Product User Manual
Document Ref:
Version:Contract No:
Creation Date:
Last Modified:WorldCover_PUM_v2.0
2.04000128231/19/I-LG
2020-09-01
2022-10-24

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DOCUMENT RELEASE SHEET
Name

Signature

Book Captain

Ruben Van De Kerchove (VITO)

Contributing
Authors

Daniele Zanaga (VITO)
Panpan Xu (WU)
Nandika Tsendbazar (WU)
Myroslava Lesiv (IIASA)
Ruben Van De Kerchove (VITO)
Olivier Arino
PublicApproval (internal)
Approval (ESA)
Distribution

Date
24-10-2022

24-10-2022

DOCUMENT CHANGE LOG
Issue

Issue datePages affected

Relevant information

0.9
1.0
2.0

2020-09-01
2021-10-15
2022-10-24

All
All
All

First version, 10% prototype
Updated for the global productUpdated for the 2021 product and version
2.0 of the algorithm

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Table of Content
TABLE OF CONTENT...................................................................................................................... 3
LIST OF FIGURES........................................................................................................................... 4
LIST OF TABLES ............................................................................................................................ 4
ACRONYMS AND ABBREVIATIONS ................................................................................................ 5
1

INTRODUCTION .................................................................................................................... 6
1.1
1.2
1.3

2

ALGORITHM ......................................................................................................................... 8
2.1
2.2

3

Project objectives and approach ............................................................................................ 6
Purpose of the document ....................................................................................................... 6
Overview ................................................................................................................................. 7

Summary ................................................................................................................................. 8
Differences to the previous version ...................................................................................... 10

PRODUCT DESCRIPTION ...................................................................................................... 13
3.1
File naming ............................................................................................................................ 13
3.2
File format ............................................................................................................................. 14
3.3
Product content .................................................................................................................... 14
3.3.1
Map: Land cover classification ...................................................................................... 14
3.3.2

InputQuality: Quality layer ............................................................................................ 16

3.4
Product characteristics ......................................................................................................... 16
3.4.1
Projection and grid Information ................................................................................... 16
3.4.2

Spatial Information ....................................................................................................... 16

3.4.3

Temporal information ................................................................................................... 16

4

KNOWN LIMITATIONS......................................................................................................... 17

5

DATA ACCESS AND CITATION .............................................................................................. 19
5.1
License................................................................................................................................... 19
5.2
Citation .................................................................................................................................. 19
5.3
Delivery Mechanisms ............................................................................................................ 19
5.3.1
Explore .......................................................................................................................... 19
5.3.2

Data Download ............................................................................................................. 20

5.3.3

Google Earth Engine...................................................................................................... 23

5.4
Supporting files ..................................................................................................................... 23
5.4.1
Symbology ..................................................................................................................... 23
5.5

Contact .................................................................................................................................. 23

6

VALIDATION ....................................................................................................................... 24

7

REFERENCES ....................................................................................................................... 26

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List of Figures
Figure 1: The WorldCover 2021 Map v200 ............................................................................................. 7
Figure 2: Combined workflow to generate the WorldCover land cover product ................................... 8
Figure 3: WorldCover 2020 map (topleft), WorldCover 2021 map (topright), Sentinel-2 median false
colour composite for 2020 (middle left), Sentinel-2 median false colour composite for 2021 (middle
right), Sentinel-1 median false colour composite for 2020 (bottom left) and Sentinel-1 median false
colour composite for 2021 (bottom right) over an area in Egypt (31.35°E, 23.00°W) showing a
difference in the permanent water bodies and cropland classes (i.e. actual land cover changes)
between 2020 and 2021 as well as a better mapping of cropland (i.e. algorithm change) in 2021 (lesscropland omission) compared to 2020 ................................................................................................. 11
Figure 4: WorldCover 2020 map (topleft), WorldCover 2021 map (topright), Sentinel-2 median false
colour composite for 2020 (middle left), Sentinel-2 median false colour composite for 2021 (middle
right), Sentinel-1 median false colour composite for 2020 (bottom left) and Sentinel-1 median false
colour composite for 2021 (bottom right) over an area in Uganda (31.35°E, 23.00°W) mainly showingchanges due to the improved retrieval algorithm in v200 leading to a better cropland and wetland
mapping in the WorldCover 2021 map compared to WorldCover 2020.............................................. 12
Figure 5: Scheme of the 3 x 3 degree tiles ............................................................................................ 13
Figure 6: Screenshot of the WorldCover viewer ................................................................................... 20
Figure 7: Spatial distribution of all validation sample sites for different (sub) continents .................. 24

