National Crop Mapping Using Sentinel-1 Time Series: A Knowledge-Based Descriptive Algorithm

Abstract

National-level mapping of crop types is important to monitor food security, understand environmental conditions, inform optimal use of the landscape, and contribute to agricultural policy. Countries or economic regions currently and increasingly use satellite sensor data for classifying crops over large areas. However, most methods have been based on machine learning algorithms, with these often requiring large training datasets that are not always available and may be costly to produce or collect. Focusing on Wales (United Kingdom), the research demonstrates how the knowledge that the agricultural community has gathered together over past decades can be used to develop algorithms for mapping different crop types. Specifically, we aimed to develop an alternative method for consistent and accurate crop type mapping where cloud cover is quite persistent and without the need for extensive in situ/ground datasets. The classification approach is parcel-based and informed by concomitant analysis of knowledge-based crop growth stages and Sentinel-1 C-band SAR time series. For 2018, crop type classifications were generated nationally for Wales, with regional overall accuracies ranging between 85.8 % and 90.6 %. The method was particularly successful in distinguishing barley from wheat, which is a major source of error in other crop products available for Wales. This study demonstrates that crops can be accurately identified and mapped across a large area (i.e., Wales) using Sentinel-1 C-band data and by capitalizing on knowledge of crop growth stages. The developed algorithm is flexible and, compared to the other methods that allow crop mapping in Wales, the approach provided more consistent discrimination and lower variability in accuracies between classes and regions.

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remote sensing
Article
National Crop Mapping Using Sentinel-1 Time Series:
A Knowledge-Based Descriptive Algorithm
Carole Planque 1, *, Richard Lucas 1, Suvarna Punalekar 1, Sebastien Chognard 1, Clive Hurford 1,
Christopher Owers 1, Claire Horton 2, Paul Guest 2, Stephen King 3, Sion Williams 4and Peter Bunting 1


Citation: Planque, C.; Lucas, R.;
Punalekar, S.; Chognard, S.; Hurford,
C.; Owers, C.; Horton, C.; Guest, P.;
King, S.; Williams, S.; et al. National
Crop Mapping Using Sentinel-1 Time
Series: A Knowledge-Based
Descriptive Algorithm. Remote Sens.
2021,13, 846. https://doi.org/
10.3390/rs13050846
Academic Editor: Sergii Skakun
Received: 4 February 2021
Accepted: 22 February 2021
Published: 25 February 2021
Publisher’s Note: MDPI stays neutral
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth SY23 3DB, UK;
rml2@aber.ac.uk (R.L.); smp15@aber.ac.uk (S.P.); sec27@aber.ac.uk (S.C.); clh33@aber.ac.uk (C.H.);
cho18@aber.ac.uk (C.O.); pfb@aber.ac.uk (P.B.)
2ESNR—EPRA, Welsh Government, Aberystwyth SY23 3UR, UK; claire.horton@gov.wales (C.H.);
paul.guest@gov.wales (P.G.)
3ESNR—ERA—Rural Payments Wales, Welsh Government, Aberystwyth SY23 3UR, UK;
stephen.king@gov.wales
4ESNR—ERA—Land, Nature & Forestry, Welsh Government, Aberystwyth SY23 3UR, UK;
sion.williams@gov.wales
*Correspondence: cap33@aber.ac.uk
Abstract:
National-level mapping of crop types is important to monitor food security, understand
environmental conditions, inform optimal use of the landscape, and contribute to agricultural policy.
Countries or economic regions currently and increasingly use satellite sensor data for classifying
crops over large areas. However, most methods have been based on machine learning algorithms,
with these often requiring large training datasets that are not always available and may be costly
to produce or collect. Focusing on Wales (United Kingdom), the research demonstrates how the
knowledge that the agricultural community has gathered together over past decades can be used to
develop algorithms for mapping different crop types. Specifically, we aimed to develop an alternative
method for consistent and accurate crop type mapping where cloud cover is quite persistent and
without the need for extensive in situ/ground datasets. The classification approach is parcel-based
and informed by concomitant analysis of knowledge-based crop growth stages and Sentinel-1 C-band
SAR time series. For 2018, crop type classifications were generated nationally for Wales, with regional
overall accuracies ranging between 85.8% and 90.6%. The method was particularly successful in
distinguishing barley from wheat, which is a major source of error in other crop products available
for Wales. This study demonstrates that crops can be accurately identified and mapped across a large
area (i.e., Wales) using Sentinel-1 C-band data and by capitalizing on knowledge of crop growth
stages. The developed algorithm is flexible and, compared to the other methods that allow crop
mapping in Wales, the approach provided more consistent discrimination and lower variability in
accuracies between classes and regions.
Keywords: land cover classification; crop type; SAR; Sentinel-1; time series; growth stage
1. Introduction
Globally, the cover of crops and other agricultural land covers (e.g., grasslands under
pastoral management) ranges from <5% (e.g., Norway) to >80% (e.g., Uruguay) [
1
]. High-
quality crop mapping has become a requirement for most nations given its importance in
national and international economics, trade, and food security [
2
] and is a major topic of
interest in the domains of policy, economics, land management, and conservation. Monitor-
ing agricultural practices is also essential as demand for food has placed huge pressures on
landscapes and particularly natural ecosystems, with these impacting (often adversely) on
soils, air, water, and biodiversity [
3
–
9
]. By knowing and understanding the distributions,
types, and management regimes (e.g., rotational cycles) of crops, changes in management
Remote Sens. 2021,13, 846. https://doi.org/10.3390/rs13050846 https://www.mdpi.com/journal/remotesensing

Remote Sens. 2021,13, 846 2 of 30
practices can be better implemented to reduce pollution, conserve and/or restore biodiver-
sity, and control the spread of crop diseases. Whilst many systems have been put in place
worldwide, detecting and mapping different crop types and their dynamics over time, in
detail and across large areas, still remains a significant challenge.
Most countries or economic regions currently and increasingly use satellite sensor
data for land cover mapping. Since the 1990s, the value of high temporal frequency obser-
vations for such mapping has been demonstrated using 1 km resolution NOAA Advanced
Very High-Resolution Radiometer (AVHRR) and other similarly coarse resolution sensors
(e.g., [
10
–
16
]). Wardlow et al. (2007) [
17
], for example, used dense time series of 250 m
Moderate Resolution Imaging Spectrometer (MODIS) data to classify major crop types in
the US Central Great Plains, where fields were ~32.4 ha or larger. However, the use of these
data has been less successful in other regions where average field sizes are smaller (e.g.,
Western and Southern European regions mainly have average field sizes of <10 ha [18]).
The recent provision of free and open access Landsat sensor and Sentinel-2 optical
data [
19
,
20
] has resulted in access to 10–30 m spatial resolution observations with a high
temporal repeat (e.g., 5-day for Sentinel-2). However, despite the high temporal frequency
of observations, usable acquisitions can be reduced by cloud cover and associated shadows,
as these prevent the reflectance characteristics of the surface from being captured. This
becomes a particular issue for regions where cloud cover is persistent (e.g., Wales), noting
that the global total cloud cover over the Earth is about 73% when considering clouds with
optical depth >0.1 and sub-visible cirrus [
21
]. Contrary to optical sensors, Synthetic Aper-
ture Radar (SAR) penetrates clouds and observations do not depend on solar illumination
or most atmospheric conditions [22,23].
The use of SAR data in combination with optical data is, therefore, preferred in regions
with persistent cloud cover and higher classification accuracies have been obtained as a
result [
24
–
27
], largely because of the greater frequency of data acquisitions. For example,
Fisette et al. (2015) [
27
] showed that the use of RADARSAT-2 SAR data in addition to
optical imagery increased the overall accuracies by up to 16%. For this reason, the SAR-
optical combined datasets have been widely used for generating regional and national crop
maps [
25
,
27
–
33
], etc. A notable example is Canada where the government department
Agriculture and Agri-Food Canada has been using the combination of Landsat-8 and
RADARSAT-2 data to operationally deliver annual national crop maps with a reported
overall accuracy of approximately 85% [
22
,
25
,
28
]. Operational use of Earth observation
(EO) data for national and regional agricultural monitoring is most established amongst
several governmental entities [34].
For operational mapping and monitoring of crops, satellite-based methods need to be
(a) consistent, (b) flexible, and (c) geographically portable over large areas, where timely
access to information might not be easy to obtain [
22
,
35
]. However, several issues were
noted with current methods when trying to map crops in Wales, where cloud cover is
persistent and no existing in situ training dataset was available. First, the use of methods
based on SAR-optical combined datasets has shown limitations in terms of consistency
and portability. Indeed, as the method partially relies on optical imagery, there is still
a dependency on cloud-free imagery. Davidson et al. (2017) [
22
] highlighted that there
is often significant regional variability in cloud cover, which leads to discrepancies in
accuracy between regions. Similar issues can occur between years. Until recently, the
availability and spatial/temporal resolution of SAR data were limiting their sole use [
35
],
but the launch of Sentinel-1 SAR in 2014 has provided new opportunities for crop mapping
using SAR alone, with many concluding that crop types could be discriminated with
relatively high accuracy [
36
–
40
]. Second, most crop classifications have been based on
machine learning algorithms and, more recently, deep learning. Machine learning has
generally produced crop maps with accuracies that are considered sufficiently high for
many applications [
29
,
30
,
33
,
38
,
41
]. However, these algorithms often require large training
datasets, that are not always available (e.g., Wales) and may be costly to produce or collect
(e.g., large field campaigns may lead to high financial and time costs). Additionally, the

