Report from the Commission on remote sensing applied to agricultural statistics during the period 1994-1998
Date: 1998-10-29
Institutions: European Commission
Subjects: action programme, agricultural statistics, project evaluation, remote sensing

COMMISSION OF THE EUROPEAN COMMUNITIES 

Brussels, 29.10.1998 
COM(1998) 613 final 

REPORT FROM THE  COMMISSION 

on remote sensing applied to agricultural  statistics 
during the period  1994-1998 

Contents 

1. INTRODUCTION 

PART I - OPERATIONAL ACTIVITIES 

1 ACTIVITY A: REGIONAL INVENTORIES 

1.1  Objective and methods 

1.2 Progress of the work 

1.3 Financial aspects 

1.4 Conclusions and policy 

2 ACTIVITY B: EUROPEAN RAPID ESTIMATES 

2.1 Objective and methods 

2.2 Progress of the work 

2.3 Working procedures and the main developments 

2.4 The role of the ground surveys 

: 

2.5 Use of financial  resources 

2.5.1 Images 

2.5.2 Image processing and estimate production 

2.5.2.1 Project execution and management 

2.5.2.2 Project launch 

2.5.2.3 Preparations for  the season 

2.5.2.4  Acquisition  and  preliminary  processing  of  satellite 

data 

2.5.2.5 Interpretation  of satellite  data 

2.5.2.6 Current situation 

2.5.2.7 Training 

2.5.2.8 Maintenance 

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7 

...7 

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16 

2.5.2.9 Internal project  control 

2.5.2.10 Reports 

2.5.2.11 Product delivery 

2.5.2.12  Cost of image processing  and  production  chain for 

estimates 

2.5.3 Software  maintenance 

2.5.4 Ground surveys 

2.5.5 Financial management 

2.6 Results obtained and error analysis 

2.7 Evaluation of the action 

: 

2.8 Conclusions 

PART II - ACTIVITIES FOR DEVELOPMENT 

3  ACTIVITY  C:  THE  ADVANCED  AGRICULTURAL 

INFORMATION 

SYSTEM... 

3.1 C.l. Vegetation condition indices 

3.2 C.2. Yield prediction models 

3.2.1 Meteorological data 

3.2.2 Pedological data and agronomic parameters: 

3.2.3  Yield prediction: 

3.2.4 Information  technology: 

3.3 C.3: Integrated agricultural  information  system and MARS bulletin 

3.4 C.4. Integrated system at national and regional level 

4  ACTIVITY  D:  MONITORING  AND  PREDICTION  OF  HARVESTS 

OUTSIDE THE COMMUNITY 

5 ACTIVITY E: APPLICATION  OF NEW METHODS OR SENSORS 

5.1 Pilot project  for uses of microwave images in estimating acreages 

16 

16 

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18 

18 

19 

20 

22 

24 

24 

24 

25 

26 

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27 

27 

27 

29 

30 

31 

31 

5.2 Re-engineering of Activity B (REACT_B) 

5.3 The aeropalynological model 

32 

33 

6 CONCLUSIONS CONCERNING THE ACTIVITIES BEING DEVELOPED 

34 

ANNEXES 

1. 

2. 

3. 

4. 

5. 

6. 

7 

MARS Activity  B acreage  estimates  for  the main crops and 
deviations in relation to Eurostat (1994): EUR-12 

MARS Activity  B acreage  estimates  for  the main crops and 
deviations in relation to Eurostat (1995): EUR-12 

MARS Activity  B acreage  estimates  for  the main  crops and 
deviations in relation to Eurostat (1996): EUR-15 

MARS Activity  B acreage  estimates  for  the main crops and 
deviations in relation to Eurostat (1997): EUR-15 

Operational activity contractors 

List of MARS publications 1994-1998 

Abbreviations used 

34 

34 

35 

36 

37 

38 

39 

48 

Introduction 

On  26  September  1988,  the  Council  adopted  Decision  88/503/EEC  adopting  a  pilot 
project  on  remote  sensing  applied  to  agricultural  statistics  (better  known  as  the  MARS 
project).  This  pilot  project  was  intended  to  develop  and  test  analysis  techniques  and 
methods  to  improve  the  information  on  agriculture  in  the  European  Union  by 
incorporating  more  space  data  in  the  Community  statistical  information  system.  The 
development  project  was planned  to  last  ten  years, but  the  above  decision  covered  only 
the first five years (1989-1993). 

At the end of that five-year  period, and in accordance with that Council Decision, a report 
on  the  first  phase  of  the  pilot  project  (1989-1993)  was  drawn  up  and  submitted  to  the 
Council,  together  with  a  proposal  for  continuing  the  applications  which  had  become 
operational.  On  that  basis,  on  14  November  1994  the  Council  adopted  Decision 
n° 94/753/EC  on  the  continued  application  of  remote  sensing  to  agricultural  statistics 
during the period  1994 to  1998\ 

The purpose of the applications covered by that decision was more particularly: 

• 

• 

to  facilitate  the  use  of  remote  sensing  by  interested  Member  States  with  a  view  to 
improving  their  national  agricultural  statistics  systems  (Activity  A  -  Regional 
inventories); 

to  provide  estimates,  before  harvest,  of  acreages  and  potential  production  of  the  main 
crops  at  European  and,  if  possible,  national  level  within  the  limits  of  the  financial 
resources available (Activity B - European rapid estimates). 

Article 5 of Council Decision 94/753/EC  stipulates that the Commission shall submit to the 
European Parliament and to the Council, by  31 July  1998, a report  on the  implementation 
of the measures and on the use of the financial  resources put at its disposal for the purpose, 
"including,  where  necessary,  proposals  on  the  continued  application  of remote  sensing  to 
agricultural statistics". Part I of this report is devoted to those activities. 

The Council also noted the Commission's  intention to continue, during implementation  of 
the  4tn  framework  programme  for  research  and  development,  a  number  of  measures 
forming  part  of  the  MARS  project  but  requiring  further  research  before  they  could  be 
applied at European level. This concerns the following activities: 

1 O.J. n°L 273 of 05.10.1988. 
2 Document COM(94) 332 final of 19.09.1994. 
3 O.J. n° L 299 of 22.11.1994, page 27. 

establishing  an  advanced  agricultural  information  system  at  Community  level  by 
combining  information  obtained  by various  actions  developed  and tried  out  during  the 
programme (Activity C - Integrated System); 

devising  a method  similar to that developed  for  the  European  Union  to allow  forecasts 
and  early  estimates  of  harvests  in  certain  regions  bordering  the  European  Union, 
particularly East European countries and possibly the Maghreb countries, in liaison with 
the  programmes  for  co-operation  with  these  countries  (Activity  D  -  Monitoring  and 
prediction  of harvests outside the  Community); 

• 

continued  research  on  the  potential  use  of  new  sensors,  such  as  radar,  for  statistical 
applications,  to  complement  the  high-resolution  satellites  and  meteorological  satellites 
currently  used  in  the  MARS  project  (Activity  E  -  Application  of  new  methods  or 
sensors). 

Part  II  of  this  report  contains  a  brief  description  of  these  activities,  carried  out  by  the 
Ispra  Joint  Research  Centre  (JRC)  in  association  with  the  Directorate  General  for 
Agriculture (DG VI) and the Statistical Office  of the European Communities (Eurostat). 

PART I - Operational activities 

1.  ACTIVITY A: REGIONAL  INVENTORIES 

1.1 

Objective and methods 

The  purpose  of  this  activity  is  to  meet  the  need  for  accurate,  objective  annual 
information  on  acreages  and  yields  at regional  level  for  the  main  crops  of  interest. 
The methodology combines "high resolution" satellite data (covering the whole area 
of  interest)  and  data  obtained  by  "area  sampling"  surveys  on  the  ground. 
Development  and evaluation related mainly to the regression estimator method.  The 
action was organised in two separate parts: 

•  objective  ground  observation  with  a  sampling  plan  established  or  refined  by 

remote sensing; 

• 

the automatic classification  of spatial data, improving the estimates of the ground 
surveys by a regression method. 

Remote  sensing  also  supplies  the  survey  officer  with  documents  enabling  him  to 
identify  the exact location of plots on the ground. 

1.2  Progress of the work 

During  the  pilot  phase  of  the  MARS  project,  this  action  comprised  three  separate 
phases: 

•  1988-1989:  during  this  phase  a  strictly  identical  methodology  was  applied  to 

5 regions; 

•  1990-1991:  during  this  period,  local  adjustments  were  made  and  the  active 

participation of the regions or national authorities was required; 

•  1992-1993: during this phase the project was used to support national or regional 

initiatives. 

At  the  end  of  phase  1 of  the  pilot  project  on  remote  sensing  applied  to  agricultural 
statistics, responsibility  for  implementing  this activity  had been  largely transferred  to 
the  Member  States.  However,  in  accordance  with  Council  Decision  94/753/CE, 
technical  assistance  and  finance  could  be  provided  under  the  Community  budget, 
particularly  as regards aid for technical  statistics, the execution of pilot studies aimed 
at encouraging  the use of area surveys to improve national  agricultural  statistics, and 
improvements to software. 

During  phase  2  the  Spanish  Ministry  of  Agriculture,  Fisheries  and  Food  (MAPA) 
and  the  Portuguese  statistical  services  received  assistance  from  the  Commission  in 
financing  two  projects  in  this  sphere  of  activity.  In  particular,  MAPA  received  a 
subsidy  to  develop  a  land-based  panel  for  the  early  estimation  of  acreages  and 
production  of  the  main  crops.  The  Portuguese  Ministry  of  Agriculture  received 

Community  finance  to carry out a project  on area survey methodology  applied  to the 
production  of statistics on land use. In both cases, the Community  contributed  around 
40% of the total cost of the project. 

In  connection  with  this  activity,  the  JRC  provided  technical  assistance  for  several 
European  Union  Member  States  and  for  countries  taking  part  in  the  PHARE 
programme.  In  particular,  Slovenia,  Poland  and  the  Czech  Republic  received 
technical  assistance  with  regard  to  statistical  methodology  and  sampling  plans  (cf. 
for  some 
technical  support 
project  Activity  D).  The  JRC  also  provided 
Mediterranean  countries  for  the  purpose  of  establishing  regional 
inventories 
(notably  Tunisia  and  Turkey)  and  arranged  scientific  exchanges  with  Morocco  on 
this  subject. 

1.3  Financial  aspects 

Table  1 shows the EU's contribution to the two projects mentioned above. 

Table 1 - EU contribution  to "Activity A" (ECU) 

Ministry of Agriculture  (Spain) 
. 
Ministry of Agriculture (Portugal) 
Total 

1997 

1996 
60,911  65,880 
60,150 
121,061  65,880 

1998 
-- 

- 

Total 
126,791 
60,150 
186,941 

1.4  Conclusions  and policy 

In  phase  2  of  the  project  the  "Regional  inventories"  activity  was  confined  to 
financial  and  technical  assistance  for  national  statistical  services  requesting  it, 
within the limit of the available resources. 

The regional  inventories  are still  a rapid, objective  and  often  more  efficient  system, 
in terms  of  accuracy,  when  specific  handicaps  make  farm  surveys  more  difficult  or 
less  reliable.  The  scientific  methodology  developed  could  be  used 
the 
harmonisation of land use estimates for the whole of the European Union. 

in 

The area survey system  is particularly  appropriate for the PHARE countries, since it 
is  difficult  to  maintain  an  up-to-date  list  of  farmers,  owing  to  the  current  farming 
reforms.  This  approach  may  however  also  prove  useful  for  the  whole  of  the 
European  Union  in meeting a growing need for  information  on land  use, the  impact 
of  agricultural  activity  on the  environment,  the  territorial  dimension  of  agricultural 
policy and rural development measures, etc. 

Given  the  high  cost  of  the  images,  exhaustive  annual  coverage  in  high-resolution 
images and use of the regression estimator method are not a viable way of producing 
statistics  at  EU  level  and  are therefore  not  recommended.  That  could  change  in  the 
future,  either  with  the  arrival  of  new  sensors  and  a  reduction  in  the  price  of  the 
images,  or  if  statistics  are  produced  in  combination  with  cartographic  products: 
hence  the  desirability  of  maintaining  a  technological  watch  on  methodological 
aspects  and  the  introduction  of new  sensors, including  radar.  We  should  also  make 
better  use  at  European  level  of  the  potential  offered  by  area  surveys  to  obtain 

information  on  land  use  and  on  other  statistical  variables  with  a  territorial 
dimension, for which traditional methods are less  efficient. 

2.  ACTIVITY  B: EUROPEAN RAPID  ESTIMATES 

2.1  Objective and  methods 

The  purpose  of  Activity  B  is to  provide  an  early,  objective  estimate  of  changes  in 
the  acreages  of  the  main  annual  crops  at  European  level  by  interpreting  high-
resolution  satellite  images  obtained  on  a  sample  of  60  sites  (40x40  km  in  size) 
distributed  over  the  whole  of  the  European  Union.  The  method  is  based  partly  on 
estimating  for  each  site the  surface  changes  against  the previous  year and partly  on 
extrapolating  these  changes  at  European  level  on  the  basis  of  the  previous  year's 
official  statistics on acreages (source: Eurostat). 

The  method  does  not  use  any  ground  data  for  the  current  year  but  incorporates  the 
previous  year's  ground  observations,  also  permitting  a  retrospective  check  on  the 
method's validity. 

This activity has also supplied qualitative information  during the agricultural  year. 

2.2  Progress of the work 

In phase  1  of the MARS project, this activity was set up in three stages: 

•  1987-1988: finalisation  of detailed  methodology; 

•  1989-1991 : establishment of the system and entry into production; 

•  1992-1993: full-scale  test of the activity (53 sites). 

During phase 2 this activity became operational. The main stages were as follows: 

•  1994: Transitional  year  pending  the  Council  decision;  no  significant  changes  in 

methodology  or organisation of the work. 