List of Tables
Table 1: Successive versions of the WorldCover products. .................................................................. 10
Table 2: Land Cover layer names in the filename ................................................................................. 14
Table 3: Coding of the Map layer and definition of the classes............................................................ 15
Table 4: Overall accuracy for the WorldCover 2021 v200 product at the continental level and sample
sites used (SSU- secondary sample units, PSU – primary sample units) .............................................. 24

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

Amazon Web Services
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 Synthetic Aperture Radar (SAR) data such as Sentinel-1 to produce large area land cover maps isstill in its infancy.
For this purpose and inspired by the 2017 WorldCover conference (attended by more than 400
participants) the European Space Agency (ESA) initiated the WorldCover project. The key outcome ofthis 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 andindependently validated with a global overall accuracy of 74.4%.
Following the positive feedback from the users, ESA decided to extend the WorldCover project andrequested the WorldCover consortium to also produce a 2021 version of the product with even higher
quality. This new WorldCover map for 2021 was released on 28 October 2022 and resulted in a global
overall accuracy of 76.7%.
Since the WorldCover maps for 2020 and 2021 were generated with different algorithm versions(v100 and v200, respectively), changes between the maps include both changes in real land coverand changes due to the used algorithms.

1.2 Purpose of the document
This Product User Manual (PUM) describes the WorldCover Land Cover products and contains therequired information to use the products.
The PUM is the primary document that users should read before handling the product. It gives an
overview of the product characteristics, in terms of algorithm and technical characteristics anddescribes the known limitations and a summary of the independent validation results.

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1.3 Overview

Figure 1: The WorldCover 2021 Map v200
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 isprovided (section 6).

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2 Algorithm
The ESA WorldCover global land cover products build 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 products 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).

2.1 Summary
Below more details are provided for v200 of the WorldCover retrieval algorithm. For more details onv100 of the algorithm version used in the 2020 map, the reader is referred to V1.0 of the Product User
Manual available at esa-worldcover.org/en/data-access.
The methodology for producing the WorldCover land cover products can be split in 3 separate
workflows (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 productAs 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WorldCover

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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). The same features are calculated on the S1 VV and VH
backscatter time series as well. For S1 bands, an additional multitemporal speckle filter is used before
compositing the timeseries. 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 are also included: the position of each pixel (latitude & longitude) and the biome and realm
features derived from the Ecoregions2017 layer (Dinerstein et al., 2017). Yearly meteoreologicalfeatures are also derived from the TerraClimate dataset (Abatzoglou et al., 2018). The yearly meanand standard deviation is computed for 7 variables from the dataset (reference evapotranspiration,snow water equivalent, minimum temperature, maximum temperature, vapor pressure, vapor
pressure deficit and wind speed). Overall, 64 features are extracted from Sentinel-2, 12 features from
Sentinel-1, 2 features from the DEMs, 23 positional features and 14 metereological features, for a
total of 115 features.
In the training phase, these features are extracted for each training location, from which training
points are randomly sampled. For each class, 10% of the points are sampled, with a maximum of 30
points per class per location. The training points selected are randomly splitted in training and test
sets and used for training different models (scenarios) with a gradient boosting decision tree algorithm(CatBoost) (Prokhorenkova et al., 2018).Three models are trained in total, one using all the extracted
features (combined model), one excluding S1 features (L2A model), and one excluding S2 features(GAMMA0 model). In the classification phase, the same steps are carried out until the features are
extracted, at that point the models/scenarios trained during the training phase are loaded and used
to predict the labels 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, 2021), Global Mangrove Watch (Bunting et al., 2022), Global Human
Settlement Layer (Corbane et al., 2020a, 2020b), World Settlement Footprint 2019 (Marconcini et al.,2021) and ESA WorldCover 2020 (Zanaga et al., 2021). 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 classattribution. These expert rules address two main limitations of the current methodology: artefacts
deriving from artefacts or low-quality input signal (100% cloud cover/ice/snow areas) and spectral
mixing leading to confusion between bare areas and urban and between mangroves and trees.