Remote Sens. 2021,13, 846 3 of 30
quality of in situ/ground training datasets and sampling approaches may significantly
impact on the reliability of the maps [
22
]. As such, the use of these methods in operational
systems for countries such as Wales can be problematic.
Matton et al. (2015) [
42
] demonstrated that crops could be automatically classified on
an annual basis without in situ/ground training datasets, but by considering five stages in
their cycle that were discernible within optical time series. An overall accuracy of > 85% was
obtained. This approach is useful as crops often pass through distinct changes and these
are well documented. Since 1974, growth stages of crops have been carefully observed and
described [
43
]. In 1992, the first version of the Biologische Bundesanstalt, Bundessortenamt
und CHemische Industrie (BBCH) scale, which is now used and recognized worldwide,
was published [
44
] and then regularly improved. The BBCH scale is a system for a uniform
coding of phenologically similar growth stages of all mono- and dicotyledonous plant
species [
45
] and describes nine principal growth stages, each of which is comprised of
substages. A range of scientific publications and various technical reports/guides give
information about the average changes in biomass and other physical structures of crops,
as well as the average timing of these.
This research sought to address the issue around the need for large training datasets
when trying to produce consistent and accurate crop maps over large areas, particularly
where access to in situ/ground information (e.g., timing of growth stages in various fields
across the country during the whole vegetation growth period) might not be easy to obtain.
In this paper, we convey how the knowledge that individuals, groups, or organizations
(e.g., farmers, scientists, agricultural institutes, and companies) have gathered together
over past decades can be used as an alternative to develop algorithms for mapping different
crop types. More specifically, we aimed to develop a method to classify crop types over
a large area where cloud cover is persistent without the need for large in situ/ground
training datasets. The classification approach is parcel-based and informed by concomitant
analysis of knowledge-based crop growth stages (i.e., theoretical changes in structure and
timing) and Sentinel-1 C-band SAR time series. The method was developed and applied to
Wales, in the United Kingdom (UK), and across the national landscape.
2. Materials and Methods
2.1. The Landscape of Wales
Wales is one of the four countries of the UK. It is a hilly/mountainous country of
20,779 km
2
and much of the land is over 150 m above mean sea level, particularly in the
north and center. The climate is primarily oceanic because of proximity to the Atlantic
Ocean and is characterized by mild conditions (annual mean temperature of 9.5–11
◦
C
at low altitudes), clouds that are quite persistent, particularly in the mountains, and
predominantly wet (annual precipitation of 1200 mm) and windy conditions [
46
]. The
period of greatest cloud cover is from October to the end of April, which coincides with
the beginning of the growing season for several crops. However, cloud cover remains
high during the whole year and as a consequence, through the major crop growth periods.
December and January are the cloudier months with, on average, cloud cover higher than
40% (i.e., partly cloudy to overcast) that persists for ~80% of the time. July has more
sunshine hours, but clear conditions (characterized as <20% cloud cover) occur, on average,
only 24% of the time [47].
Most of Wales is vegetated and largely managed, with the uplands primarily support-
ing sheep grazing whilst the lowlands, particularly those in the southwest and southeast,
are used for growing arable crops. A total of 88% of the land area is used for agriculture [
48
].
Over 75% of this area (1.332 M ha) is dominated by permanent pasture (grassland), 10%
(0.180 M ha) is used for common rough grazing, and 14% (0.246 M ha) is suitable for
growing crops [
48
]. The most commonly grown crops in Wales are spring barley (31%),
winter wheat (21%), maize (18%), winter barley (10%), potatoes, spring wheat and winter
rapeseed (4% each), and beets (3%). Other crop types are grown but these represent less
than 1% each and 5% together. This study focused on the eight main crop types of Wales,

Remote Sens. 2021,13, 846 4 of 30
noting that these are likely to remain as such in the future. The majority of crops are
mainly located in the flat lowlands in the south, with the rest of the country being used as
permanent pasture or supporting semi-natural vegetation.
2.2. Validation Sites
Sites for validating the crop type maps were selected in the main crop growing
regions (i.e., Pembrokeshire, the Vale of Glamorgan, and central Monmouthshire;
Figure 1
).
Within the three selected regions, different crop types, land management regimes, and
soil/weather conditions occur.
Remote Sens. 2021, 13, 846 4 of 30
(31%), winter wheat (21%), maize (18%), winter barley (10%), potatoes, spring wheat and
winter rapeseed (4% each), and beets (3%). Other crop types are grown but these represent
less than 1% each and 5% together. This study focused on the eight main crop types of
Wales, noting that these are likely to remain as such in the future. The majority of crops
are mainly located in the flat lowlands in the south, with the rest of the country being used
as permanent pasture or supporting semi-natural vegetation.
2.2. Validation Sites
Sites for validating the crop type maps were selected in the main crop growing re-
gions (i.e., Pembrokeshire, the Vale of Glamorgan, and central Monmouthshire; Figure 1).
Within the three selected regions, different crop types, land management regimes, and
soil/weather conditions occur.
Pembrokeshire is characterized by very complex and diverse landscapes. The north
is rural with many small settlements [49]. The slopes and valleys are covered with small
broadleaved woodlands and mixed or coniferous plantations but many of the flatter areas
consist of a mix of fields with pasture, cereals, or hay meadows. In the south, the diverse
agricultural land uses include cereal cropping, dairying, sheep rearing, and rough grazing
[50]. The Pembrokeshire landscape is a mosaic of fields of varying size bounded by hedge-
rows, hedgerow trees, and hedgebanks. The average size of agricultural fields is 3.76 ha
(4.62 ha when excluding grasslands, i.e., crops only). The agricultural area not covered by
grasslands consists about equally of horticulture, winter cereals, and spring cereals
[source: 2018 Land Parcel Identification System (LPIS)]. The Vale of Glamorgan is a dis-
tinctive, gentle lowland landscape, largely comprising a rolling limestone plateau with a
patchwork of fields and woodlands [51]. The field sizes are larger than in Pembrokeshire,
with an area average of 5.44 ha but 7.64 ha for croplands [source: 2018 LPIS]. Monmouth-
shire is also predominantly low lying with the landscape comprised of undulating hills,
valleys, and floodplains. Sheep grazing and dairy farming are commonplace and arable
farming is largely confined to the fertile floodplains. As farming is intensive, the extent of
semi-natural or ecologically rich habitats is quite limited with the exception of the wood-
lands [52]. The average sizes of all fields and those used only for crops are 3.85 and 5.46
ha, respectively.
Figure 1.
Regions of Wales. The red rectangles represent the three areas used for the validation step.
Pembrokeshire is characterized by very complex and diverse landscapes. The north
is rural with many small settlements [
49
]. The slopes and valleys are covered with small
broadleaved woodlands and mixed or coniferous plantations but many of the flatter areas
consist of a mix of fields with pasture, cereals, or hay meadows. In the south, the diverse
agricultural land uses include cereal cropping, dairying, sheep rearing, and rough graz-
ing [
50
]. The Pembrokeshire landscape is a mosaic of fields of varying size bounded by
hedgerows, hedgerow trees, and hedgebanks. The average size of agricultural fields is
3.76 ha (4.62 ha when excluding grasslands, i.e., crops only). The agricultural area not
covered by grasslands consists about equally of horticulture, winter cereals, and spring
cereals [source: 2018 Land Parcel Identification System (LPIS)]. The Vale of Glamorgan
is a distinctive, gentle lowland landscape, largely comprising a rolling limestone plateau
with a patchwork of fields and woodlands [
51
]. The field sizes are larger than in Pem-
brokeshire, with an area average of 5.44 ha but 7.64 ha for croplands [source: 2018 LPIS].
Monmouthshire is also predominantly low lying with the landscape comprised of undulat-
ing hills, valleys, and floodplains. Sheep grazing and dairy farming are commonplace and
arable farming is largely confined to the fertile floodplains. As farming is intensive, the
extent of semi-natural or ecologically rich habitats is quite limited with the exception of the
woodlands [
52
]. The average sizes of all fields and those used only for crops are 3.85 and
5.46 ha, respectively.

Remote Sens. 2021,13, 846 5 of 30
2.3. Data
2.3.1. Sentinel-1 C-band SAR
Sentinel-1A C-band images from 5th October 2017 to 17th November 2018 acquired
over Wales were downloaded in Level-1 Ground Range Detected (GRD) format. These data
were acquired in Interferometric Wideswath (IW) mode, which presents a dual polarization
(VH and VV) and is the pre-defined mode over land [
53
]. Only data acquired in the
descending orbit were used as these provided greater coverage of Wales, consistent viewing
(at incident angles ranged from 30 to 46
◦
) every 12 days at 10 m spatial resolution, and
reduced processing needs. Wood et al. (2002) [
54
] indicated that the choice of orbit does
not significantly impact on the ability to differentiate crops.
Sentinel-1 GRD products are already partially pre-processed (e.g., by multi-looking
and projecting to ground range using an Earth ellipsoid model). Further pre-processing
(i.e., advanced SAR calibration, geocoding and co-registration) was undertaken using
the proprietary GAMMA software [
55
] (http://www.gamma-rs.ch/ accessed on 9 June
2020). GAMMA allows automatic and routine processing of Sentinel-1 SAR data [
56
].
Ticehurst et al. (2019) [
57
], who compared Sentinel-1 backscatters after Sentinel Application
Platform SNAP [
58
] and GAMMA processing, found good agreement between the two. In
order to improve SAR data processing, the national 2m-resolution Digital Elevation Model
(DEM) distributed by National Resources Wales (NRW) on the national Lle geo-portal
(http://lle.gov.wales/
accessed on 12 August 2020) was used rather than the Shuttle Radar
Topography Mission (SRTM) DEM. The national DEM was obtained from airborne-LiDAR
surveys carried out over several years [
59
]. The data were converted to backscattering
coefficients (decibels; dB) and reprojected to the national coordinate reference system, the
British National Grid (EPSG: 27700).
The Sentinel-1 SAR transmits and receives C-band microwaves, whose interactions
with vegetation depend upon the amount and structure of plant material and also the
ground conditions, including moisture and surface roughness [
53
]. However, the relative
importance of these factors depends on the polarization, with VH and VV backscatters
both influenced by the vegetation but VV being more sensitive to ground conditions.
However, VH backscattering is not insensitive to soil effects. Towards the peak of the
growing season, the sensitivity of SAR C-band backscattering to the ground conditions
progressively decreases as crops establish and grow [
60
] but is greater at the beginning and
end of the growing season. To reduce the effect of soil on VH backscattering, the VH/VV
index was developed and used in several studies [23,53,61,62], as well as here.
In order to reduce the effect due to diversity in individual plant growth stage, this
study was conducted at a parcel-based level. Kussul et al. (2016) [
30
] have shown that
parcel- rather than pixel-based approaches allow crops to be classified with greater accura-
cies. In this study, and for each date, median VH, VV, and VH/VV backscattering were
calculated for each parcel, with these defined using the Land Parcel Identification System
(LPIS) available from the Welsh Government (see Section 2.3.2). Time series of VH/VV, VH,
and VV values were then constructed for the period 5th October 2017 to 17th November
2018, which covers the crop year for Wales.
2.3.2. Land Parcel Identification System (LPIS)
The LPIS is a vector layer with associated attributes that depicts the geographic
boundaries of all fields in Wales, which have been mapped through interpretation of
aerial photography. The mapping is annually updated, and all fields are attributed with
information on the owner, the area, and crop type, with this collected through farmers’
declarations by Rural Payments Wales. The field boundaries from the 2018 LPIS for the
whole of Wales were used to generate the crop type map. The crop type data collected
through farmers’ declarations were used to assess the accuracy of the resulting crop map for
the three validation sites (i.e., Pembrokeshire, the Vale of Glamorgan, and Monmouthshire)
(Table 1).

