COMMISSION OF THE EUROPEAN C0MV1UNITIES 
COM(94) 332 final 
Brussels» 19.09.1994 
REPORT FROM THE COMMISSION 
REPORT OF THE FIRST PHASE 1989-1993 
OF THE PILOT PROJECT ON REMOTE SENSING 
APPLIED TO AGRICULTURAL STATISTICS 
INDEX 
1. INTRODUCTION 
2. ACTION 1: REGIONAL INVENTORIES 
2.1 Objective and methods 
2.2 Work 
23 The results 
2.4 Technical spin-offs for other programmes 
2.5 Conclusions, future developments 
3. ACTION 2: VEGETATION CONDITIONS AND YIELD INDICATORS 
3.1 Objective and methods 
3.2 Work 
3.3 The results 
3.4 Technical spin-offs for other programmes 
3.5 Conclusions, future developments 
4. ACTION 3: YIELD PREDICTION MODELS . 
4.1 Objective and methods 
4.2 Agrometeorolgical model 
4.3 Results 
4.4 The technical fallout for other programmes 
4.5 Conclusions, future developments 
5. ACTION 4: EUROPEAN RAPID ESTIMATES OF ACREAGE AND 
POTENTIAL YIELDS 
5.1 Objectives and methods 
5.2 Work 
* 5.3 Results 
5.4 Conclusions, future developments 
6. BACK-UP AND SYNTHESIS ACTIONS 
6.1 Action 5: Advanced agricultural information system 
6.2 Action 6: Area survey systems 
6.3 Action 7: Long-term studies 
6.4 Information and communication 
7. INTERNAL STUDIES RELATING TO THE PROJECT 
8. BUDGETARY MATTERS 
9. RELATIONS WITH REPRESENTATIVES OF THE MEMBER STATES 
10.CONCLUSIONS 
ANNEX 1 : MAIN PROJECT CONTRACTORS IN 1990 AND 1991 
ANNEX 2: COST OF THE PILOT PROJECT 
ANNEX 3: PROJECT PUBLICATIONS 
1 
REPORT ON THE FIRST PHASE 
OF THE PILOT PROJECT ON REMOTE SENSING 
APPLIED TO AGRICULTURAL STATISTICS 
1. INTRODUCTION 
This document is the final report on the first five years of the Pilot Project 
on Remote Sensing Applied to Agricultural Statistics, 1989-1993. A final report 
on the conduct of the project is provided for in Article 3 of Council Decision 
88/503/EEC of 26 September 1988, to be presented to the Council in the first 
half of 1994. It also covers work done in 1987 and 1988 in relation to the 
project. Following a proposal by the European Parliament, funding was granted 
as early as 1987 for preparatory work on the project. 
The ten-year programme for the application of remote sensing to 
agricultural statistics was initiated by DG VI Agriculture in cooperation with 
the Statistical Office of the European Communities (SOEC) following work by 
the Joint Research Centre (JRC) in this field. The JRC is responsible for 
implementing the programme in close cooperation with national laboratories 
ana organizations. The Council Decision has a budgetary impact on only the 
first five years, during which the project was covered by a special technical 
appropriation. 
The objective set for the project was to develop methods for improving 
agricultural statistics in the Community through the use of remote sensing 
techniques. Such methods were to be tested on fairly large areas and were to be 
developed to a stage where they could be put into operational use. This entailed 
the use of satellite data for which there would be guaranteed continuity. 
The priorities set for the projects were as follows: u 
inventories of acreages; 
inventories of production; 
•* production forecasts. 
In this last action, the prediction of foreign harvests, which was initially 
one of the priorities, has been postponed to the second phase. 
The crops targeted were the ones in which there is the biggest market, to 
the exclusion of crops consumed on the farm, such as fodder crops. 
Representativeness was sought not only at Community level, but at 
regional and national level too. 
In order to meet these objectives, the pilot project was organized into • 
seven actions shown in the above table. 