•  1995:  Call  for  tenders  for  the  purpose  of  awarding  the  contract  for  photo 

interpretation and acreage estimation. 

•  1995:  First  effective  year  of 

the  Council  decision. 
Establishment  of  the  GRIPS  and  ORCA  software  by  the  JRC,  for  image 
correction,  processing  and  classification  respectively.  Establishment  of  seven 
new sites to extend the activity to the three new Member States. 

implementation  of 

•  1996: Extension of the activity to 60 sites, with 7 covering the three new Member 

States (Austria, Finland and Sweden). 

•  1996-1997: Establishment  of the CAMEL  software  for the current situation task. 

•  1997: Adaptation of the GRIPS software  to 1RS satellite images. 

•  1998:  Final  year  of  implementation  of  the  Council  decision.  Early  use  of 
windows for obtaining images to enhance  the efficiency  of the estimates in terms 
of stability and earliness. 

10 

2.3  Working procedures and the main  developments 

During  phase  2  of  the  MARS  project,  working  procedures  for  Activity  B  were 
essentially  no  different  from  those  in  phase  1. Nevertheless,  although  this  activity 
was  considered  operational,  several  significant  changes  were  made  to  both  the 
system and the tools: 

•  Replacement  of  the  software  used  up  to  1995  by  software  developed  under  the 

supervision of the JRC, namely: 

GRIPS, for the correction of satellite images; 

ORCA, for the classification  and photo-interpretation  of the images; 

CAMEL for  analysis of the results and the data processing  necessary  to 
prepare the final estimates on the basis of the classified  images. 

GRIPS  and  ORCA  were  used  throughout  phase  2.  The  CAMEL  software  was 
used  from  1997  onwards.  By  using  these  software  packages  it  was  possible  to 
rationalise  tasks  and  gain  control  over  all  phases  of the  execution  of the  activity, 
thus  ensuring  quality  control  over  the  whole  production  chain.  The  GRIPS 
software  was adapted  between  1997 and  1998 to the new 1RS satellite images (up 
to then, the only satellites used were SPOT and LANDSAT). 

•  Introduction  of  7 new  sites  for  the  three  new  Member  States, namely  three  sites 
in  Sweden,  two  in  Finland  and  two  in  Austria.  One  effect  of  this  was  better 
representation  of spring crops. 

•  Establishment  of  a "radiometric  knowledge  base" to  help the  photo  interpreters, 
taking  account  of the  spectral  signatures  and  climatic  conditions  observed  in the 
past. This knowledge base is updated  and extended  each year, thus improving the 
system's  quality. 

The main results of Activity B were included  in the MARS bulletin (see Activity C). 

Between  1994 and  1998, the salient points were as follows: 

•  1994 

The activity  continued  in this transitional  year between the research  and  operational 
stages.  It  followed  the  same  procedures  as  in  1993.  Altogether,  155  images  were 
obtained on 53 sites. 

•  1995 

Altogether,  153  images  were  obtained,  covering  53  sites.  The  results  of  the 
estimates  were  published  in  7 MARS  bulletins  during  the  season.  During  this  year 
the  activity  proceeded  satisfactorily  despite  some  specific  problems  encountered  in 
validating the estimates: 

11 

-  very  large changes  in the acreages of certain  crops of interest,  such as  sunflower, 

partly owing to the effects  of the CAP, 

-  over-estimation  of  the  change  in  the  acreages  of  certain  crops  such  as  colza, 

owing to the fact that this crop is poorly represented  in the sample, 

- 

the severe drought in Spain and Portugal, which made it very difficult  to interpret 
the  photographs  and  led  to  dubious  estimates  for  a  large  proportion  of  summer 
crops. 

In  the  case  of  sunflower  and  colza,  the  error  increased  the  smaller  the  number  of 
sites  containing  colza  and  sunflower,  so  that  estimates  are  greatly  influenced  by 
relocation of these crops in Europe, as happened  following  the CAP  reform. 

•  1996 

The  number  of  sites  processed  was  59  out  of  60  (there  being  no  images  for  the 
Koping  site), and  the  whole  of the  European  Union  was  covered  for  the  first  time. 
Altogether,  171  images  were  obtained.  The  results  were  published  in  7  MARS 
bulletins during the agricultural  year. 

•  1997 

Altogether,  183  images  were  obtained  covering  all  60  sites. The  main  results  were 
included  in six MARS bulletins and three intermediate reports. 

•  1998 

Although the final results were not available by the time this report was written, it is 
worth  mentioning  that  a  number  of  changes  were  made  to  meet  the  need  for 
improvements to results and  management: 

-  acquisition  dates were rationalised  to improve discrimination  between winter and 
spring crops, to achieve  earlier  stability  in cereal  estimates  and to identify  winter 
colza  more  accurately.  This  was  made  possible  by  the  database  of  sowing  dates 
produced  from  ground  survey data associated  with Activity B. 

-  adaptation  of the GRIPS  software  to process  the  images  from  the  Indian  IRS-1C 
satellite,  which  will  increase  the  likelihood  of  obtaining  images  on  the  sites  on 
the set dates. 

2.4  The role of the ground  surveys 

Implementation  of  Activity  B  during  the  second  phase  of  the  MARS  project 
benefited  from  the  results  of  the  ground  surveys  conducted  on  the  same  sites  with 
point  sampling  in  a  number  of  segments  (on  average,  16  segments  1400x1400 
metres per site). The purpose of these surveys (known as «Action 6» during phase  1) 
was as follows: 

12 

a) 

b) 

c) 

d) 

to  supply  photo  interpreters  with  ground  information  to  establish  the 
reference  system  for  each  site  for  year  n-1 and  to  validate  the  results 
obtained via satellite data at the end of year n; 

to obtain  direct  estimates  of  acreages  and  yield  forecasts  for  the  main 
crops at European  level, independently  of remote sensing techniques; 

to  supply  acreage  and  yield  data  for  the  main  annual  crops  for  each 
site, to be used as a statistical  model for extrapolating the results; 

to  supply  additional  information  on  certain  aspects  which  come  into 
play  in the agro-meteorological  model  (sowing, flowering and  harvest 
dates, crop rotation, plans for the next year's sowings, etc.). 

All  the  results  of  these  surveys  were  stored  in  a  database  at  the  JRC  and  had  a 
positive  impact  on  other  aspects.  For  example,  the  ground  surveys  were  used  to 
produce a database on spectral signatures and provide real data on sowing dates, etc. 

2.5  Use of financial  resources 

Table 2 below gives a summary  of expenditure on the operational  implementation  of 
Activity  B from  1994 to  1998. It reveals  that the overall  cost of this activity  for  the 
EU  budget  during  the  period  was  ECU  11.8  million,  or  an  average  of  ECU  2.4 
million per annum, though  it was closer  to  ECU  2.1 million  in  1997 and  1998. This 
decline, which  is all  the  more  marked  in  comparison  with  the  start  of the  period  if 
we take  account  of the  increased  number  of  sites, is due  mainly  to  cost  savings  on 
image processing  for  1997 and  1998 following  a call for  tenders put  out in  1995  for 
the award of this contract.  The increase  in  1996 is due to investment  in  information 
technology  in that year by the contractor  selected. 

Table 2 - Total expenditure  on "Activity BM 

4 

Processing 
Images 
Software  maintenance 
Ground  surveys 
Total 

1996 

1994 

632,007 

1995 
1.323,470  1,125,000  1,357,807 
666,400 
633,719 
102,952 
581,966 

Total 
5,474,658 
3,247,581 
427,970 
2,611,619 
2,510,330  2,248,638  2,709,125  2,153,473  2,140,262  11,761,828 

1998 
837,614 
660,000 
161,360 
481,288 

1997 
830,767 
655,455 
163,658 
503,593 

489,919 

554,853 

Broadly speaking, the operating costs can be divided into four types: 

•  purchase of images, 

•  expenditure on image processing and production of estimates, 

•  software  maintenance, 

•  ground surveys. 

4  The  number  of  images  for  1998  is  estimated. 

13 

Here is a brief description of each type of expenditure. 

2.5.1 

Images 

On  average,  3  images  per  site  are  processed  in  the  course  of  each  season;  in 
practice, all the images are obtained  between the beginning  of April  and the end 
of  September.  If  an  image  is  to  be  usable,  there  must  be  very  few  clouds.  In 
some  parts  of  Europe,  cloudy  weather  may  make  it  impossible  to  obtain  any 
images during highly interesting periods in crop  development. 

Table 3 - Statement of images used by Activity B 

Year 

1994 
1995 
1996 
1997 
1998* 
total 

satellite 
LANDSAT 
18 
16 
12 
57 
40 
143 

SPOT 

137 
149 
159 
126 
120 
691 

(*)  Forecast 

TOTAL 

1RS 

Images 

155 
165 
17.1 
183 
180 
854 

20 
20 

Costs 
632007 
633719 
666400 
655455 
660000 
3247581 

During  1994 to  1997 a total of 674 images were obtained  and processed.  During 
those  years,  two  image  suppliers  were  competing  to  supply  the  most  suitable 
images  at any time:  Spot  Image, which  has  exclusive  rights  to the  images  from 
the  European  satellite,  SPOT,  and  Eurimage,  which  has  exclusive  rights  to  the 
images  from  the American  LANDSAT  satellite. To  some extent,  the  SPOT and 
LANDSAT  images  are  interchangeable.  However,  in  practice  the  only  way  of 
ensuring  that  an  image  is  obtained  when  and  where  necessary  is  to  obtain  the 
first  available  image for the chosen  sites in the periods  selected.  The  possibility 
of  programming  the  SPOT  satellite  considerably  increases  the  likelihood  of 
obtaining  good  images  from  that  satellite  for  Activity  B,  which  explains  the 
large  number  of  images  obtained  from  SPOT  as  opposed  to  LANDSAT.  Very 
recently (April  1998), GAF/Euromap - the exclusive  supplier of images obtained 
by  the  Indian  satellite, IRS-1C  - also  re-entered  this  market.  About  180 images 
are to be bought in  1998 from  the three suppliers, as indicated in Table 3. 

2.5.2 

Image processing and estimate  production 

The  term  "processing"  covers  all  the  practical  operations  which  take  place 
following  acquisition  of  the  image  and  are  necessary  to  prepare  the  estimates 
which  the  Commission  expects.  For  a  better  understanding  of  the  nature  and 
level  of  expenditure  on this, the  sections  below  will  give  a brief  description  of 
the activities involved in "processing" the images. 

2.5.2.1  Project execution and management 

Management  activities  concern  the  organisation  of  the  project  as  a  whole, 
mobilising  the  necessary  equipment  and  human  resources  and  planning  the 
timetable.  During  the  execution  phases,  the  management  is  responsible  for  the 

14 

operation  of the production process, co-ordination  between  successive years and 
resolving problems and contingencies. 

2.5.2.2  Project  launch 

This action  takes place  during the preparatory  phase  and  concerns the launch  of 
the entire production chain, i.e. the installation  of the data processing  equipment 
and  commissioning  of  the  software  and  the  communications  network.  When  a 
long-term  production  chain  is installed  (several  consecutive  years),  expenditure 
on this type of activity is charged to the first year only. 

2.5.2.3  Preparations for  the season 

This  work  can  be  divided  into  two  main  parts: validation  and  site  preparation. 
Validation  consists  in  evaluating  the  quality  of  the  photo-interpretation  results 
by  comparing  them  with  ground  surveys  conducted  on  each  of  the  sites  in  the 
previous  season.  Site  preparation  consists  in  collating  all  the  information  on 
previous  agricultural  years;  this  information 
is  preserved  to  improve  the 
knowledge  base  on  the  sites  (crop  rotation  tables,  site  profile,  image  reference 
system, definition  of the regions, etc.) 

2.5.2.4  A cquisition and preliminary processing  of satellite data 

When  the  image  is  received,  it  is  checked  to  ensure  that  it  is  acceptable  (date 
taken,  image  quality,  rate  of  cloud  cover,  etc.).  Next,  the  image  undergoes  a 
series  of  processes  aimed  at  eliminating  any  influence  due  to  atmospheric 
conditions,  the  satellite  photography  parameters,  the  type  of  satellite  and  the 
local  topography.  These  processes  may  be  roughly  classified  into  two  types: 
radiometric and geometric; they are carried out using the GRIPS software  owned 
by the Commission. 

2.5.2.5  Interpretation  of satellite data 

This  fundamental  phase  comprises  several  operations  which  we  can  divide  into 
six  consecutive  stages: a) data  integration,  if the preceding  correction  phase  has 
been  validated;  b)  automatic  classification,  by  allocating  the  image  to  one  of 
about thirty homogeneous classes; c) segment analysis for thematic allocation of 
each  plot  in  the  segment;  d)  preparation  of  the  land  use  map  by  incorporating 
each  segment  in  the  automatic  classification;  e)  transmission  to  the  current 
situation unit; f) preservation of the results. Stages b) and c) are carried out using 
the ORCA software  owned by the Commission. 

2.5.2.6  Current  situation 

The  purpose  of the  current  situation  task  is to  estimate  the  acreages  of  each  of 
the  17 crops of interest  at European  level, every two weeks from  April onwards. 
The  estimate  is  based  on  extrapolation  of  the  data  obtained  by  processing  a 
maximum  of four  images per site. "Current  situation system" software  and some 
other auxiliary packages are used in this phase. 

15 

2.5.2.7  Training 

Training  is  divided  into  two  stages.  The  first  comprises  initial  training,  to 
establish  the  production  team.  It  is  followed  by  on-the-job  training  to  meet  a 
need to develop the production tools and therefore  to modify  operating methods. 

2.5.2.8  Maintenance 

Maintenance  ensures  that  all  the  data  processing  equipment  involved  in  the 
production  chain  and  provided  by  the  entity  responsible  for  image  processing 
and estimate production is available at all times. 