Remote Sens. 2021,13, 846 6 of 30
Table 1.
Proportion (in%) of each of the main crop types declared by farmers in the three validation areas [source: 2018
Wales Farmers’ declaration].
Sites WB WW WR SB SW MA PO BT GR
Pembrokeshire (% crops)
(% parcels)
11
4
19
6
8
3
31
10
1
0
9
3
18
6
2
1
-
67
Vale of Glamorgan (% crops)
(% parcels)
19
8
43
18
14
6
11
5
0
0
11
5
1
0
2
1
-
57
Monmouthshire (% crops)
(% parcels)
8
3
46
15
15
5
5
2
0
0
26
9
0
0
0
0
-
67
WB = winter barley, WW = winter wheat, WR = winter rapeseed, SB = spring barley, SW = spring wheat, MA = maize, PO = potatoes,
BT = beets, GR = grassland.
2.3.3. Planet CubeSat Data: PlanetScope Constellation
The PlanetScope satellite constellation consists of multiple groups of individual small
satellites which have been placed in orbit through multiple launches since the end of
2016. The PlanetScope constellation captures the entire Earth’s landmass every day at 3 m
spatial resolution in four different spectral bands—the visible red, green, and blue and
the near infrared (NIR). In this study, the PlanetScope data available on the Planet Lab
platform
(https://www.planet.com/
accessed on 12 August 2020) through Planet Lab’s
Ambassador Program were used to cross-check the farmers’ declarations.
2.3.4. Reference Crop Maps for Wales
For comparative purposes, the crop map generated in this study through time series
analysis of the Sentinel-1 C-band SAR were compared with the two crop maps currently
existing nationally for Wales. These were the UK Centre for Ecology and Hydrology (CEH)
Land Cover plus [63] and OneSoil crop maps [64].
The UKCEH Land Cover plus: Crops are based on the Land Cover Map parcel
framework and maps were generated for the years 2016, 2017, and 2018 by CEH. To achieve
the mapping, they used an automated parcel-based crop classification algorithm, which
exploited the Sentinel-1 C-band SAR and Sentinel-2 optical data. Crop maps were validated
using farmers’ declarations collected by the Rural Payments Agency (for England), Rural
Payments Wales, and the Scottish Government Rural Payments and Services. The CEH
crop classification for Wales consists of ten classes, namely winter wheat (including oats),
spring wheat, winter barley, spring barley, rapeseed, field beans, potatoes, sugar beet,
maize, and improved grass, and only takes into account fields larger than 2 ha. The overall
accuracy reported by CEH for the 2016 map was 87% at the UK level, but accuracies for the
years 2017 and 2018 were not available at the time of this study. The accuracy assessment
report highlighted that some data were missing due to the dependency of the map on the
CEH Land Cover Map 2007 [
65
] for identifying agricultural land prior to crop classification
(https://www.ceh.ac.uk/ceh-land-cover-plus-crop-map-quality-assurance, accessed on
12 August 2020).
For the OneSoil map, neural network algorithms were used to detect field boundaries
and crops were then classified from Sentinel-2 optical images to generate the map. OneSoil
uses in situ data provided by farmers to train its neural network algorithm and improve
the accuracy of the crop maps. The algorithm was able to detect 27 crop types across
Europe and the USA with (according to OneSoil) high accuracy. For Wales, the main
crop types in the OneSoil map are grass, barley, wheat, maize, potatoes, sugar beet, and
rapeseed. Of note is that the OneSoil map does not separate winter and spring varieties for
barley and wheat. At the time of this study, maps were available for 2016, 2017, and 2018
(https://map.onesoil.ai/, accessed on 12 August 2020).

Remote Sens. 2021,13, 846 7 of 30
2.4. Methods for Crop Type Mapping
The methodology for classifying crop types in this study comprised three successive
main steps: (a) benchmarking, (b) development of the algorithm, and (c) implementation
of the algorithm to generate the crop map.
2.4.1. Benchmark Temporal SAR Dynamics
The benchmarking step consisted of a concomitant analysis of knowledge-based crop
growth stages and VH, VV, and VH/VV Sentinel-1 time series to define the SAR dynamics
corresponding to the key growth stages of each crop type. A range of scientific publications
and various technical reports/guides, produced over past decades, give information about
the average changes in biomass and other physical structures of crops, as well as the
average timing of these, over Wales and the British Isles [
66
–
95
]. This study capitalized on
this knowledge (i.e., theoretical/average changes in structure and timing) to undertake the
benchmarking rather than relying on in situ/ground data (i.e., precise growth stage timing
in the fields used for benchmarking during the whole 2018 vegetation growth period).
Knowledge on growth stages and their average timings was reviewed, synthesized, and
used for benchmarking of the eight crop types. This knowledge is detailed in Appendix A
and summarized in Figure 2.
Remote Sens. 2021, 13, 846 7 of 30
2.4. Methods for Crop Type Mapping
The methodology for classifying crop types in this study comprised three successive
main steps: (a) benchmarking, (b) development of the algorithm, and (c) implementation
of the algorithm to generate the crop map.
2.4.1. Benchmark Temporal SAR Dynamics
The benchmarking step consisted of a concomitant analysis of knowledge-based crop
growth stages and VH, VV, and VH/VV Sentinel-1 time series to define the SAR dynamics
corresponding to the key growth stages of each crop type. A range of scientific publica-
tions and various technical reports/guides, produced over past decades, give information
about the average changes in biomass and other physical structures of crops, as well as
the average timing of these, over Wales and the British Isles [66–95]. This study capitalized
on this knowledge (i.e., theoretical/average changes in structure and timing) to undertake
the benchmarking rather than relying on in situ/ground data (i.e., precise growth stage
timing in the fields used for benchmarking during the whole 2018 vegetation growth pe-
riod). Knowledge on growth stages and their average timings was reviewed, synthesized,
and used for benchmarking of the eight crop types. This knowledge is detailed in Appen-
dix A and summarized in Figure 2.
Note: canopy size = any change in green biomass, green leaf area index (GLAI), canopy cover, or height.
Figure 2. Key knowledge-based growth stages of the eight crop types with associated theoretical periods in Wales and
indications of changes in canopy size.
To determine the benchmark temporal trends in Sentinel-1 C-band backscatter for
each of the eight crop types selected, VV, VH, and VH/VV time series were extracted from
ten fields (per crop type) randomly selected in Pembrokeshire and median time series
were calculated (see Figure 3). The resulting trends were interpreted with reference to the
growth stages (knowledge-based) for each crop type (cf. Appendix A and Figure 2). From
this, the uniqueness and similarities of SAR dynamics of the various crop types were an-
alyzed, and key SAR dynamics were derived. The results (i.e., key SAR dynamics) from
this benchmarking are shown and detailed in Section 3.1 and were used to support the
development of the classification algorithm (see Section 2.4.2).
2.4.2. From Key SAR Dynamics to Crop Type: A Descriptive Decision Algorithm
To differentiate and map the different crop types from temporal SAR data, a decision
algorithm was developed.
(a) Key SAR dynamics (mathematical measures)
Figure 2.
Key knowledge-based growth stages of the eight crop types with associated theoretical periods in Wales and
indications of changes in canopy size. Note: canopy size = any change in green biomass, green leaf area index (GLAI),
canopy cover, or height.
To determine the benchmark temporal trends in Sentinel-1 C-band backscatter for
each of the eight crop types selected, VV, VH, and VH/VV time series were extracted from
ten fields (per crop type) randomly selected in Pembrokeshire and median time series
were calculated (see Figure 3). The resulting trends were interpreted with reference to
the growth stages (knowledge-based) for each crop type (cf. Appendix Aand Figure 2).
From this, the uniqueness and similarities of SAR dynamics of the various crop types were
analyzed, and key SAR dynamics were derived. The results (i.e., key SAR dynamics) from
this benchmarking are shown and detailed in Section 3.1 and were used to support the
development of the classification algorithm (see Section 2.4.2).
2.4.2. From Key SAR Dynamics to Crop Type: A Descriptive Decision Algorithm
To differentiate and map the different crop types from temporal SAR data, a decision
algorithm was developed.
(a) Key SAR dynamics (mathematical measures)
First, the key SAR dynamics resulting fromthe benchmarking (see results in
Section 3.1
)
were translated into quantifiable variables using mathematical measures, namely magni-
tude, noise, trend, and slope (see Figure 3and Appendix ).

Remote Sens. 2021,13, 846 8 of 30
In order to measure trends (e.g., increase or decrease), Mann–Kendall and Sen’s slope
statistical tests were used over the key SAR dynamics’ periods. The Mann–Kendall (MK)
test [
96
,
97
] is a rank-based non-parametric statistical trend test which has been widely used
to detect significant monotonic trends and changes in time series (e.g., [
98
–
104
]). One of the
main advantages of this statistical test is that, compared with parametric statistical tests,
it is more suitable for non-normally distributed data, which are frequently encountered
with environmental variables [
105
]. The MK test has been used mainly for analyzing
time series of climatic and environmental data, but any type of data can be evaluated.
Recently, Dabrowska-Zielinska et al. (2018) [
106
] used MK to interpret time series of
Sentinel-1 C-band SAR for estimating soil moisture variations over wetlands. In this
study, a p-value of 0.01 was used in the MK test to determine trend significance. Trends
with
p-value > 0.01
were considered not significant (c.f.,
MKno
in Appendix ). Following
detection of a significant trend by the MK test, Sen’s slope test [
107
–
109
] was used to
measure the orientation of the slope (i.e., positive/negative) and quantify magnitude (c.f.,
Ssl+
,
Ssl−
, and Mag in Appendix ). Sen’s slope is a non-parametric test which is based on
the median slope rather than the mean slope (as in the case of parametric linear regression).
Note that, even though these two tests are based on a monotonic trend hypothesis, the
presence of outliers does not decrease their relevance [
110
–
112
], as they are non-parametric
tests. Hamed (2008) [
111
] mentioned that the use of non-parametric trend tests is more
suitable for detecting trends in time series which may contain outliers.
Finally, in order to measure noise, the average difference between original values and
smoothed values during the key SAR dynamics period was calculated and used. Smoothed
values were calculated using locally weighted least squares regression (i.e., loess) [
113
,
114
]
which combines the simplicity of the classical least squares method with the flexibility of
non-linear regression [
115
]. It is a non-parametric method where least squares regression is
performed in localized subsets, which renders it a suitable candidate for smoothing volatile
time series. It has been widely used in the literature to smooth curves and filter noise
in various types of dataset e.g., [
116
–
120
]. In this study, we used loess to detect if noise
occurred during the key SAR dynamics’ periods. “No noise” (c.f.,
Nseno
in Appendix ) was
attributed where an average difference of ~0 dB was found between original values and
smoothed values during the analyzed period.
(b) Decision algorithm
The key SAR dynamics (mathematical measures) were hierarchically organized in
order to develop a conditional decision algorithm. The hierarchical organization in the
algorithm was defined after analysis of the uniqueness and similarities of the key SAR
dynamics resulting from the benchmarking (see Section 3.1), and is detailed in Appendix .
The developed algorithm allows (a) a physical description of the temporal SAR time series
of fields using the previously mentioned mathematical measures over key analysis periods,
and then (b) makes decisions regarding the crop type based on conditional decisions (c.f.,
Appendix ). The key analysis periods used by the algorithm are based on knowledge of the
different growth stages (see Figure 2) and the analysis of the results of the benchmarking
(see Sections 3.1 and 4). In the algorithm, first, long periods (i.e., the beginning of November
to the end of March and the beginning of April to the end of the growing season) are used
by the algorithm to make decisions regarding the broad crop category (i.e., winter and
spring crops), c.f. Appendix . Then, using shorter periods (i.e., 2 months and 1 month)
and the mathematical conditions listed in Appendix , decisions regarding the crop type
are made (i.e., wheat, rapeseed, and barley for winter crops or potatoes, barley, maize,
beets, and wheat for spring crops). The grassland category is assigned when none of the
mathematical conditions are met.