TABLE I: OUTLINE OF THE AGRICULTURE PROJECT 
Action Methodology Geographic localization Input 
(.. Regional 
nventories 
acreages) {Regression estimator p selected administrative 
regions High-resolution satellite 
Hata (SPOT, Landsat-TM) 
Vegetation 
Conditions and 
meld indicators Spatial or temporal 
comparison of VI and Ts 
[integrated indices) Selected regions and 
ampling of sites, then 
af Europe Low-resolution satellite] 
allbata (mainly AVHRR) 
Models of yield 
prediction Improvement of existing 
agrometeoro-logical 
models 
Integration of satellite 
lata in agro-
meteorological models 
Deriving agronomic 
parameters (AET) 
Direct relationships Meteorological data 
Agronomic information 
Low-resolution satellite 
lata 
High-resolution satellit 
data 
i. European rapid 
sstimates of 
acreages and 
potential yields Computer-assisted 
bhoto interpretation Sample of some 50 sites 
throughout Europe [High-resolution satellite 
Hata 
(Backup activities 
5. Advanced 
igricultural 
information 
system Comparison with 
previous years Entegration of all 
vailable results 
5. Area frame 
ampling; 
associated surveys Sstimates obtained by 
integration of the above 
methods with 
conventional ones Stratification, 
segmentation, sample 
surveys on the ground h t 
conjunction with farmei s 
Long-term 
fetudies {Backup documents or 
Idata for other actions Using new sensors; 
Geographical 
Information Systems 
Each of actions 1 to 5 corresponds to a specific operational objective, 
action 6 is a backup action and action 7 involves a number of long-term studies 
with no specific link with any particular operational objective. As regards the 
transition to practical applications, various schedules have been set for the 
different actions: 
- short term (3-5 years): actions 1,2 and 4; 
- medium term (5-10 years): actions 3 and 5; 
- long term (over 10 years): action 7. 
The project team, comprising nine members supplemented by national 
experts, was recruited in 1987 and 1988. Action 1 got under way in 1988 and 
the rest of the work in 1989, the first year of the project. The setting-up of 
institutes within the Joint Research Centre in December 1988 gave an official 
structure to the project team, which was grouped together in a special 
agriculture unit reporting directly to the Director of thé Institute for Remote 
Sensing Applications. This restructuring was the result of a desire to direct the 
JRC towards scientific and technical activities targeted at clearly identified 
external users, in particular the other departments of the Commission. This 
facilitated relations both between Commission departments and between the 
Commission and the national authorities and consultancy companies. 
These close links^were provided for in both the legislation and the 
technical guidelines for the~project. The bulk of the work was to be done by 
national companies and agencies. In addition to receiving regular reports on 
the progress of the project, the statistical services or ministries of agriculture 
occasionally had the opportunity to participate directly in certain actions. In 
this way, they obtained a more practical view of the project. 
The validity of the 1993 results can only be verified when the official 
figures will have become available (in early 1994). A first evaluation indicates 
that the Action 4 estmates of acreage and production are, for most crops, in 
agreement with the EUROSTAT estimates. However, the total cereal 
production, and mainly of soft wheat, appeared to be difficult to asses in 1993. 
This is probably due to the new Common Agricultural Policy, particulary is 
related to the set-aside regulations. If this analysis is confirmed, it would mean 
that the lands put into fallow during this first year of CAP, were generally the 
lest fertile ones. The common models used to estimate yield, without taking 
into account this shift, were therefore difficult to apply. 
2. ACTION 1: REGIONAL INVENTORIES 
2.1 Objective and methods 
The aim of this action was to meet the need for accurate and objective 
annual information on acreage and production at regional level covering the 
main crops. The method adopted was to establish close links between satellite 
data and observations on the ground. Development and evaluation was to focus 
on the so-called regression estimator method. This action comprised two 
separate components: 
objective observations in the field with a sample design established or 
enhanced by remote sensing; 
automatic classification of the satellite data in order to improve the 
estimates generated by the above-mentioned surveys on the ground 
using the regression method. 
Remote sensing also came into play in providing the enumerator with 
documents enabling him to identify plots of land accurately on the ground. 
The first component was developed as part of backup action 6 because the 
method was stanaardized for all project worle* involving ground-based 
monitoring^ particularly action 4. However, it is presented here. 
2.2 Works 
This action was implemented in three separate stages: 
first phase: 1988-1989, in which strictly identical methods were applied 
in five regions; 
second phase: 1990-1991, in which local adaptations were made and the 
active participation of the regions and national bodies was required; 
third phase: 1992-1993, in which the project was used as a basis for 
national and regional initiatives. 
