# From parcel to continental scale - A first European crop type map based on
Sentinel-1 and LUCAS Copernicus in-situ observations

Raphaël d’Andrimont 1,∗, Astrid Verhegghen1,∗, Guido Lemoine 1, Pieter
Kempeneers1 , Michele Meroni1 and Marijn van der Velde 1 1 European
Commission, Joint Research Centre (JRC), Ispra , Italy
∗ These authors contributed equally to this work.

###### Abstract

Detailed parcel-level crop type mapping for the whole European Union (EU) is
necessary for the evaluation of agricultural policies in the European Union
(EU). The Copernicus program, and Sentinel-1 (S1) in particular, offers the
opportunity to monitor agricultural land at a continental scale and in a
timely manner. However, so far the potential of S1 has not been explored at
such a scale. Capitalizing on the unique LUCAS 2018 Copernicus module, we
present the first continental crop type map at 10-m spatial resolution for the
European Union (EU) based on S1A and S1B Synthetic Aperture Radar observations
for the year 2018. We combined time series of 10-day average VV and VH S1
sigma backscattering coefficients from January to end of July with crop type
information from the LUCAS 2018 survey. Random forest classification
algorithms are tuned for two strata (a northern continental and a southern
Mediterranean) and a two-phase classification procedure that distinguishes
three main vegetation classes in the first phase and 19 different crop types
(or crop type groups) in the second phase. Non-vegetation land cover classes
are masked out using auxiliary information and areas above 1,000 m and with a
steep slope are discarded. We assess the accuracy of this EU crop map with
three approaches. First, the accuracy is assessed with independent LUCAS core
in-situ observations across the EU. Second, an accuracy assessment is done
specifically for main crop types from farmers declarations (GSAA, geospatial
aid application) from 6 EU member countries or regions totaling $>$3M parcels
and 8.21 Mha. Finally, for the main crops, the crop areas derived by
classification are compared to the subnational (NUTS 2) area statistics
reported by Eurostat. The overall accuracy for the map is reported as 80.3%
when grouping main crop classes and 76% when considering all 19 crop type
classes separately. Highest accuracies are obtained for rape and turnip rape
with user and produced accuracies higher than 96%. Crop specific and phenology
driven features determine the classification accuracy throughout the growing
season. We highlight confusion between common wheat and barley, mainly due to
the crop structure and phenology. A confusion between sugar beet and maize is
also observed and likely due to the early time constraint we imposed for the
classification production, i.e. end of July. The correlation between the
remotely sensed estimated and Eurostat reported crop area ranges from 0.93
(potatoes) to 0.99 (rape and turnip rape). Finally, we discuss how the
framework presented here can underpin the operational delivery of in-season
high-resolution based crop mapping for the EU.

###### keywords:

Copernicus , Monitoring , Sentinel-1 , Sentinel-2 , LUCAS , Crop modeling ,
Crop type , Crop yield forecasting , Climate change , Crop production ,
Classification , Validation , Time series , Parcel

††journal: Remote Sensing of Environment

##  1 Introduction

The European Union (EU) is the largest global exporter of agri-food products,
with exports reaching €138 billion in 2018 [European Commission, 2019].
Farmlands are an important feature of the EU landscape with about 42% of the
EU’s land area dedicated to agriculture. At the same time, the EU’s Common
Agricultural Policy (CAP) makes up about 37% of the European Commission’s (EC)
budget (€58.8 billion in 2018). Besides underpinning resilient food
production, the CAP could contribute to mitigate climate change, improve the
sustainable management of natural resources, and preserve biodiversity and
landscapes. This is in line with the European Green Deal, and the Farm to Fork
and Biodiversity strategies111See the respective regulations COM/2019/640,
COM/2020/381 and COM/2020/380.. Updated EU-wide information on crop types is
essential to evaluate the implementation of these agricultural policies. The
EU’s Copernicus space programme and technological advances provide the
opportunity to transform the ways in which we monitor crops, agricultural
practices, as well as CAP expenditure. Moreover, in order to forecast crop
yields and production in the EU, independent and timely information is needed
to support decisions regarding agricultural markets of cereals and other
crops. [van der Velde et al., 2019].

The Copernicus Sentinel-1 (S1) mission adds unique features to our global
Earth Observation (EO) capacity. The frequency of observations is guaranteed
by the S1A and S1B polar-orbiting satellites acquiring all-weather (not
affected by atmospheric conditions) C-band Synthetic Aperture Radar imagery.
Here we exploit this capability for continental scale agricultural monitoring
at 10-m resolution. The use of microwave detection for large scale crop type
mapping remains underexploited, opposed to applications using optical imagery,
e.g. Belgiu & Csillik [2018], Immitzer et al. [2016] and Defourny et al.
[2019]. Recently, the use of high performance cloud computing on platforms
such as Google Earth Engine (GEE, Gorelick et al. [2017]) has been
demonstrated for detailed continental scale crop-type mapping. Massey et al.
[2018] and Teluguntla et al. [2018] used large collections of 30-m optical
LANDSAT imagery to respectively classify crops over North America, and over
China and Australia.

Besides the recent developments in computational capacity to handle such large
data streams and related algorithms, complementary in-situ observations remain
essential for training and validation purposes. In the EU, continental in-situ
data collection is organised through the Land Use/Cover Area frame Survey
(LUCAS) [Gallego & Delincé, 2010, Eurostat, 2018a]. In 2018, the Copernicus
module - a survey protocol specifically tailored to EO - was introduced
[d’Andrimont et al., 2021], providing a unique set of polygons with
homogeneous land cover (covering up to 0.52 ha) with land cover as well as
crop specific information. Another source of in-situ data is the parcel-level
crop declarations by farmers in the context of the CAP, which are increasingly
publicly available. Here, we bring these elements together to create the first
validated crop type map for the EU at 10-m resolution. After reviewing the
pertinent literature in detail, we present the specific objectives of this
study.

###  1.1 Crop mapping with Sentinel-1

The SAR S1 constellation provides minimum consistent 6-day revisits over the
EU since 2016. The negligible dependence of microwave backscatter on
atmospheric conditions provides consistent calibrated time series that are
particularly well-suited for automated processing and machine learning. The
sensitivity of microwave backscattering to crop canopy structure [Dobson &
Ulaby, 1981, Ulaby et al., 1981, Veloso et al., 2017, d’Andrimont et al.,
2020a, Meroni et al., 2021], along with its sensitivity to soil surface
structure and moisture content [Dobson & Ulaby, 1981] makes it an ideal data
source for crop mapping. One of the overall principles in electromagnetic
scattering models of crop canopies is that (back)scattering depends on leaf
size and shape, relative to the microwave wavelength, their orientation in
space, relative to the polarization plane of the microwaves, and the water
content of those leafs. Leaf geometry and orientation determines, to a large
extent, the preferential (back)scattering direction. This explains the large
differences in vertically structured crop canopies (e.g. cereals) versus
broad-leaf crops (e.g. sugarbeet, potatoes). It also implies that
backscattering changes are expected for phenological stages of the crop that
cause structural change in the canopy (ear formation in cereals, fruit
formation after flowering). The leaf water content relates to the effective
dielectric properties of the canopy, which determines (back)scattering
intensity. Vigorous, well-watered crop canopies are more effective scatterers
than stressed or senescent crops, which scattering behavior may be modulated
by simultaneous change in leaf orientation. Scattering of bare soil is
primarily dependent on surface roughness and soil moisture content, which has
parallels to canopy scattering because the relative size and orientation of
the surface structure is important, as well as row direction of the soil
cultivation and soil water content, which determines scattering effectivity
via the dielectric properties. It follows that scattering of partially
vegetated soil, e.g., at the crop emergence stage, is related to the ensemble
of canopy and soil scattering effects and that variation over time is related
to the relative contributions of each component. Polarization of the
microwaves determines the (Fresnel) reflection coefficients of the canopy and
soil material.

Synthetic Aperture Radar (SAR) has been used as a data source for crop type
mapping in different studies [McNairn & Brisco, 2004, Kenduiywo et al., 2018,
Clauss et al., 2018], as well as in synergistic combination with optical data
[Van Tricht et al., 2018, Gao et al., 2018, Kussul et al., 2018, Orynbaikyzy
et al., 2020, Chakhar et al., 2021]. Despite all the advantages of SAR,
relatively few studies have focused on crop mapping based on S1 only. Kussul
et al. [2017] and Van Tricht et al. [2018] derived a crop type map from a
combination of S1 with optical data over Ukraine and Belgium respectively.
Other local studies over the Chinese Fuyu City [Wei et al., 2019] and the
French Camargue region [Ndikumana et al., 2018], demonstrate the potential of
using S1 for crop type mapping. The consistent availability of S1 observations
is particularly relevant in machine learning contexts. Such consistency is
difficult to achieve with optical Sentinel-2 data, due to unpredictable cloud
cover gaps in a multi-annual monitoring setting. Early results with the use of
S1 time series in a deep neural network approach [Lemoine et al., 2019]
demonstrated this successfully in a national EU crop mapping context.

###  1.2 In-situ data in the European Union

There is growing interest in using LUCAS in-situ data for land cover and land
use research [Pflugmacher et al., 2019, Weigand et al., 2020]. However, until
the recent harmonization of the five past LUCAS surveys by d’Andrimont et al.
[2020b], use of different LUCAS survey data remained difficult. In addition,
the LUCAS protocol was designed for EU-wide standardized reporting of land
cover and land use area statistics and not for Earth Observation (EO)
applications.

Past remote sensing studies that incorporated LUCAS core data have been
limited to statistical area estimates [Gallego & Delincé, 2010]. The LUCAS
sampling frame was used to assess availability of crowd-sourced pictures
potentially relevant for agriculture across the EU [d’Andrimont et al.,
2018b]. Little experience exists in using LUCAS data for large scale land
cover generation processes, especially on the usability of the LUCAS data set
as training data base for supervised classification approaches [Mack et al.,
2017]. The 2015 survey was used in several land cover and land use mapping
exercises. Close et al. [2018] provided a Sentinel-2 LUCAS-based
classification over southern Belgium in the context of Land Use, Land-Use
Change, and Forestry (LULUCF) monitoring. Pflugmacher et al. [2019]
demonstrated the potential of using LUCAS survey 2015 to map pan-European land
cover (13 classes) with Landsat data. Weigand et al. [2020] showed the
suitability of the LUCAS 2015 survey as reference information for high
resolution land cover mapping using Sentinel-2 imagery at the scale of Germany
(7 classes).

In 2018, a new LUCAS module (the Copernicus module) specifically tailored to
EO was introduced, see d’Andrimont et al. [2021]. A specific protocol was
designed to collect in-situ information with specific characteristics fitting
EO processing requirements. As a result, a total of 58,428 polygons are
provided with a level-3 land cover (66 specific classes including crop type)
and land use (38 classes) information. This represent a unique set of data
opening the possibility for applications with higher thematic detail as
compared to previous LUCAS surveys, such as crop type mapping.

###  1.3 Objectives

The overall aim of this study is to assess the potential of combining the
LUCAS Copernicus module with S1 data to generate an EU-wide crop type map at
10-m spatial resolution for crops in the year 2018. Beyond the production of
the map, this study aims to set a benchmark for continental crop type mapping
at 10-m spatial resolution. More specifically, we address the following
research objectives:

  * 1.

To highlight the untapped potential of the LUCAS Copernicus survey as training
data for land monitoring applications in general and crop type mapping in
particular;

  * 2.

To demonstrate the value of S1 data for continental crop type map production;

  * 3.

To evaluate the effectiveness of various S1 indices and time periods for the
discrimination of specific crop types;

  * 4.

To benchmark accuracies against independent LUCAS core point data and EU
farmers’ declarations at parcel level;

  * 5.

To determine the crop specific evolution of mapping accuracy throughout the
growing season relevant for prospective operational tasks;

  * 6.

To assess crop surface areas derived from the 10-m crop type map against
reported EU statistics at subnational level.

##  2 Materials and Methods

The study area is the EU-28 as in 2018, covering $4,469,169$ km2 (Fig. 2). A
10-km buffer was applied around the EU-28 to avoid mapping issues at the
border.

Fig. 1 provides a flow chart illustrating the general approach and specific
methodological steps of the study. First, the input data is detailed: S1 in
section 2.1 and in-situ data in Section 2.2. Then, Section 2.3 defines the
legend used for the study. While Section 2.4 sets out how the features and the
parameters for the crop classification models are selected, Section 2.5
describes how the classification is applied at the continental scale. Finally,
Section 2.6 presents the three accuracy assessment approaches.

Fig. 1: General overview (references to section number are highlighted in
grey).  Fig. 2: The study area is the European Union 28 (EU-28). The study
area is divided in 2 strata based on a combination of biomes from Dinerstein
et al. [2017] as described in Section 2.4.3.

###  2.1 Sentinel-1 data

The S1 C-band operates at a central frequency of 5.404 Ghz in the microwave
portion of the electromagnetic spectrum which corresponds to a wavelength of
5.55 cm. S1 acquisitions over the European land mass are in the so-called
interferometric wave (IW) mode, which registers the backscattering of a
vertically transmitted microwave signal in a vertical and horizontal receiver,
creating a VV and VH polarized band, respectively.

