Discussion document on the introduction of monitoring to substitute OTSC : supporting non-paper DS/CDP/2017/03 revising R2014/809.
Date: 2017-11-10
Institutions: Joint Research Centre
Subjects: agricultural land, aid to agriculture, data processing, information technology, land use, remote sensing, research report

Discussion document on the 
introduction of monitoring to 
substitute OTSC 

Supporting non-paper 
DS/CDP/2017/03 
revising R2014/809 

Wim Devos, Dominique Fasbender, 
Guido Lemoine, Philippe Loudjani, 
Pavel Milenov, Csaba Wirnhardt 

EUR 28830 EN 

 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science 
and knowledge service. It aims to provide evidence-based scientific support to the European policymaking 
process. The scientific output expressed does not imply a policy position of the European Commission. Neither 
the European Commission nor any person acting on behalf of the Commission is responsible for the use that 
might be made of this publication. 

Contact information 
Name:  Wim DEVOS 
Email:  wim.devos@ec.europa.eu 
Tel.: +39 0332 78 58 95 

JRC Science Hub 
https://ec.europa.eu/jrc 

JRC108353 

EUR 28830 EN 

PDF 

ISBN 978-92-79-74279-8 

ISSN 1831-9424 

doi:10.2760/258531 

Ispra: European Commission, 2017 
© European Union, 2017 

Reuse is authorised provided the source is acknowledged. The reuse policy of European Commission documents 
is regulated by Decision 2011/833/EU (OJ L 330, 14.12.2011, p. 39). 

For any use or reproduction of photos or other material that is not under the EU copyright, permission must be 
sought directly from the copyright holders. 

How to cite this report: Devos W. et al., Discussion document on the introduction of monitoring to substitute 
OTSC, 2017, ISBN 978-92-79-74279-8, doi:10.2760/258531 

All images © European Union 2017. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Acknowledgements ................................................................................................ 3 

Abstract ............................................................................................................... 4 

1  Elements of a monitoring system for the checks of area-based aid schemes and area-
related support measures. ...................................................................................... 5 

1.1  What data sources can offer regular observation? ............................................ 5 

1.2  What is meant by implementing systematic observations? ................................ 8 

1.3  What and where to track? ........................................................................... 10 

1.4  How to assess eligibility? ............................................................................ 11 

1.4.1  A tiered approach .............................................................................. 11 

1.4.2  A reductive/channelling aspect ............................................................ 12 

1.4.3  A targeted aspect .............................................................................. 12 

1.4.4  A tentative workflow .......................................................................... 14 

1.5  What is the duration of the monitoring? ........................................................ 15 

1.6  When to trigger follow-up action? ................................................................ 16 

1.6.1  Triggers for follow-up action ................................................................ 16 

1.6.2  Types of  follow-up action ................................................................... 16 

1.6.3  Preventive action: reduce the need for additional data and information .... 16 

1.7  Quality management of the monitoring approach ........................................... 17 

2  ANNEX: Examples, tools and suggestions ........................................................... 18 

2.1  The concept of markers .............................................................................. 18 

2.2  Examples of ‘Markers’ ................................................................................ 20 

2.2.1  Detection of agricultural practices ........................................................ 20 

2.2.1.1  Grassland mowing........................................................................ 20 

2.2.1.2  Ploughing of winter cereals stubble ................................................ 22 

2.2.1.3  Robust markers in Sentinel-1 ........................................................ 24 

2.3  Examples/illustration of tests / possibilities on the use-cases .......................... 26 

2.3.1  Appropriateness of agricultural parcels for monitoring: ........................... 26 

2.3.1.1  Verifying homogeneity of the LPIS reference parcels as a reliable 
representation of the agricultural parcel ..................................................... 26 

2.3.1.2  Assessing the impact of parcel area and shape on the effectiveness of 
the monitoring using Sentinel .................................................................... 30 

2.3.1.3  Identifying regions potentially not suitable for monitoring using Sentinel 
data 

31 

2.3.2  Analysing the ability of the LPIS design to support GSAA ........................ 33 

2.3.3  Improving the signal to noise ratio for less than perfect parcels ............... 35 

2.3.3.1  Filtering out anomalies. ................................................................ 35 

2.3.3.2  Focusing on the central area of interest. ......................................... 37 

2.3.3.3  How small can a parcel be to allow efficient monitoring? ................... 38 

i 

2.3.4  Example of holding processing through monitoring................................. 39 

2.3.5  Activities/practices/use on the land that manifest themselves via phenomena 
easy to monitor ........................................................................................... 40 

2.3.5.1  Observing of the basic land use - grassland ..................................... 40 

2.3.5.2  Impact of eligibility conditions. ...................................................... 44 

2.3.6  Assigning traffic lights as a result of monitored manifestation .................. 47 

2.3.6.1  The Basic procedure ..................................................................... 47 

2.3.6.2  The “after payment” monitoring procedure ...................................... 49 

2.4  Variable timing for different holdings ............................................................ 50 

2.5  Activation and switching of “traffic lights” ..................................................... 51 

2.6  Early warning ............................................................................................ 53 

2.7  Additional sources of information ................................................................. 54 

List of abbreviations and definitions ....................................................................... 55 

ii 

Acknowledgements 

The  Authors  acknowledge  the  valuable  contribution  of  the  colleagues  of  DGAgri  and  the 
many initiatives developed by the Member States. 

Authors 

Wim Devos,  

Dominique Fasbender 

Guido Lemoine,  

Philippe Loudjani, 

Pavel Milenov,  

Csaba Wirnhardt 

Contributors 

Paer Astrand 

Aleksandra Sima 

3 

 
Abstract 

This  document  describes  the  main  concepts  and  components  of  the  future  'monitoring 
approach'.  These  need  to  be  considered  and  developed  for  substituting  the  current 
sample  approach  (on  the  spot  checks)  of  aid  applications  or  payment  claims  by  a  new 
monitoring system covering all of the applications or claims. The goal is simplification and 
reduction of the burden of controls. This approach aims for a substantial reduction in the 
number of field visits. 

Such a substitution requires a shift in both thinking and procedures, based upon adopting 
technology now available. These topics are elaborated in some detail. 

An annex provides illustrations, examples, field cases and elaborations of the key topics. 

This document constitutes the Commission’s (draft) interpretation of common standards. 

4 

 
1  Elements of a monitoring system for the checks of area-
based aid schemes and area-related support measures. 

This discussion document builds upon the DG AGRI working document DS/CDP/2017/03 
to  introduce  the  possibility  for  substituting  the  OTSC  by  a  system  of  monitoring  for 
checking the fulfilment of land use and land cover related CAP requirements. 

For this technical entry, the JRC proposes a working definition of monitoring: 

“A  procedure  based  on  regular  and  systematic  observation,  tracking  and  assessment  of 
the  fulfilment  of  eligibility  conditions  and  agricultural  activities  over  a  period  of  time, 
which involves, where and when necessary, appropriate follow-up action.” 

From this working definition follows that the main differences with OTSC are that  

● 

● 

the timing of a decision for both payment and recovery depends primarily on the 
information accumulated through the regular observations on the land instead of 
operating along a fixed calendar  
there is no sampling of holdings because the whole territory is monitored. 
However, some scheme or measure related requirements, commitments and 
other obligations may require explicit field visits and hence sampling.  

Furthermore,  the  approach  developed  in  this  document  does  not  necessarily  guarantee 
applicability  for  all  eligibility  conditions.  Specific  eligibility  conditions  and  certain 
commitments under RDP, corresponding to a large extent to a complex set of agronomic 
practices,  can  most  likely  not  be  monitored  by  the  proposed  framework  and  MS  should 
develop a specific control mechanism. 

Whereas this technical document is primarily based on experiences and practices for the 
1st pillar aid schemes and their applications, monitoring can in principle be applicable to 
2nd pillar measures and their payment claims too. The suitability of monitoring 2nd pillar 
measures  and  claims  must  be  assessed  on  a  case  by  case  base.  Meanwhile,  the  term 
payment scheme can also be read as support measure and the term aid application can 
be read as payment claim. 

The  main  part  of  the  document  discusses  the  various  technical  elements  of  monitoring; 
the  annex  illustrates  some  of  the  key  aspects  of  the  monitoring  approach  and  provides 
recommendations in the form of case studies and proofs of concept.  

Declaration:  the  document  provides  details  of  the  current  status  of  thinking 
concerning  the  monitoring  approach,  and  should  be  viewed  as  provisional. 
There  are  gaps  in  some  areas  and  further  elaboration  will  be  added  following 
discussions  with  the  main  stakeholders  and  practitioners  involved  in  the 
processing and management of aid application process.  

1.1  What data sources can offer regular observation? 

The  main  sources  of  free  and  EU-wide  satellite  images  across  all  Member  State  are 
offered by Europe’s Sentinel 1 (S-1 Radar) and Sentinel 2 (S-2 optical) satellites. 
Both  complement  one  another  and  the  nominal  revisit  of  the  twin  A  and  B  sensors  is 
every  5  days  for  S-2  and  every  6  days  for  S-1  over  the  continental  territory  of  EU. 
Overlapping  orbits  and  ascending/descending  combinations  of  S-1  provide  denser 
coverage locally: 

5 

 
 
 
 
 
●  The optical bands of Sentinel 2 are in general suitable for identifying bare and 
(partly) vegetated soil, vegetation characteristics, photosynthetic activity 
(indication of vegetation/crop growth), characteristic phenological states (e.g. 
flowering, senescence) and non-agricultural land cover (e.g. water, forest, built-
up areas) 

●  The microwave polarized intensity bands of Sentinel 1 are influenced by canopy 

structure, volume and water content and soil surface roughness and moisture; 
abrupt changes in backscattering and inter-scene interferometric coherence can 
often be attributed to a mechanical activity on the land. 