First phase: 1988-1989 
- This action was able to start as early as 1988 thanks to a European 
Parliament decision, which provided for finance and setting up a project team. 
It was implemented in five administrative regions in Germany, Spain, 
France, Greece and Italy. These areas (see Fig. No 1), were very representative 
of the most important annual crops and presented a variety of climatic 
conditions and farming practices. They covered a total area of about 100 000 
km2. 
In 1988 and 1989, the work was carried out independently of the national 
statistical services and regional authorities, using strictly identical procedures 
and methods, by national services companies (see Annex 1: list of contractors). 
The method used in 1988 was as follows: 
stratification on the basis of existing topographic 1 ;i;j,n: ,/ibuts and 
maps; 
selection of a sample of 600 square segment» uf hO hed ai a^, 
survey on the ground on the basis of aerial photographs; 
simultaneous acquisition of full coverage of the region i,y Loth Spot and 
LandsatTM; 
automatic classification of the satellite data,- analybis ot lie results. 
In 1990 the following changes were made: 
improvement of the stratification using 1988 t>a tcilite liudgea, 
further improvement of the effectiveness of the sample by reallocating it 
into strata using the 1988 results; 
a single coverage was introduced giving preference to Laudsat; 
a yield component was added by selecting points in the segment, Un 
which the farmer was identified and interviewed; 
a yield by remote sensing component was added by establishing a 
relationship between vegetation indices drawn from satellite data and 
reported yields. 
Second phase: 1990-1991 
The study areas were extended with the cooperation and joint financing 
of national and regional authorities (see Fig No 2). Methods were slightly 
modified to accommodate local characteristics or programmes, such as the use 
in Italy of segments from the Agrit programme, Teruti photographs in France, 
etc. 
Methodological studies were carried out to solve particular problems or 
extend the statistical system to data on the structure of holdings: weighted 
segments as opposed to the so-called closed segments used for acreage and 
yields, use of Corine Land Cover data in Portugal for stratification purposes, 
etc. 
Third phase: 1992-1993 
The work was carried out on the initiative of the Member States and the 
level of participation in the project varied widely (see Fig. 3).Activity tended to 
be limited to the provision of technical and scientific support, either directly to 
local or national authorities or more often to services companies working for 
them. Several requests were received for the development of methods for 
countries outside the European Community. 
Internal work, relieved of the administration of the actions, which was very 
time-consuming in the first years of the project, concentrated on certain 
aspects, computer programmes, stratification, and methodological research. 
However, coordination and even the mere provision of information on work 
accomplished became more difficult. 
Figure 1: Action 1 study areas, 1988-1989 phase 
Figure 2: Action 1 study areas, 1990-1991 phase 
ACTION ONE SITES 
IW2 
• liai AIMMAIIVI tin inl 
(3 (Ml MKMAllVt.SIILlNUN-IO 
U NAIKIMAI Mil nun tumiati 
(J IM'IINU AI («I AHUkAlk*. 
Figure 3: Action 1 sutdy areas, 1992-1993 phase 
2.3 The results 
A distinction must be made between the results of the two stages 
mentioned above: stratification and ground surveys on the one hand, and 
automatic classification of satellite data on the other. 
1, Component 1 
Stratification is very economical as, generally speaking, it reduces the 
cost on the ground by a factor of two for the same degree of precision. It pays for 
itself in the first year and can be used for several years in a row. Satellite 
images are the ideal means of detecting homogeneous areas of land use and 
physical boundaries, such as roads, rivers, paths and the edges of forests, for 
selecting segments and assessing in advance difficulties arising from the 
number of plots. Since the period in which the images are obtained is not 
critical, the method is applicable throughout Europe. 
The square segments method is quick and effective. The best tool for the 
enumerator is an enlarged aerial photograph, but nearly everywhere the use of 
a 1/10 000 enlargement of the Spot images of the segment means this can be 
dispensed with if there are problems using it. 
<a 
The identification of the farmer followed by a normal interview survey 
can be carried out to provide a complete survey system not restricted to crop 
acreage. 
The results of component 1 were published in Teleagrinews Nos 1 to 5 
(see above example of Macedonia taken from Bulletin No 3 of May 1990). 