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2.2 Differences to the previous version
Since the WorldCover maps for 2020 and 2021 were generated with different algorithm versions(v100 and v200, respectively), changes between the maps should be treated with caution, as they
include both changes in real land cover and changes due to the used algorithms (see Figure 3 andFigure 4). In fact, most changes between both maps are due to changes in the used algorithms.
Compared to v100, v200 mainly uses an improved training dataset and features leading to an overall
improved retrieval of the different land cover classes (Table 1). Additionally, v200 uses slightly
optimized settings for training the algorithm as well as an optimized set of expert rules. The productdescription (see section 3; i.e. file naming, file format, product content & product characteristics) is
however identical for both versions.
Table 1: Successive versions of the WorldCover products.
Algorithm
Versionv100
v200

Main characteristics
Used to generate the WorldCover 2020 product. A total of 259734 training
locations was used and 131 features.
Used to generate the WorldCover 2021 product. A total of 319415 training
locations was used and 115 features

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Figure 3: WorldCover 2020 map (topleft), WorldCover 2021 map (topright), Sentinel-2 median false
colour composite for 2020 (middle left), Sentinel-2 median false colour composite for 2021 (middle
right), Sentinel-1 median false colour composite for 2020 (bottom left) and Sentinel-1 median false
colour composite for 2021 (bottom right) over an area in Egypt (31.35°E, 23.00°W) showing a
difference in the permanent water bodies and cropland classes (i.e. actual land cover changes)
between 2020 and 2021 as well as a better mapping of cropland (i.e. algorithm change) in 2021 (lesscropland omission) compared to 2020

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Figure 4: WorldCover 2020 map (topleft), WorldCover 2021 map (topright), Sentinel-2 median false
colour composite for 2020 (middle left), Sentinel-2 median false colour composite for 2021 (middle
right), Sentinel-1 median false colour composite for 2020 (bottom left) and Sentinel-1 median false
colour composite for 2021 (bottom right) over an area in Uganda (31.35°E, 23.00°W) mainly showingchanges due to the improved retrieval algorithm in v200 leading to a better cropland and wetland
mapping in the WorldCover 2021 map compared to WorldCover 2020.

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3 Product description
The ESA WorldCover products are provided per 3 x 3 degree tile (Figure 5), 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_2021_v200_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. 2021)•<VERSION> shows the product version. The version denoted as vMmr (e.g. v200), with ‘M’representing the major version (e.g. v2), ‘m’ the minor version (starting from 0) and ‘r’ theproduction run number (starting from 0)•<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 5)
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 2; e.g. Map)

Figure 5: Scheme of the 3 x 3 degree tiles

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Table 2: Land Cover layer names in the filename
Layer in filename

Description

Map

Main discrete classification according to FAO LCCS scheme, see Table 3
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 3: Coding of the Map layer 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 thisclass. 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 products are delivered in a regular latitude/longitude grid (EPSG:4326) (Figure 5) withthe 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 either 2020 or 2021
except for areas in Antarctica (see Figure 5). In the north, the WorldCover product is limited to82.75°N.

3.4.3 Temporal information
The products represent the land cover for 2020 (WorldCover 2020 v100) or 2021 (WorldCover 2020v200), from 01 January to 31 December. Sentinel-1 and Sentinel-2 data covering the reference year isused in its processing.

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4 Known limitations
Below we identify a non-exhaustive list of limitations in the WorldCover v200 (2021) products. For
more details on the limitations in WorldCover v100 (2020), the reader is referred to V1.0 of the
Product User Manual available at esa-worldcover.org/en/data-access.
•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. 31.40°E, 13.20°N): In areas with a high confusion between land coverclasses, Sentinel-2 orbit borders or block borders might be visible due to sensitivity of themodel inference to the different amount of observations/input signal between neighbouring
areas.

•

Water on glaciers/ice sheets/mountain shadows (e.g. 73.28°W, 49.89°S): Some areas in theablation zone of glaciers & mountain shadows are sometimes misclassified as water bodies.

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Agriculture vs wetland confusion (e.g. 56.73°W, 27.28°S): Due to the high spectral similarity
between (irrigated) agriculture and herbaceous wetland, sometimes misclassifications
happen between those classes

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 is in preparation
If you are using the data as a layer in a published map, please include the following attribution text:'© ESA WorldCover project [year] / Contains modified Copernicus Sentinel data ([year]) processed by
ESA WorldCover consortium’
With year either 2020 or 2021 for the WorldCover 2020 and 2021 map, respectively
To cite these maps as data source in your publication, please add:
• WorldCover 2020
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. doi:10.5281/zenodo.5571936.
• WorldCover 2021
Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G.,
Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N.E., Xu, P.,
Ramoino, F., Arino, O., 2022. ESA WorldCover 10 m 2021 v200. doi:10.5281/zenodo.7254221.

5.3 Delivery Mechanisms
The
ESA
WorldCoverproducts
aredeliveredin 3x3degreetiles 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 products without installing any software. Furthermore, it also allows
users to compare the map with Sentinel-1 and Sentinel-2 median composites for 2020 & 2021, viewland 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 products either byadministrative area, or customized region of interest.