As  illustrated  by  Figure  1,  both  sources  are  equally  important  because  they  address 
different and often complementary physical and operational aspects. 

Processing  of  these  Sentinel  data  can  be  improved  by  combining  these  with  additional 
imagery  (e.g.  from  free  or  commercially  available  HR/HHR  satellites)  and  non-imagery 
sources. 

The  secondary  source  of  data  comes  from  the  farmer,  as  he  evidences  his/her 
agricultural  activity  by  providing  geotagged  photos.  There  should  be  not  need  to 
generally  tap  into  this  complementary  source.  Rather  this  data  capture  mechanism 
should  focus  on  those  practices  and  conditions  where  the  Sentinels  alone  are  known  to 
offer insufficient data to successfully complete the monitoring process. 

In  addition  to  these  data  from  the  Sentinels  and  geotagged  photos,  data  from  other 
satellite sources, precision farming tools, third-party government data, etc., can provide 
valuable  input  complementing  the  monitoring  process  and 
improving  analyses. 
Monitoring does not  exclude a priori any  other source  of supplementary information. All 
relevant and reliable data which contribute in a cost-effective way to the end-result (i.e. 
the full diagnosis of aid application) should be considered. 

Recommended MS actions: 

1.  R1.1 Familiarise with the Sentinel family of data and related services 
2.  R1.2 Introduce the use of microwave data (radar) according to local conditions 
3.  R1.3 Familiarise with the relevant additional data sources and their use 

6 

 
Figure 1:  Sentinel 1 (microwave) and Sentinel 2 (optical) images of agricultural parcels 

● 
● 

left: Sentinel 2a, 13 April 2015, false colour composite with abundant clouds 
right: Sentinel 1a, 4th-16th-18th April 2015, VH composite unaffected by clouds 

7 

 
 
 
1.2  What is meant by implementing systematic observations? 

Whereas  the  current  on  the  spot  check  mechanism  provides  a  specific  “snapshot” 
verification  of  a  sample  of  applications,  a  monitoring  approach  requires  a  complete 
system rethink. For monitoring, both the procedure (actions and milestones) and the ICT 
requirements are fundamentally different. 

Both  classical  and  CwRS  OTSC  data  capture  relies  on  the  ability  of  trained  experts  to 
visually  interpret  the  controlled  land  and  to  come  up  with  an  eligibility  verdict  or  area 
measurement.  In  CwRS,  a  limited  set  of  image  “snapshots”  allows  the  operator  (in  a 
computer-assisted environment) to apply the same cognitive logic as if in the field, albeit 
from  a  different  viewing  perspective.  In  the  context  of  monitoring,  which  implies  a 
systematic observation of all agricultural areas subject to declaration for direct aids, such 
human  judgement  can  no  longer  be  directly  applied  given  the  vast  amounts  of  data 
involved  and  the  need  to  assess  every  single  application.  In  addition,  greening,  cross-
compliance  and  some  rural  development  measures  would  require  checks  going  beyond 
the mere detection of agricultural area and activity. 

To tackle these challenges, systematic monitoring of all farmer declarations and detection 
of  complex  land  phenomena  monitoring  requires  a  different  approach  to  extract  the 
relevant information from the stack of images. This will require automation of mass data 
handling, in particular in the fields of image and data processing and decision making.  

In  addition,  since  monitoring  will  be  primarily  based  upon  the  use  of  EU-wide,  free  and 
open Sentinel data, the generation of timely parcel evidence is not per se limited to the 
control authorities, as in the current OTSC. It should be plausible to consider third party 
expertise  based  on  transparent  analyses  of  such  external  sources.  In  this  case,  the 
methodological  approach  used  in  the  monitoring  should  ensure  sufficient  reliability, 
robustness and reproducibility of the results.  

It is unrealistic to expect that every Member State or Paying Agency will be able to fully 
develop  such  an  automated  system  for  its  local  needs;  rather  it  will  need  to  customize 
and  reuse  basic  common  components  residing  in  the  cloud  or  available  centralized 
services  (such  as  Copernicus  Data  and  Information  Access  Services  -  DIAS).  As  a 
consequence,  image  and  application  processing  and  data  storage  will  no  longer  take 
place in the local ICT environment but most probably use CAP-community-wide hubs and 
servers. The bulk of the local processing tasks will deal with the launch of spatial queries 
and the documentation of results and decisions. 

As  regards  legal  aspects,  most  are  not  different  from  the  current  OTSC  provisions: 
results  and  methods  can  be  audited  or  possibly  disputed  by  farmers  and  the 
methodological  approach  used  should  ensure  reliability,  robustness  and  independent 
reproducibility.  In  addition,  as  most  of  information  will  be  processed  through  cloud 
computing,  the  inclusion  of  data  encryption  and  protection  techniques  could  be 
considered to address data privacy and sensitivity issues. 

Recommended MS actions: 

1.  R2.1 Acquire knowledge on new ICT technologies such as cloud processing and 

use of standards, and prepare an appropriate transition plan 

2.  R2.2 Participate in or stay informed about one of the several EU level projects on 

ICT developments for the CAP (e.g. DIAS, Sen4CAP) 

8 

 
 
 
Figure 2: Actual data capture from 3 orbit cycles ((e.g. 30 days for S2, 36 days for S1) over the EU 

● 

● 

left: sentinel 2A optical min = 3; max = 9. Note this image availability will double 
soon with the availability of Sentinel 2B optical (already launched) 
right: sentinel 1A and 1B microwave: min = 10; max = 28 

9 

 
 
 
 
1.3  What and where to track? 

The basis for area-related payment schemes is the land area and the land use as detailed 
by  the  declared  agricultural  parcel.  For  rural  development  measures,  non-agricultural 
area  can  also  be  declared  through  a  dedicated  spatial  management  unit.  So,  depending 
on  the  scheme  and  its  eligibility  conditions,  the  behaviour  and  condition  of  the 
agricultural parcel form the smallest entity that will be tracked.  

As the Sentinel data are not suitable for parcel delineation and area measurement in line 
with the current scale and precision requirements, the precondition of tracking is that the 
spatial  extent  (and  the  precise  area  value)  of  the  agricultural  parcel  or  unit  of 
management is already reliably known and does not have to be derived from the Sentinel 
data. Hence, the full introduction of the geospatial aid application (GSAA) in combination 
with  an  LPIS  with  appropriate  design  and  high  quality  is  required  (cf.  Art.  17  of 
Regulation (EU) No 809/2014). 

Sentinel  data  provide  spatial  indications  of  a  parcel  or  management  unit,  but  this 
depends  upon  the  local  neighbourhood  of  other  declared  parcels.  There  will  be  cases 
where  the  conclusion  on  the  regularity  of  the  application  would  depend  on  parcels  of 
small  size  or  with  specific  shape  that  cannot  be  processed  by  Sentinel  data  alone.  For 
such cases, monitoring will need to include follow up actions to complete the processing. 
This  is  expected  to  occur  in  areas  where  the  prevalent  parcel  size  is  less  than  the 
threshold  of  0.5/0.3  ha;  JRC  analysis  of  LPIS  populations  (as  a  proxy  to  the  agriculture 
parcel  populations)  suggests  that  this  could  involve  some  12  percent  of  the  EU 
agricultural areas. In these territories, alternative data sources to Sentinel will have to be 
envisaged,  e.g.  monitoring  with  sensors  of  higher  spatial  resolution  (at  a  financial  cost) 
or  use  of  geotagged  photographs  or  field  inspection,  depending  on  what  the  applicable 
legal framework will allow. 

Recommended MS actions: 

1.  R3.1 Optimize reference parcel and GSAA designs to ensure area management 
2.  R3.2 Investigate to what extent small agricultural parcels or parcels with a 

particular shape, or landscape-related characteristics require specific workarounds 
to be able to make conclusions on the payment in question at the level of the 
holding 

10 

 
 
 
1.4  How to assess eligibility? 

1.4.1  A tiered approach 

In  addition  to  the  GSAA  guiding  alerts  and  administrative  cross-checks,  the  monitoring 
process  will  aim  at  ensuring  that  a  declared  agricultural  parcel  complies  with  the 
eligibility conditions related to the availability of agriculture area. 

The assessment will be tiered along: 

●  Area of interest corresponding to the declared one: In order to be able to 

conclude on the application, is the declared agricultural parcel/spatial 
representation of the declared area the true spatial unit of agricultural activity in 
the field (check the assumption of spatial homogeneity of the parcel); If not, 
can sub-parcels or parcel aggregates be identified for further processing? 

●  Correct behaviour on the land: Is the monitored land “behaving” as the 

scheme/measure associated with the declared agriculture parcel requires? Can the 
requested aid be confirmed or rejected based on the crops and activities the 
monitoring detects? Such observed behaviour is scheme dependent; e.g. in many 
cases, basic payment requires evidence of an agricultural activity, greening 
payment might require detection of crop type.  