Over Europe, S1 acquires data both in descending and ascending orbits, as it
does not rely on solar illumination (as opposed to optical sensors). This
leads to a doubling of the revisit cycle, though from two distinct sensor
viewing configurations due to different azimuth and look directions.
Furthermore, the S1 swath width is designed to guarantee gap free coverage at
the equator. At higher latitudes, this design leads to considerable overlap
between adjacent orbits, and an effective higher revisit, more than daily at
latitude above 55 degrees North.

####  2.1.1 Geocoded backscattering coefficients

Application ready S1 data are accessed on the GEE platform. On GEE, S1 data
are processed from the GRD Level-1 product with the S1 Toolbox to remove
thermal noise and to create geocoded and radiometrically calibrated
backscattering coefficients ($\sigma^{0}$) at 10-m pixel spacing.

Due to the side-looking nature of the SAR sensor, the incidence angle varies
over the swath in the range direction, for S1 IW mode between 25-40 degrees.
Normalization of the incidence effect is normally desired, and simple
corrections can be applied to convert $\sigma^{0}$ to $\gamma^{0}$. A more
robust approach would be to apply a correction that takes the local terrain
height variation into account, known as terrain flattening (Small [2011]),
which can also partially compensate for the look angle differences in
ascending and descending orbit acquisitions. However, terrain flattening is
not applied in the GEE processing workflow and our analysis is thus based on
$\sigma^{0}$ as most of the crops are typically on flat areas and the steep
slopes are masked out as described in Section 2.5.

To be ingested in the machine learning classification algorithm, a spatially
and temporally consistent time series needs to be derived from the S1 VV and
VH $\sigma^{0}$ scenes. In addition, the backscattering coefficient ratio
VH/VV (cross polarization ratio, CR) is computed from the VV and VH scenes.
Several studies highlighted the advantages of the CR for mapping and
monitoring of crops (Veloso et al. [2017], Meroni et al. [2021]). We proceeded
as follows to generate the required archive:

  1. 1.

Edge masking. The edge of each scene should be masked before the compositing.
This is done by masking groups of neighboring pixels with values lower than
-25 dB in the VV polarization (as described in d’Andrimont et al. [2018a]).

  2. 2.

Averaging over a 10-day period. $\sigma^{0}$ values are then averaged over
subsequent time periods of 10 days for each pixel and for all ascending and
descending acquisitions available in that period, separately for the VV and VH
polarizations. The averaged $\sigma^{0}$ value is converted to decibels (dB).

  3. 3.

Computing the cross polarization ratio. The CR is computed for each scene and
averaged to the same 10-days periods.

In this way, we generate a regularly timed, gridded, equal sized set of
temporal features for VV, VH and CR to use as input for the subsequent machine
learning runs (n = 36 10-day composites over the period 2018-01-01 to
2018-12-31), independent of the number of actual S1 acquisitions in the 10-day
period. The S1 data were processed in GEE and then downloaded.

###  2.2 LUCAS 2018 in-situ data

This section describes the LUCAS 2018 survey data that were used for training
the classification models and assessing the accuracy of the crop type map.

####  2.2.1 LUCAS 2018 survey

The LUCAS 2018 survey collected 97 variables at $337,854$ core points. Most of
the points surveyed fall in a homogeneous area for which the minimum mapping
unit is about 7 m2(a circle of 1.5 m radius). When the land cover is not
homogeneous, for example when it is composed of trees or shrubs interspersed
with grass, the scale of observation is extended to classify it. In these
cases, a systematic observation of the “environment” in the vicinity of the
point, which in LUCAS is called the extended window of observation, has to be
adopted. The extended window of observation expands to a radius of 20 meters
from the point (representing an area of 0.13 ha) for forest and shrublands.
Detailed information about the survey can be found in Eurostat [2018b]. The
land cover surveyed is classified according to a harmonized 3-level legend
system Eurostat [2018c]. Additionally to the core variables collected, other
specific modules were carried out on demand on a subset of points, such as (i)
the topsoil module and (iii) the grassland module. The LUCAS 2018 core data
are available in an harmonised open database in d’Andrimont et al. [2020b].

####  2.2.2 LUCAS Copernicus module

The LUCAS 2018 Copernicus module was applied to a subset of points to collect
the land cover extent up to 51 meters in four cardinal directions around a
point of observation, offering in-situ data compatible with the spatial
resolution of high-resolution sensors (see d’Andrimont et al. [2021] for the
open ready-to-use dataset). The LUCAS Copernicus dataset contains $63,287$
polygons at level-2. When filtering the data for which a level-3 legend is
available, $58,426$ polygons with a level-3 land cover (66 specific classes
including crop type) and land use (38 classes) are available. The minimum
mapping unit (MMU) of the in-situ data is 78.53 m² (i.e. a circle of 5-m
radius) as the Copernicus module survey is not executed for smaller areas. The
data from the LUCAS Copernicus module is used to train the classification
models.

###  2.3 Legend definition

The LUCAS survey legend is re-organised to serve the purpose of the study (for
details of the LUCAS legend classification see Eurostat [2018c] and
Supplementary material). As the objective of the study is to classify the main
EU-28 crop types based on LUCAS in-situ data, the class definitions differ
slightly from the LUCAS legend. The four vegetation classes of LUCAS
(B-cropland, C-woodland, D-shrubland and E-grassland) are organized here in
three main classes: arable land, woodlands and shrubland, and grasslands. The
arable class is further divided in 19 specific classes of crop types or crop
groups. The final legend is presented in Table 1.

The main differences with the LUCAS legend are the following. The woodlands
and shrublands classes are regrouped in a single class. The LUCAS cropland (B)
class is re-organized to keep the arable land classes separated from the
grasslands and permanent crops. First, the temporary grasslands (B55) are
grouped with the grasslands class (E). Second, the permanent crops (B70 and
B80) are grouped with the woody vegetation class. Third, the bare arable land
(F40) with an agricultural land use (U111/112/113) is regrouped with the
arable land classes. The grassland class encompasses grasslands from natural
and agricultural land uses.

Table 1: The legend of the EU crop map. For the two-phase hierarchical
classification procedure, the legend is divided into two levels: level 1 with
seven broad land cover classes, and level 2 containing 19 crop types or crop
groups.

Level 1 |  | Level 2 |  |  |
---|---|---|---|---|---
100 |  |  | Artificial land |  | A11, A12, A13, A21,
---
A22
200 |  |  | Arable land | See below |
| Cereals | 211 | Common wheat | B11 |
|  | 212 | Durum wheat | B12 |
|  | 213 | Barley | B13 |
|  | 214 | Rye | B14 |
|  | 215 | Oats | B15 |
|  | 216 | Maize | B16 |
|  | 217 | Rice | B17 |
|  | 218 | Triticale | B18 |
|  | 219 | Other cereals | B19 |
| Root crops | 221 | Potatoes | B21 |
|  | 222 | Sugar beet | B22 |
|  | 223 | Other roots crops | B23 |
|  | Non permanent
---
industrial crops
230 |  | Other non permanent
---
industrial crops
B34, B35, B36, B37 |
|  | 231 | Sunflower | B31 |
|  | 232 | Rape and turnip rape | B32 |
|  | 233 | Soya | B33 |
|  | Dry pulses,
---
vegetables and flowers
240 | Dry pulses, vegetables and flowers |  | B41, B42, B43, B44,
---
B45
| Fodder crops | 250 |  | Other fodder crops
---
(excl. temp. grasslands)
B51, B52, B53, B54 |
| Bare arable land | 290 | Bare arable land | F40* |
300 |  |  |  | Woodland and shrubland
---
type of vegetation
| B71-B77, B81-B84,
---
C10, C21, C22, C23,
C31, C32, C33, D10,
D20
500 |  |  | Grassland (permanent and temporary) | B55, E10, E20, E30 |
600 |  |  | Bare land and lichens/moss | F10, F20, F30, F40** |

*U111/112/113 (agriculture), **other than U111/U112/U113

###  2.4 Crop type classification

This section describes the training data (input reference data and
classification features) selection and the parametrization of the
classification models used for the continental crop type mapping.

####  2.4.1 Reference training data

The classifiers are trained with the in-situ information collected in the
LUCAS Copernicus module. The polygons corresponding to the LUCAS labels
mentioned in Table 1 are selected and reclassified according to the legend of
the study. In total, $58.178$ polygons from the $63.287$ Copernicus polygons
available are selected to train the classifier.

####  2.4.2 Stratification

To take into account climatic and ecological spatial gradients, the EU-28 is
stratified in two strata based on the main biomes defined by Dinerstein et al.
[2017]. One main continental northern stratum and one southern Mediterranean
stratum (see map in Fig. 2):

  * 1.

Stratum 1 (Str1): Temperate Broadleaf & Mixed Forests and Temperate
Grasslands, Savannas & Shrublands, Boreal Forests/Taiga and Tundra, Temperate
Conifer Forests

  * 2.

Stratum 2 (Str2): Mediterranean Forests, Woodlands & Shrub

More detailed stratification schemes (e.g. country- or a regional-level) have
been adopted in previous crop type mapping exercises in Europe [Defourny et
al., 2019, Wang et al., 2019]. However, this is feasible where crop type in-
situ data are abundant, e.g. provided by national crop data survey. In this
study, we had to face a trade-off between stratification detail and sample
size available for the resulting strata and we opted for a simple and broad
stratification.

####  2.4.3 Two-phase classification

For each stratum, a two-phase classification procedure is followed. The first
step distinguishes between five broad land cover classes: Artificial land,
Arable land, Woodland and shrubland type of vegetation, Grassland and Bare
land (Level 1 in Table 1). In a second step, the Arable land class is further
classified into 19 different crop types or crop groups (Level 2 in Table 1).
Therefore, a total of four supervised classification (2 strata x 2
hierarchical levels) models are trained.

For the supervised classifications, we trained Random forests (RF) models on
the S1 time series to map level 1 and 2 classes. RF is an ensemble machine
learning method known to be robust against multi-colinearity and overfitting
[Breiman, 2001] and currently is a standard classification algorithm in the
remote sensing domain [Belgiu & Drăguţ, 2016]. RF was reported to yield
classification accuracies comparable to more sophisticated algorithms such as
support vector machines, but with a much lower computational complexity
[Inglada et al., 2017]. RF models are also stable with respect to the choice
of parameters [Pelletier et al., 2016] and easy to implement, making them
suited for operational processing chains (such as Sen2-Agri [Defourny et al.,
2019]).

####  2.4.4 Features selection

In order to produce a single crop type map at the continental map, the most
significant features in term of S1 backscatter and time period are selected.
To do so, different combinations of the 10-day averaged S1 indices, and
different time periods from January to December are evaluated using the
training reference dataset.

The first part of the features selection evaluates, throughout the growing
season period, the progression of the overall accuracy (O.A.) of the
classifications and the F-score for the main crop types. The accuracy is
evaluated using hindcasting with monthly time step from January to December,
thus extending the analysed period beyond the end of the average European time
of harvest. For the two classification levels and in the two strata, RF models
are trained for twelve different time period using a 80/20 split of the
reference (polygon) training data. The resulting O.A. and F-score are
evaluated at the continental and the stratum level to select the best time
period. From the analysis presented in section 3.1, we consider the period
from January to July (included) as a good trade-off between accuracy and
timeliness. The period of January to end of July is used for all further
processing.

Second, the performance of the two S1 $\sigma^{0}$ (VV and VH), one index
(VH/VV), and their combinations are evaluated in terms of overall accuracy
over the period January to end of July (included) .

###  2.5 Classification at scale

A final map is produced at the scale of EU-28 using the best features defined
by the previous analysis. This final map derived with S1 information from
January to the end of July is referred to as the EU crop map in the
manuscript.

The RF models are trained for the two strata with the best features in term of
indices and time period.

A random search cross validation is used to define the best hyperparameters
for each of the four RF models (level-1 and level-2 for each strata). We are
tuning seven parameters (Supplementary Table S3) and define a grid of ranges,
and randomly sample from this grid of possible parameter values to perform a
3-fold cross-validation with 100 possible combination of values, totalling 300
fits. Among the seven parameters, two are considered as key parameters: the
number of decision trees (Ntrees) and the number of features to consider when
looking for the best split when growing the trees (Mtry) Belgiu & Drăguţ
[2016]. For Ntrees, we defined a range from 300 to 1200 with a step size of
100 and and for Mtry, we tested two options: the square root of the number of
features and the binary logarithm of the number of features.

The RF classification algorithms and the hyperparameter tuning is implemented
using the Python’s scikit-learn (Pedregosa et al. [2011]) packages
RandomForestClassifier and RandomizedSearchCV. The final hyperparameters used
for each RF model are summarized in the supplementary materials (Supplementary
Table S3).

The classification was performed at the continental scale on the JRC Big Data
Analytics Platform (BDAP) using an HTCondor environment (Soille et al.
[2018]).

The final map is further processed for distribution. The main focus of the map
being the arable land, areas located at an altitude above 1000m and on a slope
of more than 10 ∘ is masked out. The ALOS Global Digital Surface Model ”ALOS
World 3D - 30m (AW3D30)” is used to mask steep slopes. In addition, classes
that were poorly represented in the LUCAS Copernicus module and consequently
poorly classified (i.e. built-up, water, wetlands, and bare lands classes) are
masked out with auxiliary products. Built-up areas are masked with the JRC
GHSL European Settlement Map for 2015 at 10 m [Corbane et al., 2020, Sabo et
al., 2019]. Water is masked with the permanent water from the 2019 version of
the JRC Global Surface Water product[Pekel et al., 2016]. Then, a number of
classes are masked with the Corine Land Cover 2018 map [EEA, 2018] (inland and
coastal wetlands, bare land, moors and heathlands) see Supplementary Table S4
for details.