2.5.2.9  Internal project  control 

This activity  is meant  to ensure that the production  chain  operates  efficiently  at 
all times, that deadlines are met and that the set quality  standards are  maintained 
during the various estimate preparation phases. 

2.5.2.10  Reports 

.The  main product  of this  activity  is the supply  of estimates  of  changes  in  crops 
of  interest  for  the  Commission.  These  estimates  are delivered  during the  season 
and  in real  time,  in various  forms:  tables by  geographical  level, tables  by  crop, 
reports of the current situation regarding acreages, etc. 

2.5.2.11  Product  delivery 

This activity  consists  in providing  the  Commission  almost  instantaneously  with 
all  auxiliary  data  ahead  of  the  statistical  results  produced  by  the  processing 
chain. This  information  represents  a vast, harmonised  historical  database  which 
can be used for other research or other applications. 

2.5.2.12  Cost of image processing  and  production  chain for  estimates 

Table  4 sets out  expenditure  on  image  processing  during  the  operational  phase. 
NB:  in  1994 and  1995 the processing  concerned the  53 sites of the EU of  12, as 
the current 60 sites did not form part of the operational phase until  1996. 

16 

Table 4 - Breakdown  of expenditure  on "Processing" 

PRODUCTION 
Project execution and management 
Project  launch 
Preparations for the season 
Collection and preliminary 
processing of satellite data 
Interpretation  of satellite 
data 
Current situation 
Training 
Maintenance 
Equipment 
F R U J L C l l U N l R UL 
REPORTS 
PRODUCTS 
ISPRA  EXPERT 
TOTAL 

1996 

1994 

1995 
1,280,170  1,081,700  1,144,841 
62,474 
552,919 
106,082 

90,000 
— 
123,300 

90,000 
— 
123,300 

1997 
612,624 
64,172 

1998 
621,399 
66,007 

109,135 

106,033 

49,500 

49,500 

32,205 

33,026 

33,914 

388,800 
76,500 
— 
50,400 
501,670 

388,800 
76,500 
— 
50,400 
303,200 

218,967 
92,474 
31,688 
48,031 

64  3 61 
32,895 
26,000 
19,516 
17,300 
96,194 
— 
1,323,470  1,125,000  1,357,807 

26,000 
17,300 
— 

225,222 
108,797 

231,959 
111,483 

72,272 
«- 

72,003 
— 

66  1 57 

6 0 6 89 

33,395 
20,084 
98,506 
830,767 

33,957 
20,695 
100,874 
837,614 

For  the  years  1994  and  1995  the  Commission  had  used  the  contractual 
framework  of the pre-operational  phase, but  from  1996 onwards these  activities 
were  entrusted  to  companies  outside  the  Commission,  selected  by  tendering 
procedures  based  on  free  competition.  The  higher  expenditure  in  1996 was  due 
to the launch activities described  earlier. 

2.5.3 

Software  maintenance 

to  the  JRC 

Produced  at  the  request  of  the  Joint  Research  Centre,  the  GRIPS  and  ORCA 
software  was  supplied 
in  October  1994  and  August  1995 
respectively.  The  GRIPS  software  is  used  for  the  radiometric  and  geometric 
rectification  of the  SPOT  and  LANDSAT  images. The  ORCA  software  is used 
to  back  up photo  interpretation  in  Activity  B. Recently,  GRIPS  was  adapted  to 
process IRS/LISS images incorporated  in Activity B at the start of 1998. 

The  purpose  of  maintenance  is  to  ensure  that  these  two  software  packages 
operate properly in the processing and estimate production phases. It is entrusted 
to a specialist  firm. 

There  are  two  types  of  maintenance:  corrective  and  adaptive.  The  former  is 
intended  to  correct  operating  anomalies  either  in  the  hardware  or  in  the 
operating system. The latter covers research and the implementation  of any kind 
of  modification  to  the  software  requested  by  the  users.  ORCA  also  uses  two 
software  packages,  CLIPS  and  ARKEMIES,  in  its  development  and  operation; 
the  licences  are owned  by  external  firms.  Use  of these two packages by  ORCA 
entails licensing and maintenance costs. 

17 

Table 5 - Cost of using the GRIPS and ORCA  software 

GRIPS  software 
Corrective maintenance 
Adaptive maintenance 
GRIPS total 
ORCA  software 
Arkemie  licence 
Clips licence 
Computer  configuration 
Corrective maintenance 
Maintenance  of the computer  configuration 
Adaptive maintenance 
Corrective maintenance (ARKEMIE and CLIPS) 
Total ORCA 
Total GRIPS and ORCA 

1996 

1997 

1998* 

13,415 
8,200 
21,615 

23,000 
20,664 
43,664 

23,000 
48,186 
71,186 

18,175 
10,935 
32,730 
19,497 

55,000 
55,000 
3,125 
3,125 
23,049 
52,869 
9,000 
9,000 
81,337  119,994 
90,174 
102,952  163,658  161,360 

2.5.4  Ground  surveys 

As already  stated, the  images alone  are not  enough  for  reliable  determination  of 
the  various  types  of  land  use.  We  have  also  mentioned  the  need  to  correct  the 
results of the automatic image classification  and the importance of establishing a 
well-documented  historical  database  for  the  segments  forming  part  of  each site. 
This  means  that  we  need  auxiliary  information  of  the  "ground  survey"  type. 
These  surveys  are  conducted  either  by  private  firms  or  by  statistical  authorities 
of the Member  States, selected  on  the  basis  of  open  tendering.  The table  below 
gives  detailed  information  on  the  cost  of  ground  surveys  per  annum  and  per 
Member State. 

Table 6 - Cost of ground surveys in ECU  (1994-1998) 

Sites in: 

1994* 
-- 
14,400 
35,650 
-- 

1995* 
-- 
14,270 
33,630 
-- 
75,063 
99,315 
21,900 
(1) 
74,998 
20,550 
107,363 

Austria 
Benelux 
Denmark 
Finland 
France 
Germany 
Greece 
Ireland 
Italy 
Portugal 
Spain 
Sweden 
United  Kingdom 
Total 
(1) The costs are included  in those of the United  Kingdom; (*)  53 sites 

135,250 
104,132 
24,600 
(1) 
83,821 
20,600 
89,409 
-- 
46,991 
554,853 

1996 
25,800 
8,500 
34,265 
28,960 
77,458 
101,739 
22,000 
(1) 
49,940 
18,300 
113,130 
53,734 
48,140 
581,966 

1997 
22,980 
14,250 
23,330 
27,563 
65,650 
78,173 
20,500 
13.440 
48,760 
16,682 
85,233 
51,000 
36,032 
503,593 

1998  ~ 
28,157 
10,340 
25,777 
30,461 
56,790 
78,538 
18,600 
11.670 
48,950 
17,000 
76,620 
44,500 
33,885 
481,288 

-- 
42,830 
489,919 

2.5.5 

Financial  management 

Moving  on to the  operational  phase  has also meant  changes to the  management 
of Activity B. Apart from  1994, which is a transitional  year between the research 
phase  and the  operational  phase  of the project,  Activity  B was  financed  mainly 

by  appropriations  available  under  heading  B2-513  of  the  general  budget 
(«Restructuring  of agricultural  survey  systems »). However,  some of the  ground 
surveys  were  conducted  using  appropriations  available  under  heading  B6-792 
(«Activities  providing  scientific  and  technical  support  for  Community  policies 
on a competitive basis »). 

During  this phase,  several  measures  were  initiated  to reduce  the  operating  costs 
of  Activity  B  while  keeping  within  the  limits  imposed  by  the  methodology. 
These  efforts  focused  on  three  main  areas  where  it  was  considered  possible  to 
cut  costs:  image  purchase,  processing  and  ground  surveys.  The  table  below 
shows the changes in the average cost per site during  1994 to  1998: 

Table 7 - Changes in the average cost per site (ECU thousands) 

Processing 
Images 
Ground  surveys 

Total  per  site 

1994 

1995 

1996 

1997 

1998 

25.0 
11.9 
10.5 
47.4 

21.2 
12.0 
9.2 
42.4 

22.6 
11.1 
9.7 
43.4 

13.8 
10.9 
8.4 
33.2 

14.0 
11.0 
8,0 
33.0 

As  we  can  see, the  average  cost  per  site  dropped  from  ECU  47,400  in  1994  to 
ECU  33,000  in  1998,  a  reduction  of  around  one  third.  The  cut  in  costs  was 
greatest  for  processing  (down  44.4%)  and  more  modest  for  images  (down 
12.6%) and ground surveys (down 23.8%). 

2.6  Results obtained and error analysis 

The results of the acreage estimates obtained by Activity  B are set out in the  MARS 
bulletin,  published  monthly  from  January  to  October  by the  Joint  Research  Centre. 
This Bulletin  also contains  an examination  of the  agro-meteorological  situation  and 
predicted  yields for  the main  crops. This bulletin  is normally  prepared  very  quickly 
(within  a  few  days  of  the  latest  images  being  obtained)  and  forwarded  promptly  to 
the  Commission  departments  concerned,  as  well  as  to  members  of  the  Eurostat 
working  party  on  "Vegetable  product  statistics",  and  to  a number  of  experts  in  the 
various Member States. 

Each  year,  in  accordance  with  Council  Decision  94/753/EC,  the  Commission  also 
submits  to  the  Standing  Committee  on  Agricultural  Statistics  a  report  on  the 
implementation  of  that  Council  decision.  When  this  report  is  discussed  by  the 
working  party  on  "Vegetable  product  statistics",  the  Commission  also  has  the 
opportunity  to  present  and  discuss  the  results  of  Activity  B  and  the  procedures  for 
conducting that activity. Annexes  1  to 4 show all the results of Activity  B compared 
to  the  Eurostat  official  statistics.  The  error  analysis  for  1994  to  1997  is  outlined 
below. 

This analysis is based on a comparison  of the acreage estimates for the various crops 
as  obtained  from  Activity  B  against  the  Eurostat  official  statistics  (generally 
available on  15 May in the following  year on the CRONOS database). Although  this 
approach  is not,  strictly  speaking,  a genuine  validation  of the  results  of  Activity  B, 
since even the official  statistics are not error-free,  it is nevertheless useful  to have an 

19 

idea of the  discrepancies  between  the  two  sources  and  hence  the margin  of  error  in 
the acreage estimates produced by Activity B. 

The table below  gives a general  view  of the average  percentage  deviations  between 
the  monthly  estimates  obtained  by  Activity  B  and  the  official  figures  for  1994  to 
1997 (the figures  for  1998 were not yet available when this report was written). 

Table 8 - Average  monthly  deviation  of Activity  B between  1994 and  1997  (%) 

APRIL  MAY 
1.2 
1.1 
1.6 

Cereals 
Wheat 
Common wheat 
Durum wheat 
Barley 
Maize 
Colza 
Sunflower 
Rice 
Beet 

1.0 
0.9 
1.0 
30 
2.7 
3.8 
8.3 
16.0 
9.4 
5.7 

JUNE 
1.4 
1.8 
2.7 

3.6 
3.1 
4.1 
12.5 
7.3 
11.7 
4.0 

JULY  AUG.  SEPT.  OCT.  Average 
1.1 
1.5 
1.8 

1.1 
1.8 
1.9 

1.0 
1.6 
1.7 

1.2 
1.8 
1.8 

1.0 
1.6 
1.7 

3.9 
2.4 
4.9 
14.4 
6.9 
9.3 
3.8 

3.9 
2.4 
4.9 
13.5 
6.7 
9.2 
5.6 

3.9 
2.2 
4.9 
12.3 
6.3 
7.9 
4.9 

3.8 
2.2 
4.7 
12.3 
6.9 
7.9 
4.6 

3.7 
2.6 
4.3 
12.1 
8.7 
9.7 
5.0 

4.0 
2.9 
2.7 
11.7 
10.8 
12.2 
6.3 

The  last  column  in  Table  8  shows  the  average  error  over  the  whole  period  for  the 
main  crops.  For  cereals  as  a  whole,  we  find  that  the  average  error  is  1.1%  but  it 
increases  to  1.8%  in  the  case  of  common  wheat,  2.6%  for  barley,  3.7%  for  durum 
wheat  and 4.3% for  maize. The error is even  greater  for  sunflower  (averaging  8.7%) 
and  colza  (12.1%),  and  is  also  very  marked  in  the  case  of  beet  (5.0%)  and  crops 
which are poorly  represented  at the  sites  such  as rice  (average  9.7%).  Moreover,  we 
must  not  lose  sight  of  the  fact  that,  even  though  the  margin  of  error  is  smaller  for 
cereals than for  other crops in percentage terms, a  1.1% error nevertheless  represents 
an  area  of  around  400,000  hectares,  roughly  equivalent  to  2.5  million  tonnes  of 
cereals.  Even  this  margin  of  error  may  be  considered  excessive,  given  the 
requirements concerning the management  of this market. 

Over the years there has admittedly been a slight improvement  in the margin of error 
for winter crops. However, the estimation  error  is still too high for  spring  crops and 
oilseeds. 

2.7  Evaluation of the action 

The  report  on Activity  B  at  the  end  of phase  2 of  the  MARS  project  presents  both 
strengths and weaknesses. The strengths may be summarised as follows. 

•  It is technically possible, via remote sensing, to obtain rapid estimates of changes 
in  the  acreage  of  the  main  crops  at  European  level.  This  can  be  done  by 
monitoring a relatively  small sample of sites, at least for the main cereal crops. 

•  In  terms  of  introducing  technological  innovation  and  setting  up  more  modern 
systems for  collecting  agricultural  statistics, the MARS project  and Activity  B in 
particular  are  certainly  a  success.  Apart  from  the  remote  sensing  applications 
mentioned  above, which  were  developed  to  meet  the  needs  of  the  Commission, 
there are currently  many  applications, not  only  in several  EU  Member  States but 

20 

also  in  some  East  European  countries  which  are  aiming  to  improve  national 
statistical systems by using the lessons learnt from  the MARS project. 