●  Benefit of the doubt: Is there evidence of a non-compliance impacting on the 
payment within the period of monitoring? If not, the declaration is accepted as 
true for the scheme. For example, a permanent crop is considered present even 
without clear evidence of specific agricultural activity, such as pruning or mulching 
(depending on the detailed rules applicable in the MS); however, there should be 
no indication that this permanent crop is abandoned. Note that some schemes 
(e.g. VCS, greening) will require explicit confirmation of the declaration, the 
absence of counter-evidence will not offer plausible doubt.  

●  Variable timing for the conclusion on the payment to the holding: Is the 

monitoring process complete for the application? For each holding and scheme a 
verdict at dossier level can be made as soon as a sufficient number of parcels, 
representing a sufficient area have been confirmed. Often it will not be necessary 
to obtain a conclusive monitoring result for 100% of parcels. For some schemes or 
commitments (e.g. AEM) it might take a longer time to come up to a conclusion, 
as a result of the local agricultural situation and practices. 

●  Early warning/Prevention of incompliances: Can the applicant be informed 
on an imminent non-compliance according to the evidence collected for activities 
not yet conducted? If so, a reminder-notification (warning alerts) can be sent to 
farmers, 
“After payment care”: monitoring continues to screen any scheme-specific 
infringements or commitments under RDP that occur later during the season (e.g. 
permanent grassland ploughed late in autumn); retroactive recovery should be 
launched upon such detection. 

11 

 
 
1.4.2  A reductive/channelling aspect 

To  make  the  monitoring  procedure  technically  feasible  use  must  be  made  of  both 
available  information  as  well  as  the  observed  behaviour  of  the  declared  agricultural 
parcel (or unit of management). 

The  former  comes  from  the  declaration  itself  as  well  as  from  other  IACS  registers,  and 
allows  understanding  the  behaviour  during  the  monitoring  period.  The  latter  can  be 
derived  from  automatically  detecting  a  sequence  of  specific  “markers”  from  the  dense 
time series of S-1 and S-2 observations of parcels. 

The  assessment  of  expected  behaviour  on  the  land  by  these  markers  (2nd  bullet 
above) is based on a reductive/channelling approach. At the start of the application year, 
no  parcels  and  no  application  are  assessed,  even  as  information  of  the  land  is  present 
and being gathered. Then, when the declaration makes clear which conditions need to be 
monitored and when monitoring sources become available, parcels are assigned codes by 
applying  any  logic  that  relates  to  the  observed  conditions  in  relation  to  a  particular 
scheme/measure  compliance.  Parcel  state  can  be  tracked  using  a  proposed  colour-code 
expressed as 'traffic lights' for each scheme/measure.  

1.  Black (no light), parcel declaration not yet available, still the parcel is taken in 
scope because it was declared in the previous years and some multiannual 
commitments might apply. Furthermore, in many cases, legacy information on the 
unit of land can be relevant for the declaration itself. 

2.  White: parcel declaration available, but no assessment yet available (assessment 
results of the parcel is relevant for concluding on the compliance with the declared 
scheme / support measure) 

3.  Blinking Yellow/Yellow: parcel assessed and likely to make the declared 
scheme / support measure non-compliant due to absence of farmer action 
(warning alerts to farmer should be sent) 

4.  Blinking blue: expert judgement required: parcel assessed and likely to make 

the declared scheme / support measure non-compliant, additional information 
from non-monitoring sources is needed to complement the monitoring 

5.  Yellow: parcel assessed, but insufficient evidence to either explicitly confirm or 
reject for the declared scheme / support measure because there is no impact on 
payment. 

6.  Green: parcel assessed, and confirmed as compliant with the conditions of the 

declared scheme / support measure in question 

7.  Red: parcel assessed, and confirmed as non-compliant with the conditions of the 

declared scheme / support measure in question 

Follow-up  action  and/or  additional  information  are  needed  for  the  'blinking  light'  states 
only. 

1.4.3  A targeted aspect 

A  marker  is  defined  as  “something  that  demonstrates  the  existence  or  presence  of  a 
particular quality or feature”. 

In this context of automated data processing of satellite imagery, “something” translates 
into an observed continuous state of data values or a change of state of the data values. 

12 

 
 
 
Such  data  values  relate  to  raw  data,  calibrated  data,  composite  indicators  and  any 
combination thereof.  

The time series of these values is called “the signal” whereupon the marker is applicable. 

Markers directly and solely reflect the state or behaviour of the land. They describe state 
and  changes  of  the  land  phenomena,  without  presumption  on  particular  (CAP-relevant) 
rules or expectations. Markers can be generically approached. 

However,  the  conditions  of  a  scheme  imply  the  occurrence  or  absence  of  practices, 
changing  or  preserving  the  state  of  the  land.  These  expectations  often  present  a  well-
defined  scenario  in  which  a  particular  set  of  markers  should  occur.  The  observed 
sequence  of  applicable  markers  then  offers  a  reliable  indicator  of  the  occurrence  or 
absence of agricultural activity or agricultural practices. Scenarios are by definition locally 
dependent. 

For such scenarios to be more effective, the type of agricultural area or land cover (e.g. 
permanent grassland) as recorded in the LPIS, will be used to pre-determine the possible 
agricultural  activities.  This  helps  selecting  the  set  of  markers  expected  to  detect  their 
presence  (e.g.  for  mowing,  not  mowing,  temporal  events).  This  in  turn  requires  the 
spatial  disambiguation of  arable  land,  permanent  grassland  and  permanent  crops  in  the 
LPIS. 

Recommended MS actions: 

1.  R4.1 Review the existing eligibility conditions adopted at national level, and 

assess whether the monitoring approach described above could be applicable and 
decide how to proceed. 

2.  R4.2 Design an appropriate workflow based on the tiered approach while 

considering the reductive and targeted aspects 

3.  R4.3 Identify what key markers would be picked up in the monitored land and 

what they represent in terms of the underlying agricultural practices 

4.  R4.4 Select which combination of markers and conditions would allow to assign a 

traffic light status for each declared parcel 

5.  R4.5 Identify which combination of assessed parcels enables to make a verdict on 

the holding 

13 

 
 
 
 
 
 
1.4.4  A tentative workflow 

The tentative workflow  below offers  a simplified illustration (for payment schemes  only) 
on how these aspects could ensure the reliable processing of each application. 

14 

 
1.5  What is the duration of the monitoring? 

Monitoring  overcomes  the  main  constraints  of  the  current  OTSC  concept,  where  a  few 
limited  observations  are  taken  in  predefined  and  fixed  acquisition  windows.  Time  series 
of  S-1  and  -2  data  or  other  relevant  data,  allow  the  continuous  and  territory-wide 
temporal capturing of the majority of agronomic phenomena. This enables establishing a 
more  flexible  workflow  with  the  possibility  to  timely  collect  complementary  evidence  on 
the field, and this at lower cost for the organisation and to better benefit of the farmer. 

The temporal continuity of monitoring has 2 further advantages:  

1.  a stack of image information is already available by the time the digital parcel 

declarations are ready for processing, allowing an immediate start of the parcel 
diagnosis, possibly to support the declaration process itself, and  

2.  the generation and analysis of markers can be largely automated and constantly 

upgraded based on experience from the past years. As monitoring, contrary to the 
OTSC, is applied to the whole territory all-year round, parcel information and 
behaviour from previous years can be gathered and directly integrated in the 
diagnosis.    

In  general,  this  continuous  and  systematic  nature  of  monitoring  provides  a  more 
consistent  setting  to  integrate  [machine]  learning  approaches  as  it  is  relatively 
straightforward  to  evaluate  newly  learned  location  specific  patterns  and  evaluate  how 
these impact on parcel and area diagnosis. The temporal density of the data series is a 
pre-condition to monitor specifically timed measures (e.g. grassland mowing, catch crop 
presence), which is not possible with the classical approach.  

Although  the  monitoring  is  year-round,  its  highly  automated  data  processing  and  the 
spread of decisions on the various holdings should offer the opportunity to spread dossier 
diagnosis  activities  over  that  calendar  year,  reducing  the  peak  workload  situation 
observed in the current OTSC processes. 

Recommended MS actions: 

1.  R5.1 Set up an e-government system to encase the monitoring algorithm and 

procedures 

2.  R5.2 Identify the value added from connecting parcel and holding information 

over the years  

15 

 
 
 
 
 
 
1.6  When to trigger follow-up action? 

1.6.1  Triggers for follow-up action 

When the fulfilment of all scheme requirements of the declaration have been confirmed, 
it is possible to proceed with the corresponding payment 
On the contrary, when the scheme requirements of the declaration have in part or wholly 
been rejected (even after original payment), administrations should proceed with 
remedial action (i.e. recalculation, penalties, recovery, yellow card issuing,...) 

For cases intermediate to the two previous ones, i.e. when fulfilment of the scheme 
requirements is deemed unlikely but cannot be confirmed based on the monitoring data 
alone, follow-up actions have to be triggered for applications where the 
monitoring result would affect the final payment.  If the results of monitoring do 
not have an impact on payment, then follow-up actions are not necessary. 

In the above paragraph, these triggers are indicated by blinking lights. 