Culture 
Blé tendre 
Blé dur 
Orge 
Légumes secse 
Maïs 
Tournesol 
Pomes de terre 
Betteraves 
Riz 
Coton 
Tabac Official 
statistics 
(x 1000 Ha) 
1988 
195.5 
150.3 
5.0 
30.4 
16.3 
4.0 
10.3 
12.0 
57.7 
20.5 M.A.R.S. estimate 
(x 1000 Ha) 
1988 
158.5(11.7) 
175.4 (12.3) 
60.4 (6.4) 
0.6 (0.3) 
27.5 (3.3) 
16.6(3.5) 
6.0(3.0) 
15.9(3.8) 
14.1(1.1) 
73.9 (6.2) 
31.7(4.7) 1989 
163.6(11.0) 
161.1 (10.0) 
65.5(5.5) 
2.2(1.0) 
22.5 (2.0) 
4.0(1.2) 
0.9 (0.7) 
13.9(1.2) 
9.9(0.5) 
60.5 (0.4) 
17.7(2.2) relative efficiency: 
with with 
stratification T.M-
1988 
1.25 
1.17 
1.12 
1.44 
1.70 
1.16 
0.67 
1.36 
7.04 
2.26 
1.30 1989 
1.39 
1.49 
1.29 
1.72 
1.99 
1.09 
1.10 
1.99 
6.27 
2.47 
1.60 1988 
1.84 
2.17 
(*) 
3.56 
1.67 
1.38 
0.99 
1.23 
6.31 
1.54 
1.06 1989 
1.99 
2.13 
1.67 
C) 
3.11 
1.41 
7.61 
7.16 
18.86 
3.19 
2.21 Crop 
Common wheat 
Durum wheat 
Barley 
Dried pulses 
Maize 
Sunflower 
Potatoes 
Sugar beet 
Rice 
Cotton 
Tobacco 
The figures between brackets are standard errors. 
(*) The regression correction has not been applied 
Table 2: Results for Macedonia 
2. Component 2 
The regression estimator method was applied in all the regions. The Spot 
and Landsat resolutions are very suitable for automatic processing, which is 
easier with Landsat. However, this method is subject to strict constraints, both 
technical and financial, e.g. regarding the acquisition of the images. 
With the exception of Greece in 1989, it was, in practice, never possible 
to cover the whole of a region of 20 000 km2 within the optimum period, which 
was about 40 days, with a single type of satellite.lt was nearly always possible 
to cover the region by using both Spot and Landsat, which complicates 
processing, and by straying outside the optimum period for certain portions of 
the territory, which reduces efficiency. The technical aspect of the operation 
also caused some problems. 
The services companies selected for the first phase did not have major 
problems with the technical aspect of the operation; however, such problems 
arose when national constraints affected the choice. The technique of automatic 
classification over a vast area was technically difficult for government agencies 
or services companies not specialized in remote sensing. For this reason, costs 
were high for results that were not reliable for all crops. Except in favourable 
conditions, such as in Greece, for example, with the current state of 
technology,the cost of images, etc. component 2 should not be recommended 
from an operational point of view for regional or national statistical services. 
J£> 
2.4 Technical spin-offs for other programmes 
The methods developed in action 1 had many positive spin-offs for other 
programmes and work, whether statistical or not. 
PHARE programme 
The Commission of the European Communities has set up a programme 
of assistance to central and eastern European countries. Component 1 led to the 
development of a certain number of indicators for agricultural statistics, trends 
in crop acreage and the transformation of farm structures. A method based on a 
list of holdings would not be very effective in view of annual changes in 
holdings. Following two test studies, this method is being widely adopted under 
the PHARE programme. 
CORINE programme 
In support to the CORINE Landcover Update programme of DGXI-EEA-
TF, the methodology for the updating of the CORINE Landcover data base was 
established in 1991. It relates to the stratification method. On the basis of the 
methodology, a prototype informatic s system was redalized. In 1993, the test of 
this system were ongoing. The final system will be distributed to the Member 
(schedule for 1994). 
Management and monitoring 
Agricultural statistics are a means of monitoring applications for 
individual subsidies at a fairly general national or regional level. As statistics 
are usually obtained by interviewing farmers, bias may creep in. It was possible 
to bring such distortions to light in the case of the 1990 statistics on durum 
wheat in southern Italy and northern Greece. 