•  The whole technical chain of Activity  B, data acquisition and analysis, in relation 
to operational  production  systems  and  results, is also a success  in so far  as it has 
made the  information  sought  available  within  a very  short  time  after  acquisition 
of the basic data (satellite images). 

•  The establishment of Activity  B as part  of the MARS project  has generated  other 
applications outside agricultural  statistics, such as those relating to supervision  of 
the  acreages  declared  by  farmers  under  the  CAP,  and  has  shown  that  new 
applications  are  also  possible,  e.g.  in  monitoring  the  condition  of  forests  or  the 
agri-environmental  situation. 

In  addition  to  these  strengths,  the  MARS  project  and  more  particularly  Activity  B 
are  still  encountering  certain  limits  to  the  application  of  remote  sensing  to 
agricultural  statistics  as  regards  estimating  acreages.  The  weaknesses  may  be 
summarised as follows: 

•  The  sample  used  does  not  yield  reliable  estimates  at  national  level, even  for  the 

largest countries. 

•  Although Activity  B permits  a correct  overall  assessment  of the change in cereal 
acreages  as a whole  at  an  earlier  date  than  the  official  statistics,  it performs  less 
well  in  the  case  of  more  specific  crops  (beet,  maize,  colza,  etc.).  This  is  partly 
because  the  sample  used  offers  a  less  satisfactory  level  of  representation  for 
individual  crops,  and  partly  because  in  certain  cases  the  different  crops  are 
difficult  to separate. Furthermore,  remote  sensing  cannot  identify  the  destination 
of  the  product  or  the  purpose  for  which  it  is  grown:  common  wheat  or  durum 
wheat,  grain  maize  or  fodder  maize,  food  or  non-food  colza,  subsidised  fallow 
land (set aside) or land lying fallow  under a system of crop rotation, etc. 

•  While the initial  acreage  estimates  supplied  by  Activity  B, i.e. those available  at 
the  beginning  of  May  or  June,  are  generally  earlier  than  the  official  statistics, 
they  have  not  yet  been  stabilised  and  are  not  very  reliable,  given  the  small 
number of images and  sites  on which  they  are based  and the greater difficulty  in 
separating the different  crops  at this  stage. It  is not until the  end of June that  the 
information  becomes  relatively  stable. Nevertheless,  at that  stage the spring  crop 
acreages are still not easy to record by remote sensing. 

•  The images are still expensive, despite  some  savings made in recent years, and it 
has not  been  possible  to  achieve  any  significant  reduction  in the  cost  except  for 
Eastern  Europe, where  there  is  less  demand  for  satellite  images. This  constraint, 
together  with  the  conflicts  between  programming  and  acquisition,  considerably 
restricts  any  scope  for  increasing  the  number  of  sites  in  order  to  improve  the 
representation  of  crops  of  interest  and  thus  reduce  the  margin  of  error  in  the 
estimates. 

•  Despite  the  adaptations  made  over  the  years  (e.g.,  better  integration  of  ground 
surveys and remote sensing estimates), the system  suffers  from  excessive rigidity 

21 

in  the  face  of  the  possible  changes  in  the  location  of  crops  and  the  innovations 
which  one  would  like  to  introduce  (for  instance,  Activity  B  did  not  detect  the 
relocation of colza production in  1994 following  the CAP  reform). 

2.8  Conclusions 

In the light  of the foregoing,  it does not  seem  desirable  simply  to renew  Activity B 
of the MARS project  using the same methods and actions as in phase 2. Despite the 
improvements  made  in  recent  years,  with  the  scientific  and  financial  resources 
currently  available,  the  acreage  estimates  produced  by  remote  sensing  with  the 
methodology  hitherto  applied  are  not  entirely  satisfactory,  either  in  terms  of  lead 
times  or  as  regards  reliability,  particularly  at  the  level  of  the  specific  crops  of 
interest to those administering the Common Agricultural  Policy. 

Representation  could  certainly  be  improved  by  increasing  the  number  of  sites,  but 
the costs would  then outweigh the benefits  (e.g. the total  cost  of Activity  B for  100 
sites  is around  ECU  5 million  in the  first  year  and  almost  ECU  4 million  once  it is 
up  and  running.  It  is  uncertain  whether  100  sites  would  be  sufficient  to  achieve 
satisfactory  representation). Finally under  current  conditions, the scope  for  reducing 
the cost of images and ground surveys seems relatively  limited. 

However,  it does not seem desirable  to abandon  Activity  B, particularly  on  account 
of the  following: 

• 

• 

• 

• 

the  Commission  has  devoted  substantial  financial  resources  to  this  subject  in 
order to gain a unique technological  lead in this field  at both European and world 
level; the applications may not yet have been fully  exploited; 

the  needs  which  gave  rise  to  the  project  (to  provide  an  independent  means  of 
producing early estimates of areas and yields) still exist; 

to  abandon  Activity  B would  probably jeopardise  other  potential  applications  in 
different  areas of agricultural  statistics  (e.g., monitoring  the  condition  of  forests, 
agri-environmental  indicators, etc.); 

the JRC ought to maintain a technically  skilled team to provide technical  support 
for the Commission, or for isolated analyses which might be needed (e.g. analysis 
of the impact of drought on harvests, etc.). 

Taking  account  of  these  various  points,  the  JRC  should  be  instructed  to  examine 
alternative  methods  or  significant  improvements  to  the  current  approach,  with  the 
aim  of  increasing  the reliability  and  lead  times  of the  area  estimates  while  keeping 
within the bounds of the current financial  constraints. 

Work on this subject  began in  1997 and  is intended  to continue after  1998. It covers 
various alternative  methodological  approaches  including  major  modifications  to the 
conventional  approach to remote sensing, new sampling  methods, etc. This research 
needs to continue  for  several years before  we can move on, if appropriate, to a new 
operational  phase.  If  this  work  leads  to  alternatives  to  the  current  method  which 

22 

prove  satisfactory  in  terms  of  data  reliability,  lead  times  and  costs,  we  should 
ultimately consider funding  them out of the Commission's operating appropriations. 

23 

PART II - Activities for  development 

3. 

ACTIVITY  C:  THE  ADVANCED  AGRICULTURAL  INFORMATION 
SYSTEM 

Activity C is divided  into 4 sub-objectives: 

•  C.l:  to  complete  current  development  concerning  the  establishment  of  vegetation 

condition indices. 

•  C.2:  to  set  up  yield  prediction  models  (transition  to  the  operational  stage  of  agro-

meteorological  models). 

•  C.3: to  create  an  integrated  agricultural  information  system  at  European  Union  level 
by combining the information  obtained from  Activity B and Activities C.l  and C.2. 

•  C.4: to examine  how  and under  what  conditions  an integrated  system  can  be set up at 

national and regional level. 

Below  is  a  brief  description  of  the  work  done  during  phase  2  of  the  MARS  project  on 
these various activities. 

3.1  C.l 

Vegetation  condition  indices 

Continuing  the  work  of phase  1, this action  aimed  to  set up an operational  chain  to 
process  and  store  the  daily  images  of  the  NOAA-AVHRR  meteorological  satellite 
and to extract data on the condition of vegetation. The phase 2 work focused  mainly 
on the following  aspects: 

Setting up a receiving station at the JRC (task completed). 

Installing  hardware  capable  of  processing  more  than  one  image  at  a time  (task 
completed). 

Parametric  maintenance  of  the  SPACE  software 
processing  (permanent task). 

for  preliminary 

image 

Installing the SPACE system on PC (task completed). 

Improving  filtering  and  synthesis  techniques  for  the  NDVI  (Normalised 
Difference  Vegetation Index)^ (task completed but implementation  in progress). 

Improving  the  derived  thematic  cartography  for  qualitative  use  of  the  NDVI  in 
analysis of the current situation (task completed). 

5 This index  indicates the photosynthesis  activity of plants. 

24 

Developing  numerical  statistical  models  for  the yield  forecasts  derived  from  the 
NDVI (task in its final  test and validation year). 

Developing  a  model  to  estimate  biomass  on  the  basis  of  the  NDVI  (study 
completed  and now being applied). 

The production chain provides a cartographic  view of the main factors disrupting the 
farming  year: drought, delayed or early plant development, etc. 

Future policy 

On the basis  of the results  and  validations  during  1998  it  will  be possible  to  assess 
the  extent  to  which  this  sub-objective  can  be  considered  operational.  The  same 
approach  will  be applied  in  future  to  the  VEGETATION  sensor  launched  in  March 
1998.  The  advantage  of  this  European  sensor  is  that  it  permits  easier  parametric 
management  of the  images  to extract  the  vegetation  indices. The sensor was  in  fact 
designed  from  the start for the observation and monitoring  of small-scale vegetation, 
and not for meteorological  observations, as was the NOAA-AVHRR. 

3.2  C.2 

Yield prediction  models 

Yield  prediction  is  one  of  the  key  objectives  assigned  to  the  project.  This 
particularly  concerns the yields of the main annual  crops, but also those of the main 
perennial crops such as vines and olives. 

The  yield  prediction  models  developed  are  based  on  the  agro-meteorological 
simulation  models  incorporated  in  the  CGMS  (Crop  Growth  Monitoring  System) 
designed, applied and improved by the JRC during phases  1  and 2. 

To  develop  the  model  it  was  necessary  to  establish  a  data  base  on  soils,  climate, 
plants  and  yields.  This  data  base  was  updated  year  by  year  and  extended  during 
phase 2. 

As  planned  in  phase  1,  the  yield  predictions  are  obtained  from  a  statistical 
correlation between the historical  yield  series and the historical  series of quantitative 
parameters obtained from  the following  sub-models: 

•  A potential évapotranspiration sub-model. 

•  A global radiation  sub-model. 

•  A sub-model for the spatial  interpolation  of meteorological  data. 

•  A sub-model  for usable water reserves for each crop. 

•  A sub-model for the physiological  growth of crops. 

Each  of  these  sub-models  and  the  associated  database  were  improved  by  specific 
research and actions, in particular: 

25 

3.2.1  Meteorological  data 

As in phase  1, historical  meteorological  data were acquired over a fairly  long 
period  of 25 to  30 years. The data  collected  are those  normally  available on 
the  GTS  network  (Global  Telecommunications  System)  and  used  as  input 
data for  the models or sub-models. These are daily data which  concern more 
particularly: 

rainfall, 

temperature (maximum, minimum ), 

water vapour pressure or relative humidity, 

average wind  speed, 

hours of sunshine or cloud cover. 

In  phase  2  the  number  of  stations  with  historical  records  was  doubled  to 
around  700 (in the extended area of the European continent  and the Maghreb 
region, see Activity D). Other improvements made were as follows: 

Improvements  to the network  of meteorological  stations used  (700 
in  phase  1,  of  which  350  had  historical  records  and  350  only 
current  data,  2000  in  phase  2  including  700  with  historical 
records). (Task completed). 

Improvement in data interpolation techniques (study completed and 
in experimental phase). 

Thematic  improvement  in derived  products  such  as the  production 
of  maps  showing  meteorological  extremes,  the  risk  of  frost  (on 
going task). 

Improvement  in  the  algorithms  for  calculating  parameters  such  as 
global radiation (study completed and in experimental  phase). 

Improvements to the presentation  of results incorporated  in MARS 
Bulletins (Activity  C.3). 

3.2.2  Pedological data and agronomic  parameters: 

The  final  data  used  in the  statistical  models  and  in connection  with  Activity 
C.3 are as follows: 

abnormal meteorological  conditions, 

development  indices for the ten-day  period and since the beginning 
of  the  vegetation  cycle,  namely:.biomass;  seed  production;  water 
reserves;  phenological  stage;  water  balance;  excess  water;  leaf 
index. 

26 

The  main  crops  of  interest  are: wheat,  barley,  seed  maize, colza,  sunflower, 
sugar  beet,  potatoes,  peas  and  protein  beans.  The  principal  improvements 
made in phase 2 are as follows: 

Introduction  of information  from  the soil map of the European  Soil 
Bureau (JRC), (task completed on the EU of 15). 

Maintenance  of  the  agronomic  parameters  relating  to  each  crop 
(sowing date, harvest, phenological dates, etc.). (Task in progress). 

Thematic  extension  of  the  agro-meteorological  model  to  include 
vines  and  olives.  This  gave  rise  to  a  separate  project  known  as 
OLIWIN  1  and  2.  The  results  were  incorporated  in  the  MARS 
bulletin  in  1997 and  1998. Validation  of the  results  will  follow  in 
1998. 

Improvements  to the presentation  of the results incorporated  in the 
MARS Bulletins (Activity  C.3). 

3.2.3  Yield prediction: 

Improvement  and validation of statistical models for the  calibration 
of  yield  data  obtained  from  the  model  (task  still  in  progress  in 
1998). An initial study on the accuracy  of the results was published 
by the JRC. 

3.2.4 

Information  technology: 

Improvement  in access to data and interfaces with users. 

Installation on PC of the CGMS Model. 

The Support  Group for  Agrometeorology  (SuGrAm),  set up in  1990, made  it 
the 
possible  to  validate  the  network  of  meteorological  stations  and 
interpolation  method,  as  well  as  the  sub-models  for  calculating  potential 
évapotranspiration  and  global  radiation.  This  group  is  co-ordinated  by  the 
World  Meteorological  Organisation.  During  phase  2,  this  group  formed  an 
essential  scientific  reference  point for  continued  improvements to the  CGMS 
and validation of all kinds of associated results. 