Remote Sens. 2021,13, 846 9 of 30
Remote Sens. 2021, 13, 846 9 of 30
Figure 3. Flowchart of the methodology. Note: K-based crop map = knowledge-based crop map.
(b) Decision algorithm
The key SAR dynamics (mathematical measures) were hierarchically organized in
order to develop a conditional decision algorithm. The hierarchical organization in the
algorithm was defined after analysis of the uniqueness and similarities of the key SAR
dynamics resulting from the benchmarking (see Section 3.1), and is detailed in Appendix
B. The developed algorithm allows (a) a physical description of the temporal SAR time
series of fields using the previously mentioned mathematical measures over key analysis
periods, and then (b) makes decisions regarding the crop type based on conditional deci-
sions (c.f., Appendix B). The key analysis periods used by the algorithm are based on
knowledge of the different growth stages (see Figure 2) and the analysis of the results of
the benchmarking (see Section 3.1 and Section 4). In the algorithm, first, long periods (i.e.,
the beginning of November to the end of March and the beginning of April to the end of
the growing season) are used by the algorithm to make decisions regarding the broad crop
category (i.e., winter and spring crops), c.f. Appendix B. Then, using shorter periods (i.e.,
2 months and 1 month) and the mathematical conditions listed in Appendix B, decisions
regarding the crop type are made (i.e., wheat, rapeseed, and barley for winter crops or
potatoes, barley, maize, beets, and wheat for spring crops). The grassland category is as-
signed when none of the mathematical conditions are met.
2.4.3. Generation and Validation of Crop Map
The third step is algorithm implementation which allowed generation of the crop
type map for Wales. The classification of crop types was confined to the cultivated/man-
aged vegetated area of Wales. For each of the LPIS parcels within the cultivated/managed
vegetated area, the decision algorithm was applied to the parcel VH/VV, VH, and VV time
Figure 3. Flowchart of the methodology. Note: K-based crop map = knowledge-based crop map.
2.4.3. Generation and Validation of Crop Map
The third step is algorithm implementation which allowed generation of the crop type
map for Wales. The classification of crop types was confined to the cultivated/managed
vegetated area of Wales. For each of the LPIS parcels within the cultivated/managed
vegetated area, the decision algorithm was applied to the parcel VH/VV, VH, and VV
time series in order to generate the crop map (see Figure 3). The grassland category was
assigned to parcels that exhibited no key temporal signature in the Sentinel-1 C-band SAR
time series.
Whilst the decision algorithm was applied at a national level, the classification was
validated for the main agricultural regions of Pembrokeshire, the Vale of Glamorgan, and
Monmouthshire using the crop types declared by farmers for each of the LPIS parcels.
As the farmers’ declarations may contain errors, they were cross-checked through visual
interpretation of the very high resolution and temporal frequency PlanetScope images
acquired over the study period. Parcels were discarded where the declared crop type was
different from that observed in the PlanetScope imagery or was not listed as one of the
eight studied crop types (or grassland). For Pembrokeshire, fields used for benchmarking
were removed. The size of the validation samples within each agricultural area was similar
(Table 2). For each area, confusion matrices were generated, and a range of metrics for
quantifying accuracy were calculated, including overall accuracy (OA), omission and com-
mission errors, and producer and user accuracies (respectively, PA and UA). As concerns
have been raised about the use of the kappa agreement [
121
–
127
], this measure was not
used in this study.
Although these three regions were selected because they were the main arable areas
of Wales, grassland remained the predominant cover, accounting for approximatively 60%
to 70% (see Table 1). When trying to quantify the accuracy of land cover maps, the total
size of the validation sample and the proportion of each class within that sample are of

Remote Sens. 2021,13, 846 10 of 30
great importance. Allocating approximately equal sample sizes to each class is a relatively
common practice in accuracy assessment [
125
] and allows approximately equal precision
for the estimated user’s accuracy of each class to be provided. However, larger sample
sizes can be allocated to specific classes depending on the objectives. This study aimed to
classify crop types in cultivated/managed vegetated areas at a national level and hence,
over large areas.
Table 2.
Number and crop type of the parcels used for validation of the national crop maps for Wales
(i.e., proposed knowledge-based, CEH, and OneSoil maps).
WB WR WW SB BT MA PO SW GR Total
Pembrokeshire 23 17 40 65 5 20 39 2 433 644
Vale of Glamorgan 49 38 113 28 5 29 2 0 353 617
Monmouthshire 18 32 100 11 1 56 0 1 435 654
WB = winter barley, WW = winter wheat, WR = winter rapeseed, SB = spring barley, SW = spring wheat,
MA = maize, PO = potatoes, BT = beets, GR = grassland
To validate the capacity of the method to accurately classify the cultivated landscape,
accuracy was first assessed using samples where the proportion of each class was repre-
sentative of those occurring in the landscape (i.e., including grassland). For this purpose,
we used a systematic selection method, as it allows more precise estimates [
127
]. Secondly,
to determine the capacity of the method to distinguish the different crop types, accuracy
was estimated using normalized confusion matrices containing only the eight crop types
(i.e., excluding grassland). At the same time, and using the same validation samples and
method, the accuracy of the other national crop maps for Wales (i.e., CEH and OneSoil)
was assessed for comparative purposes (Figure 3).
3. Results
3.1. Key Temporal SAR Signatures (VH/VV, VH, and VV)
The benchmarking exercise identified differences in the temporal C-band VH, VV,
and VH/VV signatures of both winter and spring crops (Figures 4and 5) which could be
interpreted through reference to the knowledge-based crop growth stages (GS), cf. Figure 2
and Appendix A.
3.1.1. Winter Crops
All three winter crops (i.e., barley, wheat, and rapeseed) exhibited a slow but slightly
increasing value of VH/VV during November, December, and January, with this corre-
sponding to winter emergence and leaf development (Figure 4). From February, a more
rapid increase in the VH/VV marked the beginning of the spring component of the leaf-
development phase, which continued until the beginning of April. By comparison, the
spring crops (Figure 5) exhibited a decreasing or largely stable trend in the VH/VV from
the beginning of November to the beginning of April. Thereafter, a rapid increase in the
VH/VV slope was observed in winter crops, with this corresponding to a period of stem
elongation (cf. Figure 2). Maximum VH/VV was observed during May or June, with this
varying by species. Wheat attained its maximum VH/VV around mid-May (maximum of
~
−
3 dB) whilst barley and rapeseed were around mid-June (with values of
−
3 and
−
4 dB,
respectively). The observations concurred with the knowledge-based GS periods for these
species (cf. Figure 2).

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Remote Sens. 2021, 13, 846 11 of 30
3.1.1. Winter Crops
All three winter crops (i.e., barley, wheat, and rapeseed) exhibited a slow but slightly
increasing value of VH/VV during November, December, and January, with this corre-
sponding to winter emergence and leaf development (Figure 4). From February, a more
rapid increase in the VH/VV marked the beginning of the spring component of the leaf-
development phase, which continued until the beginning of April. By comparison, the
spring crops (Figure 5) exhibited a decreasing or largely stable trend in the VH/VV from
the beginning of November to the beginning of April. Thereafter, a rapid increase in the
VH/VV slope was observed in winter crops, with this corresponding to a period of stem
elongation (cf. Figure 2). Maximum VH/VV was observed during May or June, with this
varying by species. Wheat attained its maximum VH/VV around mid-May (maximum of
~−3 dB) whilst barley and rapeseed were around mid-June (with values of −3 and −4 dB,
respectively). The observations concurred with the knowledge-based GS periods for these
species (cf. Figure 2).
Figure 4. Median VH/VV, VH, and VV time series (in red) and standard deviation (in grey) for the ten plots of winter
barley (a,d,g), winter wheat (b,e,h), and winter rapeseed (c,f,i) used for benchmarking. Key dynamics are indicated with
reference to the knowledge-based crop growth stages.
Figure 4.
Median VH/VV, VH, and VV time series (in red) and standard deviation (in grey) for the ten plots of winter barley
(
a,d,g
), winter wheat (
b,e,h
), and winter rapeseed (
c,f,i
) used for benchmarking. Key dynamics are indicated with reference
to the knowledge-based crop growth stages.
A dynamic response was also observed in the VH time series, which corresponded
with distinct periods during the crop growth and phenological cycle. For wheat (Figure 4),
when the VH/VV reached a maximum (i.e., around mid-May), the VH started to increase
until the beginning of August. For barley, the VH increase commenced around mid-May
(i.e., before the maximum of the VV/VH) and the maximum was reached at the same
time as the maximum VH/VV (i.e., mid-June). Hence, the timing of the VH increase
aligned differently with the VH/VV dynamics for wheat and barley. Reference to the
knowledge-based GS (Appendix A) indicated that this change aligned with the different
inflorescence emergence time for barley and wheat crops. The increase in VH also occurred
over a shorter period for barley compared to wheat and hence, the slope was steeper.
As with barley, rapeseed exhibited a marked increase in VH between mid-May and
mid-June and the maximum occurred at the same time as the VH/VV. However, contrary
to barley and wheat, which showed an important decrease in VH between the beginning of
April and mid-May, rapeseed VH almost plateaued (Figure 4). According to the knowledge-
based GS, this occurred during the concomitant occurrence of stem elongation with bud
development and flowering. This led to high VH values (i.e.,
−
15 dB) around mid-May in
rapeseed fields compared to the other winter crops (i.e., around
−
20 dB). As VH values
are already high around mid-May and further increase until mid-June, maximum VH is
largely higher in rapeseed’s field plots compared to barley’s or wheat’s, respectively,
−
11,
−
15, and
−
17 dB. These are the key dynamics that allowed us to distinguish barley, wheat,
and rapeseed (see Appendix ). The shape of the VV signal was similar to VH throughout
the time series but the magnitude varied because of different interaction with the plant
components. These differences were reflected in the dynamics observed in the VH/VV
ratio.