Under the new common agricultural policy, there is a danger that 
farmers' statistical returns will be biased. A more objective method applied 
periodically could prove essential. 
Assistance for services companies 
National services companies often answer calls for tenders from 
international organizations for statistical work in developing countries. These 
may be one-off operations or involve setting up more routine programmes. The 
methods developed are very suitable in certain conditions. The project 
personnel often act as consultants, thus enhancing the expertise of European 
services companies. 
2.5 Conclusions, future developments 
Statistical services are now fairly familiar with the potential of remote 
sensing in the area of crop inventories at regional or local level. The 
introduction of remote sensing entails the adoption of area sampling 
techniques, previously little used in Europe, where the prevailing method is 
surveys based on lists of farmers. 
Putting the methods developed in action 1 into operational use means, in 
practice, supplementing structural surveys and compulsory censuses with an 
area survey generating more rapid, objective and accurate vegetation statistics. 
This appears to be an attractive alternative in countries where there are 
occasional weaknesses or where current systems do not fully meet the demands 
created by the new measures of the common agricultural policy. In practice, it 
is the countries of southern Europe that experience such difficulties owing 
partly to the large number of farmers. It is also in these areas that remote 
sensing is the most effective, and the system of area surveying is therefore to be 
recommended. It is also in southern Europe that operational programmes have 
been set up: Greece, Spain, Italy, etc. 
11 
The regression estimator method is not to be recommended per se, not for 
reasons of technical feasibility, but because it requires too big an effort to 
implement, unless highly specialized services companies are brought in, for 
results that are not particularly outstanding frm an economic point of view, 
except perhaps in Greece and southern Spain. 
The project has generated a body of technical know-how among both statistical 
services and services companies which will enable them to respond and adapt to 
unforeseen developments. By using statistical systems based on lists of farmers 
or area systems based on either points or segments, through familiarity with 
the operational potential and limits of remote sensing, by stratification or 
automatic processing of satellite data, it is possible to select the method best 
suited to the case in point. 
It is recommended that limited action should continue in support of national 
statistical services that are engaged in putting these methods into operation. 
This should cover methodological aspects, hut also the development of 
operational software and training. 
It is also expected that EUROSTAT will provide good coordination of these 
actions. 
Technological developments relevant to component 2 of this action on 
regression estimators should be monitored in order to identify changes that 
might be introduced by the use of new sensors, such as radar. 
12 
3. ACTION 2: VEGETATION CONDITIONS AND YIELD INDICATORS 
3.1 Objective and methods 
The objective of action 2 was to use satellite meteorological data for monitoring 
vegetation conditions and providing indicators of the yields of the main crops. 
In addition to the objectivity of the method, the main attraction was the 
possibility of providing such indicators at various geographical levels: local, 
regional, national and European. 
The method consisted in processing the satellite data to generate two 
indicators: a vegetation index and surface temperature. Since these indices are 
directly related to the state of vegetation and crops, a spatial and temporal 
comparison of these data with other years or areas should make it possible to 
assess comparative yield levels. 
3.2 Work 
Two tvpes of work were done independently: 
- the development of application methods; 
- the pre-processing of AVHRR data, which in raw form are unsuitable ^br 
ground observation. 
These studies were carried on in parallel asihey are interdependent; however, 
the bulk of the work on methods was done at the beginning of the project and 
the bulk of the work on processing at the end. 
Application methods 
The development of application methods was carried out under contract (see 
report SP-I.89.04 EN and the map below showing the various study sites and 
the corresponding research teams). 
Subject!: Exploration of agronomie applications 
The study was carried out by analysing two types of data on a 
multi-annual basis: the vegetation index (NDVI), which is composed from data 
on visible and near infra-red radiation and its development profile, and thermal 
infra-red radiation, with the calculation of surface temperatures (Ts) and the 
search for drought indicators. 
Subject 2: Generating models of vegetation response on the basis of NOAA-
AVHRR data. This is done by incorporating external data, either in the form of 
high-resolution satellite images, or more traditional data, such as those from 
weather stations. 
Subject 3: Definition of useful products for a centralized agricultural 
information network. The first product identified so far is the classification of 
NOAA-AVHRR data according to land use. 