3.3  C.3 

Integrated  agricultural information  system and MARS bulletin 

All  data relating  to  the  monitoring  of  vegetation,  growth  simulation,  monitoring  of 
meteorological  extremes and auxiliary data (timetable of field work, knowledge  base 
concerning  crops,  etc.)  available  under  the  MARS  project  are  combined  in  a 
"current  situation"  analysis  to  establish  the  expected  yields  for  the  main  crops  at 
national and European level. 

The current  situation task  is carried  out by the JRC  at the end of each month  during 
the farming  season. 

27 

On  the basis  of the  current  situation  analysis,  the numerical  indications  obtained 
from  the  various  statistical  models  are  validated  and  published  in  the  MARS 
bulletin, which represents the end product of this activity. 

The  acreage  estimates  obtained  by Activity  B are also  incorporated  in the MARS 
bulletin,  as  supplied  by the external  contractor  responsible  for this  activity. The 
exchange of information  between  Activity B and Activities C.l and C.2  has  proved 
useful  in explaining the results obtained. 

Since  1994, 31 MARS bulletins have been published  and 6 (plus a fax version) are 
planned  for  1998. The print  run averages  300 copies.  They  are circulated  to the 
Commission's  Directorate  General  for Agriculture  (DG VI) and the other  interested 
DGs,  Eurostat,  the Member  States  (representatives  of delegations  in the Eurostat 
Working  Party  on  "Plant  product  statistics")  and  to  other  international  bodies 
concerned  such as the FAO. The  following  are some of the  improvements  made in 
recent years: 

In  1997 the bulletin  included  the results of the OLIWIN project,  i.e. the results 
of the analysis of olives and vines. 

From the  end of 1996 the results of Activity  D (prediction  of harvests  outside 
the  Community)  were  also  included  in the bulletin.  With  these  data  it was 
possible to produce  initial  experimental  predictions for wheat yields in central 
and  east  European  countries  (PHARE  and  TACIS  countries),  Turkey,  and the 
Maghreb countries (see Activity D). 

An  internal  report  was  produced  which  examined  the error  in analysis  of the 
current situation and the various associated statistical models. The results  show 
that  the forecasts  are earlier  and the error  is smaller  than  for other  available 
sources. 

In  1998, extension of the model to include countries bordering on the EU of 15 
(countries  covered  by the PHARE  and TACIS  programme)  and the Maghreb 
countries will be completed with the introduction of other crops of interest. 

Table 9 -1997  results for the EU of 15 

April 

May 

June 

July 

Aug. 

Sept. 

Oct.  Eurostat* 

Cereals 

Estimated  yield 

% deviation 

Total wheat 

Estimated  yield 

% deviation 

Common wheat  Estimated  yield 

% deviation 

Dunimwhca. 

Estimated  yield 

5.43 

0.64 

5.71 

3.50 

6.4 

2.70 

2.7 

5.44 

0.79 

5.7 

3.20 

6.4 

3.10 

2.5 

% deviation 

18.90 

11.30 

Barley 

Estimated  yield 

% deviation 

Seed maize 

Estimated  yield 

% deviation 

* Final figures  in May  1998 

4.4 

0.30 

8.4 

-6.1 

4.5 

2.50 

8.4 

-6.1 

5.46 

1.24 

5.72 

3.60 

6.5 

4.00 

2.4 

6.20 

4.5 

2.60 

8.5 

-5.2 

5.41 

0.33 

5.69 

3.10 

6.4 

3.30 

2.4 

7.90 

4.5 

1.60 

8.5 

-5.5 

5.42 

0.51 

5.63 

2.00 

6.4 

2.30 

2.4 

4.70 

4.5 

1.50 

8.6 

-3.5 

5.42 

5.41 

5.39 

0.39 

5.63 

2.00 

6.4 

2.30 

2.4 

4.70 

4.5 

1.60 

8.6 

-4.2 

0.36 

5.63 

2.00 

6.4 

2.30 

2.4 

4.70 

4.5 

1.60 

8.5 

-4.5 

5.52 

6.24 

2.26 

4.41 

8.95 

28 

3.4  C.4 

Integrated system at national and regional level 

Following  various  contacts  with  a  number  of  EU  Member  States,  the  systems 
available under the MARS project  were transferred  and adapted to the real  situation 
in the various regions. 

Under the Hydre project  and  its continuation,  Pop-SICILY, the  agro-meteorological 
system  was transferred  to  the  local  authorities  and  adapted  for  the  monitoring  and 
statement  of  regional  water  resources.  The  regions  taking  part  were  Sicily  and 
Sardinia in Italy, Languedoc-Roussillon  in France and Andalucia in Spain. 

Via  co-operation  on  request,  the  agro-meteorological  system  was  adapted  and 
transferred  to the Finnish statistical  service. 

29 

4.  ACTIVITY  D:  MONITORING  AND  PREDICTION  OF  HARVESTS 

OUTSIDE THE  COMMUNITY 

The  purpose  of  Activity  D  was  to  perfect  and  extend  methods  developed  by  the  other 
MARS  project  activities  to  countries  bordering  on  the  EU, particularly  central  and  east 
European  countries  and  the  Maghreb  region,  in  order  to  predict  harvests  in  those 
countries. The main developments in this field were as follows: 

In  1996  a  first  bulletin  was published  which,  by  way  of  experiment,  included  the 
first  extensions  of  the  CGMS  model,  now  known  as  System  D.  This  bulletin 
referred  to  the  agro-meteorological  aspects  of  wheat  growing  in  the  countries 
mentioned. 

In  1997  the  analysis  was  incorporated  in  Activity  C.3  and  the  first  yield  and 
production  forecasts  were supplied for  12 countries outside the EU of  15, including 
Russia,  Ukraine,  the  PHARE  programme  countries  and  the  Maghreb  region,  plus 
other qualitative information  on harvest trends in  14 other countries. 

In  1998 the  results  will  be  extended  to  other  crops  of  interest  such  as  barley  and 
maize. 

30 

5.  ACTIVITY E: APPLICATION  OF NEW METHODS OR  SENSORS 

5.1 

Pilot project for uses of microwave images in estimating  acreages 

In  1996  a  pilot  project  was  launched  on  the  incorporation  of  microwave  (radar) 
images  obtained  from  the  ERS  1  and  2/SAR  satellites  into  the  system  of  quick 
estimates  of  agricultural  acreages.  The  advantage  of  using  radar  is that  images  can 
be  obtained  even  in  cloudy  conditions.  Also  the  information  obtained  is  related  to 
the geometry and ruggedness of the targets rather than the radiometric  content, as in 
the  case  of  optical  sensors.  This  offers  the  advantage  that  the  images  can  be 
correlated  with crop development  sooner than with  optical  sensors. Research  on the 
use of radar has led to a pilot application  spread over two years: 

•  1996: a larger-scale feasibility  study to introduce radar images recorded by the European 
satellite ERS-2 was completed.  The application  simulated  an Activity B at a number of 
sites in northern Europe. In view of the encouraging results, it was possible to move on 
to the next phase. 

•  1997: the  application  was  extended  to  all  60  sites  of  Activity  B. The project  aimed  to 
improve  the  lead  times  and  accuracy  of  agricultural  acreage  estimates  by  synergy 
between optical/infra-red  data obtained  during Activity  B of the MARS project  and the 
ERS/SAR  radar  data.  In  the  project,  almost  300  radar  images  were  obtained  at  the  60 
Activity  B  sites  during  the  winter  of  1996  and  the  spring  of  1997. Acreage  estimates 
were obtained for the main groups of crops as early as January. 

The  acreage  estimates  derived  by  ERS/SAR  radar  for  the  1997  seasons  were 
compared  with those of Activity  B (which  uses only  optical  images) and the  results 
of  ground  surveys.  The  results  of  the  two  pilot  studies  showed  that  the  ERS/SAR 
data for the autumn, winter and spring can produce useful  information  for  estimating 
acreages  of  crops  sown.  However,  the  application  gives  better  results  in  areas 
composed  of  large  fields  with  greater  uniformity  in terms  of  farming  practices  and 
soil  texture  and  structure.  In  more  disparate  areas, the  results  have  to  be  integrated 
with  auxiliary  information  on  the  variables  mentioned.  Additional  research  has 
shown that, by the combined use of optical data and ERS/SAR radar, it is possible to 
complete  chronological  optical  image  series  which  are  incomplete  owing  to  cloud 
problems. 

Future policy 

New sensors with different  characteristics  will be available  in the near future.  These 
could create scope for improving the MARS project  methods. The use of all-weather 
radar,  sensitive  to  geometric  variations  in  the  terrain,  could  bring  the  acreage 
estimates forward  to before the spring. 

A pilot  project  using  ERS/SAR  radar  has  also  shown  the  potential  effectiveness  of 
using remote sensing to speed up winter estimates. 

31 

The  new  very  high  resolution  sensors,  available  within  the  next  few  years,  should 
subsequently  enhance  the  value  of the  methodology  by  reducing  the  uncertainty  of 
discrimination between certain crops. 

5.2  Re-engineering  of Activity B  (REACTS) 

In  1997 a project  for examining all possible  improvements to the current methods of 
applying  remote  sensing  to agricultural  statistics  (e.g. improvement  in  classification 
methods)  was  launched.  This project  was  dubbed  REACT_B (i.e. re-engineering  of 
Activity B). 

The  REACTB  project  was  launched  in  January  1997  with  a  list  of  users' 
requirements. These requirements  are the  benchmark  for  analysing  the  performance 
of the current approach. 

The plan of work provides for updating the whole chain of Activity B: 

image acquisition, 

image processing, 

image interpretation and  classification. 

However,  the  accent  will  be  on  sampling  in  conjunction  with  the  use  of  satellite 
mini-images  rather  than  entire  scenes,  and  on  liaison  with  other  MARS  project 
activities. 

A  statistical  study  has  shown  that  it  is feasible  to  use  sites smaller  than  the  current 
ones. However, operational use has yet to be demonstrated. 

An  increase  in  the  number  of  sites  should  make  the  sample  more  representative  as 
regards  crops  of  interest  and  improve  the  estimate  accuracy.  The  two  aspects, 
« representativity  » and  « accuracy » are  critical  for  the needs of users. The  various 
sampling  scenarios  are  now  being  evaluated  with  the  image  suppliers  to  check  the 
technical feasibility  of such an approach. 

A large-scale exercise will produce data for one EU region (the Bavarian site, NUTS 
II).  For  this  test  zone,  all  ancillary  data  have  been  acquired  and  incorporated  in  a 
geographical  information  system.  Images  produced  by  operational  sensors  (SPOT, 
LANDSAT, IRS-IC and ERS/SAR) have been obtained  for comparative analysis of 
the  performance  of  the  various  classifications.  The  tests  are  based  on  comparison 
with the results of a ground survey covering the whole zone. 

On  the  basis  of  this  feasibility  study  and  the  data  obtained,  the  techniques  and 
methods used will be improved  as part  of a detailed evaluation  in  1998.  Operational 
tests  in  parallel  with  Activity  B  will  be  conducted  at  the  sites  concerned.  The 
RADARSAT  and  SPOT  4  satellites,  and  possibly  the  RESOURS  satellite,  will 
provide other information  of use to the study. 

32 

One very  important  element  of the project  is the evaluation  of the cost/benefit  ratio, 
which will have to be acceptable before any operational system is implemented. 

5.3  The aeropalynological  model 

This  research  activity  was  completed  in  1997.  The  purpose  of  this  project  was  to 
supply  grape  harvest  predictions  (or  wine  production  predictions)  based  on  the 
conditions during flowering  of the vines. These conditions  were monitored  in terms 
of quality and quantity of plant fertilisation  using a pollen sensing method. 

By  using  a  reliable  and  reproducible  technique  for  sampling  and  analysing 
atmospheric  pollen  flows  it  is  possible  to  produce  early  harvest  predictions  about 
three  months  before  the  grapes  are  picked.  However,  it  is  necessary  to  obtain  an 
expert agronomist's opinion to validate the laboratory results used. 

The  proposed  method  can  be  used  to  produce  valid  predictions  both  for  the  zones 
sampled and at national and Community  level. At local level, the method provides a 
totally  satisfactory  answer in around  50% of situations. The effect  of  post-flowering 
conditions, which can greatly  modify  the potential  present  at the time of  flowering, 
is the main cause of the  deviations  found.  It  is therefore  essential  for  the  first  early 
predictions  obtained  by  pollen  analysis  using  agro-meteorological  models  to  be 
updated  during  the  season.  But  there  are  also  - often  clearly  identified  - profiles  in 
which  this  method  cannot  be  used.  On  a  national  scale,  the  results  are  considered 
satisfactory  where an adequate network of pollen sensing posts is established. 

From the pattern of the pollen emission curve it is also possible to identify  precisely 
the  dates  of  the  flowering  period  and,  via  an  overall  assessment  of  phenological 
homogeneity  in  the  zone  sampled,  to  arrive  at  an  initial  approximation  of  the 
vintage's potential. 

The  activity  was  conducted  with  the  support  of  CEMAGREF,  Montpellier 
University and various national agencies in the countries considered. 

In  conclusion,  these  methods  can  now  be  considered  operational,  but  they  can 
continue  only  if  they  are  taken  over  by  national  operational  entities.  The  JRC  has 
arranged  this  transfer,  but  success  will  depend  on  the  presence  of  a body  which  is 
both technically competent and able to afford  the cost of the operation. 

33 

6. 

CONCLUSIONS CONCERNING THE ACTIVITIES BEING DEVELOPED 

After two five-year cycles of research, significant results have been obtained: 

•  The  agro-meteorological  part  (low-resolution  model  and  satellite  data)  can  be 
considered  satisfactory  and  operational,  even  though  there  is  still  scope  for 
improvement. The results are available at European, national and regional level. 

•  A  number  of  limitations  in  Activity  B, particularly  representativity  at  national 
level and for certain products, seem capable of being overcome by the use of new 
sensors  and  new  sampling  methods,  but  their  operational  use  has  yet  to  be 
demonstrated. 