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Remote Sens. 2021, 13, 846 12 of 30
Figure 5. Median VH/VV, VH and VV time series (in red) and standard deviation (in grey) for the ten plots of spring barley
(a,f,k), maize (b,g,l), potatoes (c,h,m), spring wheat (d,i,n), and beets (e,j,o) used for benchmarking. Key dynamics are
indicated with reference to the knowledge-based crop growth stages.
A dynamic response was also observed in the VH time series, which corresponded with
distinct periods during the crop growth and phenological cycle. For wheat (Figure 4), when
the VH/VV reached a maximum (i.e., around mid-May), the VH started to increase until the
beginning of August. For barley, the VH increase commenced around mid-May (i.e., before
the maximum of the VV/VH) and the maximum was reached at the same time as the maxi-
mum VH/VV (i.e., mid-June). Hence, the timing of the VH increase aligned differently with
the VH/VV dynamics for wheat and barley. Reference to the knowledge-based GS (Appendix
A) indicated that this change aligned with the different inflorescence emergence time for bar-
ley and wheat crops. The increase in VH also occurred over a shorter period for barley com-
pared to wheat and hence, the slope was steeper.
Figure 5.
Median VH/VV, VH and VV time series (in red) and standard deviation (in grey) for the ten plots of spring barley
(
a,f,k
), maize (
b,g,l
), potatoes (
c,h,m
), spring wheat (
d,i,n
), and beets (
e,j,o
) used for benchmarking. Key dynamics are
indicated with reference to the knowledge-based crop growth stages.
3.1.2. Spring Crops
The time series of VH/VV, VH, and VV for the spring crops of barley, wheat, maize,
potatoes, and beets also showed a dynamic seasonal signal (Figure 5), which reflected
different components of the crop cycle identified in the key knowledge-based GS. The
temporal signatures for winter and spring barley were similar, with the VH/VV again
showing a slow gain from mid-April to the beginning of May and steeply increasing
thereafter to a maximum around mid-June. The VH time series was characterized by a
steep increase for one month (i.e., during July) followed by a decrease which started at
the same time as the VH/VV decrease (i.e., August). However, contrary to winter barley,
which showed an increase in VH/VV in parallel to the VH increase, spring barley’s VH/VV
time series was marked by a plateau. Reference to the knowledge-based GS indicated that
this corresponded to the period when canopy size plateaus soon after flag leaf emergence
until the spring barley ears are fully developed (Appendix Aand Figure 2). The maximum
VH/VV value for spring barley was 20% lower compared to the winter variety. The
knowledge-based GS also indicated that the peak canopy size was, on average, 15–20%

Remote Sens. 2021,13, 846 13 of 30
less for spring barley [
78
,
88
]. As with spring barley, spring wheat exhibited a plateau in
the VH/VV time series during July and a lower maximum VH/VV value compared to the
winter variety, but a smaller increase in VH was found compared to barley during the ear
development (~1.5 and ~5 dB, respectively).
For potatoes, a high variability in VH/VV between fields was observed throughout
the entire growing season (Figure 5), which presented limited opportunities for discrimina-
tion. However, from the median VH/VV, three phases identified in the knowledge-based
GS (vegetative, reproductive with maintenance of green parts, and reproductive with
senescence of green parts) could be associated with steep and less steep increases and a
decrease in values, respectively. These phases were also evident in the VH time series, and
with a low standard deviation in each time step. Of note was that potatoes displayed a
large increase in VH similarly to spring barley (~5 dB) followed by a decrease of ~3 dB.
However, for potatoes, the VH increase commenced in mid-May (rather than the beginning
of July for barley), with this leading to a comparatively longer period of increasing VH (i.e.,
two rather than one month). Furthermore, a corresponding increase in VV with VH was
observed whilst, for spring barley, a decrease in VV occurred during that period.
For maize, two periods of growth can be distinguished, with a slower and subsequent
steeper increase corresponding to the emergence and development of the first leaves and
period of stem elongation, respectively (cf. Figure 2). As with beet, the VH/VV and VH of
maize plateaued from July to September but whilst this continued for beet, a decrease in
VH/VV occurred from mid-September to mid-October in maize crops. Additionally, in
contrast to maize, the VV of beets increased and then plateaued. Throughout the growing
season, the VH/VV increase was significantly lower (~2 dB) for maize compared to other
cereals (at least 4 dB).
3.2. A Crop Map for Wales
On the basis of the median VH/VV, VV, and VH time series, key SAR dynamics for
discrimination of spring and winter barley and wheat, winter rapeseed, maize, potatoes,
and beets were identified (see Section 3.1) and translated to quantifiable mathematical
measures and a decision algorithm (see Appendix ) allowing crop mapping for each field in
Wales. These were defined by the Welsh Government LPIS within the cultivated/managed
landscape. Each of the functions of the algorithm was associated with a description of the
crop phases obtained through reference to the knowledge-based GS.
The mapping captures the extent of crop lands as well as grasslands in agricultural
Wales landscape and estimated that 86.1% of cultivated/managed area is covered by
grasslands, with the remainder occupied by maize (21.7%), winter wheat (19.6%), spring
barley (18.5%), winter barley (17.4%), potatoes (10.6%), spring wheat (7.75%), rapeseed
(3%), and beets (1.5%). The national map and subsets of the map, showing the extent of
cultivated crops and grasslands, are given in Figure 6.
The accuracy of the crop and grassland map was quantified for Pembrokeshire, the
Vale of Glamorgan, and Monmouthshire using the datasets presented in Section 2.4.3 and
Table 2. Overall accuracies (OA) (Table 3) were also generated for the CEH and OneSoil crop
maps using the same validation datasets. At the agricultural landscape level (i.e., including
grassland, “all parcels” in Table 3) and for all validation sites, the OA ranged from 82.0% to
90.2% when using the knowledge-based proposed methodology (i.e., mentioned K-based
map in the rest of the paper). When winter and spring varieties (i.e., “seasonal crops” in
Table 3) as well as grasslands (i.e., “all parcels” in Table 3) were considered, the highest OAs
for Pembrokeshire (90.2%), the Vale of Glamorgan (89.6%), and Monmouthshire (82.0%)
were all obtained with the K-based map. With the exception of Monmouthshire (92.2%
with OneSoil), the K-based map also displayed the highest accuracies when considering
grasslands and crops, with no distinction between winter and spring varieties (i.e., “annual
crops” in Table 3). Over the three areas, regardless the product, grassland showed user
accuracies (UA) greater than 95%, with only the exception of CEH in Pembrokeshire (i.e.,
84.7%), not shown here.

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Remote Sens. 2021, 13, 846 15 of 30
Figure 6. 2018 crop map for Wales obtained using the proposed methodology (i.e., knowledge-based), with zooms. Zoom
(a,b) show Anglesey and Wrexham regions and zoom (c–e) show Pembrokeshire, Vale of Glamorgan, and Monmouthshire
validation areas, respectively.
Figure 6.
2018 crop map for Wales obtained using the proposed methodology (i.e., knowledge-based), with zooms. Zoom
(
a
,
b
) show Anglesey and Wrexham regions and zoom (
c
–
e
) show Pembrokeshire, Vale of Glamorgan, and Monmouthshire
validation areas, respectively.

Remote Sens. 2021,13, 846 15 of 30
Table 3.
Overall, accuracies (%) of the new product (i.e., K-based), CEH, and OneSoil, using farmers’
declarations as ground-truth for Pembrokeshire, Vale of Glamorgan, and Monmouthshire. “Seasonal”
indicates the results for the crop map when a distinction is made between winter and spring crop
types (e.g., winter barley and spring barley), whereas “annual” indicates that no distinction is
made between winter and spring varieties (e.g., barley). “All parcels” indicate that crops as well as
grasslands are considered, whereas “crops” indicate that only the crop lands are taken into account.
Product Site Seasonal Crops Annual Crops
All
Parcels Crops All
Parcels Crops
K-based
Pembrokeshire
Glamorgan
Monmouthshire
90.2 *
89.6 *
82.0 *
88.6 *
90.6 *
85.8
90.2 *
89.8 *
82.0
87.1 *
90.1 *
83.5 *
CEH 1
Pembrokeshire
Glamorgan
Monmouthshire
60.8
70.3
61.9
87.6
79.4
94.2 *
-
-
-
-
-
-
OneSoil
Pembrokeshire
Glamorgan
Monmouthshire
-
-
-
-
-
-
83.9
88.0
92.2 *
70.5
83.5
76.8
* Method providing the highest accuracy. 1Excludes parcels of 2 ha or less, with many being grasslands.
Using normalized confusion matrices containing each crop type and excluding grass-
lands (i.e., labelled as “crops” in Table 3), the capacity of the proposed methodology to
classify crop types was evaluated and compared to other maps available for Wales (CEH
and OneSoil). With the exception of Monmouthshire, where the CEH map OA was ~94%,
the K-based map showed higher accuracy with OA ranging between 85.8% and 90.6%
(Table 3).
The omission and commission errors as well as the producer and user accuracies
(PAs and UAs, respectively) of each of the crop types for the K-based, CEH, and OneSoil
maps were quantified, using normalized confusion matrices containing each crop type
and excluding grasslands. Depending on the region, the crops types differ. Only the crop
types with a significant number of parcels were analyzed. In the three validation areas,
the number of beets and spring wheat field plots was too small to be considered in the
accuracy analysis. Potatoes fields were significantly present in Pembrokeshire but not in
the two other regions and very few spring barley fields were present in Monmouthshire.
For Pembrokeshire, the K-based map provided higher PAs for five of the six crop
types considered, with the exception of potatoes (79% compared to 100% for CEH;
Table 4
).
PAs exceeded 90% for winter barley, winter rapeseed, and maize and 80% for winter
wheat and spring barley in the K-based map (see
Table 4
). Winter barley and winter
rapeseed also displayed accuracies greater than 90% in the CEH product. However, the
CEH PAs for winter wheat and spring barley were less than 80% (respectively, 65.96% and
77.78%). Compared to the K-based and CEH maps, OneSoil performed very poorly in the
Pembrokeshire region with PAs of 45.24%, 50.00%, and 62.07%, respectively, for wheat,
potatoes, and barley and UAs of 57.21% and 63.78% for barley and maize (see Table 5).
In the Vale of Glamorgan, the PA for all crops was > 80% for the K-based map, with the
exception of maize (at 77%). The CEH PA for spring barley was very low because of high
misclassification to spring wheat and the UA was low because of a commission error of
39.7% from winter wheat. Similarly, poor results in the OneSoil map in this region came
from a confusion between barley and wheat, leading to a wheat PA of 59.29% and a barley
UA of 61.24%. For Monmouthshire, within the K-based product, only the maize PA was
under 80% (i.e., ~76%). In both Monmouthshire and the Vale of Glamorgan, this <80%
maize PA was attributed to misclassification of maize into several other but not any specific
crop types. In Pembrokeshire, the PA for maize was 90.0%, suggesting that other factors
(than the algorithms) might be contributing to the map accuracy (e.g., parcel size, mixed
SAR signal).