1.6.2  Types of  follow-up action 

Different processes and techniques can be considered as follow-up actions. All aim to 
collect additional data or information to complete the assessment process. Possible 
activities are: 

•  An expert CAPI inspection of the considered parcel from available data 

sources; 

•  Collecting of an authenticated geotagged photo to provide evidence of the 

compliance with the requirement; 

•  The submission of scanned seed labels to evidence a specific type of crop 

(e.g. Durum wheat) or crop mixture (e.g. mixture as cover crop); 

•  Consulting other types of imagery (HR, VHR, satellite, aerial, UAV, google 

earth …); 

•  Gathering of applicable evidence from third party data sources 

(stakeholder sourcing)  

•  An inspection in the field. This follow-up action should be considered only 
as a ‘last resort’ option, that is if all else fails. The monitoring processing 
aims to reduce field visits to a very small number to minimise the burden 
and costs. So, among follow-up actions, field visits should be limited in 
number and avoided as much as possible. 

1.6.3  Preventive  action:  reduce  the  need  for  additional  data  and 

information 

Because  all  holdings  are  monitored,  any  collection  of  additional  data  should  be 
considered  while  taking  into  account  the  cost-effectiveness  of  the  monitoring  approach 
when  local  policy  options  are  discussed.  Addressing  this  challenge  will  probably  require 
two ex-ante considerations: 

16 

 
 
 
 
 
 
1.  A careful implementation of local measures and policy choices focussed on 

phenomena that can be observed through monitoring in order to reduce the need 
for additional information. 

2.  A thorough rethinking of the role of farmer and inspector to make the farmer the 
main supplier of reliable and current data on his agricultural parcel, relieving the 
inspector of time consuming field activities. 

Recommended MS actions: 

1.  R6.1 Decide on the relevance rules for additional data and information collection 
2.  R6.2 Ensure that the preventive actions are implemented. 
3.  R6.3 Set up data catalogues, CAPI instructions and workstations for office 

processing 

4.  R6.4 Set up simple mechanisms that enable farmers to capture and deliver 

current field data 

5.  R6.5 Design an appropriate RFV action, considering the logistical constraints and 

other priorities 

1.7  Quality management of the monitoring approach 

Given  the  importance  of  the  budgets  allocated  to  the  area  based  aid  schemes, 
substituting  the  existing  OTSC  mechanism  with  a  newly  developed  monitoring  approach 
inevitably raises some concern on the effectiveness of the new approach. 

It is a key task of the Member States to correctly implement the CAP and to assure the 
new  control  mechanism  is  effective  and  cost-efficient.  The  easiest  way  to  provide  such 
assurance  is  by  designing  and  implementing  an  appropriate  quality  policy  for  the 
monitoring system. The resulting quality management of the monitoring system is rather 
straightforward and comparable to any other quality management system. Among other 
things, it is required to 

•  Validate the applied monitoring methodologies that lead to the assessment of an 

application 

•  Perform quality control tests on the operational procedures and their result 
•  Launch remedial actions where necessary. 

As  part  of  a  larger  quality  policy,  these  quality  management  activities  should  be  well 
documented and quality awareness should a key competence of the staff involved. 

To  provide  assurance  to  external  stakeholders,  a  quality  assurance  framework  could 
eventually be set up. 

17 

 
 
 
 
 
2  ANNEX: Examples, tools and suggestions 

This  Annex  compiles  some  thoughts,  discussion,  examples  and  illustrations  that  are 
relevant  for  one  or  more  of  the  various  ideas  and  recommendations  of  the  main 
document. It is divided into two parts:  the compilation itself and a register of references 
to find additional information. 

2.1  The concept of markers 
Markers  can  be  considered  as  a  unique  collection  (combination)  of  property  values  of  a 
data signal that evidence the existence or presence of a particular continuous state or a 
change  of  state  of  the  land  phenomenon.  As  explained  above,  the  “data  signal”  can  be 
the  raw  or  calibrated  DN  values  of  the  image  time  series  (reflectance  in  a  given 
wavelength range, backscattering coefficient) or a composite indicator (vegetation index, 
SAR coherence). The “shape” of the evolution of the signal intensity over time is the key 
property that defines a given marker.  

Not surprisingly for the monitoring context, markers have an important temporal aspect, 
and general markers can be described by: 
•         Optional: pre-conditions   (presence/absence of other markers) 
•         A period of occurrence (earliest and latest date) 
•         Optional: a minimum or maximum duration 
•         Conditions on start value 
•         Condition on end value 
•         Optional condition on values in between 
•         Optional post-conditions (presence/absence of other markers) 

The figure below provides a graphical illustration of the key temporal aspects (properties) of 
a marker. 

Fig. A11: General concept of a Marker 

By  default,  markers  are  generic.  They  relate  to  the  state  of  land  and  not  the  CAP-related 
rules. However, when they are applied in a particular locally-dependent CAP scenario, they 
benefit from being “parameterized”. This means that the marker properties and they values 
(see  Fig.  A11)  need  to  be  defined  to  adequately  reflect  the  local  conditions.  This 
“parametrization” is performed using: 

•  A  priori  information  (crop  calendars,  prescriptions  for  established  local  practices, 

farmed application, data from IACS registries) 

•  Statistical  analysis  of  the  image  time-series  within  the  population  of  agriculture 

parcels/units of management that are in the scope of the particular scenario 

The figure below shows possible marker property values for a particular land phenomenon. 

18 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. A12: Example of marker “parametrization” – plant growth 

It is also important to understand that the basic feature subject to monitoring is not the 
image  pixel  itself,  but  the  agricultural  parcel  or  the  unit  of  management.  In  our  image 
domain,  the  marker  property  values  are  derived  from  statistical  operators  (mean, 
median, standard deviation and others) on the multitude of image pixels representing the 
object of monitoring. As a result, the marker property values are “data aggregates” that 
can be easily related to the physical properties of the land phenomenon, such as material 
(e.g. vegetation), appearance (herbaceous) and life cycle (temporal behaviour). 

The  following  examples  aim  to  illustrate  the  selection,  parametrization  and  use  of 
markers  in  particular  scenarios,  related  to  specific  agricultural  activities  or  agricultural 
practices. 

19 

 
 
 
 
 
 
 
2.2  Examples of ‘Markers’ 

2.2.1  Detection of agricultural practices 

2.2.1.1  Grassland mowing 

Grassland  mowing  is  a  farmer  activity  occurring  regularly  on  managed  grasslands  that 
results in cutting and removal of the upper layer of grass which, after a short period, is 
able to regrow naturally. From the land cover perspective, this means that for a specific 
time-period, a substantial amount of herbaceous vegetation is absent from the land. This 
absence can occur almost instantly. As the roots and stems of the vegetation remain on 
the land, the surface is not completely bare, but partly covered. The vegetation gradually 
reappears and usually regains its height and cover, prior to the removal. Since mowing is 
often performed several times within a given year, the associated removal and regrowth 
of vegetation can occur more than once. 

The  presence  and  absence  of  vegetation  is  well  represented  by  the  Normalized 
Vegetation Index (NDVI) alone, which can play a role as a marker in this scenario. NDVI 
can  be  easily  derived  from  Sentinel  2  only.  Grassland  mowing  is  detected  by  a 
characteristic  sharp  but  shallow  drop  of  NDVI-values  (averages)  of  the 
agriculture  parcels  with  managed  grassland.  The  drop  is  followed  by  a  fast 
recovery, which can be followed by other drops of the same nature during the following 
year. 

The  NDVI  temporal  profile  derived  from  Sentinel-2 
images shows the development of the grassland with 
2 mowing dates: 

1st mowing date: between 11th June and 19th June  

2nd mowing date: between 26th July and 5th August 

False colour composite time series (RGB 8,11,4) 

NDVI time series 

20 

 
 
 
 
 
           
 
  
 
Jun 15 

   Aug 2 

Figure A13: a case of grassland mowing 

21 

 
 
 
 
 
 
 
2.2.1.2  Ploughing of winter cereals stubble 

Ploughing of annual winter crops is a farmer activity occurring on arable land that results 
in  turning  up  of  the  top  layer  of  the  soil  for  bringing  fresh  nutrients  to  the  surface, 
removing  of  the  weeds  and  preparation  of  the  land  for  seeding  (e.g.  late  spring  crops). 
This  event  usually  occurs  once  in  a  given  year.  From  the  land  cover  perspective,  this 
means that for a specific period, which starts usually end of spring/beginning of summer 
for the major part of Europe, the soil is completely absent of vegetation. Moreover, due 
to  the  turning  up  of  the  top  soil  layer,  the  surface  is  marked  by  long  trenches  of  more 
fertile and more humid soil.  Often the soil is left to dry out, and is then harrowed before 
planting. The plants of the new crop gradually appear and cover the soil in late summer. 

Here, the Normalized Vegetation Index (NDVI) alone, can play again a role as a marker 
in  this  scenario.  Ploughing  can  be  detected  by  a  single  very  deep  drop  of  NDVI-
values.  The  NDVI  values  stay  very  low  for  a  while  and  then  start  gradually  to 
increase. 

The  NDVI  temporal  profile  derived  from  Sentinel-2 
images shows the development of the winter cereals, 
June  and  ploughed 
harvested  at  the  end  of 
immediately (preparing the land for maize) 

False colour composite time series (RGB 8,11,4) 

NDVI time series 

Jun 26 

Jul 6 

22 

 
 
 
 
 
 
 
         
 
 
 
 
 
  
 
 
Figure A14: a case of ploughing arable land 

23 

 
 
 
2.2.1.3  Robust markers in Sentinel-1  

Both cases above demonstrate that each mechanical agricultural activity triggers abrupt 
and manifest changes in the  “appearance” of the land within the parcel that are picked 
up by the monitoring satellites.  The above detection cases relied on Sentinel 2 only, but 
the combination with Sentinel 1 can dramatically increase the sensitivity and robustness 
of the detection of such manifestations. 