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Figure 6: Screenshot of the WorldCover viewer
5.3.1.2 Alternative WorldCover viewer
As an alternative, the WorldCover 2020 and 2021 products can also be viewed atworldcover2020.esa.int and worldcover2021.esa.int hosted by ESA.
5.3.1.3 Google Earth Engine App
A dedicated Earth Engine App has been made which allows users to visualize the ESA WorldCoverproducts and compare with Sentinel-1 and Sentinel-2 composites.
5.3.1.4 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 & WORLDCOVER_2021_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

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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
terracatalogueclient•

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_2021_V2")# 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_2021_V2",
geometry=geometry)### Download# download the products to the given directory
catalogue.download_products(products, "downloads")

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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 open apython environment and run

pip install awscli.

•run

aws s3 sync s3://esa-worldcover/v200/2021/map/ /local/path --nosign-request(please modify /local/path to the desired download location)•or alternatively use this python snippet to download a subset by country from AWS
URLs

import geopandas as gpd
s3_url_prefix = "https://esa-worldcover.s3.eu-central1.amazonaws.com"# load natural earth low res shapefile
ne = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))# get AOI geometry (Italy in this case)country = 'Italy'
geom = ne[ne.name == country].iloc[0].geometry# load worldcover grid
url = f'{s3_url_prefix}/v100/2020/esa_worldcover_2020_grid.geojson'
grid = gpd.read_file(url)# get grid tiles intersecting AOItiles = grid[grid.intersects(geom)]# use requests library to download them
import requests
from tqdm.auto import tqdm # provides a progressbar
for tile in tqdm(tiles.ll_tile):url =f"{s3_url_prefix}/v200/2021/map/ESA_WorldCover_10m_2021_v200_{tile}_M
ap.tif"r = requests.get(url, allow_redirects=True)
out_fn = f"ESA_WorldCover_10m_2021_v200_{tile}_Map.tif"
with open(out_fn, 'wb') as f:f.write(r.content)

4. Zenodoo WorldCover 2020 v100 can be downloaded as a single tar.gz file from Zenodo:
https://zenodo.org/record/5571936#.YXA3ehpBxPYWorldCovero

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WorldCover 2021 v200 can be downloaded as zipped macrotiles (60x60 degrees)from Zenodo: https://doi.org/10.5281/zenodo.7254221

5. worldcover2020.esa.int & worldcover2021.esa.int
The 2020 & 2021 datasets can be downloaded as 18 60x60 degree macro tiles, whereby
each macro tile contains a zipped version of all covered 3x3 degree tiles
Please note that the Map data layer can be downloaded from all 5 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 2020 data is available with Asset ID ESA/WorldCover/v100
The WorldCover 2021 data is available with Asset ID ESA/WorldCover/v200•

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 2021 v200 product was statistically validated using an independent validation
dataset. The validation followed the CEOS (the Committee on Earth Observation Satellites) Validation
guidelines (Stage 4).
The statistical validation used the multi-purpose Global Land Cover Validation dataset developed for
the Copernicus Global Land Service- Land Cover 100 product (the CGLS-LC validation dataset) which issuitable for validation of 10 m resolution global land cover maps. This dataset was updated for thereference year 2021 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 7). Each PSU consists of 100
secondary sample units (SSU) which correspond to Sentinel-2 10 m resolution pixels.

Figure 7: Spatial distribution of all validation sample sites for different (sub) continents
The validation results showed that the global overall accuracy of the WorldCover 2021 v200 productis 76.7±0.5. In terms of class-specific accuracies at the global scale, grassland, cropland, bare/sparsevegetation, and permanent water classes have slightly higher accuracies for the WorldCover 2021v200 compared to the WorldCover 2020 v100; Built-up, herbaceous wetlands, and lichen/moss classes
have seen a significant increase in the accuracies.
Overall accuracies at the continental level are above 72%, with the highest accuracy of 82.1% for Asiaand the lowest accuracy of 72.5% for Oceania-Australia and Eurasia (Table 4). Compared to theWorldCover 2020 v100 product, all continents have an improved overall accuracy in the WorldCover2021 v200. The most increase in accuracy was observed in North America, followed by Africa and Asia.
Table 4: Overall accuracy for the WorldCover 2021 v200 product at the continental level and sample
sites used (SSU- secondary sample units, PSU – primary sample units)
Overall accuracy Total SSU

Total PSU

Global76.7 +/- 0.5

2,162,366

21,624Africa76.5 +/- 1.3

359,900

3,599

Europe77.9 +/- 1.0

311,800

3,118

Eurasia72.5 +/- 1.3

300,271

3,003

Asia

82.1 +/- 1.0

306,400

3,064

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Oceania72.5 +/- 1.3

296,100

2,961

North America

74.6 +/- 1.2

287,995

2,880South America

77.9 +/- 1.1

299,900

2,999

Detailed information on the validation of this product and comparison results can be found in the
product validation report available at esa-worldcover.org/en/data-access. Overall, the WorldCover2021 v200 product shows promising improvements in characterizing the World’s land cover at 10 m
resolution.