Remote Sens. 2021,13, 846 16 of 30
Table 4.
Producer accuracies (PAs) and user accuracies (UAs) in% for the new (i.e., K-based) and
CEH maps over Pembrokeshire, Vale of Glamorgan, and Monmouthshire.
Measure Crop
Type Pembrokeshire Vale of Glamorgan Monmouthshire
K-Based CEH 1K-Based CEH 1K-Based CEH1
PAs
WB 100.00 * 95.24 89.58 100.00 * 88.89 100.00 *
WR 100.00 * 100.00 * 94.74 100.00 * 90.63 100.00 *
WW 82.35 * 65.96 100.00 * 92.38 87.37 * 76.74
SB 80.36 * 77.78 82.61 * 4.35 - -
BT------
MA 90.00 * 86.36 76.92 100.00 * 76.19 100.00 *
PO 78.95 100.00 * - - - -
SW------
UAs
WB 100.00 * 86.48 97.15 * 95.45 90.37 * 83.50
WR 100.00 * 100.00 * 100.00 * 100.00 * 100.00 * 100.00 *
WW 96.90 * 93.27 86.43 100.00 * 82.72 100.00 *
SB 72.63 82.04 * 97.54 * 60.34 - -
BT------
MA 76.08 95.89 * 89.84 * 88.46 100.00 * 100.00 *
PO 93.65 * 85.22 - - - -
SW------
WB = winter barley, WW = winter wheat, WR = winter rapeseed, SB = spring barley, SW = spring wheat,
MA = maize
, PO = potatoes, BT = beets. * Method providing the highest accuracy.
1
Excludes parcels of 2 ha
or less.
Table 5.
Producer accuracies (PAs) and user accuracies (UAs) in% for the new (K-based) and OneSoil
maps over Pembrokeshire, Vale of Glamorgan, and Monmouthshire.
Measure Crop
Type Pembrokeshire Vale of Glamorgan Monmouthshire
K-Based OneSoil K-Based OneSoil K-Based OneSoil
PAs
Barley 85.90 * 62.07 88.73 92.21 * 80.77 * 68.97
Rapeseed
100.00 * 100.00 * 94.74 100.00 * 90.63 100.00 *
Wheat 80.56 * 45.24 100.00 * 59.29 86.46 98.00 *
Beets - - - - - -
Maize 90.00 95.00 * 76.92 82.35 * 76.19 * 40.38
Potatoes 78.95 * 50.00 - - - -
UAs
Barley 74.57 * 57.21 97.12 * 61.24 74.07 82.67 *
Rapeseed
100.00 * 100.00 * 100.00 * 100.00 * 100.00 * 96.67
Wheat 95.37 * 84.90 80.87 91.94 * 83.39 86.74
Beets - - - - - -
Maize 76.57 * 63.78 96.47 98.45 * 100.00 * 97.58
Potatoes 95.35 95.60 * - - - -
* Method providing the highest accuracy.
For all fields and regardless of their location, the OAs and PAs were analyzed by field
size (Figure 7a,b). The accuracies increased with field size up to an area of 8 ha (Figure 7b)
but then remained relatively similar thereafter. This was especially the case for the spring
varieties. Maize and spring barley, which had the lowest accuracies in the K-based map
(see Table 4), respectively increased from 60.0% to 83.3% and 50.0% to 100.0%, for fields
that were on average <2 to 8 ha (Figure 7b). This led to an increasing OA in the K-based
map as field size increases (Figure 7a).

Remote Sens. 2021,13, 846 17 of 30
Remote Sens. 2021, 13, 846 18 of 30
Figure 7. (a) Overall accuracy and (b) producer accuracies by field size for the new (K-based) map.
These plots consider all the fields regardless of the region. WB = winter barley, WW = winter
wheat, WR = winter rapeseed, SB = spring barley, MA = maize.
4. Discussion
By using knowledge gathered by the agricultural community to inform a descriptive
decision algorithm based on the Sentinel-1 C-band SAR time series, eight different crop
types were able to be mapped across Wales for 2018 with overall accuracies of between
85.8% and 90.6% depending on the area. The classifications were generated without the
use of in situ/ground training datasets nor machine learning but provided accuracies that
exceeded those of other products that included these techniques in their mapping. The
concomitant analysis of knowledge-based GS and time series of VH, VV, and VH/VV Sen-
tinel-1 C-band SAR data allowed interpretation of signatures through biophysical mean-
ing. The VH/VV trends were similar to those observed in the knowledge-based GS and
particularly related to changes in canopy size (see Figure 2). This is consistent with several
previous studies (e.g., [53]) that have shown that the VH/VV ratio is correlated with fresh
(green) biomass. As the VH/VV ratio reduces sensitivity to soil effects, as observed with
VH and VV, detection of slow and small trends in vegetation growth during the winter
season (barley, wheat, and rapeseed) was discerned. This allowed us to separate winter
and spring crops with very high accuracy (> 95%).
With the start of spring, winter crops display an increase in the VH/VV because of
the increase in fresh biomass through leaf production. Winter barley and wheat both dis-
played a large decrease in VH and VV during the stem elongation stage. These trends are
consistent with the literature and seem to occur during this growth stage regardless of the
European region and period. Veloso et al. [53] in the southwest of France during 2015 and
Khabbazan et al. [23] in their study case in the Netherlands during 2017 found similar
trends for barley and wheat crops. The decreases in VH and VV during stem elongation
are both induced by vegetation rapid growth, but for different physical reasons. Whereas
the VV signal is dominated by both direct ground and canopy contributions, VH is influ-
enced by double-bounce scattering between the stem and ground, as well as by volume
Figure 7.
(
a
) Overall accuracy and (
b
) producer accuracies by field size for the new (K-based) map.
These plots consider all the fields regardless of the region. WB = winter barley, WW = winter wheat,
WR = winter rapeseed, SB = spring barley, MA = maize.
4. Discussion
By using knowledge gathered by the agricultural community to inform a descriptive
decision algorithm based on the Sentinel-1 C-band SAR time series, eight different crop
types were able to be mapped across Wales for 2018 with overall accuracies of between
85.8% and 90.6% depending on the area. The classifications were generated without the
use of in situ/ground training datasets nor machine learning but provided accuracies that
exceeded those of other products that included these techniques in their mapping. The
concomitant analysis of knowledge-based GS and time series of VH, VV, and VH/VV
Sentinel-1 C-band SAR data allowed interpretation of signatures through biophysical
meaning. The VH/VV trends were similar to those observed in the knowledge-based GS
and particularly related to changes in canopy size (see Figure 2). This is consistent with
several previous studies (e.g., [
53
]) that have shown that the VH/VV ratio is correlated with
fresh (green) biomass. As the VH/VV ratio reduces sensitivity to soil effects, as observed
with VH and VV, detection of slow and small trends in vegetation growth during the winter
season (barley, wheat, and rapeseed) was discerned. This allowed us to separate winter
and spring crops with very high accuracy (> 95%).
With the start of spring, winter crops display an increase in the VH/VV because
of the increase in fresh biomass through leaf production. Winter barley and wheat both
displayed a large decrease in VH and VV during the stem elongation stage. These trends
are consistent with the literature and seem to occur during this growth stage regardless
of the European region and period. Veloso et al. [
53
] in the southwest of France during
2015 and Khabbazan et al. [
23
] in their study case in the Netherlands during 2017 found
similar trends for barley and wheat crops. The decreases in VH and VV during stem
elongation are both induced by vegetation rapid growth, but for different physical reasons.
Whereas the VV signal is dominated by both direct ground and canopy contributions, VH is
influenced by double-bounce scattering between the stem and ground, as well as by volume
scattering [
53
,
128
]. Hence, the VV decrease is attributed to the increasing attenuation of the

Remote Sens. 2021,13, 846 18 of 30
vertically polarized wave led by the growing vertical structure of cereals on both forward
and return propagation paths [
23
,
53
,
129
,
130
]. As stems elongate, the double-bounce and
the volume fraction of the vegetation change, which affects the VH.
During the second part of May, VH and VV start to increase. The difference of timing
alignment between VH and VV trends and maximum VH/VV for barley suggests (from
the knowledge-based GS) a contribution from inflorescence (i.e., ear) development. In-
deed, barley and wheat have a very similar structure, but one of the major differences is
that winter barley reaches its maximum green biomass at the end of the ear emergence,
whereas wheat’s maximum canopy size occurs between flag leaf emergence and ear emer-
gence
[78,87,88]
. Khabbazan et al. (2019) [
23
], who compared the time series of Sentinel-1
backscatter with ground measurements, mentioned that VV increase in winter wheat is
due to flag leaves and/or inflorescence emergence, with this supported by Mattia et al.
(2003) [
60
]. Similarly, Veloso et al. (2017) [
53
], who analyzed the temporal behavior of
Sentinel-1 C-band backscatter in a field plot of winter barley, related this phenomenon to
heading (i.e., inflorescence emergence). Note that inflorescence development also leads
to increasing fresh biomass in barley but not in wheat crops. This induced an increase in
VH/VV for barley fields but not for wheat, strengthening the correlation between the ratio
and fresh biomass and showing the importance of the inflorescence emergence period to
distinguish barley from wheat.
Contrary to winter cereals, in rapeseed crops, the VH and VV signals approximately
followed the same temporal dynamics as the VH/VV signal. Unlike barley and wheat,
rapeseed does not display a large decrease in VH but instead, slightly decreases and almost
plateaus. According to the knowledge-based GS, this occurs during the concomitant occur-
rence of stem elongation with bud development and flowering. Fieuzal et al. (2013) [
62
]
and Wiseman et al. (2014) [
131
] observed a similar trend in the RADARSAT-2 C-band
VH data. They showed that the minimum of the small decrease in VH is reached when
flowering is occurring, which agrees with our results. As pods develop, VH shows a steep
increase and the increasing VH/VV rate lowers. As no predominant orientation exists,
volume scattering increases, which explains the increase in VH during that period [62].
In most crops, VH/VV is generally well correlated with fresh biomass and this allowed
detection of a ~20% lower biomass in the spring variety compared to the winter variety of
barley, which is commonly reported in the literature [
78
,
88
]. However, in potato crops, VH
was found to be of greater use, as the VH/VV displayed large standard deviations during
almost the entire growing season but particularly just before and during the emergence
and vegetative stages. This sudden increase in the standard deviation of VH/VV from mid-
April (i.e., the planting period) was largely due to greater sensitivity of the VV backscatter
compared to the VH. The standard deviation decreased as the VH/VV (fresh biomass)
increased, with minimal standard deviation obtained at maximum canopy size. Hence,
the high variation in the VH/VV during the first stages of potato growth was probably
due to the soil ridges within which the potato crops are planted. Differences in the
orientation of the ridges with respect to the radar viewing direction result in variations in
the backscatter from bare soil [
23
]. As mentioned in the literature, VV is more sensitive
than VH to directional scattering induced by ridge and/or row orientation [
23
,
128
,
132
,
133
]
and sensitivity decreases as canopy covers bare soil.
For maize, the changes in VH/VV mirrored those in canopy size given by the
knowledge-based GS. The VH displayed a similar trend to VH/VV. However, contrary to
other crops where VV follows a similar trend as VH, in maize, the VV time series remained
flattened during the growing season. This absence of a correlation between the VV and
the biomass in maize crops has been reported in the literature [
23
]. Vreugdenhil et al.
(2018) [
134
], who studied the sensitivity of Sentinel-1 C-band backscatter to vegetation
dynamics using in situ reference data in Austria, showed that VV backscatter is mainly
sensitive to soil moisture in maize crops due to the distance between maize rows. As
row spacings are generally large (i.e., ~70 cm), the VV is still influenced by direct ground
contribution until late stages, which leads to a poor correlation with biomass. Additionally,