13 
At a later stage, some more basic research work was carried out, 
particularly to derive actual évapotranspiration figures using data drawn from 
the thermic bands of the AVHRR sensor and air temperature. 
The development of methods covered only data from the NOAA-AVHRR 
satellite. Research work tended to demonstrate the importance of data from the 
Meteosat satellites, which were left out of the project in order to simplify 
matters and avoid spreading efforts too thinly. 
EQUIPES et SITES-TEST 
Localised Studies 
National Studies 
LOGICA 
2§^ NUTIS Univ. Reading 
Univ. Athens 
<» • SUj\iv. Thcïîiloniki 
0 
Figure 4: The sutdy sites and research teams related to Action 2 
Processing of data 
The development of processing and flow procedures for NOAA-AVHRR 
data was done in cooperation with the European Space Agency (ESA), which 
was responsible for the acquisition and storage of data, which entailed 
coordination with the receiver stations. The ESA undertook to acquire and 
store all the data needed for daily coverage of Europe and a series of historical 
data.The project undertook to develop processing algorithms with the highest 
possible degree of automation for transforming the raw data into a 
geometrically and radiometrically adjusted product (level 2) in which the 
initial image is stored. This would be supplied to the ESA and the national 
stations belonging to the network. In 1989, the precise specifications of the 
algorithms were defined. External contracts were signed for writing the codes 
and determining certain coefficients for the algorithms, e.g. calibration 
coefficients of the sensors of various satellites. These elements were delivered 
at the end of 1990 
14 
Starting in 1991 a second processing phase got under way to generate the 
required products: 
- daily mosaic of the Community generated from NOAA-AVHRR data (level 3); 
- database for incorporating data for a homogeneous area or a given period, e.g. 
ten days, and for generating a more elaborate synthetic product (level 4). These 
could be time profiles for a given area or cartographical products for a given 
period. 
Since 1992, the improvement of the software has gone on in parallel to 
the operational processing of historical data. 
ACTION 2 
RAW SATELLITE IMAGES 
GENERATE 
DAILY MOSAICS OF EUROPE 
T 
'M< 
T DAILY MOSAICS 
OF EUROPE 
GATHER PARAMETERS 
ON A10X20 KM GRID 
GENERATE 
ND VI TIME PROFILES 
NDVT 
TIME PROFILES i .PARAMETERS 
TIME SERIES 
GENERATE 
NDVI EVOLUTION MAPS 
ND VI EVOLUTION 
MAPT 
Figure 5: The NOAA-AVHRR data flux and processing 
3.3 The results 
The.results of these studies were very satisfactory, but it proved much 
more difficult than expected to convert them into operational procedures, which 
entail automated operational sequences. 
^<T 
The studies 
The qualitative and quantitative results of the studies are 
particularly good in that they faithfully reflect crop and vegetation 
conditions. A spot validation of the results is impossible owing to the 
very low resolution of the NOAA-AVHRR sensors (1km); however, the 
variations in the vegetation indices faithfully reflect the variations 
in the yields of annual crops that occupy fairly large land areas. (See 
reference of reports in annexe). 
However, these results must be placed in perspective, in that they are 
still obtained retrospectively, by which time the variations sought are 
already known, and the processing procedure is very variable and 
unstandardized. As the results are very sensitive to the choise of 
images and to the processing of the data, doubts persist as to their 
validity in operational terms. 
The pre-processing of the data was the most laborious part of the 
studies, while the quality of the results depended very much on the 
ability to process a large number of images. 
The results are particularly interesting when an exceptional event 
occurs, such as a major drought. 
The data are enhanced by linking with agrometeorological models, which 
could be envisaged in a subsequent phase of the project. 
Processing the data 
By the end of 1993 the data are processed internally. There is no 
obvious error in the software and it is fully automated. Not only it is 
possible to process the satellite data practically in real time, but 
there are facilities for processing existing files from 1982 onwards. 
The sensitivity of the results in reflecting variations in the state of 
vegetation is not known, and neither are the response times. At 
present, we do not know whether the software is able to eliminate 
certain factors of disturbance. Parameters suchs as the thickness of 
the atmosphere, the viewing angle and the angle of the sun can all 
influence radiometric quality. Cloud cover in certain areas can reduce 
the number of ground control points, which can in turn reduce the 
geometric quality of the images. 