The EU crop map is re-projected to the Lambert azimuthal equal area
(ETRS89-LAEA, EPSG:3035) projection for the analysis.

###  2.6 Accuracy assessment

Three approaches to estimate the accuracy of the EU crop map are tested. The
first approach is taking as reference data the high quality LUCAS core points
not surveyed by the Copernicus module. The second approach is comparing the EU
crop map with a selection of GSAA data based on farmers’ declarations. The
third approach compares the area of several main crops obtained from the EU
crop map to the corresponding official subnational statistics.

####  2.6.1 LUCAS 2018 core points

In the first approach, validation was focused on all the vegetation classes
(i.e. crop types, woodland and shrubland, and grassland). To obtain data
relevant for the assessment, LUCAS core points were filtered to keep only high
quality information. While the LUCAS core points were not initially designed
for EO analysis, Weigand et al. [2020] showed that the data can be used in
land cover applications although no detailed analysis for crop type mapping
applications were done to date. We therefore defined four criteria to select
only high-quality points suitable for crop type mapping applications: keep
only direct and in-situ observations, remove parcels smaller than 0.1 ha and
only keep data with an homogeneous land cover class. In addition, the subset
of (63,364) core points also surveyed for the Copernicus module and used for
training were not included in this validation set. This screening resulted in
a total of 87,853 LUCAS core points covering the selected vegetation classes
(spatial distribution overview in Figure Supplementary Fig. S4).

The reference data is used in combination with the EU crop map to report the
confusion matrix, the overall, user, and producer accuracy. LUCAS is a two-
phase sampling scheme. The LUCAS 2018 survey followed a stratified random
sample design [Scarno et al., 2018]. The first-phase is a systematic sampling
scheme and we treat the second-phase, the 2018 samples, as collected under a
stratified random sampling, due to the large amount of collected data.
Accordingly, the accuracy metrics are reported based on the estimated area
proportion of correctly and mis-classified classes according to the equations
of Olofsson et al. [2014] for a 95% confidence level. The accuracy metrics are
provided first for a set of grouped crop classes (Table 1) and second
considering all the classes.

####  2.6.2 Comparison with GSAA

In the second approach, a subset of the classes are assessed and compared
wall-to-wall with vector parcels from the GSAA for specific regions. The GSAA
refers to the annual crop declarations made by EU farmers for CAP area-aid
support measures. The electronic GSAA records include a spatial delineation of
the parcels. A GSAA element is always a polygon of an agricultural parcel with
one crop (or a single crop group with the same payment eligibility). The GSAA
is operated at the region or country level in the EU-28, resulting in about 65
different designs and implementation schemes over the EU. Since these
infrastructures are set up in each region, at the moment data is not
interoperable, nor are legends semantically harmonized. Furthermore, most GSAA
data is not publicly available, although several countries are increasingly
opening the data for public use. In this study, six regions with publicly
available GSAA are selected representing a contrasting gradient across the EU
(Table 2). The selected regions along with the acronym used in this study are:
bevl2018 (Flanders in BE), dk2018 (DK), frcv2018 (Centre - Val de Loire in
FR), nld2018 (NL), nrw2018 (North Rhine-Westphalia in DE)) and si2018 (SI).
The six selected regions with GSAA data have comparable sizes, ranging from
401,833 parcels in Flanders to 659,302 parcels in the Netherlands,
facilitating the comparison. As each national GSAA records set can contain
more than 300 distinct classes, only the parcels for crop classes covering at
least 1% cumulative GSAA area of a given region were selected (see
Supplementary Fig. S2 for the distribution and Supplementary Fig. S3 for the
count distribution). Excluding marginal classes with percentage cover smaller
than 1% results in a coverage of the total area ranging from from 84%
(Denmark) to 92% (Slovenia) of the total parcels for each respective GSAA. In
total, 3,149,334 parcels were extracted covering 8.21 Millions of hectares. Of
our pixel-based classification predicted classes within each GSAA parcels, we
extracted the class mode and compared it to the declared crop, mapped to the
LUCAS legend (semantic mapping in Supplementary Table S10). See also the
Discussion Section 4.2 about semantic harmonization of GSAA.

For each of the selected parcels, the majority predicted class was computed
for the enclosed pixel ensemble and then compared to the declared crop label,
mapped to the LUCAS legend. Then Producer Accuracy (PA) and User Accuracy (UA)
were computed. The extraction of the data was implemented with the Python
”Checks by Monitoring” (CbM) package (https://pypi.org/project/cbm/). This
package provides Big Data Analytics routines for parcel crop monitoring using
the Sentinel sensors and cloud compute infrastructure.

Table 2: Summary of GSAA data used in the study. Region |  Description |  Area (Mha) |  Area Ratio of the total (%) |  Parcels (#)
---|---|---|---|---
bevl2018 |  Flanders (Belgium) |  0.59 |  85 |  409,884
dk2018 |  Denmark |  2.23 |  84 |  445122
frcv2018 |  Centre - Val de Loire (France) |  1.95 |  85 |  336,212
nld2018 |  Netherlands |  1.62 |  87 |  659,302
nrw2018 |  North Rhine-Westphalia (Germany) |  1.39 |  91 |  613,012
si2018 |  Slovenia |  0.43 |  92 |  685,802
Total |  |  8.21 |  - |  3,149,334

####  2.6.3 Comparison with reported subnational statistics

In the third and final validation approach, the area of the crop obtained from
the EU crop map is compared to the official subnational statistics for the
main crops in EU-28. In order perform do the comparison, subnational area data
is obtained from the ”Area (cultivation/harvested/production) (1000
ha)”222From the tab of the APRO_CPSHR table reporting on crop production in EU
standard humidity. Data is downloaded from the Eurostat Data Browser portal
(https://ec.europa.eu/eurostat/databrowser/view/APRO_CPSHR/default/table).
Detailed description along with metadata can be found in
https://ec.europa.eu/eurostat/cache/metadata/en/apro_cp_esms.htm. by NUTS 2
regions. The legend of the Eurostat crop classes is then semantically matched
with the EU crop map legend as described in Supplementary Table S17.

The areas estimated from the EU crop map are obtained with a zonal histogram
per administrative zone and then compared to the one reported by Eurostat at
the finest spatial detail available (i.e. NUTS2 regions for most of the EU-28
except for the UK and DE where only NUTS1 is reported). For each crop with
enough data available, the Pearson correlation coefficient is calculated.
Additionally, the relative percentage difference in area is computed for each
administrative region and mapped, highlighting the difference between the
reported and here estimated surface area for each crop. The distribution of
these differences for each crop is finally summarized to draw conclusion at
the crop level.

##  3 Results

###  3.1 Features selection

The reference data consists of S1 time series retrieved from $58,178$ polygons
derived from the LUCAS Copernicus polygons corresponding to 2,296,889 S1 10-m
pixels (Supplementary Table S1). The training data are distributed into 23
thematic classes and two geographical strata (Str1 and Str2) as described in
the methodology. Each of the pixels thus have time-series of synthesized
10-day - or 36 dekads per year - with VV, VH and VH/VV data. The VV and VH
temporal $\sigma^{0}$ signatures for the 12 main crops are averaged per
stratum and presented in Fig. 3 for the crop growing season.

Fig. 3: Evolution of VV and VH backscatter for the growing season for the 12
main crops. The value are averaged per stratum. The ribbon around the line
corresponds to one standard deviation.

Fig. 4 summarizes the overall accuracy through time. An initial steep increase
in accuracy is observed from February to June when only very small increases
are achieved by adding more data. By the end of July the accuracy reaches a
value of 78.8% (Fig. 4).

The dynamic of the Fscore through time for each crop separately (Fig. 5) shows
expected patterns. The rape and turnip rape Fscore is booming when the crop is
flowering in May (Fscore 0.86) indicating that the model could identify them
quite early in the growing season (March). The figure also shows that common
wheat has a steep increase in Fscore in April which coincides with the
reproductive growth period. Grain maize, a summer crop which is harvested
later in the season than winter wheat and rape and turnip rape, is reaching
the plateau in Fscore at the beginning of July. Interestingly, the Fscore for
sugar beet continuous to increase until October which is logical as sugar beet
is harvested in late autumn in Europe. Finally, as expected, we observe a very
low Fscore value for mixed classes (such as other fodder crops).

As detailed earlier, and considering also any future operational application,
we opted for using time-series from January until the end of July for further
processing (evaluation of indices and final training of the RF algorithms).

Fig. 4: Overall accuracy at a monthly time step for the crop type level and
the two strata combined  Fig. 5: Evolution of The F-score through time for the
12 mains crops over the EU-28.

From the comparison (Table 3) of the overall accuracy for the polarization
backscattering coefficients (VV and VH), the cross-ratio index (VH/VV) along
with their combinations, we observe that the combination of VV and VH gives
the highest accuracy (79.89 %) and is selected for the EU crop map. The
training data for the EU crop map therefore consists of 10-day time series of
VV and VH backscatter coefficients from 1st of January to 31st of July (22
dekads).

Table 3: Overall accuracy for S1 indices (dates are fixed from Jan to end of July). Indice | Overal Accuracy
---|---
VV | 71.96
VH | 72.70
VH/VV | 50.90
VV and VH | 79.89
VV and VH and VH/VV | 77.91

###  3.2 Crop type over EU

####  3.2.1 Visual assessment

The EU crop map presented in Fig. 6 is representative of a number of crop
types and main vegetation classes and covers 91 Mha of cropland (9,174 M 10 m
pixels). The map is available for download and visualization (see section Data
dissemination).

Fig. 6: EU crop type map for 2018.

The resulting map is consistent over different landscapes at the level of the
main land cover classes. A naive area estimation by pixel counting for each
class is provided in Supplementary Table S5. Woodlands and shrublands are
regrouped in a single class covering more than 2 M km2; i.e. more than half of
the area mapped because it is a very broad class covering broadleaved and
needleleaved natural and managed forests as well as permanent crops. The
arable land class has an extend of about 0.9 million km2. The grassland class,
grouping the permanent and temporary grasslands covers 0.7 million km2. The
woodland and shrubland class covers forests patches but also tree lines and
hedges in agricultural landscapes.

Over the EU-28, the three most detected crop types in term of area are common
wheat, maize and barley. Despite using a pixel based classification approach,
separated parcels can clearly be recognized. The difference in the size of
parcels is well captured, as illustrated in Fig. 7, by comparing Fig. 7 (b)
Austria and the Czech Republic Fig. 7 (c). The crop types are identified over
the EU without a priori knowledge of country practices. The ability of the map
to represent different agricultural landscapes is well illustrated in the
examples of Fig. 7 for North of Orleans, France (a) where the diversity of
crop types cultivated in the area are distinctly classified (wheat, barley,
sugar beet, maize, potatoes, rape and turnip rape, dry pulses). North West of
Valladolid, Spain Fig. 7(c) the major crops cultivated in Castilla y Leon:
barley and sunflower are clearly detected together with some parcels of wheat,
dry pulses and bare arable land. In Romania, North East of Bucharest Fig. 7
(d), the diversity of small and large parcels of wheat, maize, rape and turnip
rape, sunflower and barley are well captured.

Fig. 7: EU crop map for (a) Orleans (France), (b) border between Austria and
Czech Republic, (c) Castilla and Leon (Spain), (d) (Romania).

####  3.2.2 F-score per crop type and administrative unit

As agricultural practices vary across Europe, in terms of crop type cultivated
and crop calendar, we expect our mapping approach using a minimal
stratification to represent the field reality with different fidelity in
different areas. To investigate this aspect, a first assessment of spatial
discrepancy for arable land class accuracy is done by calculating the F-score
at country level, when enough LUCAS points were available, for the six main
crops over EU, i.e. common wheat, barley, maize, sugar beet, sunflower, rape
and turnip rape. This allows to quickly grasp how well the classification is
performing spatially as highlighted in Fig. 8 (see Supplementary Table S9 for
number details). The F-score for most crops is higher in the northern part of
the study area than in the southern part. The F-score for common wheat is
about 0.7 for Belgium, Bulgaria, Czech republic, Denmark, France, Luxembourg
and Slovakia. Maize reaches high scores in the North (above 0.8 for Germany,
Czech Republic, Hungary and Slovenia) and rape and turnip rape have an F-score
higher than 0.9 in Austria, Czech Republic, Germany, Lithuania, Luxembourg,
Latvia, Poland and Slovakia.

Fig. 8: The F-score per country for the 6 main crops in Europe.