Remote Sens. 2021,13, 846 19 of 30
whereas the VH/VV trends accurately matched the changes in biomass, we noticed that
just before emergence, the VH/VV for maize was higher than other crops. This led to a
2 dB increase during the growing season, which contrasted with the (at least) 4 dB of winter
crops. Some potato fields also displayed a high VH/VV at the beginning of the growing
season and this was attributed to the establishment of the soil ridges and their arrangement
in rows. However, for potatoes, this period was also associated with high variability
in VH/VV between the fields, which was not the case with maize crops. Moreover, no
such phenomenon has been reported in the literature. In this study, the small change in
the VH/VV for maize during the growing season can be explained by the configuration
of the landscape. In Pembrokeshire, where benchmarking field plots were selected, the
multiple small fields (3.76 ha on average) form a mosaic bounded by hedgerows and
hedgerow trees [
50
]. During the second part of May (i.e., the period of maize emergence),
the semi-natural vegetation (including hedgerows and hedgerow trees) is fully developed,
which influences the parcel-based averaged SAR backscatter. Trees have a VH/VV value
of ~
−
5 dB during that period, which could explain the high VH/VV of late crops such as
maize.
Compared to studies analyzing the Sentinel-1 time series using in situ/ground mea-
surements, very similar results based on knowledge acquired from various technical reports
were observed. In this study, it has been shown that knowledge-based growth stages can
be used to interpret and detect key dynamics in SAR time series. The concomitant analysis
of knowledge-based crop growth stages and average Sentinel-1 C-band time series allowed
us to understand the temporal dynamics of the C-band SAR observations for the eight
main crop types. In general, VH/VV and VH were shown to be the most correlated to
changes in vegetation structure during the growing season and, thus, were the two most
important variables for defining the key dynamics. From this benchmarking, translation
into quantitatively measurable variables was possible, with this leading to the development
of the descriptive decision algorithm. Using this algorithm on parcel-based Sentinel-1 time
series allowed mapping of crop types across Wales with an OA exceeding 85% in the
majority of cases.
Two methods were used to evaluate the crop map, with the first using samples where
the proportion of each class was representative of the landscape (i.e., grasslands were
included) and the second considering the crops only and using equal sample sizes for
each class. Overall, the accuracies were comparable and often exceeded those obtained
for the CEH and OneSoil maps, and the K-based product was found to be more consis-
tent. The OneSoil map achieved a high level of classification accuracy in the mapping
of grasslands and hence, a large overall accuracy, but different crop types were not able
to be discriminated to the same level as with K-based. By contrast, CEH showed good
capacity to distinguish crop types, but the overall accuracy at the landscape level was lower
(i.e., 60% to 70%) due to the high number of missing data. The K-based product provided
accuracies that were generally > 85% overall for both classifying the agricultural landscape
and distinguishing crop types. In terms of crop types, it was particularly effective in
distinguishing barley from wheat. Confusion between these two crop types is a major
source of error in the other two map products but distinction was achieved by the K-based
classifier, which capitalizes on the knowledge of the different growth stages of the two
species.
In general, the accuracy in the mapping of spring crops was lower than that of winter
crops. Accuracies were also found to be a function of field size, which partially explains
this outcome. The Welsh landscape is characteristically a mosaic of small fields bounded by
hedgerows and hedgerow trees. Giordano et al. (2018) [
32
], who proposed a parcel-based
methodology allowing crop type mapping from a sequence of satellite acquisitions (radar
and optical), also demonstrated a high sensitivity of radar images to parcel size, especially
in fragmented landscapes. The GRD Sentinel-1 data were similarly used as input data.
As the pixels of the GRD product are averages of five pixels of the Single Look Complex
(SLC) product, they suggested the use of SLC images in order to improve the accuracy of

Remote Sens. 2021,13, 846 20 of 30
classification, as well as the use of a high-resolution Digital Terrain Model instead of the
SRTM. Radar speckle filtering was also mentioned as a potential source of error in small
objects. Of note is that the SAR processing in this study utilizes a national 2m-resolution
Digital Elevation Model (DEM).
The size of parcels can only partially explain differences in the accuracies obtained as
winter and spring crops generally do not have significantly different field sizes. Figure 7b
highlighted that whilst all crop types are sensitive to parcel size, spring crops are affected in
a stronger way. Semi-natural elements (e.g., hedgerows, hedgerow trees, and grass strips)
can contribute to the signal and the overall response from a field. This is most likely to
occur during late April–May, when some of the spring crops emerge whilst semi-natural
elements have already started to develop (e.g., through growth and leaf flush). The smaller
the parcel, the higher the effect of edge elements is likely to be, with this resulting in a
lowering of accuracy. As the first growth stages are crucial for the classification of crop
types, noise in the SAR signal during that period is likely to introduce error. Errors are,
therefore, likely to be greater for small parcels of spring crops surrounded by hedgerows or
hedgerow trees. Denize et al. (2019) [
135
] mentioned hedgerows as noisy features in land
use mapping and removed them from the images using a 5 m negative buffer. A similar
method was tried in this study, but the remaining area within the parcels was too small to
clearly characterize key SAR dynamics.
Despite these limitations, the knowledge-based descriptive decision algorithm pro-
vided crop and grassland maps with an accuracy that was similar but often improved
over those provided by CEH or OneSoil. The K-based method is more consistent in that it
provides less variability between classes and regions compared to the other maps. This is
due to both the type of data and algorithms used in this study. Timing is very important
when addressing the issue of crop type mapping. In this study, we used only SAR time
series. A particular advantage of SAR in regions with frequent cloud cover, including
Wales, is the ability to obtain a complete time series from the images that are available. As
such, all growth stages can be captured because of observations throughout an annual cycle,
and the quality during the whole year and over the whole study area remains the same,
regardless of local weather. The maps generated by OneSoil and CEH relied, respectively,
totally or partially on optical data, which would have introduced discrepancies in the final
product because of missing data and/or perpetual change in the proportion of optical data
used and their quality, due to clouds.
The proposed approach also provides more consistent mapping as it allows tailoring
to regional and/or local land management and weather/soil/plant conditions. The use of
the MK non-parametric statistical trend and Sen’s slope tests ensures that the approach is
robust to a few outliers and the developed algorithm is sufficiently flexible to account for
temporal shifts in growth patterns as a result of regional/local weather, soil condition, plant
stress, and/or timing of crop management actions. In this study, 12-day temporal frequency
data were used. A delay of one date in the time series (i.e., one outlier) is, therefore, a shift
of 12 days in the growing season. In many parts of Europe, between years and regions, the
growing season is usually shifted by a maximum of one month. For example, Khabbazan
et al. [
23
] showed a shift of less than two weeks for maize, potatoes, and winter wheat crops
in the Netherlands during 2017 compared to Wales in 2018. Similarly, Veloso et al. [
53
]
and Vreugdenhil et al. [
134
], respectively, showed a shift of (approximatively) one month
in southwest France during 2015 and two weeks in Austria during 2017 compared to the
growing season observed for Wales. By tolerating a few outliers, the accuracy of the final
product displayed no correlation with the distance to benchmarking field plots, therefore
allowing the system to be consistent when mapping crops in different regions of Wales.
This also glimpses the potential of the proposed method for mapping crop types over other
large areas and potentially several countries or regions in Europe.

Remote Sens. 2021,13, 846 21 of 30
5. Conclusions
For crop mapping over large areas, a consistent and flexible descriptive knowledge-
based (K-based) algorithm that solely used Sentinel-1 C-band SAR annual time series
has been developed, with this being informed by knowledge gathered by the agricultural
community on crop growth stages. The method was developed for Wales and the accuracies
of classification are comparable or exceed those generated in previous efforts, with these
based on optical or combined SAR-optical data. As Wales is frequently covered with clouds,
the use of Sentinel-1 C-band data alone is advantageous as scenes are currently available at
a national level every 12 days (when using one orbit), which allows consistent application
and outputs for all regions.
In general, VH/VV and VH were shown to be the most correlated to changes in vegeta-
tion structure during the growing season and, thus, were the two most important variables
for crop type mapping. Whereas the VH/VV ratio has shown to be very correlated with
canopy size (i.e., fresh biomass), VH shows especially good capacity to detect inflorescence
(i.e., ears) emergence in winter cereals. Wheat and barley have a very similar structure
but timing alignment between the beginning of the VH increase (i.e., ear emergence) and
the maximum VH/VV (i.e., maximum green biomass) is different, with this period being
crucial for separating wheat from barley. By capitalizing on this fundamental difference,
these two crop types were particularly well differentiated, which is a significant advance
given these are not well captured in other products available for Wales. The accuracies
of classification, however, varied as a function of field size in this parcel-based approach
and increased progressively up to an area of 8 ha but then remained relatively similar
thereafter. This was attributed to contributions from structures located on the boundaries of
fields, which generally increase with decreasing parcel size. The edge effect was especially
evident in spring crop fields. Foliage production in hedgerows and/or hedgerow trees (i.e.,
semi-natural elements of the landscape) has generally already started when spring crops
emerge. This introduces noise in the overall SAR response during the first crop growth
stages, which are critical for crop type mapping.
Despite the size effect and the small size of plots in Wales, the overall accuracies of crop
type mapping using the proposed method were between 85.8% and 90.6%. Compared to
other methods that have been used for crop mapping in Wales, the developed method was
more consistent as variability in accuracies between classes and regions was lower. This
was attributed largely to the sole use of Sentinel-1 C-band data, without dependence on
optical data, but the algorithms used in the method also played a key role in the consistency.
The Mann–Kendall non-parametric statistical trend and Sen’s slope tests ensured that the
approach was robust to a few outliers and was sufficiently flexible to account for temporal
shifts in growth patterns as a result of regional/local weather/soil/plant conditions and
timing of crop management actions. By tolerating a few outliers, the accuracy of the final
product displayed no correlation with the distance to benchmarking field plots, therefore
allowing the system to be consistent when mapping crops in different regions.
By relying on (a) Sentinel-1 C-band data only, (b) knowledge, and (c) flexible algo-
rithms, the proposed method has shown to be a good alternative for consistent and accurate
crop mapping over large areas with persistent cloud, where access to in situ/ground infor-
mation and optical data was not be easy to obtain. The method was applied at the national
level in Wales, but this study also foresees the potential of the method for mapping crop
types over other areas and potentially several countries or regions in Europe.
Author Contributions:
Conceptualization, C.P. and R.L.; methodology, C.P.; validation, C.P. and C.H.
(Clive Hurford); writing—original draft preparation, C.P.; writing—review and editing, R.L., S.P., S.C.
and C.O.; resources, C.H. (Claire Horton) and P.G.; data curation, S.K. and S.W.; supervision, R.L.
and P.B. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the European Research Development Fund (ERDF) Sêr Cymru
II program award (80761-AU-108; Living Wales).