The graphs below demonstrate the monitoring “signatures” of agricultural land as picked 
up  by  Sentinel  1’s  microwave  bands.  The  all-weather  capacity  of  the  microwave  sensor 
ensures  a  predictable  data  capture,  in  contrast  to  the  cloud  free  requirement  of  the 
Sentinel 2 cases above. 

The  3  graphs  show  the  monitoring  results  of  known  wheat  fields  in  the  Netherlands, 
France and Italy over the period January 1st - September 1st. This period yielded in 120 
observations (images), i.e. on average every 2 days. 

Figure A15a: microwave signatures of wheat in the Netherlands 

Figure A15b: microwave signatures of wheat in France 

24 

 
Figure A15c: microwave signatures of wheat in Italy 

Comparison of the 3 graphs shows a single trend across these EU production areas, and 
that  this  trend  is  explained  by  the  phenology  of  the  wheat  crop.  In  particular  can  be 
seen: 

1.  the steep drop in VV at stem elongation,  
2.  followed by an increase in VV after ear formation, a senescence phase and 

harvest. 

3.  a characteristic maximum in the VH/VV ratio at the breakpoint between 

elongation and ear formation.  

In general it is scientifically documented that: 

1.  VH/VV ratios below -10 dB signal bare soil (or sparsely vegetated) conditions, e.g. 

at start of the period and post-harvest.  

2.  The timing, magnitude and duration of the trends depend on specific crop species 
and development due to varying agro-meteorological conditions, with a clear 
South to North sequence (earliest in Italy, latest in Netherlands). Detection of the 
elongation trend is possible from mid-April (Italy) to mid-May (Netherlands), i.e. 
even before or shortly after parcel declarations are filed.  

The  data  collected  by  Sentinel-1  (being  an  active  sensor)  provides  much  more  complex 
information  on  the  physical  status  of  the  Earth  cover,  in  comparison  with  Sentinel-2. 
Given  the  multi-parametric,  multi-temporal,  multi-dimensional  data  available,  it  is  not 
appropriate to relate only a single variable or parameter to a particular phenomenon but 
rather use the multivariate observation.  Thus, in the context of S1, the “marker” would 
rely  on  the  multivariate  analysis  domain  to  come  up  with  a  more  complex  set  of 
properties  aiming  to  reflect  any  characteristic  state  or  change  of  state  of  the  land 
phenomenon. 

25 

 
 
   
 
2.3  Examples/illustration of tests / possibilities on the use-cases 

2.3.1  Appropriateness of agricultural parcels for monitoring:    

2.3.1.1  Verifying homogeneity of the LPIS reference parcels as a reliable 

representation of the agricultural parcel 

The applicability of monitoring is dependent on the quality of the LPIS and the ability of 
the  GSAA  to  delineate  individual  parcels  reliably  inside  LPIS  reference  parcels  that 
contain  more  than  one  agricultural  parcel.  In  the  figures  below  we  show  extracts  from 
one 2012 LPIS that is available online, to demonstrate increasing difficulties that can be 
expected  in  a  monitoring  setup  due  to  heterogeneous  quality  of  the  parcel  reference 
system.  The  LPIS  is  overlaid  on  a  Sentinel  2A  image  of  4  May  2016.  The  proximity  of 
these extracts is so that they are all located inside this single Sentinel frame. 

The  first  extract  spans  a  zone  that  can  be  considered  appropriate  for  monitoring.  The 
LPIS  reference parcels  clearly represent the physical reality of the agricultural land use, 
with  a  large  proportion  of  one-to-one  correspondence  between  reference  parcel  and 
agricultural  parcel.  A  clear  sub-parcelling  of  the  other  reference  parcels  into  distinct 
agricultural parcels could be easily achieved with the support of the GSAA.   

Figure A21a: LPIS extract 1: The majority of the reference parcels represents single units of 
management 

In  the  second  extract,  the  LPIS  still  matches  the  physical  reality  of  the  agricultural 
parcels  patterns,  but  most  reference  parcels  are  now  large  and  contain  many  individual 
agricultural parcels, often with irregular spatial arrangements. The LPIS condition is less 
than optimal as several large blocks show permanent boundaries (e.g. roads) that would 
allow  a  more  appropriate  subdivision,  as  inclusion  of  ineligible  areas  is  evident  even  in 
the  Sentinel-2  imagery,  and  as  some  reference  parcels  perimeters  (e.g.  in  the  North-

26 

 
 
West  corner)  do  not  match  the  agricultural  parcel  boundaries.  GSAA  may  still  assist  in 
resolving some of the larger block sub-parcelling, but the matching of monitoring results 
with  declared  parcel  attributes  will  require  additional  (manual)  work  beyond  the 
automatic  processing  stages.    Improvement  of  the  LPIS  quality  through  the  update 
process  and  appropriate  remedial  actions  (if  required  by  the  LPIS  Quality  assessment) 
would address the identified issues. 

Figure A21b: LPIS extract 2: Many reference parcels contain more several distinct units of 
management 

In the third extract, there is no longer a simple (i.e. one-to-one, one-to-few) but rather a 
complex many-to-many relation between the LPIS reference parcels and the agricultural 
parcels. It is impossible in this case to reliably establish agricultural parcel outlines from 
multiple reference parcels. The LPIS no longer represents the physical reality of units of 
agricultural area, as the Regulation requires. Monitoring will not work in this set-up as it 
will require extensive, and likely imprecise, subdividing and re-grouping of both reference 
and agricultural parcels fragments to assess any compliance.  

27 

 
 
Figure A21c: LPIS extract 3: Most of the reference parcel do not represent true units of 
management 

In 2011, JRC expressed three RP properties that any LPIS design should ideally strive to 
approximate as much as possible to support the 2009 declaration and OTSC process. 
(http://ies-webarchive-
ext.jrc.it/mars/mars/content/download/2092/11055/file/S0_Devos_Outline.pdf  slide  25 
and following):  

1.  One spatial unit represents one applicant 
2.  One spatial unit is 100% eligible 
3.  One spatial unit represents one unit of application 

The monitoring approach aspires to the exact same ideals. 

The fourth extract constitutes a solution that could address the conditions of figure A21b where the 
LPIS  still  matches  the  physical  reality  of  the  agricultural  parcels  patterns,  but  many  reference 
parcels contain many individual agricultural parcels. In this real world example, the GSAA assists in 
resolving sub-parcelling and thus makes the monitoring approach applicable. 

28 

 
 
Figure A21d: LPIS and GSAA extract: The combination of parcel declared through GSAA (black 
boundaries) and LPIS (yellow boundaries) provides the conditions to apply monitoring  

29 

 
 
 
 
 
2.3.1.2  Assessing the impact of parcel area and shape on the effectiveness of 

the monitoring using Sentinel 

Theoretical  considerations  on  the  Sentinel  GSD  (10m-20m)  and  the  provisional 
experiences  of  MS  tests  indicate  that  the  Sentinel  family  should  operate  well  on 
agricultural  parcels  of  0.3  to  0.5  ha,  depending  on  their  shape  and  neighbourhood 
conditions. 

An  analysis  of  the  impact  of  size/shape  on  the  effectiveness  of  the  monitoring  with 
Sentinels  was  performed  with  all  declared  parcels  of  EU  Member  States.  The  basic 
assumption  was  that  in  order  to  derive  meaningful  information  for  the  markers,  the 
declared parcel/unit of management should contain at least 20 to 30 pixels. 

For  each  parcel  in  the  agricultural  parcel  population,  a  shape  index  is  computed  (the 
reference is 1 for a square), together with the expected maximum number of 10m pixels 
in the parcel (upper graph of Fig.22) and in a negative buffer of the parcel (lower graph 
of  Fig.22).  The  colours  correspond  to  a  given  range  of  numbers  of  pixels,  with  green 
indicating all cases where pixels are more than 30. The red line is the 0.5 ha limit. 

These graphs lead to two key conclusions:  

1.  the area limit of 0.5 ha is indeed a good “proxy” for the usability of the Sentinel 
data. Except for the very complex shapes, all parcels above the 0.5 ha threshold 
contain a sufficient number of pixels.  

2.  the 0.5 ha limit is not always an obstacle, as the shape also plays an important 

role and a sufficient number is still achieved for smaller parcels.  

The parcels with more than 5 pixels (all the dots but the dark blue ones) represent about 
94% of the points in the upper graph and 76% in the lower graph. For comparison, there 
are  67%  of  parcels  with  more  than  0.5ha.  On  the  other  end  of  the  graph,  there  is 
expectation  that  less  than  5  pixels  (the  dark  blue  points),  might  not  be  sufficient  to 
provide a (rough) estimation of the parcel average value.  

Figure A22: impact of size/shape on the effectiveness of the monitoring with Sentinels 

●  upper: expected maximum number of 10m pixels in the parcel (green is > 30 pixels) 

30 

 
● 

lower: expected maximum number of 10m pixels in the parcel in a negative buffer of the 
parcel (green is > 30 pixels) 

2.3.1.3  Identifying regions potentially not suitable for monitoring using Sentinel 

data 

Each  MS  considering  the  application  of  monitoring  should  analyse  which  parts  of  its 
territory have landscapes where suitable conditions occur. 

Below is a simulation of such analyses at EU level based on three assumptions 

1.  The critical agricultural parcel size is 0.5 ha (this size should exclude any impact 

from shape and condition) 

2.  Monitoring will work in territories where approximately 90% of agricultural area is 

covered with agricultural parcels above the critical size. 