###  3.3 Accuracy Assessment

####  3.3.1 LUCAS core points

The accuracy of the EU crop map is evaluated using $87.853$ points selected
from the LUCAS 2018 survey. An overall accuracy of 76.1% (Supplementary Table
S2) is achieved over the EU when considering the 19 crop type classes with the
woodland and shrubland and grassland classes. An accuracy of 80.3% (Table 4)
is achieved when grouping the crops by the main crop type classes (groups
defined in the Table 1). The woodland and shrubland class is well
discriminated from the rest (PA of 97% and UA of 83%). A confusion is observed
between the grasslands class and the woodland and shrubland class, as
highlighted by a PA of 58% for grasslands indicating an omission error. The UA
of grassland is 82% indicating a commission error for grassland and again a
confusion between the grassland and woodland and shrubland class. The pixels
mapped as woodland and shrubland instead of grassland and vice versa could be
linked with the class definition of woodland and shrubland including open
tree/shrub cover canopy (10%). In terms of land cover some classes of
grasslands are very similar to open canopy class of woodland and shrubland.
The point information provided by the LUCAS core dataset is also not the most
suitable to assess more complex land cover classes characterised by
discontinuous canopies.

Regarding arable land, cereals are well discriminated, reaching a producer
accuracy (PA) of 84% and user accuracy (UA) of 72%. Omission errors for the
cereals class are mainly occurring with the woodland and grassland classes
while commission errors are happening mainly with grasslands. Root crops have
a lower PA of 59%, with many points labelled as root crops being mapped as
cereals instead. However the root crops present a high UA of 90% showing
little commission errors with the other classes. The non permanent industrial
crops have high PA (89%) and UA (79%) with good discrimination of sunflower
and rape and turnip rape. The fodder crops class including cereals and
leguminous cultivated for fodder is not well discriminated and is confused
with the cereals and grassland class (PA of 7% and UA of 29%).

Regarding specific crop type (Table 5), the LUCAS accuracy assessment
highlights a difference between the northern and the southern stratum with an
OA of respectively 78% and 70.8%. The overall best-performing crop classes are
rape and turnip rape, sunflower and maize. In the northern stratum, common
wheat, maize, sunflower and rape and turnip rape have PA above 80% but the
value of their UA is lower, indicating commission errors. The commission error
is specifically high for the common wheat.

Table 4: Confusion matrix for crop type group class for the EU-28. The values
represent weighted area UA Users accuracy PA Producer Accuracy.

|  | Reference class (LUCAS point)
---|---|---
| Map Class | 210 | 220 | 230 | 240 | 250 | 290 | 300 | 500 | UA (%) | SE (%) | OA(%)
210 | Cereals | 0.13 | 0.003 | 0.002 | 0.002 | 0.005 | 0.009 | 0.002 | 0.026 | 72.3 | 0.6 | 80.3
220 | Root Crops | 0 | 0.006 | 0 | 0 | 0 | 0 | 0 | 0 | 90.1 | 2 |
230 | Non permanent industrial crops | 0.001 | 0 | 0.024 | 0.002 | 0 | 0.001 | 0 | 0.001 | 79.5 | 6.2 |
240 | Dry pulses, Vegetables and Flowers | 0 | 0 | 0 | 0.002 | 0 | 0 | 0 | 0.001 | 43.6 | 4.4 |
250 | Fodder Crops | 0.001 | 0 | 0 | 0 | 0.001 | 0 | 0 | 0.002 | 28.8 | 3.3 |
290 | Bare Arable Land | 0.001 | 0 | 0 | 0 | 0 | 0.006 | 0.001 | 0.003 | 50.3 | 2.7 |
300 | Tree and Shrub Cover | 0.01 | 0 | 0 | 0.001 | 0.004 | 0.004 | 0.483 | 0.077 | 83.4 | 0.4 |
500 | Grassland | 0.01 | 0 | 0 | 0 | 0.009 | 0.001 | 0.013 | 0.151 | 82.1 | 0.5 |
| Producer accuracy (%) | 84.4 | 58.6 | 89.3 | 23.6 | 6.9 | 25.7 | 96.9 | 58.1 |  |  |
| Standard Error (%) | 0.8 | 3 | 1.3 | 3.9 | 0.8 | 1.7 | 0.1 | 0.6 |  |  |

Table 5: Weighted area accuracy metrics, overall accuracy for the crop types classification (level-2) and producer and user accuracies for the main crop type classes (for the whole study area and for the two strata) | All | stratum 1 | stratum 2
---|---|---|---
class | P.A. | U.A | P.A. | U.A | P.A. | U.A
common wheat | 78.2 | 49.6 | 82.5 | 49.9 | 44.5 | 46.0
durum wheat | 25.9 | 49.7 | 1.8 | 92.3 | 45.2 | 49.0
barley | 53.8 | 51.6 | 50.0 | 57.8 | 67.2 | 44.2
maize | 84.0 | 58.5 | 87.6 | 58.3 | 22.7 | 66.7
potatoes | 37.3 | 73.5 | 39.8 | 74.1 | \ | \
sugar beet | 57.1 | 75.0 | 57.9 | 75.0 | \ | \
sunflower | 86.7 | 62.2 | 90.7 | 72.0 | 64.3 | 32.4
rape and turnip rape | 82.7 | 80.2 | 83.5 | 80.2 | 17.9 | 78.9
Overall accuracy | 76.1 | 78.0 | 70.8

####  3.3.2 GSAA

The LUCAS core points are giving a first evaluation of the accuracy of the
crop type at the continental level. A more detail assessment is done with the
GSAA data for specific countries or regions. For each region, the classified
crop of each parcel from a crop representing an area greater than 1% of the
region is compared to the one from the GSAA. After removing the grassland and
woodland classes the confusion matrices are computed. The confusion matrices
along with the PA and UA for each region are presented in Supplementary
Material see (Supplementary Table S11 for bevl2018, Supplementary Table S12
for dk2018, Supplementary Table S15 for nld2018, Supplementary Table S16 and
Supplementary Table S13 for si2018). The PA and UA for the main crops are
summarized in 9(a) and 9(b) respectively.

The analysis shows very promising results for the detection of common wheat,
barley, maize, sunflower and rape and turnip rape in all the regions analysed.
Rape and turnip rape are detected without almost any confusion in the three
regions where it is present (dk2018, frcv2018 and nrw2018), reaching PA above
98% and UA above 97%. It is worth noting that common wheat and barley have
much better accuracies for both PA and UA compared to the LUCAS points based
assessment, with the exception of si2018. Except for nrw2018 and si2018,
common wheat has a PA above 80%, while the UAs are above 92% in all regions
except si2018. In nrw2018, the most common confusion for common wheat are
omission error towards barley and triticale classes. On the other hand any
parcels of triticale in GSAA are correctly mapped as triticale. The confusion
of common wheat with triticale is due to the similarity of the two crops, both
in general crop structure and seasonality. In si2018, omission errors are
observed towards barley and the other cereals class. Barley is present in the
six regions with a PA above 92% except for dk2018 (88%) and nld2018 (82%). In
Denmark, this is explained by a confusion between common wheat and barley
while in nld2018 the confusion is mainly with maize. The UA for barley are a
bit lower than the PA value but still above 80% for bevl2018, dk2018 and
frcv2018. Both in nrw2018 (UA 76%) and nld2018 (UA 64%), the main commission
error is due to a confusion with common wheat. Sugar beet presents very
promising results in bevl2018 (PA 97% and UA 93%) and nrw2018 (PA 84% and UA
85%). In bevl2018, dk2018 and nld2018 where UA are lower, the confusion is
mainly with maize and to a lesser extent with potatoes. The class potatoes,
presents in four regions, shows high PA (91% in bevl2018, 96% in dk2018, 95%
in nld2018 and 94% in nrw2018) and relatively low UA. In nrw2018, dk2018 and
bevl2018, the UA (respectively 47%, 54% and 50%) of potatoes is explained by
commission errors with maize. However, the UA of maize is showing little
commission errors (99%). In nld2018, confusion is happening with maize and
sugar beet and probably linked to the time period made available to the
classifier. Maize and sugar beet maturity phases are reached in autumn,
between September and November. A better discrimination would thus be expected
later in the season.

(a) PA

(b) UA

Fig. 9: User and Producer accuracy obtained from the comparison of the EU crop
map and the parcel data from the GSAA. The detailed confusion matrices are in
Supplementary Material.

####  3.3.3 Subnational statistics

The crop area data is extracted and compared for 220 administrative regions
covering most of the EU-28. The matching with the reported statistics was done
at the NUTS2 level(Supplementary Table S17). For DE and UK no data is
available at NUTS2 level, the comparison was thus done at NUTS1 level for
which statistics are available. Fig. 10 shows how the area estimated by the EU
crop map compared with the area reported by Eurostat. The calculated Pearson’s
correlations (r) range from 0.93 for potatoes and rye to 0.99 for rape and
turnip rape. Additionally, the relative difference in area (%) is computed for
each region and presented in Supplementary Fig. S5 (Supplementary Fig. S6
provides the histogram of the differences for each crop). The maps show that
while for common wheat and maize the EU crop map estimations are generally
higher compared to Eurostat without a distinct spatial pattern in observed
differences, this is not the case for other crops, whose overestimation and
underestimation are more localised. Barley for example is underestimated in
the center of Europe but overestimated in both the north and the south of
Europe. The distribution of the difference shows that common wheat and maize
are underestimated in our EU crop map compared to Eurostat. Barley is under-
estimated in the northern part and over-estimated in the southern part. The
other main crop area estimations are close to the official Eurostat
statistics.

Fig. 10: The areas reported by Eurostat at NUTS2 level (except for UK and DE
at NUTS1) are compared with the area retrieved from the EU crop map. $R$ is
the Pearson correlation coefficient, $R^{2}$ is the the coefficient of
determination.

##  4 Discussion

###  4.1 Timeliness and accuracy of the crop type classification

The final map presented in this study is built with S1 observations from
January to end of July. The overall accuracy at the end of July across all
crops is relatively high at 76%. As the season progresses, discrimination of
crop types and parcels keeps improving as shown in Fig. 11. However, for an
operational application specific crops can be mapped earlier during the season
depending on the specificity of the region and requirements regarding accuracy
for specific crop types. The timing when the F-score is highest for each
specific crop is linked to canopy structure and phenological stage as these
affect the backscattering signal. For in-season applications, a trade-off can
be found between the required level of accuracy and timely map production.

While covering only 2018, the current study shows potential for developing in
season prediction of crop type and confirms the observations of [Veloso et
al., 2017, You & Dong, 2020] at the EU scale. A key element is that the S1 SAR
signal is not affected by atmospheric perturbation. Any acquisitions can thus
be used for crop type mapping. Although dependent on year to year phenological
development, findings regarding the best timing for specific crop types are
likely to be valid for future studies.

Supplementary Fig. S7 shows that the time series of VV and VH backscatter for
specific crop types have a similar shape in the northern and the southern
stratum but often appear shifted in time. This relates to phenological
development that occurs later when going northward. As a consequence, the
F-score for the same class may peak at different times as revealed by the
analysis of the temporal evolution of the F-score in the two strata.

Fig. 11: Crop type mapping results for different time periods, considering the
data from January to respectively April, May, June, and July (the EU crop map
presented in this study) in South East Germany.

In the northern stratum (Supplementary Fig. S1), the F-score reaches a plateau
at the end of June for common wheat, rape and turnip rape. Other cereals (rye,
barley and maize) can be predicted with a high F-score at the end of July,
even if the F-score keeps increasing slightly until the end of the summer. The
F-score for sunflower, potatoes and other fodder crops reaches a plateau one
month later, at the end of August, while sugar beet reaches the plateau in
September, in agreement with its longer growth cycle that extends into autumn.
Winter wheat and rape and turnip rape that are already established in the
field at the beginning of the year, present a structure, reflected by the VV
and VH backscatter, that is already different for the other classes earlier in
the year.

Similar results were found in other studies exploring the potential of S1 for
crop type monitoring in northern European countries. For Belgium, Van Tricht
et al. [2018] illustrates that while winter cereals were already well
discriminated at the end of June, end of August was more appropriate for
potatoes, maize or sugar beets. For France, Veloso et al. [2017] showed that
S1 backscatter clearly separates winter wheat and rapeseed between March and
July during the tillering and senescence stages and distinguishes maize,
soybean and sunflower during the heading/flowering phase between June and the
end of August.

In contrast, the highest accuracies are achieved earlier in the Mediterranean
stratum. Already at the end of June, plateaus in F-score are reached for the
accuracies of wheat (common and durum wheat), rape and turnip rape, other
fodder crops, and the mixed class ”other non permanent industrial crops”
(including e.g. cotton, fibre and oleaginous crops). At the end of July, the
plateau is reached for barley, sunflower and root crops while it is reached
end of August for maize. The earlier time at which the highest accuracies are
achieved in the southern stratum can be explained by typical timings of the
growing season in the two strata. As shown in (Figure S4) Meroni et al. [2021]
there is latitudinal gradient in the timing of the growing season with earlier
growing seasons in the south of Europe. This gradient justifies the observed
lag in achieving the best performances in the two strata

To assess the accuracy of the EU crop type map produced at the end of July,
three different and complementary approaches were presented in the Section
3.3. We discuss here the pros and cons of each.

The first one using high-quality LUCAS points (Section 3.3.1) enables an
accuracy assessment for the whole continental area but on the other hand the
protocol to collect the in-situ information is not designed for EO
applications and we filtered out ”low-accuracy” points, working only with a
subset of the 2018 survey. In addition, the point information is difficult to
reconcile with the assessment of crop type classes that present spatial
heterogeneity in a small area and especially where only few acquisitions
contribute to the 10-day average (e.g. in the South). Speckle filtering, not
used in the present study, may attenuate such undesired heterogeneity.

