU) which correspond to Sentinel-2 10m resolution pixels.

Figure 5: Spatial distribution of all validation sample sites for different (sub) continents
The validation results showed that the overall accuracy of the WorldCover product is 74.4 ±0.1% for2020. In terms of land cover types, tree cover and snow/ice, cropland, water body, and bare/sparsevegetation classes had high accuracies, while shrubs, herbaceous wetland, and moss/lichen classes
were mapped with lower accuracies.
Overall accuracy at the continental level is mostly above 70%, with the highest accuracy of 80.7% forAsia and the lowest accuracy of 67.5% for Oceania (Table 3). The lower accuracy for Oceania could be
due to high shrubland and grassland confusions in the open shrublands of Australia.
Table 3: Overall accuracy for the WorldCover product at the continental level and sample sites used
(SSU- secondary sample units, PSU – primary sample units)

GlobalAfricaEuropeEurasia
Asia
Oceania
North America
South America

Overallaccuracy

Total SSU

Total PSU

74.4 ± 0.1

1,935,650

21,62473.6±0.2

313,7093,599

76.8±0.2

282,7033,11870.2±0.2

274,2803,003

80.7±0.1

284,4813,064

67.5±0.2

248,2052,961

72.2±0.2

258,4862,880

76.1±0.2

273,786

2,999

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The product was also assessed in terms of spatial accuracy to highlight areas with higher overall quality
and areas that have higher uncertainty. Detailed information on the validation of this product andcomparison results can be found in the product validation report. Overall, the WorldCover productshows promising results in characterizing the World’s land cover at 10m resolution.

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