3.  In the absence of geospatial agricultural parcel data, we used the LPIS reference 
parcel data as the best proxy for agricultural parcels. Indeed, the Regulation 
states that a reference parcel should be a unit of land representing agricultural 
area. 

The  resulting  map  shows  the  areas  in  the  EU  where,  under  these  3  assumptions, 
monitoring would be appropriate. MS are advised to analyse their IACS records and GSAA 
data and to check applicability of the third assumption, especially: 

• 

• 

that for many physical or farmer block LPIS designs, any reference parcel 
could contain several agricultural parcels;  
that for some cadastral parcel designs, the cadastral parcels have a historic 
legacy and agricultural parcels could be larger by covering several 
reference parcels  

Since the map below is derived from the data of LPIS reference parcels, it can serve only 
as  a  proxy  for  indicating  possible  areas  not  suitable  for  monitoring.  Further  analysis 
based on agriculture parcel data is required. 

31 

 
 
 
 
Figure A23: EU distribution of territories, based on 10km x 10km grid with the percentage of 
agricultural area covered by reference parcels that are bigger than 0.5 ha (blue is 0% and red is 
100%), a proxy for possible non suitability for monitoring, requiring further analysis 

32 

 
 
 
 
 
 
2.3.2  Analysing the ability of the LPIS design to support GSAA  

The  conceptual  analysis  of  the  LPIS  design  offers  more  comprehensive  information  in 
addition to the LPIS homogeneity and the RP characteristics discussed above. The tool for 
such  analysis  is  already  available  in  the  form  of  the  TG  IXIT,  a  key  component  of  the 
LPIS  Model  Test  Suite  (MTS),  part  of  the  LPIS  quality  assurance  framework.  Briefly 
elaborated  below  is  how  this  TG  IXIT  helps  with  the  preparation  for  setting  up  the 
monitoring system. 

TG IXIT can be regarded as a structured set of questions on the design/assembly of the 
LPIS reference parcel and related components such as landscape features and maximum 
eligible  area.  They  are  grouped  into  the  so-called  qualifiers,  which  correspond/reflect 
specific  requirements  set  in  the  EU  regulation  in  relation  to  LPIS  and  to  the  GSAA.  The 
combination  of  choices  made  for  these  qualifiers  by  an  EU  MS  can  reveal  the  level  of 
complexity  of  the  given  LPIS  implementation  and  a  proxy  for  the  correspondence 
between the declared agriculture parcels and the unit of management represented by the 
LPIS reference parcels. 

More specifically, IXIT can provide important information and indications on:  

● 

● 
● 

the qualities of a reference parcel in GSAA terms, (provide/confirm the true extent 
of his/her agricultural parcels and the correct value for the maximum eligible area 
per scheme) 
the individual particularities of the LPIS concept applied, 
the readiness of the given LPIS implementation to fit the purpose of the “claimless 
system” or “click and confirm system”. 

The  overview  map  below  shows  the  approaches  used  by  EU  Member  States  for  the 
creation  of  the  initial  reference  parcel  perimeter,  expressed  in  the  well  known  RP 
typology (2016) 

Figure A24: LPIS reference parcel types in 2016 (source: MTS) 

33 

 
 
 
 
For example, for an EU MS that has LPIS implementation ready and 'fit for purpose', we 
would expect the following outcomes from IXIT: 

1.  The unit of land representing agricultural area is directly provided/located by the 

farmer in an unambiguous way at the level of the single crop/management 
practice. 

2.  The RP was created through delineation or confirmation on the basis of the 

information provided by a geospatial aid application    

3.  The maximum eligible area is directly derived from this delineation and 

immediately confirmed by the reference information in the GSAA 

34 

 
 
 
 
 
 
2.3.3  Improving the signal to noise ratio for less than perfect parcels 

Monitoring  will  rely  on  mass  automation  to  extract  only  relevant  information  from  the 
stack  of  images  to  come  with  a  reliable  decision.  Although  fuzzy  logic  and  big  data 
processing are still under development, the decision algorithms can already benefit from 
existing  procedures  to  separate  the  noise  from  the  relevant  parcel  signal.  Several 
techniques are available. 

2.3.3.1  Filtering out anomalies. 

Automatic outlier detection algorithms can easily help to remove noisy captures from the 
stack  of  images.  The  example  below  shows  how  snow,  cloud  and  shadow  can  be 
eliminated  in  an  automated  way  from  an  NDVI  sequence,  using  pre-defined  cloud  and 
haze masks. 

Figure A25a: complete or raw NDVI profile 

The  detection  of  such  noise  is  greatly  facilitated  by  including  metadata  and 
meteorological data in the processing. 

35 

 
 
 
 
 
 
Figure A25b: relevant NDVI profile 

The  availability  of  concurrent  Sentinel  1  microwave  imagery  will  greatly  facilitate  the 
detection  of  noisy  captures  since  snow  and  clouds  impact  on  microwave  imagery  in  a 
completely  different  manner  than  they  do  on  the  optical  data.  Correlation  analysis 
between  the  behaviour  on  the  optical  and  microwave  images  will  be  able  to  provide 
conclusive evidence of most meteorological phenomena. 

36 

 
 
 
 
 
2.3.3.2  Focusing on the central area of interest. 

Removing  a  1  or  2  pixels  from  the  agricultural  parcel’s  perimeter  by  applying  an  inner 
buffer  will  ensure  that  only  'purer'  signal/information  from  the  field  is  analysed.  The 
buffer  indeed  holds  pixel  values  that  could  be  subject  to  spatial  displacement  (in  fact 
representing the neighbouring parcel or non-agricultural land) or that could be influenced 
by the presence of physical boundary features (hedges, ditches, tree lines). These pixels 
can introduce bias in the calculation of the image statistics within the parcel.  

The  example  below  shows  that,  although  the  pixels  for  calculation  of  the  markers  are 
slightly  reduced,  this  leads  to  higher  maxima,  lower  minima  and  lower  standard 
deviation, in other words a better signal! 

Figure A5: comparison of the NDVI profile of a complete parcel and of its center part only 

37 

 
 
 
 
 
 
 
2.3.3.3  How small can a parcel be to allow efficient monitoring? 

In a previous section, it was stated that even 5-10 pixels could be used for an estimation 
of the parcel conditions. This example, corroborates this statement. 

Experience  from  OTSC  CwRS  using  HR  imagery  shows  that  it  is  usually  difficult  to 
interpret  parcels  smaller  than  0.5ha.  However,  even  sub-pixel  parcels  may  show 
characteristic  behaviour,  especially  when  there  is  a  contrast  with  larger  neighbouring 
parcels.   

Not all the noise reduction techniques might be applicable to such parcels, but additional 
information from the GSAA should provide elements regarding the nature of the object to 
come up with a conclusive verdict. 

In the case below, a small stretch of land is clearly different from the neighbouring arable 
crop  indicating  a  different  management  regime.  This  stretch  could  be  unmanaged  i.e. 
undeclared or intentional e.g. declared as EFA.  

Figure A6: monitoring appearances of a 6m parcel 

So  even  in  absence  of  characteristic  discriminating  markers,  the  declaration  could  be 
confirmed only by the detection of the different management regime. 

38 

 
 
 
 
 
 
 
 
 
 
2.3.4  Example of holding processing through monitoring 

The  concept  of  monitoring implies  a  systematic inspection  of  all  parcels  declared  by  the 
holding. However, it does not necessarily require that a conclusive monitoring outcome is 
obtained  for  all  agricultural  parcels  declared.  All  long  as,  a  decision  can  be  drawn  at 
dossier level on the basis of the assessed number of agricultural parcels, the dossier can 
be  categorized  and  closed,  even  if  there  are  parcels  left  unresolved  due  to  insufficient 
evidence or lack of assessment results. 

In the example below, a holding is checked for the greening requirement related to crop 
diversification.  The  size  of  the  holding  required  the  presence  of  minimum  three  crops 
(crop groups). Six parcels were processed successfully, while four remained unresolved. 
However,  from  the  processed  ones,  it  is  clear  that  the  holding  respected  the  crop 
diversification requirement - the third crop group found occupies 11% of the total arable 
land  of  the  holding  (>5%)  and  both  second  and  third  crop  groups  occupy  35%  of  the 
total  arable  land  of  the  holding  (>25%).  This  example  also  shows  the  relative 
(un)importance of individual (small) parcels (or non-compliances). 

This  ultimately  means  that  the  introduction  of  monitoring  will  give  the  opportunity  to 
observe  every  agricultural  parcel  in  an  automated  way,  but  to  retain  the  flexibility  in 
relation to the amount of information needed (and manual follow up) to come up with a 
decision at dossier level. For example, when the crop is wrongly declared but nonetheless 
the  conditions  and  commitments  of  the  scheme/support  measure  are  fulfilled,  the 
payment can be made. 

Figure A7: impact of parcel verdicts on the application verdict for a scheme 

39 

 
  
 
 
 
 
 
 
2.3.5  Activities/practices/use  on  the  land  that  manifest  themselves  via 

phenomena easy to monitor 

2.3.5.1  Observing of the basic land use - grassland 

In this example of the “targeted aspect” of monitoring, the analysis focuses on outliers in 
a  specific  agricultural  land  use  class  to  tag  a  conclusive  non-compliance  or  indicate  a 
possible  one  (i.e.  to  switch  on  the  blue  blinking  light).  The  particular  example  concerns 
permanent grassland where the outlier analysis consists of 2 steps:  

1.  identify the declared permanent grassland cases as implausible (i.e. it is most 

likely not (permanent) grassland).  