The information about the crop type is then assessed more precisely with the
GSAA (Section 3.3.2). The main differences between the LUCAS point based and
GSAA parcel based accuracies are due to the parcel aggregation of the pixel
classification within the GSAA parcel, which uses a majority filter. In this
way, the analysis is not deteriorated by possible geometric inaccuracies that
can affect the LUCAS points. In addition, speckle-induced variation affecting
the pixel classification result within the parcel is removed. However, the
GSAA assessment is limited to specific areas in the northern part of the
study, due to a lack of openly accessible GSAA sets for 2018.

Finally, the comparison with the official statistics (Section 3.3.3 ) has the
obvious advantage of covering the whole area of interest but the drawback of
being a rather coarse evaluation of the classification as the comparison with
official statistics can only be performed at some aggregate administrative
spatial level.

Useful observations can be derived from the comparative analysis of of the
three complementary level of accuracy assessment. For instance, some specific
errors are detected with all approaches.

From the comparison with official statistics (Fig. Supplementary Fig. S5 and
Supplementary Fig. S6) we observe an overestimation of common wheat in the
northern part of our study. Similarly, for common wheat, an important
commission error with other cereals classes is depicted by the LUCAS points
assessments. The same confusion (common wheat versus barley and triticale) is
reported by the GSAA assessment even with a smaller severity.

An over-estimation in the northern stratum is also observed for maize. This is
consistent with what is observed in the accuracy assessment based on the LUCAS
point(a low UA associated with an important commission error). In contrast,
this is not reported in the GSAA assessment where the high UA of maize
illustrates a low commission error for that class. Contrastingly, an under-
estimation of maize is observed in the southern part of our study area. This
is in agreement with the omission errors reported in the LUCAS assessment. We
however lack of the GSAA comparison in the South to confirm this observation.

While barley is over-estimated in the southern part of the study, it is under-
estimated in the northern part according to the official statistic assessment.
The underestimation of barley in the North is in agreement with the LUCAS
point assessment reporting low PA and UA. In contrast, the GSAA assessement
reports PA are above 80% in all GSAA analyzed, not confirming an omission
error. An hypothesis is that the GSAA assessment filters the pixels that are
not yet detected as barley at the parcel level. The overestimation of barley
in the South is confirmed by the LUCAS point assessment.

The confusion observed at the level of the study area with the LUCAS points
assessment (Table 4) between the cereals and the root crops and for the GSAA
between sugar beet and maize and potatoes is directly related to the time
period (January to end of July) selected for EU crop map. The evolution of the
VH backscatter coefficient in time Supplementary Fig. S7 illustrates what is
also visible from the F-score Supplementary Fig. S1, that the discrimination
between broad leaf crops, cereals and maize increases from June onwards in the
Northern stratum. Extending the period of a few month would alleviate these
problems.

###  4.2 Recommendation for an operational service

More than 6 years after the successful launch of the first Copernicus
satellite, S1 A, Europe still lacks access to a full, free and open archive of
Level-2 Copernicus Application Ready Data for the S1 (A and B) sensors
(S1-CARD). Whereas Sentinel-2 Level-2 data is produced as a routine ESA
output, this is not the case for S1. Level 2 S1-CARD generation is a required
expensive processing task that should be done once with the results made
available to the community.

The scale of this study requires the use of advanced cloud computing solutions
closely coupled to the massive on-line data archives of Sentinel data. The
requirements for such cloud solutions are that they provide access to
application ready data (ARD) and a programming interface that allows the
application of advanced machine learning algorithms, for instance, in support
of supervised classification, as was applied in this study. GEE is currently
the only platform that provides access to consistently processed S1 ARD
(backscattering coefficients only, for now), which is processed with a
standard recipes using the open source SNAP s1tbx software333S1TBX - ESA
Sentinel-1 Toolbox - Documentation, http://step.esa.int.. Nevertheless,
although GEE offers a number of routines for supervised classification
(including RF used in this study), it has some limitations in the
hyperparameter tuning along with some processing issues when the model is
applied to very large areas. This is why the pre-processing of the S1 data
into 10-day temporal synthesized mosaics was done in GEE while the results
were downloaded on a local facility to perform the classification. For this
study, this represented about 10 TB of data. This data transfer is clearly
sub-optimal and, in addition, while GEE is free to use for non commercial
application, the download of large amount of data has to be done via a paid
Google Cloud account. In our study, we have processed the annual volume of
data for 2018 in a post-hoc fashion (i.e. the classification was done off-line
after the growing season). However, with the effective use of cloud based
solutions, our method can be run in quasi-real time, for instance as a
combination of transfer learning with early season in situ collection and GSAA
for training and testing. Cloud based capacities allow integration of daily
acquisitions in 10-day compositing with automatic re-run of the ML
classifiers. Deep learning approaches that learn to extract discriminative
temporal information from the data, such as convolutional Neural Networks and
Recurrent Neural Networks could provide a more robust alternative to RF and
should be considered for further studies.

Finally, it is interesting to reflect on the actual utility of a EU crop map
against other potential sources. We in fact acknowledge that there would be no
need for a post-harvest crop map with 70-85% accuracy for only a limited set
of major crops if the annual GSAA declaration set is available in July, with a
typical accuracy of 90-95% (for a far more significant set of crops). However,
so far, most of the EU Member States’ GSAA are not openly accessible and, when
this is the case, they are usually available after the growing season (the
Netherlands being an exception, with a preliminary version published in July
of the current year). Recently, the Integrated Administration and Control
System (IACS) spatial data sharing initiatives444Art. 65 of the Proposal for a
Regulation of the European Parliament and of the Council, COM(2018) 393
final). is promoting EU member states to share their IACS data within the
infrastructure for spatial information in Europe (INSPIRE) portal555See the
discovery portal https://agridata.ec.europa.eu/extensions/iacs/iacs.html and
see Toth & Milenov [2020].. The expected outcome of this new regulation is the
expanded public availability of GSAA.

While LUCAS is performed every three years, the GSAA data are collected every
year in Europe, making them and ideal source of ground data for crop
classification training and validation. Beside the legal and technical
challenge to make the data open access already discussed, the legend semantic
harmonization is key to use in classification approaches across different
regions. While the semantic matching was done for this specific study, a
common semantic model would be very useful to integrate GSAA in a more
detailed EU-wide classification context.

Besides LUCAS and GSAA, other information source could be considered. The use
of crowd sourcing to collect in-situ data and generate LUCAS-like information
could enrich data availability [Laso Bayas et al., 2020]. The use of geo-
tagged street-view pictures has been already investigated for agriculture
monitoring [d’Andrimont et al., 2018b] and could represent an alternative in-
situ dataset. Application like Pl@ntnet [Joly et al., 2016] or i-naturalist
[Van Horn et al., 2018] collect massive plant and species occurrence
information via their portal. Finally, leveraging mobile gaming solutions such
as the impact games (https://world.game/) could represent an additional
alternative to collect massive in-situ data.

###  4.3 The EU crop map 2018: perspectives for improvement

There are a number of limitations in the proposed mapping approach. Some are
coming from the lack of information in the LUCAS survey and other from
methodological choices.

From the LUCAS data, the limitation comes mainly form the crop type surveyed,
the number of samples per classes and details about the agriculture practices
such as irrigation. We did not distinguish between rain-fed and irrigated
parcels due to a lack of samples in each category. This particularly affects
the maize detection in the South of Europe where most of the maize is
irrigated. The Supplementary Fig. S5 shows that the maize is indeed
underestimated in Spain, southern Italy and Greece. Another limitation is that
the approach proposed doesn’t capture rice. While that class is usually easy
to discriminate from other crops, it is here missed due to the lacking of
training data (i.e. 14 polygons). The west area of northern Italy is
particularly affected, with a confusion between rice and maize. Future studies
could consider using information about the water management available in the
LUCAS survey. Better understanding and taking into account on the backscatter
signal of irrigated surfaces would also certainly improve the discrimination
of irrigated crops.

In term of methodological choices, improvements are possible taking into
consideration crop classes not considered in this study. Specifically,
permanent crops are covering a considerable extent of agricultural land and
are currently classified in the broad class Woodland and shrubland. The
Copernicus information for the two classes of permanent crops (fruit trees and
the other permanent crops such as olive and vineyard) can be used to explore
classification workflows identifying them. Distinction between temporary,
permanent and natural grasslands would also be worth considering in future
developments which would include multi-season temporal profiles.

Although we have not considered using S1 coherence in this study we are well
aware of the potential use of this alternative Level 2 product in agricultural
use contexts (e.g. Tamm et al. [2016], De Vroey et al. [2021]) 666The terrain
flattening correction that is recommended for wall-to-wall mapping
applications of S1 can be implemented as a per-orbit reference layer, which is
due to the excellent orbit stability of the S1 sensors. See
https://developers.google.com/earth-engine/tutorials/community/sar-basics.. We
realize that a future extension of S1 use in national or continental crop
mapping requires the introduction of terrain flattening to better compensate
for incidence angle and look direction differences in the multi-orbit
compositions for 10-day periods. Although we have masked terrain with steep
slopes ($>$ 10∘) above 1000 m limiting these effects somewhat, we expect
improvements especially for crops with an extended bare soil phase (e.g.
summer crops) for which angular variability in backscattering can be
significant. We have not applied speckle filtering, because speckle variance
is already reduced in the averaging step we apply to create the 10-day
composites and using polygon averaged values for the RF training. However, the
sparser revisit frequency in the Southern areas makes speckle filtering
potentially useful. Furthermore, we could consider speckle filtering with a
shorter averaging period (e.g. 5 days) in the North, to better capture abrupt
temporal changes in the $\sigma^{0}$ signatures (e.g. at harvest).

This study demonstrates the potential of S1 as a baseline for continental
mapping. However Sentinel-2 (S2) would certainly improve the results as
already demonstrated by Van Tricht et al. [2018], Gao et al. [2018], Kussul et
al. [2018], Orynbaikyzy et al. [2020], Chakhar et al. [2021]. In particular,
we expect that the inclusion of S2 data would improve the discrimination of
main cropping practices. For example helping to separate winter crops from
permanent grassland, for the detection of bare soil conditions in spring to
delineate summer crops, and to highlight crop specific phenology events (e.g.
d’Andrimont et al. [2020a]). Additionally, S2 would be particularly useful in
the South where there is less cloud coverage and also less S1 acquisitions
[Lemoine, 2017]. More generally, to facilitate S2 ingestion in the machine
learning processing, smoothed time series of cloud free biophysical variables
would be useful (e.g. the Copernicus pan-European High-Resolution Vegetation
Phenology and Productivity products (HR-VPP) [Tian et al., 2021]).

The EU crop map provides a coherent and unique continental view with a spatial
detail that is relevant for crop management interventions at the parcel level.
As such, the information can be relevant for EU wide policy monitoring, e.g.
by deriving indicators such as crop diversity [Merlos & Hijmans, 2020], and be
a source of input data for EU wide models and assessments requiring detailed
information on crop location.

Another perspective is to derive national or sub-national area statistics from
the EU crop map. This was not considered in this study. Area estimation can be
done directly from the crop map (pixel counting) but this is not taking into
account the bias inherent of remote sensing: the presence of mixed (border)
pixels and the misclassification of pure pixels Gallego [2004]. Estimating
area from a satellite-based map typically requires the use of an un-biased
estimators [Olofsson et al., 2014, Stehman, 2014, Gallego & Delincé, 2010] by
combining data from a ground survey or from another source of higher quality
data in combination with the map. LUCAS 2018 survey is a source of reference
data, providing information until the NUTS2 level. However, further
exploration is needed in order to use it for area estimation at a finer scale.
While the approach of Gallego & Bamps [2008] for area estimation with CLC as
post-stratification could be a first naive approach, a more precise approach
would be the one of Stehman [2014] to take into account the fact that the
strata from our map and the strata from the LUCAS survey are not similar.
However, for this purpose, the sampling weight of each LUCAS point should be
made available by Eurostat777It is worth mentioning here that two
institutional ongoing projects are developing methodologies and good practices
to integrate EO in statistic collection process: Sen4Stats (https://www.esa-
sen4stat.org/) and EO4stats (”Training on EO for statistics and testing of the
methodology” PN5C/03/2020/E4).. In addition, further care should used to
account for the fact that a subset of the 2018 survey was used in the
training.

This study sets up a framework that could apply for other years. While the
approach demonstrated is robust, using model trained for 2018 on other years
could result in not satisfying result. Indeed, it is noted that 2018 was a
peculiar year, with droughts and severe heatwaves affecting Europe. Drought
conditions in central and northern Europe caused yield reductions up to 50%
for the main crops, yet wet conditions in southern Europe saw yield gains up
to 34%, both with respect to the previous 5-year mean [Buras et al., 2019,
Toreti et al., 2019]. To tackle year to year seasonality and the limited
availability of in-situ data, classification methodological developments are
still needed to apply the proposed approach to other years.