•  Some methods, such as the aeropalynological  model, can be considered  only if 

they are taken on by national authorities. 

34 

MARS ACTIVITY B ACREAGE ESTIMATES FOR THE MAIN CROPS AND DEVIATIONS 
IN RELATION TO EUROSTAT (1994): EUR-12 

ANNEX 1 

1994 

APRI 

L 

MAY 

JUNE 

-  -.  *">,. 

Ctri^r^^t^&l' 

TM<mJte*j$BbL-

WOO ha 
32371,9 

478,7 

15418,9 

137,4 

Common wnea|^«#Hj«U- ; 

12408,7 

164,0 

3014,2 

% 
0,3 

1000 ha v 
32630,1 

% 
1,1 

1000 ha l 
32759,2 

% 
1,5 

1,5 

1,5 

0,9 

0,9 

1,3 

4,3 

736,9 

15531,2 

249,8 

12519,4 

274,7 

3017,1 

2,3 

23 
1,6 

1,8 

2,2 

4,4 

866,0 

15514,1 

232,7 

125563 

311,6 

2962,2 

2,7 

2,1 

1,5 

2,1 

2,5 

2,5 

Durum wheaf  **^«§fliii;u 
,,,*  Vtfwtat , 

•vFit'lv^^^k^' 
^&^ra^^BpR"^ < 
•'^^^4 j'^^p&'i 
Cofia  * ^'  ^JjSBwSi' 
r  "*'\  dcviat. 
cstim. 

Sunflower 

-22,5 

-0,74 

-19,6 

-0,6 

-74,5 

-2,5 

10182,0 

469,5 

3746,9 

74,9 

2374,2 

-118,7 

3310.7 

403,4 

-0,1 

4,8 

-0,5 

2,0 

9,5 

-4,8 

2,0 

13,9 

10222,7 

510,3 

3618,9 

-53,1 

2341,6 

-151,2 

3180,9 

273,6 

0,3 

5,3 

-3,9 

-1,4 

8,0 

-6,1 

-2,0 

9,4 

10253,3 

540,9 

3709,2 

37,3 

2283,1 

-209,7 

0,6 

5,6 

-1,5 

1,0 

53 
-8,4 

2771,9 

-14,6 

-135,4 

-4,7 

JULY 
lOOOha^r] 
32436,4 

-'i"SSSB' 
]-J0MHd^. 
32436,4 

\W;l 

0,5 

y^A'*,; 

0,5 

, 
'. 
.-  SEPT 
ii#:! 
0,4 

'#jri§.ha\y$. 
32404,1 

OCT, 

IpOQ^ha^ 
32404,1 

Zz.%^ 
0,4 

Eurostat. •* 
::f||| 

-1,18 

-MX:< 

31893,20 

Eurostat 

(000 ha) 

Aver,  dev 

iation 

fe'199? 
32275,03 

WOO ha 

% 

543,2 

15414,8 

133,4 

12470,2 

225,5 

2947,7 

-89,0 

10141,2 

428,8 

3615,1 

-56,9 

2333,0 

-159,9 

24863 
-421,0 

1,7 

1,5 

0,9 

1,4 

1,8 

2,0 

-2,9 

-0,5 

4,4 

-4,0 

-1,5 

7,6 

-6,4 

-23,4 

-14,5 

543,2 

1,7 

510,9 

15417,6 

136,2 

12470,2 

225,5 

2950,6 

1,5 

0,9 

1,4 

1,8 

2,1 

-86,1 

-2,83 

10141,2 

428,8 

3615,1 

-56,9 

2335,1 

-157,7 

-0,5 

4,4 

-4,0 

-1,5 

7,7 

-6,3 

15408,3 
126,9 

12457,9 

213,2 

2953,5 

-83,2 

10141,2 

428,8 

3554,8 

-117,1 

2335,1 

-157,7 

1,6 

1,5 

0,8 

13 

1,7 

2,2 

-2,7 

-0,5 

4,4 

-5,6 

-3,2 

7,7 

-6,3 

510,9 

15420,5 

139,0 

12470,2 

225,5 

2953,5 

-83,2 

10141,2 

428,8 

3562,4 

-109,6 

2333,0 

-159,9 

1,6 

1,5 

0,9 

1,4 

1,8 

2,2 

-2,7 

-0,5 

4,4 

-5,4 

-3,0 

7,6 

-6,4 

0,62 

15281,42 

15187,98 

-0,43 

12244,69 

12298,03 

5,08 

3036,73 

2889,95 

-4,71 

9712,43 

10192,17 

-2,49 

3671,99 

3765,72 

14,97 

2492,82 

2168,18 

2483,0 

-23,5 

2512,2 

-22,6 

2502,5 

-22,9 

-10,43 

2907,27 

3245,80 

-424,2 

-14,6 

-395,0 

-13,6 

-404,8 

-13,9 

613,44 

1,92 

172,05 

1,13 

238,29 

1,95 

7133 

235 

464,28 

4,78 

77,49 

2,11 

161,14 

6,46 

365,09 

12,56 

estim. = areas (x 1000 ha) and % change in relation to previous year estimated by Activity B 
deviat. = difference between the Activity B estimate and the Eurostat official  figures 
% = change in area in relation to previous year 
EUROSTAT final figures in May 1998 (x 1000 ha) 

35 

MARS ACTIVITY B ACREAGE ESTIMATES FOR THE MAIN CROPS AND DEVIATIONS 
IN RELATION TO EUROSTAT (1995): EUR-12 

ANNEX 2 

1995 

APRIL 

MAY 

JUNE 

JULY 

AUG. 

OCT. 

Eurostat 

WOO ha 

% 

WOO ha 

% 

1000 ha 

32499,2 

1,9 

324673 

1,8 

32307,8 

% 

13 

WOOha 

32275,9 

-430,8 

-1,3 

-462,7 

15569,5 

1,9 

15569,5 

CereaJi ."<  w. „;esrtin.;. 
•:'^^^^^^^^L 
™flw'?!?8S!^. 

• r^^^^^^^'Cl^^^'.' 
commonincites cstim; 

Durum wheat, 
'.'%'; 
Barley  .  ",; 

lestim.. 
tfeviat: 
.  ..  Vesftin. 

M a t M ' ^ ^ l ^ n ^v  cram. 

Sunflower-.  .. 

\estim»  • 

;^^^^^#M<-

1000 ha 

32850,0 

111,4 

15780,4 

% 

3,0 

0,3 

33 

-239,4 

15712,0 

-160,1 

-1,0 

-228,6 

12820,2 

16,3 

4,7 

0,1 

2957,8 

-2,6 

-179,0 

-5,71 

9935,8 

127,0 

3532,4 

-115,3 

2532,7 

-43,7 

2840,4 

366,4 

23 

1,3 

-3,8 

-3,2 

1,6 

-1,7 

- 23 

14,8 

12710,0 

-93,9 

30003 

-136,4 

9731,9 

-76,9 

36683 

20,6 

3076,1 

499,7 

2886,9 

412,9 

-0,7 

2,8 

-1,4 

3,8 

-0,7 

-1,2 

-4,4 

0,2 

-0,8 

-0,1 

0,6 

23,4 

19,4 

-0,7 

16,7 

-271,3 

15767,6 

-173,0 

12783,5 

-20,4 

2982,1 

-154,7 

9741,6 

-67,2 

3642,6 

-5,1 

3116,0 

539,6 

2831,7 

357,7 

-0,8 

3,2 

-1,1 

4,4 

-0,2 

-1,8 

-4,9 

03 

-0,7 

-0,8 

-0,1 

25,0 

20,9 

-2,6 

14,5 

% 

1,2 

-1,4 

1,9 

-2,3 

3,3 

-1,2 

-3,9 

-371,1 

12648,8 

-155,1 

29183 

-218,4 

-6,96 

9819,3 
10,5 

3539,8 

-107,9 

3190,8 

614,4 

1,1 
0,1 

-3,6 

-3,0 

28,0 

23,8 

SEPT. 
lOOOha  % 
1,4 
32339,7 

-398,9 

15618,6 

-322,0 

12697,7 

-106,1 

29183 

-218,4 

9838,7 

29,9 

3539,8 

-107,9 

3138,5 

562,0 

-1,2 

2,2 

-2,0 

3,7 

-0,8 

-3,9 

-7,0 

13 

0,3 

-3,6 

-3,0 

25,9 

21,8 

1000 ha 

32339,7 

-398,9 

156093 

-331,2 

12685,5 

-118,4 

29213 

-215,4 

9838,7 

29,9 

3569,2 

-78,6 

3141,0 

564,5 

% 

1,4 

-1,2 

2,1 

-2,1 

3,6 

-0,9 

-3,8 

-6,9 

13 

0,3 

-2,8 

-2,2 

26,0 

21,9 

-371,1 

12648,8 

-155,1 

29183 

-218,4 

9858,1 

49,3 

3580,2 

-67,6 

3230,7 

654,3 

-2,3 

3,3 

-1,2 

-3,9 

-7,0 

1,5 

0,5 

-2,5 

-1,9 

29,6 

25,4 

Eurostat (000 ha) 
1994 
1995 
31893,20 
32738,60 

Aver, deviation 

1000 ha 

% 

% 
2,65 

431 

15940,59 

15281,42 

4,57 

12803,86 

12244,69 

3,29 

3136,74 

3036,73 

0,99 

9808,79 

9712,43 

-0,66 

3647,74 

3671,99 

3 35 

2576,43 

2492,82 

350,76 

1,07 

291,95 

1,83 

108,81 

0,85 

194,25 

6,19 

66,45 

0,68 

82,62 

2,26 

532,23 

20,66 

261,24 

10,56 

2427,6 

-16,5 

2386,9 

-17,9 

2375,2 

-183 

2314,2 

-20,4 

-14,90 

2474,00 

2907,27 

-46,4 

-1,9 

-87,1 

-3,5 

-98,8 

-4,0 

-159,8 

-6,5 

estim. = areas (x  1000 ha) and % change in relation to previous year estimated by Activity B 
deviat. = difference  between the Activity B estimate and the Eurostat official  figures 
% = change in area in relation to previous year 
EUROSTAT final figures in May  1998 (x  1000 ha) 

36 

MARS ACTIVITY B ACREAGE ESTIMATES FOR THE MAIN CROPS AND DEVIATIONS 
IN RELATION TO EUROSTAT (1996): EUR-15 

ANNEX 3 

1996 

Cereals 

Total wheat 

estlih. 

deviat. 

estim. 

deviat. 

i  '•',  estim. 
Common wheat 
, 
- 

.vfij,? 

: 

deviat. 

Durum wheat 

Barley 

Maize  ' 

.  \  '-" 

: 

Colza 

Sunflower 

esnm. 

deviat. 

estim. 

deviat. 

estim. 
deviat. 
estim. 

deviat. 

estim. 

deviat. 

APRIL 

MAY 

JUNE 

JULY 

AUG. 

SEPT. 

OCT. 

Eurostat 

Eurostat  (000  ha) 

1000 ha 
36606,8 

-332,1 

16950,9 

-11,1 

13747,7 

-14,1 

3202,9 

% 
2,9 

-0,9 

2,4 

-0,1 

2,5 

-0,1 

1,8 

2,4 

0,07 

11678,9 

236,0 

3937,3 

-236,5 

2914,0 

296,1 

2955,5 

510,9 

6,1 

2,1 

4,5 

-5,7 

2,1 

11,3 

18,1 

20,9 

1000 ha 

37033,7 

94,8 

17026,0 

64,0 

14029,3 

267,6 

2992,1 

-208,4 

11502,8 

59,8 

4016,4 

-157,4 

2814,1 

196,2 

% 

4,1 

0,3 

2,8 

0,4 

4,6 

1,9 

-4,9 

-6,5 

4,5 

0,5 

6,6 

-3,8 

-1,4 

7,5 

2214,8 

-11,5 

-229,9 

-9,4 

JOOOha 
36998,1 

59,2 

17471,0 

509,1 

14404,9 

643,1 

3061,3 

-139,2 

11106,6 

-336,4 

3877,0 

-296,8 

2822,6 

204,8 

2447,5 

2,9 

% 
4,0 

0,2 

5,5 

3,0 

7,4 

4,7 

-2,7 

-4,3 

0,9 

-2,9 

2,9 

-7,1 

-1,1 

7,8 

-2,2 

0,1 

WOO ha 
36998,1 

% 
4,0 

1000ha.. 
36962,5 

59,2 

17352,7 

390,7 

14163,5 

401,7 

3187,1 

-13,4 

11282,7 

-160,3 

3813,0 

-360,9 

2865,4 

247,6 

2445,0 

0,4 

0,2 

4,8 

2,3 

5,6 

2,9 

1,3 

-0,4 

2,5 

-1,4 

1,2 

-8,6 

0,4 

9,5 

-2,3 

0,0 

23,7 

17354,7 

392,7 

14150,0 

388,3 

3202,9 

2,4 

11293,7 

-149,3 

3828,0 

-345,8 

2842,6 

224,8 

2430,0 

-14,6 

% 
3,9 

0,1 

4,8 

2,3 

5,5 

2,8 

1,8 

0,07 

2,6 

-1,3 

1,6 

-8,3 

-0,4 

8,6 

-2,9 

-0,6 

1000 ha 
37069,2 

130,4 

173443 

382,4 

14136,6 

374,9 

3206,0 

5,5 

11304,7 

-138,3 

3843,1 

-330,7 

2811,2 

193,4 

2425,0 

-19,6 

% 
4,2 

0,4 

4,7 

2,3 

5,4 

2,7 

1,9 

0,2 

2,7 

-1,2 

2,0 

-7,9 

-1,5 

7,4 

-3,1 

-0,8 

WOO ha 
37069,2 

130,4 

17330,9 

368,9 

14123,2 

361,4 

3206,0 

5,5 

11293,7 

-149,3 

3850,6 

-323,2 

2799,8 

182,0 

2425,0 

-19,6 

% 
4,2 

0,4 

4,7 

2,2 

5,3 

2,6 

1,9 

0,2 

2,6 

-1,3 

2,2 

-7,7 

-1,9 

7,0 

-3,1 

-0,8 

% 
3,83 

1996 
36938,86 

1995 
35575,08 

2,44 

16961,96 

16558,59 

2,61 

13761,77 

13412,36 

1,72 

3200,49 

3146,24 

3,96 

11442,99 

11007,49 

10,78 

4173,80 

3767,74 

-8,28 

2617,83 

2854,02 

-2,32 

2444,61 

2502,55 

Aver, deviation 
% 
WOO ha 

151,59 

0,41 

348,98 

2,06 

390,72 

2,84 

94,92 

2,97 

193,41 

1,69 

300,57 

7,20 

223,77 

8,55 

212,08 

8,68 

estim. = areas (x  1000 ha) and % change in relation to previous year estimated by Activity B 
deviat. = difference  between the Activity B estimate and the Eurostat official  figures 
% = change in area in relation to previous year 
EUROSTAT final figures  in May  1998 (x  1000 ha) 

37 

ANNEX 4 

MARS ACTIVITY B ACREAGE ESTIMATES FOR THE MAIN CROPS AND DEVIATIONS 

IN RELATION TO EUROSTAT (1997): EUR-15 

APRIL 

MAY 

JUNE 

JULY 

AUG. 