2.  find the class labels for the parcel by comparing to all cases that are not 

permanent grassland, i.e. add a plausibility estimate that the parcel is of another 
class. 

For illustrating this, we selected a site where the majority of declared parcels are either 
grassland (both permanent (64.7%) and temporary (12.1%)) or maize (15.6%), i.e. only 
7.7%  of  the  parcels  are  in  one  of  the  other  classes.  The  test  area  is  outlined  for  a 
selected municipality, where 5041 parcels (12209 ha) have been declared. The choice of 
this administrative boundary is arbitrary and can be replaced by a much larger area, e.g. 
defined  on  the  basis  of  relevant  agro-ecological  criteria  (e.g.  same  soil  type,  crop 
patterns) for which known Sentinel-1 and Sentinel-2 time profiles (basis for markers) are 
representative. 

Figure A8: Declared grassland (green), maize (red) and other (blue) on the test site 

40 

 
In  a  classical  remote  sensing  analysis  approach,  image  classification  would  be  applied, 
either  to  each  of  the  available  image  data  set,  or  to  a  stack  of  those  images.  The 
confusion  matrix  derived  for  the  classification  would  then  provide  the  most  likely 
candidate  parcels  that  are  considered  as  misclassified.  By  running  the  classification 
several times, each time with a different (randomly) chosen subset of training data, and 
tracking  which  parcels  are  consistently  misclassified,  a  high  level  of  confidence  can  be 
reached to make a classification verdict and assign a red or, at least, blinking blue light, 
to those parcels. 

A  classification  approach  is  suitable  if  the  stack  of  images  is  limited  and  noise  is  not 
impacting too much on the data quality.  

In  this  example,  5  relatively  cloud  free  Sentinel-2  images  are  available  over  the  test 
area,  for  the  period  end  of  March  to  end  of  August.  But  additionally,  the  site  is  also 
covered  by  121  (partial)  Sentinel-1  images.  Using  these  is  very  challenging  for  a 
traditional classification, but modern machine learning tools are now available to handle 
such  high-dimensional  data  stacks.  JRC  used  the  open  source  TensorFlow  library  to 
analyse  the  S-1  temporal  profiles  extracted  for  all  grassland  and  maize  parcels  in  the 
area.  

•  As noise reduction steps, we buffered the parcel boundaries with a 10 m 
internal buffer to avoid inclusion of boundary pixels and excluded parcels 
smaller than 1 ha.  

•  We resampled the profiles for the May-June period to a weekly interval. In 
TensorFlow, we chose a so-called deep neural network, and trained it with 
1500 samples. The resulting model achieved an accuracy of > 99%, i.e. it 
manages to separate the temporal S-1 signatures in the 2 distinctive 
classes (grass and maize) extremely well. 

•  We then used the trained model to predict the class labels for the samples 
that were not included in the training. Thus, for each field, we obtained the 
probability that the model assigns either the correct or incorrect class 
sample. A typical result is: 

Parcel_ID 

Area class 

Probability for 0 

Probability of 1 

1501 

1502 

1503 

1504 

1505 

1506 

… 

1 

1 

0 

1 

0 

0 

 0.67 

 1.30 

98.75 

 4.56 

99.62 

98.57 

99.33 

98.70 

 1.25 

95.44 

 0.38 

 1.43 

Provisional results!  

For instance, parcel 1501, which has label 1 (= Maize) has a probability of 99.33% to be 
correctly assigned as class 1 (last column) and only 0.67% as class 0 (= Grass). 
Similarly, parcel 1503 is most likely (98.75%) to be a correct grassland case. 

41 

 
 
 
 
Of the 1939 validation parcels,  

•  There are only 2 grassland cases for which the probability that they are 
assigned to maize is higher. In fact, that probability is > 80%, i.e. these 
are very likely red light cases.  

•  By contrast, none of the maize parcels have a higher probability of being 

assigned to the grassland class.  

•  An additional 20 grassland and maize parcels have a probability < 80% 

(but still in the range 60-70%) of being correctly assigned. These could be 
cases of yellow (orange) lights, which can be resolved either by inspecting 
additional imagery, or select for RPV. Note that we are able to assign the 
green light to 1917 samples with a single run of the model! 

Figure A8a: Simulated traffic light assignment after the first run of the model.  

In figure A8a, all grassland and maize parcels for which the class label was confirmed by 
the learning model are set to green. The 2 non-compliant grassland parcels are set to red 
(location indicated by the yellow arrow). All parcels with other crop labels which have not 
been included in the analysis are (still) white. The 20 grass and maize parcels which have 
not yet been confirmed as such are also set to yellow (orange). 

42 

 
 
The routine can be re-run with other training sample selections to ensure that all parcels 
are at least once used as a validation sample (this is similar as in classical classification 
approaches).  

The  parcel’s  time  profile  extraction  for  the  large  S-1  stack  is  fully  automatic  (using 
Google  Earth  Engine).  The  TensorFlow  model  (implemented  in  less  than  50  lines  of 
python  code),  including  training  and  validation,  requires  only  a  few  seconds  to  run  for 
this  test  case.  We  have  used  only  S-1  data  for  May  and  June,  which  demonstrates  the 
excellent quality of these data for the purpose of monitoring and the quick availability of 
parcel diagnostics for timely follow up.  

43 

 
 
2.3.5.2  Impact of eligibility conditions.  

A  more  sophisticated  example  relates  to  monitoring  of  eligibility  requirements  with 
respect to fallow land. In this particular case, an agricultural parcel was declared as land 
lying  fallow,  whose  eligibility  conditions  (defined  at  national  level)  require  that  it’s  kept 
out  of  production  for  a  period  of  at  least  8  months.  In  addition,  if  the  area  is  declared 
also  as  EFA,  no  mowing  and  any  other  agricultural  activity  should  occur  in  the  period 
from 1 of March to 31 of July. 

Since  no  agronomic  activities  are  envisaged  on  the  land  represented  by  this  agriculture 
parcel,  one  would  expect  that  a  spontaneous  growth  of  semi-natural  vegetation  will 
gradually  occur  through  the  year.  This  phenomenon  will  be  manifested  by  slow 
progressive  increase  of  the  vegetation  cover,  combined  by  notable  presence  of  the 
vegetation heterogeneity. These two characteristic can be well represented by the mean 
value of the NDVI within the parcel and its correspondent standard deviation.  

On the figure below (left), it can be seen that these two parameters follow the expected 
trend  typical  for  fallow  land.  First,  in  the  spring  months  the  NDVI  gradually  increases, 
then in the land spring-summer months (May - July) slightly decreases probably due to 
the increased presence  of dry matter (confirmed also by the PSRI index). Note that the 
standard deviation of the NDVI increases during the whole period. 

Figure A9: evidence of agricultural activity detected from markers based on NDVI and PRSI 
profiles 

Then, there are two value drops - one in the NDVI in the period 2-22 July, combined with 
decrease of standard deviation; and another in the PSRI in the period 22 July - 3 August. 

This can be considered as an evidence for intervention on the land, related to ploughing, 
provided by two independent markers:  

1.  disappearance of the green vegetation, due to clean-up activities and  
2.  drop of the dry matter (weeds, stubbles) due to the subsequent ploughing. 

Here, there are two scenarios for assessment, depending on the scheme declared: 

1.  declared only for BPS: Ploughing seems to happen at the very end of the 

commitment period. In order to confirm whether it was conducted after the exact 
completion of the 8 months, the parcel can be flagged as “blinking yellow” and the 
farmer can be asked to provide further evidence (such as GPS track log of the 
harvest machine, others.). If case of absence of such evidence and in case of no 
impact on payment, the parcel can be flagged as “yellow” and then processed as 
“green”, as it’s considered more likely that the 8 months were respected (benefit 
of doubt to the farmer). 

44 

 
 
 
 
2.  declared as EFA: Since there is substantial evidence for an intervention on the 

land well before 31 of July (change of NVDI value within short period), the parcel 
is flagged as “blinking blue” and further expert judgement is required within a 
short period to confirm the observed non-compliance. In the case of confirmation, 
the parcel is flagged as “red”. In absence of definite evidence from farmer, the 
parcel is flagged as “blinking blue” requiring further expert judgement. 

For  this  particular  case,  a  CAPI  assessment  and  field  visit  confirmed  the  assumptions 
above. 

The screenshot below represent the visual manifestations (as false colour composites) of 
the NDVI and PRSI evolution of that parcel. 

Figure A10a: False color time series 

45 

 
 
 
 
 
 
Blinking  blue:  the  late  field  visit  on  Aug  31  evidences  deep  ploughing  and  traces  of 
weed. 

Figure A10b: late in the season field evidence 

46 

 
 
 
 
 
2.3.6  Assigning traffic lights as a result of monitored manifestation  

2.3.6.1  The Basic procedure 

The process of assigning traffic lights (i.e. assigning an eligibility status to an agricultural 
parcel) is based on a channelling approach; the parcel is monitored until a characteristic 
manifestation through the applied markers is observed that allows making a decision for 
the application year.  