The software itself is difficult to transfer owing to the lack of 
detailed documentation and the fact that it was developed by trial and 
error. The products generated are nevertheless interesting and can 
start to be used, though the software requires a period of validation 
and industrialization. 
On the above map, the anomaly observed in Spain corresponds to the 
drought of 1992, which appears in the country's time profile. The two 
products reveal the areas most affected and the period of drought in 
1992. 
This analysis could be refined by profiles of more localized areas and 
cartographic reference to an average year rather than the previous 
year. 
16 
MMtMtMCT DIFFERENCE CURRENT NDVI - PREVIOUS YEAR NDVI 
01.07.93 
Figure 6: The difference between the NDVI vegetation index between 1 July 
1993 and 1 July 1992. (NDVI = " Normalized Difference 
Vegetation Index". The index is represented on a 50Kmx50Km 
grid.) 
rT>i.YfClYOCN: OQNVKHUU COMPAS BON 
<U 
07 
06 
OS 
I 04 
z 
03 
02 
01 
0*-1991 
- - - - 1992 
1993 
KO 200 
OqrNintar 
Figure 7: Examples of time profiles of the NDVI Vegetation index 
3.4 Technical spin-offs for other programmes 
The result of the automatic processing of NOAA-AVHRR data in near 
real time up to level 2, defined above, is unique in Europe. Current non-
meteorological applications involve a limited number of data processed image 
by image and not, as here, using an on-going automated procedure. The two 
types of processing each have their advantages and disadvantages, but 
automatic processing is well suited to the demands of monitoring over time.lt 
could have many applications, particularly for environmental problems .As 
part of the project, there are plans to study applications involving forage crops 
and forests in particular. 
3.5 Conclusions, further developments 
The link with the ESA entailed a certain number of constraints 
regarding formats, computer equipment, data flows, etc., but these constraints, 
which affected completion deadlines, guarantee a more operational service in 
the long term, all the more so as level 2 processing has many applications and 
should in future be covered by a joint service. Only level 3 and 4 products are 
dedicated Community agricultural statistics applications. 
In contrast to action 1, which could be put into operation sooner than 
planned in the project, the work schedule here is proceeding according to plan. 
A number of potential applications have been demonstrated by 
retrospective studies, which often required specially adapted processing of data. 
Standard processing software has been developed and should enable 
these applications to be created. However, they have not been validated with 
the new sequence, the sensitivity of which has not yet been determined with 
any degree of accuracy. 
The whole sequence for obtaining and processing the data is still rather 
labour-intensive, and it is necessary to industrialize for more operational use. 
However, the procedures would then have to be formalized, which is not yet 
possible. 
There need to be close links with the other project actions concerning 
crop monitoring, i.e. actions 3 and 4. These links, which were planned when the 
actions were devised, are only in their early stages, since the production 
sequence for level 3 and 4 products began only in 1993. 
A period of at least three years will therefore probably be required to put 
action 2 into operational use, i.e. to make it suitable for transfer, like action 1. 
On the other hand, everything is in place for the full-scale testing of the 
applications in real time and for links with the other actions in order to 
incorporate them into the advanced agricultural statistics system. 
18 
4. ACTION 3: YIELD PREDICTION MODELS 
4.1 Objectives and methods 
Yield prediction is one of the essential aims of the project. The yields of 
the main annual crops are targeted, as well as those of the main perennial 
crops: vines, olives and citrus fruits. 
So far, yield prediction is done mainly by qualitative methods, or 
information from correspondents. Only a few countries make objective 
observations on the basis of a specific statistical plan, and only for certain 
targeted crops .Models exist based mainly on data from ground meteorological 
stations; however, they are not very satisfactory in practice. 
At European level, the Agromet model has been used by Eurostat for 
annual crops, tut its main contribution concerns trends. Prediction models 
exist locally and have been developed by research bodies, but they cover mainly 
annual crops and thé input data for the models are generally not available at 
European level. 
Existing prediction models using data from remote sensing have proved 
to be either unfeasible, e.g. as part of action 1, or relatively complicated to 
implement, see results of action 2.The objective of action 3 was therefore to 
develop yield prediction models using ground data, particularly meteorological 
data available throughout the European Community, that can be enhanced at a 
later stage by data from remote sensing. 