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7 References
Abatzoglou, J.T., Dobrowski, S.Z., Parks, S.A., Hegewisch, K.C. (2018). Terraclimate, a high-resolution
global dataset of monthly climate and climatic water balance from 1958-2015, Scientific Data 5(1), 112. doi:10.1038/sdata.2017.191
Arino, O., Bicheron, P., Achard, F., Latham, J., Witt, R., Weber, J.L. (2008). GlobCover the mostdetailed portrait of Earth. ESA Bulletin, 136, 25-31.

Buchhorn, M., Lesiv, M., Tsendbazar, N.-E., Herold, M., Bertels, L., Smets, B. (2020). Copernicus Global
Land Cover Layers—Collection 2. Remote Sensing 12(6), 1044. doi:10.3390/rs12061044
Buchhorn, M., Bertels, L., Smets, B., De Roo, B., Lesiv, M., Tsendbazar, N. E., Masiliunas, D., & Li, L.
(2020). Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: algorithm
theoretical basis document. 10.5281/zenodo.3938968
Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R.M., Thomas, N., Tadono, T., Worthington, T.A,
Spalding, M., Murray, N.J., Rebelo, L.M. (2022). Global Mangrove Watch Extent Change 1996-2020:
Global Mangrove Watch Version 3.0. Remote Sensing 14(15): 3654. doi:10.3390/rs14153657.
Corbane, C., Sabo, F., Politis, P., Syrris, V. (2020). GHS-BUILT-S2 R2020A - built-up grid derived from
Sentinel-2 global image composite for reference year 2018 using Convolutional Neural Networks (GHSS2Net).
EuropeanCommission,
Joint
Research
Centre(JRC)
PID:
http://data.europa.eu/89h/016d1a34-b184-42dc-b586-e10b915dd863.
doi:10.2905/016D1A34B184-42DC-B586-E10B915DD863.
Corbane, C., Syrris, V., Sabo, F., Politis, P., Meclchiorri, M., Pesaresi, M., Soille, P., Kemper, T. (2021).
Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite
imagery. Neural Computing and Applications 33. doi:10.1007/s00521-020-05449-7.
Dinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N. D., Wikramanayake, E., Hahn, N., Palminteri,S., Hedao, P., Noss, R., Hansen, M., Locke, H., Ellis, E. C., Jones, B., Barber, C. V., Hayes, R., Kormos, C.,
Martin, V., Crist, E., Sechrest, W., Price, L., Baillie, J. E. M., Weeden, D., Suckling, K., Davis, C., Sizer,N., Moore, R., Thau, D., Birch, T., Potapov, P., Turubanova, S., Tyukavina, A., de Souza, N., Pintea, L.,
Brito, J. C., Llewellyn, O. A., Miller, A. G., Patzelt, A., Ghazanfar, S. A., Timberlake, J., Klöser, H.,
Shennan-Farpón, Y., Kindt, R., Lillesø, J. B., van Breugel, P., Graudal, L., Voge, M., Al-Shammari, K. F.,
Saleem, M. (2017). An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm, BioScience,
67(6), 534–545. doi:10.1093/biosci/bix014
ESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available
at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf.
Fahrland, E., Jacob, P., Schrader, H., Kahabka, H. (2019). Copernicus DEM Product Handbook. AIRBUS
Defence and Space, Campaign ID: GEO.2018-1988-2, Version 1.0, pp. 27.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., Gulin, A. (2018). CatBoost: unbiased
boosting with categorical features. Advances in neural information processing systems, 6638-6648.
Marconcini, M., Metz-Marconcini, A., Esch, T. , Gorelick, N. (2021). Understanding Current Trends inlobal Urbanisation – The World Settlement Footprint suite. GI_Forum, 1, 33-38.
doi:10.1553/giscience2021_01_s33.

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OpenStreetMap contributors. (2021) Planet dump retrieved from https://planet.openstreetmap.org.
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. doi:10.5281/zenodo.5571936.