There are 2 straightforward paths to that decision on a parcel: 

1.  The observation of required evidence for eligibility  
2.  The observation of conclusive evidence for ineligibility  

And there is a grey zone in-between with 

1.  The observation of in-conclusive evidence on ineligibility 
2.  In-conclusive evidence on eligibility 
3.  Delayed observation of evidence on eligibility 

For the channelling workload, what is required and conclusive is scheme and application 
dependent.  For  example,  for  BPS  an  agricultural  activity  is  often  required  and  should 
then  be  evidenced.  But  for  EFA,  parcel  evidence  may  no  longer  be  required  as  soon  as 
the  holding  meets  the  compliance  threshold.  Similarly,  conclusive  evidence  for  an 
incorrect  crop  declaration  may  be  irrelevant  in  cases  where  the  crop  diversification 
requirement is met. 

Unfortunately, this dependency complicates the evaluation workflow. 

The  parcel  workflow  below  illustrates  how  an  individual  parcel  could  be  processed  for  a 
particular scheme; in simple terms what it translates into. 

1.  The farmer’s application determines what to look for, IACS helps the 

interpretation. 

2.  If the required evidence is detected, the parcel is assigned a green light 
3.  If conclusive counter-evidence is detected, the parcel is assigned a red light 
4.  If in-conclusive counter-evidence is detected, the parcel is assigned yellow 

blinking light. If relevant, it triggers a request for additional information, possibly 
from the farmer. This information can be sufficient to re-assign a green light but if 
additional information does not come forward, it is assigned a blue blinking light 
and possibly subject to some field data collection. The result of this field activity 
should switch it in either red or green. 

5.  Any parcels that have not been assigned a red or yellow blinking light by a set 

deadline are assigned a yellow light, but treated as if were a green light further 
on. 

47 

 
 
48 

 
 
 
2.3.6.2  The “after payment” monitoring procedure 

The “after payment” monitoring procedure is  much simpler than the  Basic procedure.  It 
only looks for conclusive counter-evidence of the few selected cases that can be relevant 
for processing of the application, the so-called targeted approach. 

E.g.  

●  ploughing of a permanent grassland qualified as ESPG after payment 
●  Non respect of the period of a cover crop. 

Agricultural parcels that are not subject to such conditions are not in scope of the “after 
sales” monitoring. 

Any detected non-compliance would trigger the parcel to be assigned a red light and be 
brought  to  the  attention  of  the  holding  and  the  application  should  be  re-processed  well 
before the launch of a retro-active recovery. 

49 

 
 
 
 
 
2.4  Variable timing for different holdings 

The wheat example already mentioned above (point 2.2.1.3) shows the varying timing of 
the crop phenology over different regions of Europe. There is also the time difference in 
the management of crop within each region, as a consequence of the specific activities of 
each  individual  farmer.  Markers  need  to  be  robust  enough  to  cope  with  these  local 
differences  for  a  given  agronomic  practice,  in  order  to  provide  meaningful  results.  For 
example,  the  property  value  “A  period  of  occurrence”  will  be  set  in  a  way  to  correctly 
reflect the period where ploughing of certain crops might occur. 

The  NDVI  temporal  profile  derived  from 
Sentinel-2  

The  NDVI  temporal  profile  derived  from 
Sentinel-2  images  shows  the  ploughing  of 
land  of  two  neighbouring  maize  parcels  in 
North Italy with 1 month difference.  

False colour composite time series (RGB 8,11,4) 

NDVI time series 

The linked image cannot be displayed.  The file may have been moved, renamed, or deleted. Verify that the link points to the correct file and location.

The linked image cannot be displayed.  The file may have been moved, renamed, or deleted. Verify that the link points to the correct file and location.

50 

 
 
 
 
 
 
 
 
      
Figure A14: Two nearby maize crops with different sowing dates (20 April and 17 May) 

2.5  Activation and switching of “traffic lights” 

This example provides an illustration of the way different “traffic lights” are activated. 

•  An agriculture parcel is declared with winter wheat. At the beginning of the 
monitoring, the parcel is flagged as WHITE, since only the information from 
IACS/LPIS is extracted.  

•  As soon as the schemes/measures are identified and the relevant markers 
are selected (in this case NDVI), the monitoring of the parcel starts, the 
colour switches to YELLOW – the parcel is under assessment, but there are 
no definitive result.  

The  marker  properties  and  values  are  set  based  on  the  crop  calendar  for  winter  crops 
applied in the particular agro-climatic region and the statistical information derived from 
the  image  time-series  for  the  parcels  declared  with  wheat  in  the  region.  The  marker 
depicts  a  temporal  profile  of  the  expected  NDVI  mean  and  standard  deviation  within  a 
typical parcel declared with winter wheat for the area. It implies a steady development of 
homogeneous vegetation coverage over the declared parcel in the spring months with a 
peak around the end of May. This should show an increase of the NDVI, combined with a 
low  standard  deviation  due  to  the  expected  homogenous  nature  of  the  crop  coverage. 
The  same  type  of  temporal  profile  (graph  given  below)  is  gradually  generated  from  the 
continuous image dataflow for the parcel under monitoring.   

•  The comparison between these profiles (profile of monitored parcel against 

the reference one of the marker) reveals a significant higher value for the 
standard deviation of the NDVI early in the season, which indicates within-
parcel heterogeneity. This can be an evidence of non-compliance, as 
probably only part of the parcel is cropped with winter wheat. Parcel colour 
is switched to BLUE BLINKING, indicating the need of expert judgement. 

•  Visual (CAPI) verification of the time series reveals the fact that in the 
beginning of February only part of the parcel is covered with a crop. 
Further (semi-automated) check of historic imagery from autumn of the 
previous year, reveals the fact that there was a (cover?) crop on the part 
of the parcel where winter crop was not evident (which might explain why 
there was a delay of the winter crop sowing). Parcel colour remain BLUE 
BLINKING, waiting for collection of further evidence late in the season.  
•  The subsequent monitoring reveals the fact that the crop emerges on the 

remaining part of the parcel within the expected time frame (later spring). 
The situation is rectified and the parcel colour switched to GREEN. 

51 

 
Figure A15a: NDVI profile of a heterogeneous parcel 

The  above  NDVI  values  correspond  to  the  following  visual  observations  (false  colour 
composite) 

Winter crop occupies part of the 
agriculture parcel 

Figure A15b: Sentinel 2 images of a heterogeneous parcel 

52 

 
 
2.6  Early warning 

The provision of notifications (warning alerts) to farmers about agronomic activities that 
they need to conduct is an important component in any design of a system that strives to 
increase  compliance.  To  quote  Benjamin  Franklin:  “An  ounce  of  prevention  is  worth  a 
pound of cure”.  

53 

 
 
 
 
2.7  Additional sources of information  

Title  

Description  

Responsible 
Organization  

Reference link  

European  Space 
Agency 

http://esa-
sen4cap.org/ 

European  Space 
Agency 

http://copernicus.eu/n
ews/upcoming-
copernicus-data-and-
information-access-
services-dias 

SEN4CAP 

Copernicus  Data  and 
Information  Access 
Services (DIAS) 

Policy 

European 

The SEN4CAP project aims at 
providing  to  the  European 
and  national  stakeholders  of 
Common 
the 
Agricultural 
(CAP) 
algorithms, 
validated 
products  and  best  practices 
for  agriculture  monitoring 
relevant for the management 
of  the  CAP.  Special  attention 
will  be  given 
to  provide 
evidence  how  Sentinel-1  and 
Sentinel-2 
derived 
information  can  support  the 
and 
modernization 
simplification  of  the  CAP  in 
the post 2020 timeframe. 

has 

Data 

launched 
to 

The  European  Commission 
(EC) 
an 
develop 
initiative 
Copernicus 
and 
Information  Access  Services 
(DIAS)  that  facilitate  access 
to  Copernicus  data  and 
the 
information 
By 
Copernicus 
providing 
and 
information  access  alongside 
processing  resources,  tools 
and  other  relevant  data,  this 
initiative is expected to boost 
user 
stimulate 
innovation  and  the  creation 
of  new  business  models 
based  on  Earth  Observation 
data and information. 

from 
services. 
data 

uptake, 

A Large Scale Pilot to 
further 
support 
integration 
and 
digitization  of  IACS 
across Europe 

The  topic  is  pending  upon 
the  final  adoption  of  the 
Work Programme 2018-2020 

European 
Commission 
DG 
Connect 

- 
AGRI/DG 

https://ec.europa.eu/j
rc/sites/jrcsh/files/ma
hy.pdf 

Inventory of Sentinel 
Pilot projects 

A  catalogue  of 
the  MS 
projects  on  Sentinel  use  for 
the CAP 

European 
Commission 
DG JRC 

- 

https://g4cap.jrc.ec.e
uropa.eu/G4CAP/pilot
4cap 

54 

 
List of abbreviations and definitions 

AP 

Agricultural parcel 

BPS 

basic payment scheme 

CAP 

Common Agricultural Policy 

CwRS  Control with remote sensing 

EFA 

ecological focus area 

IACS 

Integrated Administration and Control System 

GSAA  Geospatial aid application 

IXIT 

implementation extra information for testing 

LPIS 

Land Parcel Identification System 

NDVI 

normalised difference vegetation index  

OTSC  On the spot check 

RP 

Reference parcel 

55 

 
 
 
GETTING IN TOUCH WITH THE EU 

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On the phone or by email 

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Direct or your local information centre (see http://europa.eu/contact). 

 
 
 
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doi:10.2760/258531 

ISBN 978-92-79-74279-8