SEPT. 

OCT. 

Eurostat 

Eurostat  (000  ha) 

A v e r,  d e v i a t i on 

1997 

Cereals 

Total wheat 

1000 ha 

37736,7 

-334,5 

17446,6 

204,5 

estim 

deviat. 

estim. 

deviat. 

Common wheat 

estim. 

14215,9 

Durum wheat 

Barley 

Maize 

Colza 

Sunflower 

deviat. 

estim. 

deviat. 

estim. 

deviat. 

estim. 

deviat. 

estim. 

deviat 

estim. 

deviat. 

195,8 

3230,3 

8,3 

0,26 

11739,4 

-169,0 

4272,7 

-87,2 

2478,0 

-304,3 

2755,1 

421,3 

2,6 

-1,4 

2,4 

-2,0 

-5,3 
-10,9 

12,7 

18,1 

% 
2,2 

-0,9 

2,9 

1,2 

3,3 

1,4 

0,9 

1000 ha 

38220,6 

149,4 

17296,3 

54,2 

14059,0 

38,9 

3237,3 

15,3 

12237,1 

328,7 

4233,9 

-126,0 

2606,0 

-176,3 

2592,8 

259,0 

% 
3,5 

1000 ha 

38209,6 

0,4 

2,0 

0,3 

2,2 

0,3 

1,2 

0,5 

6,9 

2,8 

1,4 

-2,9 

-0,5 

-6,3 

6,1 

11,1 

138,3 

17334,6 

92,6 

14101,7 

81,6 

3232,8 

10,9 

12054,0 

145,6 

4280,2 

-79,7 

2696,4 

-86,0 

2500,6 

166,9 

% 
3,4 

0,4 

2,2 

0,5 

2,5 

0,6 

1,0 

0,3 

5,3 

1,2 

2,6 

-1,8 

3,0 

-3,1 

2,3 

7,1 

1000 ha 

38294,5 

% 
3,7 

WOO ha 

38298,2 

% 
3,7 

1000 ha  ; 
38039,6 

% 
3,0 

WOO ha 

37991,6 

223,3 

173293 
87,2 

14105,8 

85,7 

3223,2 

1,3 

12119,3 

210,9 

4337,8 

-22,1 

2696,1 

-86,3 

2447,1 

113,3 

0,6 

2,2 

0,5 

2,5 

0,6 

0,7 

0,0 

5,9 

1,8 

3,9 

-0,5 

3,0 

-3,1 

0,1 

4,9 

227,0 

17320,3 

78,2 

14076,9 

56,8 

3243,4 

0,6 

2,1 

0,5 

2,3 

0,4 

13 

21,4 

0,66 

12146,7 

238,3 

43253 

-34,6 

2748,2 

-34,2 

2331,2 

-2,6 

6,2 

2,0 

3,6 

-0,8 

5,0 

-1,2 

-4,6 

-0,1 

-31,6 

17250,7 

8,6 

13999,9 

-20,3 

3251,1 

29,1 

11959,1 

50,7 

4316,1 

-43,8 

2751,9 

-30,5 

2354,9 

21,2 

-0,1 

1,7 

0,0 

1,7 

-0,1 

1,6 

0,9 

4,5 

0,4 

3,4 

-0,1 

5,1 

-1,1 

-3,7 

0,9 

-79,6 

172313 

-10,8 

13995,7 

-24,4 

3235,7 

13,7 

11930,5 

22,1 

4312,8 

-47,1 

2760,5 

-21,9 

2298,7 

-35,1 

% 
2,9 

-0,2 

1,6 

-0,1 

1,7 

-0,2 

1,1 

0,4 

4,3 

0,2 

3,3 

-1,1 

5,5 

-0,8 

'6,0 

-1,5 

% 

1997 

1996  1000  ha 

% 

3,07 

38071,22 

36938,86 

1,65 

17242,08 

16961,96 

1,88 

14020,13 

13761,77 

0,67 

3221,95 

3200,49 

4,07 

11908,40 

11442,99 

4,46 

4359,90 

4173,80 

6,29 

2782,37 

2617,83 

-4,54 

2333,74 

2444,61 

193,49 

0,51 

98,01 

0,57 

91,08 

0,65 

16,56 

0,51 

193,52 

1,63 

7136 

1,64 

141,99 

5,10 

202,48 

8,68 

estim.  =  areas  (x  1000  ha)  and  %  change  in  relation  to  previous  year  estimated  by  Activity  B 

deviat.  =  difference  between  the  Activity  B  estimate  and  the  Eurostat  official 

figures 

%  =  change  in  area  in  relation  to  previous  year 

E U R O S T AT  final  figures  in  May  1998  (x  1000  ha) 

ANNEX 5 

Operational activity  contractors 

Activity  A 

Contractor 

Instituto de Estruturas agrarias e desenvolvimento rural  (Portugal) 
Ministerio de Agricultura, Pesca y Alimentaciôn (Spain): 

Activity  B 

Image analysis 
Main contractor 
SOTEMA Group 

Image suppliers 
SPOT IMAGE,  S.A. 
EIRIMAGE, SCRL. 
G.A.F 

Subcontractor 
SOTEMA 
GEOSPACE 
CISI AID 
Bureau Veritas 
Faculté de Gembloux 

Information  technology and software 
CISI  INGENIERIE 

Ground data: 
Instituto de Estructuras Agrarias e Desenvolvimento Rural (IEADR) 
Remote Sensing Data Engineering, Sri 
Danish Institute of Plant and Soil Science 
Katholieke Universiteit Leuven 
Da Vinci  Consulting 
Ôsterreichisches Statistisches Zentralamt 
Desarrollo Agrario y Pesquero de Andalucia 
Era-Maptec 
Eftas  Fernerkundung Technologietransfer,  GmbH 
Information  Centre of Ministry of Agriculture and Forestry 
Irco s.p.r.l 
National Remote Sensing Center Limited 
Trabajos  Catastrales 
Ministère de l'Agriculture et de la Pêche 
Hunting Technical  Services Ltd 
Spazio Verde 
Strabo, s.a. 
Statistics  Sweden 

ANNEX 6 

LIST OF MARS PUBLICATIONS  1994-1998 

Monographs 

BOUMAN,  B.A.M.,  SCHAPENDONK,  A.H.C.M.,  STOL,  W.,  VAN  KRAALINGEN, 
D.,  1996.  Description  of  the  grass  growth  model  LINGRA  as  implemented  in  CGMS. 
Joint publication of the JRC Space Applications Institute and the DLO Research  Institute 
for Agrobiology  and Soil Fertility. Wageningen (NL); 83pp. 

D'SOUZA  G.,  BEL WARD  A.S.,  MALINGREAU  J.P.  (Eds.)  - Advances  in  the  use  of 
NOAA-AVHRR  data  for  land  applications.  Kluwer  Academic  Publishers,  Dordrecht, 
Boston, London (1995). 

JONES, P.D., SALMON, M.,  1996. Development  and integration  of a stochastic  weather 
generator  into  a crop growth  model  for  European  agriculture.  Contract  Report  N°  5631 -
93-12 ED ISP GB. University of East Anglia, Norwich (UK), 32 pp. 

PAQUET, F., J.V. VOGT & A. VIAU,  1996. Régionalisation  de la température de l'air à 
partir  des  données  physiographiques  et  des 
images  NOAA  AVHRR.  Cas  de 
l'Andalousie.- JRC-SAI-AIS Technical Report, Ispra/Italy, 92 S. 

VAN DER VOET, P., KRAMER, K., VAN DIEPEN, C.A.,  1996. Parametrization  of the 
Richardson  weather  generator  within  the  European  Union.  Joint  publication  of  the  JRC 
Space  Applications  Institute  and  the  DLO  Research  Institute  for  Integrated  Land,  Soil 
and Water Research. Wageningen (NL); 73pp. 

VOGT,  J.,  ASTRAND  P., BORIES,  L., NIEMEYER,  S.,  1996. Monitoring  the  Quality 
and  Quantity  of  Water  Resources  in  Sicily:  Development  of  a  Geographic  Information 
System and Integration of Remote  Sensing Derived  Parameters. Joint Research Centre of 
the EC. Space Applications Institute. Ispra (I), 61 pp + annexes. 

WOODING  M.G., DE GROOF  H. et al. - Satellite  radar  in agriculture  - experience  with 
ERS-1. European Space Agency, ISBN 90-9092-339-3, Noordwijk  (NL), October  1995 

Contributions to specialist  publications 

BURRILL  A., VOSSEN  P., VAN  DIEPEN  C.A.  - A G.I.S. database for  crop  modelling. 
In:  European  Land  Information  Systems  for  Agro-Environmental  Monitoring.  King  D., 
Jones R.J.A., Thomasson A.J. (Eds.). EUR Publication N°  16232/EN (1995),  143-154 

CARFAGNA  E., GALLEGO  F.J.  - Sulla dimensione  ottimale del segmento  per rilievi  a 
terra  e  fotointerpretazione.  Statistica  e  Telerilevamento.  AIT.  Quaderno  N°  3  (1995) 
CNR Pisa, 41-61 

GENOVESE  G.  -  The  methodology,  the  results  and  the  evaluation  of  the  MARS  crop 
yield  forecasting  "system".  In:  Agrometeorological  Applications  for  Regional  Crop 

40 

Monitoring  and  Production  Assessment.  EUR  Publication  17735 EN,  1998, Ispra,  Italy, 
100p. 

JONES  R.J.A.,  BUCKLEY  B.  -  European  Soil  Database  Information  Access  and  Data 
Distribution Procedures. EUR Publication N°  17266/EN (1996), 35 pp. 

KING  D.  &  THOMASSON  A.J.,  1996  -  European  Soil  Information  Policy  for  Land 
Management and Soil Monitoring. EUR  16393 EN, Ispra, Italy, 22p. 

LE  BAS  C,  1996 - Base de Données  Géographique  des  Sols d'Europe,  EUR  16380 FR, 
Ispra, Italy, 39p. 

MONTANARELLA  L.  -  The  European  Soil  Bureau.  European  Society  for  Soil 
Conservation  (ESSC) Newsletter nr.2, 1996, 2-5. 

NEZRY  E.,  RÉMONDIÈRE  S.,  SOLAAS  G.,  GENOVESE  G.  -  Mapping  of  next 
season's crops during the previous winter using ERS  SAR", Earth Observation  Quarterly, 
N° 50 (1995), 5 pp. 

TERRES J.M., DELINCÉ J., VAN DE STEENE M. - The use of remote sensing and GIS 
capabilities  to  support  the  Common  Agricultural  Policy  of  the  European  Community. 
Remote Sensing Review, Vol.12 (1995), 53-60 

VOGT  J.V.  (1996): Land  Surface  Temperature  Retrieval  from  NOAA  AVHRR  Data in: 
G. D'Souza, A. Belward & J.-P. Malingreau (eds.), Advances in the Use of AVHRR Data 
for  Land  Applications.-  (Kluwer  Academic  Publishers)  Dordrecht,  Boston,  London., pp. 
125-151. 

VOGT  J.V.,  VIAU  A.,  PAQUET  F.  (1996):  Mapping  Regional  Air  Temperature  Fields 
Using Satellite Derived Surface  Skin Temperatures.- submitted to Int. J. of Climatology. 

VOSSEN  P.  -  Land  information  with  remote  sensing:  the  MARS  Project.  GIM 
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41 

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GALLEGO  J.  -  Sampling  frames  of  square  segments.  EUR  Publication  N°  16317/EN 
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MONTANARELLA,  L.  (Editor),  1996.  Soils  Information  for  Europe.  EUR  Publication 
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RIJKS  D.,  TERRES  J.M.,  VOSSEN  P.  (editors)  - Agrometeorological  Applications  for 
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SCHOENMAKERS  R.P.H.M.  -  Integrated  methodology  for  segmentation  of  large 
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SUPITI. - Global Radiation. EUR Publication  15745 EN,  1994, SAI, Ispra. 