##  5 Conclusions

Arable land represents almost half of total EU area and its monitoring is
crucial for efficient environmental, agriculture and climate policies’
implementation. In this study, a framework to derive a 10-m crop type from S1
at continental level has been designed, implemented and assessed rigorously.
The proposed framework opens avenues to develop a synergistic robust
information system to monitor agriculture consistently both at fine spatial-
and-temporal scale over large areas. This demonstrates the current momentum
combining the unique Copernicus Sentinel fleet, high quality massive in-situ
data along with cloud computing. The method and data developed will serve
communities ranging from scientific modellers to policy makers.

##  6 Author contributions

R.D and A.V conceptualized the study and designed the methodology, R.D., A.V.,
G.L., P.K. processed the data. R.D., A.V., G.L., P.K., M. M. , M. V. analyzed
the data and wrote the paper.

##  7 Acknowledgements

The authors would like to thank Kostas Anastasakis as jrc-cbm developer,
Javier Sanchez Lopez for his agronomicic advices, the JRC JEODPP colleagues
for their continuous support and the Google Earth Engine team for their
technical help.

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## Data dissemination

The EU crop map masked with the non-vegetation classes and steep slopes is
available for download and visualization.888 The map can be downloaded as a
GeoTiff (6.2 Gb) from the FTP server :
https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/EUCROPMAP/2018/. The map can
be accessed via a WMS service via:
https://jeodpp.jrc.ec.europa.eu/jeodpp/services/ows/wms/landcover/eucropmap.

###### Contents

  1. 1 Introduction
    1. 1.1 Crop mapping with Sentinel-1
    2. 1.2 In-situ data in the European Union
    3. 1.3 Objectives
  2. 2 Materials and Methods
    1. 2.1 Sentinel-1 data
      1. 2.1.1 Geocoded backscattering coefficients
    2. 2.2 LUCAS 2018 in-situ data
      1. 2.2.1 LUCAS 2018 survey
      2. 2.2.2 LUCAS Copernicus module
    3. 2.3 Legend definition
    4. 2.4 Crop type classification
      1. 2.4.1 Reference training data
      2. 2.4.2 Stratification
      3. 2.4.3 Two-phase classification
      4. 2.4.4 Features selection
    5. 2.5 Classification at scale
    6. 2.6 Accuracy assessment
      1. 2.6.1 LUCAS 2018 core points
      2. 2.6.2 Comparison with GSAA
      3. 2.6.3 Comparison with reported subnational statistics
  3. 3 Results
    1. 3.1 Features selection
    2. 3.2 Crop type over EU
      1. 3.2.1 Visual assessment
      2. 3.2.2 F-score per crop type and administrative unit
    3. 3.3 Accuracy Assessment
      1. 3.3.1 LUCAS core points
      2. 3.3.2 GSAA
      3. 3.3.3 Subnational statistics
  4. 4 Discussion
    1. 4.1 Timeliness and accuracy of the crop type classification
    2. 4.2 Recommendation for an operational service
    3. 4.3 The EU crop map 2018: perspectives for improvement
  5. 5 Conclusions
  6. 6 Author contributions
  7. 7 Acknowledgements

###### List of Figures

  1. Fig. 1 General overview (references to section number are highlighted in grey).
  2. Fig. 2 The study area is the European Union 28 (EU-28). The study area is divided in 2 strata based on a combination of biomes from Dinerstein et al. [2017] as described in Section 2.4.3.
  3. Fig. 3 Evolution of VV and VH backscatter for the growing season for the 12 main crops. The value are averaged per stratum. The ribbon around the line corresponds to one standard deviation.
  4. Fig. 4 Overall accuracy at a monthly time step for the crop type level and the two strata combined
  5. Fig. 5 Evolution of The F-score through time for the 12 mains crops over the EU-28.
  6. Fig. 6 EU crop type map for 2018.
  7. Fig. 7 EU crop map for (a) Orleans (France), (b) border between Austria and Czech Republic, (c) Castilla and Leon (Spain), (d) (Romania).
  8. Fig. 8 The F-score per country for the 6 main crops in Europe.
  9. Fig. 9 User and Producer accuracy obtained from the comparison of the EU crop map and the parcel data from the GSAA. The detailed confusion matrices are in .
  10. (a) PA
  11. (b) UA
  12. Fig. 10 The areas reported by Eurostat at NUTS2 level (except for UK and DE at NUTS1) are compared with the area retrieved from the EU crop map. $R$ is the Pearson correlation coefficient, $R^{2}$ is the the coefficient of determination.
  13. Fig. 11 Crop type mapping results for different time periods, considering the data from January to respectively April, May, June, and July (the EU crop map presented in this study) in South East Germany.
  14. Supplementary Fig. S1 Evolution of The F-score for each Stratum through time for the 12 mains crops over the EU-28.
  15. (a)
  16. (b)
  17. Supplementary Fig. S2 Proportion of total GSAA declared area in 2018 by crop in the six regions of interest. Crop type covering an area smaller than 1% of the total are neglected.
  18. Supplementary Fig. S3 Area of total GSAA declared area in 2018 by crop in the six regions of interest. Crop type covering an area smaller than 1% of the total are neglected.
  19. Supplementary Fig. S4 Validation points (87,853) are high-quality point of the LUCAS core survey 2018 not used for training
  20. Supplementary Fig. S5 Relative difference of area [%] reported by Eurostat and retrieved from the EU crop map. The difference is the Eurostat reported value minus the value estimated from the EU crop map. Green values means that the EU crop map underestimates the area compared to the data reported from Eurostat while the brown values means that there is an overestimation.
  21. Supplementary Fig. S6 The areas reported by Eurostat at NUTS2 level (except for UK and DE at NUTS1) are compared with the area retrieved from the EUcropmap. The difference is the Eurostat reported value minus the value estimated from the EU crop map. Green values means that the EU crop map underestimates the area compared to the data reported from Eurostat while the brown values means that there is an overestimation.
  22. Supplementary Fig. S7 Sentinel-1 backscattering coefficient VV and VH are grouped for broad leaf crops (potato, sugar beet, sunflower), cereals (common wheat, durum wheat, barley, rye, oats, triticale), maize and rapeseed. This grouping better illustrates the differences observed in the north (stratum 1) and in the south (stratum 2).

###### List of Tables

  1. Table 1 The legend of the EU crop map. For the two-phase hierarchical classification procedure, the legend is divided into two levels: level 1 with seven broad land cover classes, and level 2 containing 19 crop types or crop groups.
  2. Table 2 Summary of GSAA data used in the study.
  3. Table 3 Overall accuracy for S1 indices (dates are fixed from Jan to end of July).
  4. Table 4 Confusion matrix for crop type group class for the EU-28. The values represent weighted area UA Users accuracy PA Producer Accuracy.
  5. Table 5 Weighted area accuracy metrics, overall accuracy for the crop types classification (level-2) and producer and user accuracies for the main crop type classes (for the whole study area and for the two strata)
  6. Supplementary Table S1 The training data consist of 58,178 polygons derived from the LUCAS Copernicus module corresponding to 2,956,889 S1 10-m pixels and associated time series. The training data are distributed into 21 thematic classes and two geographical strata (Str1 and Str2).
  7. Supplementary Table S2 Weighted area accuracy for all the classes
  8. Supplementary Table S3 Hyperparameters of the RF models used in the final version
  9. Supplementary Table S4 Masking the EU crop map for visualization and area estimation
  10. Supplementary Table S5 Area (ha) of the EU crop type classes, with the projection EPSG 3035
  11. Supplementary Table S6 Confusion matrix for the EU-28 region. The values represent sample counts. UA = User’s accuracy, PA=Producer Accuracy
  12. Supplementary Table S7 Confusion matrix for the EU-28 region. The values represent the estimated area proportions. UA = User’s accuracy, PA=Producer Accuracy
  13. Supplementary Table S8 Confusion matrix for main crop type group for the EU-28 region. The values represent sample counts. UA = User’s accuracy, PA=Producer Accuracy
  14. Supplementary Table S9 F-score by country for the 6 main crops
  15. Supplementary Table S10 Summary of GSAA legend matching data used in the study.
  16. Supplementary Table S11 Confusion matrix for bevl2018
  17. Supplementary Table S12 Confusion matrix for dk2018
  18. Supplementary Table S13 Confusion matrix for si2018
  19. Supplementary Table S14 Confusion matrix for frcv2018
  20. Supplementary Table S15 Confusion matrix for nld2018
  21. Supplementary Table S16 Confusion matrix for nrw2018
  22. Supplementary Table S17 Legend convergence between Eurostat and EU crop map classes

## Supplementary Material

Supplementary Table S1: The training data consist of 58,178 polygons derived from the LUCAS Copernicus module corresponding to 2,956,889 S1 10-m pixels and associated time series. The training data are distributed into 21 thematic classes and two geographical strata (Str1 and Str2). |  | # polygons | # pixels
---|---|---|---
class |  label | Str1 | Str2 | Sum | Str1 | Str2 | Sum
100 |  Artificial land | $157$ | $56$ | $213$ | $2449$ | $772$ | $3221$
211 |  Common wheat | $4210$ | $619$ | $4829$ | $261,290$ | $28,826$ | $290,116$
212 |  Durum wheat | $198$ | $388$ | $586$ | $10,863$ | $17,962$ | $28,825$
213 |  Barley | $1615$ | $930$ | $2545$ | $98,670$ | $44,216$ | $142,886$
214 |  Rye | $528$ | $75$ | $603$ | $31,956$ | $3259$ | $35,215$
215 |  Oats | $433$ | $209$ | $642$ | $25,021$ | $8984$ | $34,005$
216 |  Maize | $2241$ | $140$ | $2381$ | $119,697$ | $5947$ | $125,644$
217 |  Rice | $11$ | $3$ | $14$ | $661$ | $207$ | $868$
218 |  Triticale | $297$ | $32$ | $329$ | $17,686$ | $1588$ | $19,274$
219 |  Other cereals | $86$ | $4$ | $90$ | $4625$ | $99$ | $4724$
221 |  Potatoes | $285$ | $23$ | $308$ | $14,965$ | $850$ | $15,815$
222 |  Sugar beet | $396$ | $7$ | $403$ | $22,706$ | $468$ | $23,174$
223 |  Other root crops | $75$ | $19$ | $94$ | $4036$ | $884$ | $4920$
230 |  Other non perm. ind. crops | $141$ | $106$ | $247$ | $7600$ | $4400$ | $12,000$
231 |  Sunflower | $462$ | $222$ | $684$ | $24,369$ | $10,208$ | $34,577$
232 |  Rape and turnip rape | $1096$ | $15$ | $1111$ | $63,899$ | $553$ | $64,452$
233 |  Soya | $154$ | $1$ | $155$ | $7020$ | $74$ | $7094$
240 |  Dry pulse vegetables and flowers | $423$ | $285$ | $708$ | $22,440$ | $11,627$ | $34,067$
250 |  Other fodder crops | $859$ | $472$ | $1331$ | $40,453$ | $18,600$ | $59,053$
290 |  Bare arable land | $628$ | $683$ | $1311$ | $35,167$ | $28,442$ | $63,609$
300 |  Woodland | $15,507$ | $6845$ | $22,352$ | $932,467$ | $284,063$ | $1,216,530$
500 |  Grassland | $14,021$ | $3086$ | $17,107$ | $624,627$ | $108,337$ | $732,964$
600 |  Bare land | $75$ | $60$ | $135$ | $2488$ | $1368$ | $3856$
Total | $43,898$ | $14,280$ | $58,178$ | $2,375,155$ | $581,734$ | $2,956,889$
Supplementary Table S2: Weighted area accuracy for all the classes

|  | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 221 | 222 | 223 | 230 | 231 | 232 | 233 | 240 | 250 | 290 | 300 | 500 | U.A
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
common wheat | 211 | 0.039 | 0.003 | 0.007 | 0.002 | 0.003 | 0.001 | 0 | 0.002 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 | 0 | 0 | 0.002 | 0.005 | 0.001 | 0.012 | 49.6
durum wheat | 212 | 0 | 0.002 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 | 49.7
barley | 213 | 0.003 | 0.001 | 0.015 | 0.001 | 0.001 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 | 0.001 | 0.002 | 0 | 0.003 | 51.6
rye | 214 | 0 | 0 | 0 | 0.002 | 0 | 0 | 0 | 0.001 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48.3
oats | 215 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 37.3
maize | 216 | 0.001 | 0 | 0.001 | 0 | 0 | 0.033 | 0 | 0 | 0.001 | 0.001 | 0.001 | 0 | 0 | 0 | 0 | 0.001 | 0.001 | 0.002 | 0.001 | 0.001 | 0.009 | 58.5
rice | 217 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
triticale | 218 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 50.0
other cereals | 219 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57.1
potatoes | 221 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.002 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 73.5
sugar beet | 222 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.003 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 75.0
other roots crops | 223 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| other non permanent
---
industrial crops
230 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 76.5
sunflower | 231 | 0 | 0 | 0 | 0 | 0 | 0.001 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.007 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 | 62.2
rape and turnip rape | 232 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.014 | 0 | 0 | 0 | 0.001 | 0 | 0.001 | 80.2
soya | 233 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 88.9
| dry pulses,
---
vegetables and flowers
240 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.002 | 0 | 0 | 0 | 0.001 | 40.35cm
| fodder crops
---
(cereals and leguminous)
250 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 | 0 | 0 | 0.001 | 28.4
bare arable land | 290 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.005 | 0.001 | 0.002 | 49.1
| woodland and shrubland
---
vegetation (incl. permanent crops)
300 | 0.002 | 0.001 | 0.002 | 0 | 0.001 | 0.003 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 | 0 | 0.001 | 0.004 | 0.004 | 0.49 | 0.078 | 83.3
| permanent and temporary
---
grasslands
500 | 0.004 | 0.001 | 0.002 | 0.001 | 0.001 | 0.001 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.009 | 0.001 | 0.013 | 0.151 | 82.0
|  | 0.0504 | 0.0087 | 0.0286 | 0.0067 | 0.0077 | 0.0389 | 0.0009 | 0.0043 | 0.0015 | 0.0039 | 0.0058 | 0.0010 | 0.0027 | 0.0076 | 0.0168 | 0.0018 | 0.0069 | 0.0195 | 0.0209 | 0.5058 | 0.2595 |
P.A. |  | 78.1 | 22.1 | 52.7 | 31.1 | 6.0 | 83.7 | 0.0 | 1.0 | 1.6 | 38.7 | 58.3 | 0.0 | 24.6 | 85.9 | 82.1 | 7.2 | 24.7 | 6.2 | 22.0 | 96.9 | 58.1 |
O.A. | 0.766 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |

Supplementary Table S3: Hyperparameters of the RF models used in the final version  | Stratum 1 | Stratum 2
---|---|---
Level1 |  | n_estimators=1200
---
max_features=’sqrt’
min_samples_leaf=1,
min_samples_split=2,
max_depth=None,
criterion=’gini’,
bootstrap = True
| n_estimators=900
---
max_features=’sqrt’
min_samples_leaf=1,
min_samples_split=3
max_depth=None,
criterion=’gini’,
bootstrap = True
Level2 |  | n_estimators=1200
---
max_features=’log2’
min_samples_leaf=1
min_samples_split=2,
max_depth=None,
criterion=’gini’,
bootstrap = True
| n_estimators=900
---
max_features=’log2’
min_samples_leaf=1,
min_samples_split=2,
max_depth=None,
criterion=’gini’,
bootstrap = True

(a)

(b)

Supplementary Fig. S1: Evolution of The F-score for each Stratum through time for the 12 mains crops over the EU-28.  Supplementary Table S4: Masking the EU crop map for visualization and area estimation  | Classes labels | Class codes
---|---|---
Corine Land Cover 2018 |  |
| Inland and coastal wetlands | 411-412, 421-423
| Moors and Heatlands | 322
| Bare Lands | 331-333,335
| Port and airports | 123,124
JRC GHSL European Settlement Map R2019 | Residential and non residential built-up area | 250, 255
JRC Surface Water 2019 | 2019 permanent water bodies | 3
Supplementary Table S5: Area (ha) of the EU crop type classes, with the projection EPSG 3035  Class | Area (ha)
---|---
211 | $31,325,983.42$
212 | $1,842,598.45$
213 | $11,822,095.11$
214 | $1,641,600.28$
215 | $457,025.1$
216 | $22,188,829.6$
217 | $3380.94$
218 | $33,631.6$
219 | $13,944.24$
221 | $785,300.85$
222 | $1,753,491.13$
223 | $19,124.88$
230 | $393,616.4$
231 | $4,551,610.89$
232 | $6,868,240.33$
233 | $62,272.83$
240 | $1,731,230.9$
250 | $1,872,904.25$
290 | $4,375,965.4$
Total arable land | $91,742,847$
300 | $223,436,295.7$
500 | $70,941,202.43$
Supplementary Table S6: Confusion matrix for the EU-28 region. The values
represent sample counts. UA = User’s accuracy, PA=Producer Accuracy

| Reference class (LUCAS point)
---|---
| 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 221 | 222 | 223 | 230 | 231 | 232 | 233 | 240 | 250 | 290 | 300 | 500 | total | UA | OA
211 | 4624 | 367 | 833 | 287 | 348 | 99 | 7 | 269 | 25 | 8 | 11 | 12 | 52 | 4 | 93 | 3 | 47 | 205 | 598 | 69 | 1369 | 9330 | 49.6 | 74.0
212 | 68 | 402 | 61 | 2 | 37 | 3 | 1 | 3 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 10 | 60 | 43 | 5 | 111 | 809 | 49.7 |
213 | 372 | 121 | 2117 | 112 | 206 | 47 | 6 | 38 | 4 | 3 | 0 | 10 | 24 | 3 | 23 | 0 | 78 | 164 | 255 | 49 | 467 | 4099 | 51.6 |
214 | 34 | 3 | 41 | 236 | 11 | 2 | 0 | 99 | 1 | 1 | 0 | 0 | 2 | 0 | 12 | 0 | 1 | 1 | 19 | 0 | 26 | 489 | 48.3 |
215 | 26 | 3 | 14 | 3 | 56 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 15 | 8 | 1 | 21 | 150 | 37.3 |
216 | 128 | 16 | 105 | 21 | 44 | 3464 | 28 | 16 | 81 | 124 | 149 | 38 | 36 | 45 | 36 | 120 | 133 | 169 | 139 | 98 | 936 | 5926 | 58.5 |
217 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |  |
218 | 2 | 0 | 1 | 1 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 50.0 |
219 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 7 | 57.1 |
221 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 180 | 35 | 4 | 0 | 5 | 2 | 2 | 6 | 0 | 5 | 0 | 5 | 245 | 73.5 |
222 | 0 | 0 | 2 | 0 | 0 | 21 | 0 | 0 | 1 | 68 | 476 | 7 | 0 | 17 | 3 | 11 | 14 | 1 | 7 | 2 | 5 | 635 | 75.0 |
223 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 3 | 33.3 |
230 | 0 | 0 | 1 | 0 | 0 | 0 | 5 | 0 | 1 | 1 | 0 | 0 | 124 | 5 | 0 | 0 | 10 | 1 | 7 | 0 | 7 | 162 | 76.5 |
231 | 5 | 3 | 9 | 0 | 1 | 46 | 4 | 1 | 1 | 17 | 28 | 7 | 36 | 530 | 3 | 22 | 40 | 8 | 37 | 7 | 47 | 852 | 62.2 |
232 | 15 | 1 | 12 | 3 | 1 | 4 | 0 | 5 | 3 | 1 | 10 | 4 | 5 | 2 | 1415 | 1 | 34 | 11 | 135 | 8 | 94 | 1764 | 80.2 |
233 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 9 | 88.9 |
240 | 5 | 13 | 13 | 2 | 1 | 9 | 1 | 1 | 6 | 21 | 4 | 16 | 8 | 3 | 37 | 2 | 213 | 26 | 51 | 7 | 89 | 528 | 40.3 |
250 | 36 | 44 | 45 | 4 | 31 | 21 | 4 | 3 | 2 | 0 | 0 | 2 | 2 | 1 | 8 | 0 | 32 | 203 | 29 | 16 | 232 | 715 | 28.4 |
290 | 21 | 15 | 24 | 0 | 13 | 34 | 34 | 1 | 0 | 11 | 2 | 4 | 11 | 20 | 12 | 0 | 35 | 28 | 667 | 91 | 335 | 1358 | 49.1 |
300 | 137 | 93 | 117 | 26 | 47 | 179 | 8 | 18 | 11 | 10 | 9 | 2 | 14 | 14 | 62 | 9 | 70 | 275 | 251 | 31981 | 5082 | 38415 | 83.3 |
500 | 441 | 79 | 253 | 84 | 131 | 144 | 2 | 38 | 19 | 1 | 1 | 5 | 22 | 1 | 18 | 3 | 46 | 1039 | 163 | 1532 | 18327 | 22349 | 82.0 |
Total | 5914 | 1160 | 3648 | 781 | 927 | 4075 | 100 | 497 | 159 | 447 | 725 | 113 | 339 | 650 | 1725 | 181 | 772 | 2206 | 2414 | 33866 | 27154 |  |  |
| Producer ’s
---
accuracy
78.2 | 34.7 | 58.0 | 30.2 | 6.0 | 85.0 | 0.0 | 0.8 | 2.5 | 40.3 | 65.7 | 0.9 | 36.6 | 81.5 | 82.0 | 4.4 | 27.6 | 9.2 | 27.6 | 94.4 | 67.5 |  |  |

Supplementary Table S7: Confusion matrix for the EU-28 region. The values
represent the estimated area proportions. UA = User’s accuracy, PA=Producer
Accuracy

| Reference class (LUCAS point) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
Map class | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 221 | 222 | 223 | 230 | 231 | 232 | 233 | 240 | 250 | 290 | 300 | 500 | UA | SE | OA
211 | 0.04 | 0.003 | 0.007 | 0.002 | 0.003 | 0.001 | 0 | 0.002 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 | 0 | 0 | 0.002 | 0.005 | 0.001 | 0.012 | 49.6 | 1 | 76.3
212 | 0 | 0.002 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 | 49.7 | 3.4 |
213 | 0.003 | 0.001 | 0.016 | 0.001 | 0.002 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 | 0.001 | 0.002 | 0 | 0.003 | 51.6 | 1.5 |
214 | 0 | 0 | 0 | 0.002 | 0 | 0 | 0 | 0.001 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48.3 | 4.4 |
215 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 37.3 | 7.8 |
216 | 0.001 | 0 | 0.001 | 0 | 0 | 0.034 | 0 | 0 | 0.001 | 0.001 | 0.001 | 0 | 0 | 0 | 0 | 0.001 | 0.001 | 0.002 | 0.001 | 0.001 | 0.009 | 58.5 | 1.3 |
217 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |  |  |
218 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 50 | 37 |
219 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57.1 | 39.6 |
221 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 73.5 | 5.5 |
222 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.003 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 75 | 3.4 |
223 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 33.3 | 65.3 |
230 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 76.5 | 6.5 |
231 | 0 | 0 | 0 | 0 | 0 | 0.001 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.007 | 0 | 0 | 0.001 | 0 | 0.001 | 0 | 0.001 | 62.2 | 3.3 |
232 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.014 | 0 | 0 | 0 | 0.001 | 0 | 0.001 | 80.2 | 1.9 |
233 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 88.9 | 21.8 |
240 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.002 | 0 | 0 | 0 | 0.001 | 40.3 | 4.2 |
250 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 | 0 | 0 | 0.002 | 28.4 | 3.3 |
290 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.006 | 0.001 | 0.003 | 49.1 | 2.7 |
300 | 0.002 | 0.001 | 0.002 | 0 | 0.001 | 0.003 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 | 0 | 0.001 | 0.004 | 0.004 | 0.482 | 0.077 | 83.3 | 0.4 |
500 | 0.004 | 0.001 | 0.002 | 0.001 | 0.001 | 0.001 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.009 | 0.001 | 0.013 | 0.151 | 82 | 0.5 |
PA(%) | 78 | 25.5 | 53.4 | 30.2 | 5.6 | 83.8 | 0 | 1 | 1.4 | 37.6 | 57.5 | 1.6 | 26.8 | 86.7 | 82.3 | 7.5 | 25 | 6.9 | 25 | 96.9 | 58 |  |  |
SE(%) | 1 | 1.9 | 1.4 | 2.6 | 1.2 | 1.2 | 0 | 0.7 | 1 | 3.3 | 3.2 | 3.1 | 3.2 | 2.2 | 1.7 | 2 | 2.5 | 0.8 | 1.4 | 0.1 | 0.5 |  |  |

Supplementary Table S8: Confusion matrix for main crop type group for the
EU-28 region. The values represent sample counts. UA = User’s accuracy,
PA=Producer Accuracy

|  | Reference class (LUCAS point)
---|---|---
| Map Class | 210 | 220 | 230 | 240 | 250 | 290 | 300 | 500 | Total | UA (%) | OA(%)
210 | Cereals | 14904 | 358 | 240 | 270 | 614 | 1062 | 222 | 2931 | 20601 | 72.3 | 79.2
220 | Root Crops | 25 | 771 | 13 | 22 | 1 | 12 | 2 | 10 | 856 | 90.1 |
230 | Non permanent industrial crops | 8 | 1 | 132 | 10 | 1 | 7 | 0 | 7 | 166 | 79.5 |
240 | Dry pulses, Vegetables and Flowers | 51 | 41 | 10 | 213 | 26 | 51 | 7 | 89 | 488 | 43.6 |
250 | Fodder Crops | 190 | 2 | 2 | 32 | 203 | 29 | 16 | 232 | 706 | 28.8 |
290 | Bare Arable Land | 142 | 17 | 11 | 35 | 28 | 667 | 91 | 335 | 1326 | 50.3 |
300 | Tree and Shrub Cover | 636 | 21 | 23 | 70 | 275 | 251 | 31981 | 5082 | 38339 | 83.4 |
500 | Grassland | 1191 | 7 | 25 | 46 | 1039 | 163 | 1532 | 18327 | 22330 | 82.1 |
| Total | 17147 | 1218 | 456 | 698 | 2187 | 2242 | 33851 | 27013 |  |  |
| PA (%) | 86.9 | 63.3 | 28.9 | 30.5 | 9.3 | 29.8 | 94.5 | 67.8 |  |  |