Remote Sens. 2021,13, 846 22 of 30
Acknowledgments:
The authors would like to acknowledge PlanetLab for allowing access to the
PlanetScope data for Wales, as well as the Welsh Government for making 2018 LPIS data and farmer’s
declarations available.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
Appendix A. Knowledge-Based Growth Stages
Appendix A.1. Winter Wheat
Winter wheat growth is composed of over ninety stages, starting with germination
and ending with senescence, which are fully described and explained in various technical
reports [
83
,
87
,
90
]. In Wales, winter wheat is sown during the early autumn of year-1 (Y-1),
leaves start emerging during November, and the leaf emergence and tillering stages last the
whole winter [
68
]. As the development of plants is mainly governed by the temperatures
and the photoperiod, establishment of the winter crops is slow. From February–March, with
the increase in temperatures and length of days, the rate of vegetation growth increases.
Growth during early development stages is mainly driven by leaf formation; by the start
of the stem extension stage, which occurs around April, the crops have produced most
of their leaf biomass [
83
]. During the stem extension stage, canopy expansion is rapid as
stems elongate and canopy biomass typically reaches its maximum before anthesis. Indeed,
reports mention that “maximum canopy size occurs between flag leaf emergence and ear
emergence” [
87
]. After stems are fully extended, canopy expansion stops, and the amount
of green biomass starts to decline due to senescence.
Appendix A.2. Winter Barley
As with winter wheat, winter barley growth is composed of over ninety stages, starting
with germination and ending with senescence. In general, winter barley growth stages (GS)
are very similar to winter wheat [
90
]. Indeed, similarly, in Wales, winter barley is sown
during the early autumn of Y-1 and early development stages have a slow growing rate as
they occur during winter [
68
]. From February–March, the rate of leaf development starts to
increase and, from April, canopy expansion accelerates as (1) tillering and leaf emergence
continue and (2) stem elongation starts. Increase in canopy biomass and height continues
until ear emergence and flowering stages. However, contrary to winter wheat, for which
maximum green biomass occurs before ear emergence (end of stem extension), barley
reaches its maximum at the end of the ear emergence stage (due to their greater surface
due to awns) [
78
,
88
]. The ears of barley, which are composed of long awns, play a very
significant role as they represent 11% of the green area [
95
]. Then, as grain filling begins,
heads start to bend and lower leaves to die, leading to a decrease in green biomass [
78
,
88
].
Barley generally has weaker stems than wheat. The crop is more susceptible to stem
lodging at the base as well as necking further up the plant. Heads bend early in the season
when grain watery ripe starts.
Appendix A.3. Winter Rapeseed
As the two crop types previously described, winter rapeseed is sown during the
autumn. Leaf production starts before the winter with a slow growth rate and accelerates
from February–March when temperatures start to increase. Canopy biomass is further
increased from April by the stem extension stage. However, contrary to the two graminoid
crops (wheat and barley), rapeseed crops start their flower bud development and flowering
stages earlier in the season [
89
,
91
,
92
]. Alongside stem extension, flower bud development
occurs in April. This stage is followed by the flowering and pod development stages
in May and June [
68
]. In terms of total crop biomass, the pod development stage is as
important as the stem extension stage. In general, 35% of the total crop biomass is in
the stem and 30% is in the pod wall [
89
]. Compared to wheat, seed filling in rapeseed

Remote Sens. 2021,13, 846 23 of 30
is determined almost entirely by the leaf photosynthesis. Only up to 10% of rapeseed
yield comes from the remobilization of soluble carbohydrate accumulated in the stem
before flowering, compared to 20% to 50% in wheat [
89
]. Thus, green leaf biomass stays
for a longer period. Once the pods are fully developed and have reached their final size,
ripening of the pods starts, leading to a decrease in canopy size, further accelerated by the
senescence of the whole plant at the end of the season.
Appendix A.4. Spring Barley and Wheat
Spring barley growth is composed of the same GS as winter barley. However, as
spring barley is sown after the winter period, the timing of the various GS and the physical
structure of the crop are slightly different. Indeed, leaf emergence is mainly controlled
by temperatures and daylight. Thus, leaf emergence of spring sown barley is accelerated
compared to winter sown barley. In addition to the increased plant development speed,
and in order to make up for lost time induced by late sowing, spring barley also produces
fewer leaves. Studies have shown that the number of leaves is correlated to the sowing
date; the earlier a crop is sown, the more leaves it will produce [
80
]. As less time is available
for canopy development, spring barley generally also produces fewer tillers (i.e., shoots
and main stem) than winter barley [
78
,
88
]. Tiller production is one of the major factors in
the ear production as it determines the ear number per square meter. In addition to this
reduced number of tillers, the use of dwarfing genes in breeding programs has reduced the
height of many varieties and spring crops are usually 5 cm shorter than winter ones [
78
,
88
].
As for winter barley, the canopy size of spring barley is due to the number and size of
leaves, stems, ears, and awns. A smaller production in those elements leads to a smaller
canopy biomass. Thus, despite the mild temperatures during the canopy development and
expansion, peak canopy size in spring barley is, on average, 15% to 20% less than in winter
barley [
78
,
88
]. In spring barley crops, canopy size plateaus soon after flag leaf emergence.
While stems and ears continue to grow, these increases in canopy size are offset by the
death of leaves and reductions in canopy size lower down the canopy. Once the ear and
stem are fully emerged, there is no further canopy expansion and total canopy size starts to
decline [
80
]. Similar to barley, spring wheat growth is shifted in time compared to winter
wheat, with the same impact on plant growth.
Appendix A.5. Maize
Maize growth is often described by technical reports with a large number of stages,
starting with germination and emergence and ending with maturity [
67
,
69
,
70
,
90
]. All these
stages can be grouped in two categories called vegetative and reproductive stages. In
Wales, maize is generally sown during the two first weeks of May [
68
]. After emergence,
leaf production starts. During the first leaf stages, plants produce leaves, but the growing
point remains below the soil surface [
79
]. From the 6–8 leaves stage, the stem starts to
elongate, and the number of leaves continue to increase until the end of the vegetative
stage, leading to a rapid increase in canopy size [
79
,
84
]. By the time of the tasseling stage
(i.e., tassels are completely visible), plants have reached their maximum height and canopy
size [
85
,
93
]. Silks then appear, marking the beginning of the reproductive stage. The
reproductive stage is composed of two phases. First, pollination occurs, and kernels start
to develop and grains start to fill. During this phase, the plants remain green in order to
produce sufficient energy to ensure reproduction, kernel development, and dry matter
accumulation. Nguy-Robertson et al. (2012) [
76
] showed a plateau in green leaf area index
(GLAI) during the first phase of the reproductive stage of rainfed maize. In Wales and
England, most agricultural crops are rainfed, and less than 0.5% of crops are irrigated [
77
].
Moreover, most of the irrigated crops are potatoes and other vegetables and fruits; cereals
only concern 3% of the total irrigated area, and are mainly located in England, with Wales
containing less than 1% of the total irrigated area [
71
,
72
]. As soon as green leaves start to
turn brown, the maturation phase starts. During this phase, kernels dry until they reach
physiological maturity. GLAI drops to nearly zero by the time of final maturity [76].

Remote Sens. 2021,13, 846 24 of 30
Appendix A.6. Potatoes
Contrary to previous crops, potatoes are root vegetables and hence, have two cycles
happening in parallel, an over- and an under-ground one. During sprout development,
also known as emergence, and vegetative stages, branches, leaves, roots, and stolons
grow
[75,82]
. The vegetative stage lasts until inflorescences emerge on the main stem,
marking the beginning of the reproductive stages [
74
,
82
]. During the successive repro-
ductive stages, flowers appear, and fruits develop on the over-ground parts. On the
underground parts, tubers start to initiate at stolon tips and develop and enlarge. During
the first stages of the reproductive phase, shoots and leaves continue to develop, hence
ensuring, through photosynthesis, the production of the required energy. It is the most
important period for the accumulation of the production, and tuber growth is fast [
74
]. As
fruit ripening starts, canopy size decreases, the senescence of aerial parts begins, and tuber
growth slows.
Appendix A.7. Beets
Similarly, beets are root vegetables and hence, have an under-ground cycle happening
in parallel to the aerial one. After emergence, the production of leaves starts, then followed
by root filling. The production of leaves is composed of two phases (i.e., leaf development
and rosette growth), the latter one corresponding to the increase in crop cover [
90
]. Con-
trary to potatoes, the aerial green leaves of beets remain until the harvest [
66
,
73
,
81
,
86
,
94
].
Additionally, beets arrive later in the season compared to all previously mentioned crops,
leading to a plateau in canopy cover during the autumn.
Appendix B. Decision Algorithm
Table A1.
Summary of the key SAR dynamics (mathematical measures) that are used in the decision algorithm. The
key conditions are hierarchically organized as in the algorithm (i.e., starting with winter/spring crop distinction based
on mathematical conditions applied to VH/VV ratio and finishing by the classification of spring wheat). The grassland
category is assigned when none of the mathematical conditions are met.
MKno
GS1.X−GS2.X
indicates that no significant trend
(i.e., p-value > 0.01) is detected by the Mann–Kendall trend test between the beginning of GS1 (i.e., (knowledge-based)
starting date of Growth Stage 1) and the end of GS2 of the crop X.
Ssl+/−
GS1.X−GS2.X
signifies a significant (i.e.,
p-value < 0.01
)
positive/negative slope (using Sen’s slope statistical test) between the beginning of GS1 and the end of GS2 of the crop
X.
Mag≥y dB
GS1.X−GS2.X
shows a magnitude of at least ydB between the beginning of GS1 and the end of GS2 of the crop
X.
Nseno
GS1.X−GS2.X
indicates that no noise is detected between the beginning of GS1 and the end of GS2 of the crop X.
Value>y dB
GS.Xsignifies that a value of at least ydB must be detected at the GS time.
Conditions
VH/VV VH VV Decision
Broad crop categories Ssl+
eme.WW−stem.W W Winter crop
MKno
eme.WW−stem.W W Spring crop
Winter crops
Ssl+
i f l.WW −f ru.WW WW
Value>−15dB
beg sen.WR
Mag≥1dB
stem.WR−f r u.WR
Ssl≈0
stem.WR−f l o.WR
WR
ValueM AX
end i f l.WB
Ssl+
i f l.W B−i f l.W B
Mag≥2dB
i f l.W B−i f l.W B
WB

Remote Sens. 2021,13, 846 25 of 30
Table A1. Cont.
Conditions
VH/VV VH VV Decision
Spring crops
Mag≥5dB
eme.PO−f ru.PO
Mag≥3dB
eme.PO−stem.SB
Ssl−
sen.PO−sen.PO
Ssl+
eme.PO−f ru.PO PO
Ssl++
eme.SB−stem.SB
Mag≥2dB
i f l.SB−i f l.SB
Ssl++
i f l.SB−i f l.SB
Ssl−−
f lo.SB−sen.SB
SB
MKno
f lo.M A−f ru.M A
Ssl−
sen.MA−sen.M A
MKno
f lo.M A−f ru.M A. MA
Nseno
sen.MA−november
Ssl+
eme.BT−ve g.BT
MKno
sen.MA−november
Nseno
sen.MA−november
Ssl+
eme.BT−ve g.BT
BT
Ssl++
eme.SW−stem.SW
Ssl−−
i f l.SW−sen.SW
Mag<2dB
i f l.SW−i f l.SW SW
WB = winter barley, WW = winter wheat, WR = winter rapeseed, SB = spring barley, SW = spring wheat, MA = maize, PO = pota-
toes,
BT = beets;
eme = emergence, stem = stem elongation, ifl = inflorescence emergence, flo = flowering, fru = fruit development,
sen = senescence, veg = vegetative.
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