SUPIT,  L,  HOOIJER,  A.A.,  VAN  DIEPEN,  C.A.,  Edts,  1994  (reprint  1996).  System 
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CARFAGNA E., GALLEGO F.J. - Extrapolating  intra-cluster correlation to optimize the 
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CHIUDERI,  A.,  CAPPELLINI,  V.,  1996.  A  Kohonen's  Self-Organizing  Map  for 
in 
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43 

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FUENTES M., GALLEGO F.J. - Stratification  and cluster estimator on an area frame by 
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Applied Statistics to Agriculture,  1995, Kansas State Univ. Manhattan (USA), 112-121 

GALLEGO  F.J.  -  Agricultural  surveys  on  area  frame  based  on  a  square  grid.  In: 
Proceedings  of  the  50th  Session  of  the  International  Statistical  Institute,  August  1995, 
Beijing (P.R, China), Book  1, 378-379 

GENOVESE  G.  -  Yield  forecasting  and  operational  approaches  using  remote  sensing: 
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the  Seminar on  Yield  Forecasting,  24-27  October  1994, Villefranche-sur-Mer  (France), 
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GENOVESE,  G.  - The MARS  Bulletin,  an integrated  assessment  of  agricultural  status. 
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GENOVESE  G.,  NEZRY  E.,  ARCANGELI  N.  -  Stime  precoci  di  superfici  agricole 
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GENOVESE  G.,  TERPESSI  C.  -  The  use  of  remote  sensing  for the  derivation  of  crop 
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KING  D.  &  MONTANARELLA  L.  -  European  Soil  Information:  Present  status  and 
future perspectives. In: Proceedings of the National Congress of the Spanish Soil  Science 
Society, Lleida, 1996. 

LAGUETTE  S.,  VIDAL A.,  VOSSEN  P.  - Monitoring  of yield indicators in Europe by 
combined use  of NOAA-AVHRR  and an agrometeorological  model.  In: Proceedings  of 
the Conference  on Photosynthesis and Remote  Sensing, 28-30 August  1995, Montpellier 
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LAGUETTE S., VIDAL A., VOSSEN P. - Using satellite data into a productivity model. 
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LOUDJANI  P.,  DELÉCOLLE  R.,  GUÉRIF  M.,  VOSSEN  P.  -  Combined  use  of 
NOAA/AVHRR  and  SPOT/HVR  data  for  the  estimation  of  European  crop  yields  on  a 
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Yield  Forecasting,  24-27  October  1994,  Villefranche-sur-Mer  (France),  Published  by 
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MILLOT M., LOOPUYT P. - Data, software,  hardware  and  operations requirements. In: 
Proceedings of the  Seminar on Yield Forecasting, 24-27  October  1994,  Villefranche-sur-
Mer (France), Published by EUROSTAT-CESD,  543-557 

MEYER-ROUX  J.  &  MONTANARELLA  L.  -  The  European  Soil  Bureau.  In: 
Proceedings of the 2nd Workshop on Land Information  Systems, Hannover,  1996. 

NEZRY  E.,  DE  GRANDI  G.,  LEYSEN  M.  -  Speckle  and  scene  spatial  statistical 
estimators  for  SAR image filtering and texture  analysis:  some  application to  agriculture, 
forestry  and  point  targets  detection,  In:  Proceedings  of  the  European  Symposium  on 
Satellite Remote Sensing II, Proceedings of SPIE, 25-29  September  1995, Paris (France), 
Vol.2584,  11pp. 

NEZRY  E.,  GENOVESE  G.,  SOLAAS  G.,  REMONDIERE  S.  -  ERS  based  early 
estimation  of  crop  areas  in  Europe  during  the  winter  1994/1995, In:  Proceedings  of  the 
2nd ERS Applications Workshop, 6-8 December  1995, London (UK), 

NEZRY E., SOLAAS G., GENOVESE G., REMONDIERE  S. - Early estimation of next 
season's set-aside and crop surfaces  in Europe  during the winter  1994/1995 using ERS-1 
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Parameters  from  SAR  Data  for  Land  Applications,  10-13  October  1995,  Toulouse 
(France), CNES/IEEE Publication,  10 pp. 

PERDIGÂO V. - Updating a multi-sources database: the case of CORINE land cover. In: 
Proceedings  of  the  Workshop  for  Decision-Makers  on  the  Integration  of  Space  and 
Conventional  Earth  Observation  Data  for  Agricultural  and  Environmental  Management, 
FAO/EC/ESA, May  1995, Warsaw (Poland) 

PERDIGÂO  V.  -  Area  frame  sampling  and  ground  surveys.  In:  Proceedings  of  the 
Eurocourse  on  Agrometeorological  Models,  Theory  and  Applications  in  the  MARS 
Project, 21-25 November  1994, Ispra (Italy), EUR Publication N°  16008/EN (1995), 

PERDIGÂO  V.,  DALLEMAND  J.F.,  VOSSEN  P.  -  Monitoring  and  forecasting 
agricultural  production  in  central-eastern  Europe:  a  review  of  the  MARS  activities.  In: 
Proceedings of GIS/LIS '95, June  1995, Budapest  (Hungary) 

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45 

PERDIGÂO  V.,  VOSSEN  P.,  GALLEGO  J.  -  The  MARS  Project:  the  European 
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Use in Developing Countries, October  1995, Porto (Portugal) 

SCHOENMAKERS  R.P.H.M.,  LEMOINE  G.G.,  NEZRY  E.  -  Segmentation  of  multi-
temporal  ERS-1  SAR  imagery.  In: Proceedings  of the European  Symposium  on  Satellite 
Remote Sensing II, 25-29 September  1995, Paris (France), Vol. 2579, 7 pp. 

VIAU  A.,  VOGT  J.V.,  PAQUET  F.  (1996):  Régionalisation  and  Mapping  of  Air 
Temperature  Fields  Using  NOAA-AVHRR  Imagery.-  9e  Congrès  de  l'Association 
Québécoise  de  Télédétection  (AQT),  30 avril  - 3 mai  1996, Québec,  Canada.  CD  ROM 
ISBN 2-9802696-2-X,  11 p. 

VOGT J. - The use of low resolution satellite data for crop state monitoring.  Possibilities 
and  limitations.  In:  Proceedings  of  the  Eurocourse  on  Agrometeorological  Models, 
Theory and Applications in the MARS Project, 21-25 November  1994, Ispra (Italy), EUR 
Publication N°  16008/EN (1995) 

VOGT  J.,  VOSSEN  P.  -  Suivi  des  ressources  hydriques  de  la  zone  méditerranéenne  au 
nord:  premiers  résultats  du  Projet  HYDRE.  In:  Actes  des  Cinquièmes  Journées 
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VOSSEN  P.  - Early  assessment  of  national  crop  yields:  the  approach  developed  by  the 
MARS-STAT  Project  on  behalf  of  the  European  Commission.  In:  Proceedings  of  the 
Seminar  on  Yield  Forecasting,  24-27  October  1994,  Villefranche-sur-Mer  (France), 
Published by EUROSTAT-CESD,  323-347 

VOSSEN  P.,  GALLEGO  J.,  PERDIGÂO  V.,  LOOPUYT  P.,  DALLEMAND  J.F., 
GENOVESE  G. - The MARS Project and its extensions in central and eastern Europe. In: 
Proceedings  of the Equilino  Seminar  on the Application  of Remote  Sensing  in  Statistics, 
27-29 November  1995, Roma (Italy), 

Special  publications 

BESSELAT,  B.,  COUR,  P.,  1996.  Méthode  aéropalynologique  pour  la  prévision  du 
potentiel  de  la  production  viticole.  Rapport  final  1995  N°  10935-95-04  FI  ED  ISP  F. 
Publié conjointement  par le JRC, le CEMAGREF (F) et le CNRS (F). Bordeaux,  120pp. 

MARS  BULLETINS.  -  Early  crop  production  forecasting  and  crop  state  assessment  at 
European Union and National levels. 
Volume 2 (1994) N°: 1,2,3,4,5,6,7,8 
Volume 2 (1995) N°: 1,2,3,4,5,6,7,8 
Volume 2 (1996) N°: 1,2,3,4,5,6,7,8 
Volume 2 (1997) N°: 1,2,3,4,5,6,7 
Volume 2 (1998) N°: 1,2,3,4,5,6 
Joint Research Centre of the EC, Space Applications Institute, Ispra (Italy). 

46 

MARS Project,  1996. Agrometeorological  monitoring of the whole continent of Europe 
and  the  Maghreb  region.  Situation  at  the  end  of  May,  July,  August,  September  and 
November  1996.  Space  Application  Institute,  Joint  Research  Centre  of  the  EC.  Ispra 
(Italy), variable number of pages. 

Technical documents 

ACTIVITY B DOCUMENTATION - Documentation on the first 5 years of operation of 
Action 4. Volumes 1-8. Ispra, 1994, Scot Conseil and EC editors. 

BORIES  L.,  P.  VOSSEN  -  Bilan  provisoire  du  programme  1988-1995  "Méthode 
pollinique  pour  la  prévision  précoce  du  potentiel  de  production  viticole  en  Europe". 
Technical Report with Limited Circulation. Ispra (Italy), 20 pages & annexes (1995) 

DE  GROOF  H. - An  Operational  Service  for  Timely  Estimating  of Agricultural  Crop 
Areas from Satellite  Images.  Call  for  proposal  O.J.  95/C  44/5. February  1995, Space 
Application Institute, J.R.C., 1995, Ispra, 80 pp. 

DE  GROOF  H.,  COLAES  C,  ATTARDO  C.  -  Activity  B:  1996  Quality  Control 
Procedure, 1997, SAI, Ispra. 

DE  GROOF  H.,  VOSSEN  P.  -  Research  activities  related  to the  improvement  of  the 
Advanced  Agricultural  Information  System  of  the  European  Commission.  In:  Official 
Journal N° 95/C 203/07, 8 August 1995 

GENOVESE G.,  1997. The methodology,  the results and the evaluation  of the MARS 
crop forecasting "systems", 1993-1996 activities, Publication n° SPI 97.68, Office  for the 
Official  Publications  of  the  E.C.,  Luxembourg  ,  Space  Application  Institute,  JRC, 
ISPRA, pp 381. 

KERDILES  H.  (1996):  Software  for  Processing  Avhrr  data  for  the  Communities  of 
Europe (SPACE): algorithms used in SPACE version 2. Part  1 : calibration, atmospheric 
correction and cloud detection. JRC-SAI-AIS Technical Note, Ispra/Italy, 23 p. 

LOUDJANI  P.  -  Remote  sensing  and  crop  yield  estimation:  an  overview.  COST  77 
document. Joint Research Centre of the EC, Ispra (Italy) (1995), 55 pp. 

PAQUET F., VOGT J.V. & VIAU A. (1996): Régionalisation de la température de l'air à 
partir  des  données  physiographiques  et  des  images  NOAA  AVHRR.  Cas  de 
l'Andalousie.- JRC-SAI-AIS Technical Report, Ispra/Italy, 92 p. 

PERDIGÂO,  A.  -  L'application  de  la  méthode  "Comptage  du  pollen  émis  dans 
l'atmosphère"  dans le cadre  du  suivi  de la production  viticole  au niveau  du parcellaire 
d'une petite région. Technical  Report  with Limited  Circulation. Ispra (Italy) (1995), 18 
pages & annexes, 

PERDIGÂO V. - Technical  specifications  for  area frame  sampling surveys in the three 
new Member States in 1995. (1995) 

47 

TOLLEFSEN  T.  -  Area  Frame  Sampling  Survey  within  the  European  Union.  Call  for 
proposals O.J. 96/C 366/14, Space Applications Institute, 1996, J.R.C., Ispra. 

VOGT  J.  -  Suivi  des  ressources  hydnques  au  service  des  politiques  régionales,  de 
l'agriculture et de l'environnement. Projet HYDRE. Final Report, JRC, Ispra (1995) 

VOGT J. - Monitoring  agricultural production  in Europe using remote sensing  and GIS 
techniques.  1  December 1995, Université Laval, Quebec (Canada) 

VOSSEN  P. - Note  de réflexion:  les orientations  du Projet MARS  1995-1998.  Strategic 
Document. Remote Sensing Applications Institute, Ispra (Italy), (1995), 95 pp. 

VOSSEN  P.,  GALLEGO  J.,  PERDIGÂO  V.  -  Multi-country  project  for  rapid 
environmental  and  agricultural  monitoring  and  statistics  in  the  PHARE  countries.  In: 
Project  Document  for  DG  I  -  External  Affairs,  in  the  context  of  the PHARE  Regional 
Programme (1995), 55 pp. 

48 

ANNEX 7 

Abbreviations used 

Project Management  Programme specially used by Activity B 
European Community 
European Economic Community 
Crop Growth Monitoring System 
European unit of account 
Earth Remote Sensing 
Estimation 
European Union 
Food and Agricultural Organization of the United Nations 
Geometric and Radiometric Image Processing System 
Indian Remote Sensing Satellites 

CAMEL 
EC 
EEC 
CGMS 
ECU 
ERS 
estim 
EU 
FAO 
GRIPS 
1RS 
LANDSAT  System of Satellites for Earth Resources Observation 
LISS 
MARS 
NDVI 
NOAA- 
AVHRR 
OCT. 
OLIWIN 
ORCA 
CAP 
PC 
PHARE 

October 
Agro-meteorological Model for the Estimation of Olive and Grapevine yields 
Orbital Remote Sensing of Crop Areas 
Common Agricultural Policy 
Personal Computer 
Programme  of  economic  restructuring  aid  for  Central  and  East  European 
countries. 
Satellite 
September 
Experimental Earth Observation system 
Technical Assistance to the Commonwealth of New Independent States 
European Union 

Multispectral Sensors on Board of 1RS Satellites 
Monitoring Agriculture by Remote Sensing 
Normalised Difference Vegetation Index 
Constellation of Satellites with Advanced Very High Resolution Radiometer 

SAR 
SEPT. 
SPOT 
TACIS 
EU 

M^ 

ISSN 0254-1475 

COM(98) 613 final 

DOCUMENTS 

EN 

03  15  17 

Catalogue  number  :  CB-CO-98-603-EN-C 

Office  for Official  Publications of the European Communities 

L-2985 Luxembourg 

fo