Supplementary Table S9: F-score by country for the 6 main crops Country | # points | Fscore 211 | Fscore 213 | Fscore 216 | Fscore 222 | Fscore 231 | Fscore 232
---|---|---|---|---|---|---|---
AT | 1750 | 0.63 | 0.54 | 0.70 | 0.46 | 0.50 | 0.96
BE | 946 | 0.69 | 0.51 | 0.72 | 0.65 |  | 0.83
BG | 1751 | 0.68 | 0.27 | 0.71 |  | 0.97 | 0.87
CY | 487 | 0.32 | 0.15 |  |  |  |
CZ | 2084 | 0.78 | 0.65 | 0.85 | 0.98 |  | 0.96
DE | 9170 | 0.67 | 0.62 | 0.85 | 0.75 | 0.22 | 0.90
DK | 1069 | 0.57 | 0.69 | 0.77 | 0.74 |  | 0.87
EE | 666 | 0.46 | 0.57 |  |  |  | 0.70
EL | 2991 | 0.09 | 0.26 | 0.33 |  | 0.31 |
ES | 13242 | 0.53 | 0.60 | 0.42 |  | 0.46 | 0.51
FI | 3183 | 0.18 | 0.54 |  |  |  | 0.41
FR | 15024 | 0.74 | 0.57 | 0.72 | 0.80 | 0.78 | 0.87
HR | 983 | 0.36 |  | 0.74 | 0.22 | 0.84 | 0.13
HU | 1428 | 0.58 | 0.16 | 0.83 | 0.36 | 0.86 | 0.78
IE | 1329 | 0.56 | 0.81 | 0.21 |  |  | 0.67
IT | 6280 | 0.55 | 0.17 | 0.64 | 0.47 | 0.55 | 0.26
LT | 1138 | 0.56 | 0.20 | 0.33 | 0.57 |  | 0.88
LU | 106 | 0.71 | 0.75 | 0.67 |  |  | 1.00
LV | 1313 | 0.67 | 0.28 | 0.12 |  |  | 0.88
MT | 11 |  |  |  |  |  |
NL | 1437 | 0.58 | 0.23 | 0.69 | 0.58 |  |
PL | 7810 | 0.51 | 0.32 | 0.70 | 0.75 |  | 0.88
PT | 2230 | 0.05 | 0.29 | 0.48 |  |  |
RO | 3528 | 0.26 | 0.08 | 0.62 | 0.30 | 0.76 | 0.47
SE | 4756 | 0.48 | 0.50 | 0.16 | 0.40 |  | 0.74
SI | 634 | 0.55 |  | 0.81 |  | 0.67 |
SK | 818 | 0.73 | 0.54 | 0.46 |  | 0.75 | 0.92
UK | 5037 | 0.58 | 0.64 | 0.27 | 0.38 |  | 0.48
Supplementary Fig. S2: Proportion of total GSAA declared area in 2018 by crop in the six regions of interest. Crop type covering an area smaller than 1% of the total are neglected.  Supplementary Fig. S3: Area of total GSAA declared area in 2018 by crop in the six regions of interest. Crop type covering an area smaller than 1% of the total are neglected. Supplementary Fig. S4: Validation points (87,853) are high-quality point of the LUCAS core survey 2018 not used for training Supplementary Table S10: Summary of GSAA legend matching data used in the study. Region | Crop code | 211 | 212 | 213 | 214 | 215 | 216 | 218 | 219 | 221 | 222 | 223 | 231 | 232 | 250 | 290 | 300 | 500
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
bevl2018 | hfdtlt | 311 | 0 | 321 | 0 | 0 | 201,202 | 0 | 0 | 901,904 | 91 | 0 | 0 | 0 | 0 | 0 | 9711 | 60,700
dk2018 | afgkode | 2,11 | 0 | 1,10 | 15 | 3 | 216 | 0 | 0 | 151 | 160 | 0 | 0 | 22 | 0 | 318 | 0 | 101,252,254,260,263,276,308
frcv2018 | cropcode | 801 | 1 | 809 | 0 | 0 | 5,6 | 0 | 807 | 0 | 1 | 0 | 1 | 1 | 208 | 0 | 100,699 | 201,203,204
nld2018 | gws_gewasc | 233 | 0 | 236 | 0 | 0 | 259 | 0 | 0 | 2014,2015,2017 | 256 | 262 | 0 | 0 | 0 | 0 | 0 | 265,266,331,335,336
nrw2018 | mon10_cr_1 | 115 | 0 | 131,132 | 121 | 0 | 171,411 | 156 | 0 | 602 | 603 | 0 | 0 | 311 | 0 | 0 | 0 | 424,459
si2018 | sifra_kmrs | 1 | 10 | 6,7 | 0 | 0 | 4,5 | 0 | 1 | 0 | 189 | 0 | 39 | 37 | 1 | 0 | 2 | 149,150,151
Supplementary Table S11: Confusion matrix for bevl2018 | 211 | 213 | 216 | 221 | 222 | TOTAL | UA
---|---|---|---|---|---|---|---
211 | 26456 | 118 | 262 | 4 | 6 | 26846 | 0.9855
213 | 1168 | 5401 | 107 | 0 | 1 | 6677 | 0.8089
216 | 734 | 196 | 102799 | 107 | 42 | 103878 | 0.9896
221 | 37 | 10 | 10086 | 11555 | 1332 | 23020 | 0.502
222 | 16 | 7 | 2170 | 982 | 4804 | 7979 | 0.6021
TOTAL | 28411 | 5732 | 115424 | 12648 | 6185 | 168400 |
PA | 0.9312 | 0.9423 | 0.8906 | 0.9136 | 0.7767 |  |
Supplementary Table S12: Confusion matrix for dk2018 | 211 | 213 | 214 | 215 | 216 | 221 | 222 | 232 | 290 | TOTAL | UA
---|---|---|---|---|---|---|---|---|---|---|---
211 | 41660 | 3267 | 56 | 7 | 167 | 3 | 0 | 30 | 0 | 45190 | 0.9219
213 | 4593 | 100065 | 103 | 11 | 1679 | 37 | 39 | 97 | 7 | 106631 | 0.9384
214 | 2423 | 2088 | 7524 | 0 | 30 | 1 | 0 | 4 | 0 | 12070 | 0.6234
215 | 1546 | 7060 | 14 | 70 | 386 | 5 | 0 | 15 | 3 | 9099 | 0.0077
216 | 213 | 513 | 3 | 2 | 25374 | 10 | 1 | 1 | 0 | 26117 | 0.9716
221 | 9 | 26 | 0 | 0 | 1788 | 2197 | 21 | 0 | 0 | 4041 | 0.5437
222 | 11 | 52 | 0 | 0 | 679 | 28 | 1929 | 0 | 0 | 2699 | 0.7147
232 | 135 | 312 | 4 | 0 | 66 | 0 | 1 | 14467 | 1 | 14986 | 0.9654
290 | 5 | 8 | 0 | 1 | 20 | 0 | 0 | 0 | 0 | 34 | 0
TOTAL | 50595 | 113391 | 7704 | 91 | 30189 | 2281 | 1991 | 14614 | 11 | 220867 |
PA | 0.8234 | 0.8825 | 0.9766 | 0.7692 | 0.8405 | 0.9632 | 0.9689 | 0.9899 | 0 |  |
Supplementary Table S13: Confusion matrix for si2018 | 211 | 213 | 216 | 219 | 250 | TOTAL | UA
---|---|---|---|---|---|---|---
211 | 18662 | 155 | 6689 | 0 | 31 | 25537 | 0.7308
213 | 8539 | 4118 | 7979 | 0 | 98 | 20734 | 0.1986
216 | 608 | 31 | 69898 | 0 | 13 | 70550 | 0.9908
219 | 3633 | 72 | 1765 | 0 | 10 | 5480 | 0
250 | 184 | 27 | 1373 | 0 | 23 | 1607 | 0.0143
TOTAL | 31626 | 4403 | 87704 | 0 | 175 | 123908 |
PA | 0.5901 | 0.9353 | 0.797 | NaN | 0.1314 |  |
Supplementary Table S14: Confusion matrix for frcv2018 | 211 | 212 | 213 | 216 | 222 | 231 | 232 | TOTAL | UA
---|---|---|---|---|---|---|---|---|---
211 | 88981 | 0 | 233 | 224 | 10 | 10 | 122 | 89580 | 0.9933
212 | 8413 | 0 | 88 | 31 | 3 | 0 | 8 | 8543 | 0
213 | 7186 | 0 | 34060 | 636 | 13 | 9 | 75 | 41979 | 0.8114
216 | 217 | 0 | 49 | 21902 | 10 | 11 | 54 | 22243 | 0.9847
222 | 25 | 0 | 7 | 173 | 3723 | 51 | 33 | 4012 | 0.928
231 | 59 | 0 | 9 | 1317 | 74 | 6768 | 25 | 8252 | 0.8202
232 | 124 | 0 | 24 | 49 | 1 | 11 | 43836 | 44045 | 0.9953
TOTAL | 105005 | 0 | 34470 | 24332 | 3834 | 6860 | 44153 | 218654 |
PA | 0.8474 | - | 0.9881 | 0.9001 | 0.971 | 0.9866 | 0.9928 |  |
Supplementary Table S15: Confusion matrix for nld2018 | 211 | 213 | 216 | 221 | 222 | 223 | TOTAL | UA
---|---|---|---|---|---|---|---|---
211 | 17352 | 148 | 178 | 6 | 4 | 0 | 17688 | 0.981
213 | 1873 | 4224 | 473 | 4 | 3 | 1 | 6578 | 0.6421
216 | 564 | 420 | 73207 | 57 | 14 | 16 | 74278 | 0.9856
221 | 71 | 38 | 7823 | 20472 | 3565 | 9 | 31978 | 0.6402
222 | 32 | 25 | 5229 | 969 | 10416 | 7 | 16678 | 0.6245
223 | 306 | 311 | 2674 | 10 | 108 | 1210 | 4619 | 0.262
TOTAL | 20198 | 5166 | 89584 | 21518 | 14110 | 1243 | 151819 |
PA | 0.8591 | 0.8177 | 0.8172 | 0.9514 | 0.7382 | 0.9735 |  |
Supplementary Table S16: Confusion matrix for nrw2018 | 211 | 213 | 214 | 216 | 218 | 221 | 222 | 232 | TOTAL | UA
---|---|---|---|---|---|---|---|---|---|---
211 | 69259 | 274 | 164 | 268 | 0 | 4 | 15 | 45 | 70029 | 0.989
213 | 10833 | 37528 | 220 | 556 | 0 | 1 | 7 | 125 | 49270 | 0.7617
214 | 1455 | 754 | 3918 | 48 | 0 | 1 | 1 | 17 | 6194 | 0.6325
216 | 824 | 135 | 43 | 102231 | 0 | 17 | 23 | 61 | 103334 | 0.9893
218 | 12692 | 2064 | 4921 | 92 | 8 | 2 | 0 | 42 | 19821 | 4e-04
221 | 96 | 15 | 2 | 2204 | 0 | 4365 | 2564 | 29 | 9275 | 0.4706
222 | 41 | 13 | 0 | 2039 | 0 | 231 | 13238 | 5 | 15567 | 0.8504
232 | 94 | 17 | 0 | 28 | 0 | 0 | 0 | 16175 | 16314 | 0.9915
TOTAL | 95294 | 40800 | 9268 | 107466 | 8 | 4621 | 15848 | 16499 | 289804 |
PA | 0.7268 | 0.9198 | 0.4227 | 0.9513 | 1 | 0.9446 | 0.8353 | 0.9804 |  |
Supplementary Table S17: Legend convergence between Eurostat and EU crop map classes estat_crop_class | estat_crop_label | eucropmap_class | eucropmap_label
---|---|---|---
C1110 | Common wheat and spelt | 211 | common wheat
C1120 | Durum wheat | 212 | durum wheat
C1200 | Rye and winter cereal mixtures (maslin) | 214 | rye
C1300 | Barley | 213 | barley
C1410 | Oats | 215 | oats
C1500 | Grain maize and corn-cob-mix | 216 | maize
C1600 | Triticale | 218 | triticale
C2000 | Rice | 217 | rice
G3000 | Green maize | 216 | maize
R1000 | Potatoes (including seed potatoes) | 221 | potatoes
R2000 | Sugar beet (excluding seed) | 222 | sugar beet
I1110 | Rape and turnip rape seeds | 232 | rape and turnip rape
I1120 | Sunflower seed | 231 | sunflower
I1130 | Soya | 233 | soya
Supplementary Fig. S5: Relative difference of area [%] reported by Eurostat
and retrieved from the EU crop map. The difference is the Eurostat reported
value minus the value estimated from the EU crop map. Green values means that
the EU crop map underestimates the area compared to the data reported from
Eurostat while the brown values means that there is an overestimation.
Supplementary Fig. S6: The areas reported by Eurostat at NUTS2 level (except
for UK and DE at NUTS1) are compared with the area retrieved from the
EUcropmap. The difference is the Eurostat reported value minus the value
estimated from the EU crop map. Green values means that the EU crop map
underestimates the area compared to the data reported from Eurostat while the
brown values means that there is an overestimation. Supplementary Fig. S7:
Sentinel-1 backscattering coefficient VV and VH are grouped for broad leaf
crops (potato, sugar beet, sunflower), cereals (common wheat, durum wheat,
barley, rye, oats, triticale), maize and rapeseed. This grouping better
illustrates the differences observed in the north (stratum 1) and in the south
(stratum 2).