A particular effort was to be made and special techniques developed for 
perennial crops, for which the advantages of remote sensing are less obvious. 
Action 3 was divided into three separate sub-actions: 
-the development of a semi-deterministic agrometeorological model for 
predicting annual crop yields; 
the extension of Eurostat's Agromet model to perennial crops; 
the development of a model for the prediction of vine (and olive) yields 
based on pollen count methods. 
The main effort went into the semi-deterministic agrometeorological 
model, i.e. one that simulates plant growth, in contrast to the Agromet model, 
which is purely statistical.For this model, the aim was to use daily 
meteorological data to produce a yield prediction for target crops that would be 
sufficiently accurate at regional, national and European level. The models 
were to generate quantitative data for vast areas, supplemented by local 
qualitative data from action 2. 
19 
The development of the model entailed the compilation of a database on 
soils, climate and plants as well as yields. It also involved harmonising, at 
European level, sub-models such 
as potential and actual evaporation and water balance. Phenological stages and 
the quantitative parameters relating to these stages, such as leaf area indices, 
grain weight, etc., are then established by correlating the basic structural data 
with current meteorological data. 
The yield predictions are obtained by a statistical relationship between the 
historic series of yields and the historic series of the parameters defined above 
(see Figure 8). 
TEMPERATURE T 
RAYONNEMENT GLOBAL R. 
mgC02 
-R, SAISON 
\ 
\ 
\ 
\ 
BILAN D'EAU 
KTR/ETM 
RR/RU MODELISATION DE LA CROISSANCE 
besoins en eau 
temps 
racines (durée jour) 
TYPE DE SOL 
TENDANCE 
Rendement 
année PRATIQUES CULTURALES 
REGIONALES ACCIDENTS 
- météorologiques 
- phytosanitaires 
- économiques 
Figure 8: Example of a general agrometeorological model. 
(With: MET and AET = maximum and actual 
évapotranspiration; UR and AR = usable and actual reserve; S = 
sowing; CE = crop establishment; F -flowering; YM = yellow 
maturity; CM = complete maturity; Dc = critical daylight hours; T 
= temperature; T(b) = sum of temperatures, base b.) 
ZO 
4.2. Work 
4.2» 1. Agrometeorological model 
The working framework was finalized as early as 1988. During 1989 and 
1990, the main job was compiling the database, which was later gradually 
improved. 
The harmonization of the sub-models and the construction of the models 
themselves was done in two stages: qualitative models, then quantitative 
models. 
Database 
This included data relating to soils, plants and climate (see figure 9). 
DONNEES METEOROLOGIQUES^ 
SPATIALISATION I 
(50 x 50 km) | x 
DONNEES PEDOLOGIQUES 
RESERVES UTILES 
(CARTES: 1/1000.0001 
INFORMATIONS ACROMET. 
f I INFORMATIONS AGRONOMIQUES/ «-
(PAR REGION) 
STATISTIQUES AGRICOLES 
t I EUROSTAT 
(PAR PAYS OU RECION) TELEDETECTION: 
- T.. ETR 
- SPATIALISATION 
TELEDETECTION: 
- ALARMES METEO 
- CALAGE DU CYCLE 
DE CROISSANCE 
- ZONE DE CULTURE 
TELBDETECTIOH: 
- SPATIALISATION 
DES SORTIES 
• SORTIES INTECREES —, 
PREVISIONS QUANTITATIVES DES RENDEMENTS 
(A L'ECHELLE NATIONALE ET POUR LES GRANDES REGIONS)] 
SUIVI QUALITATIF DE L'ETAT DES CULTURES 
(PRODUITS CARTOGRAPHIQUES) I 
Figure 9: Organizational plan of Action 3(yield prediction models for the 
Agriculture Project) 
'3iA 
For the soil data, cooperation with the Orléans National Agronomy 
Research Institute (France) and the setting-up of a "support group On soils and 
geographical information systems" led to the production of a 1/1 000 000 
pedological map of the European Community, from which information on 
usable ground water reserves" was extracted. This stage has been completed 
for the requirements of the model. 
Various contracts were signed for the collection of agronomic data for 
each of the target crops, or, to be more precise, the agrometeorological 
parameters relating to each crop (see annex). Details of optimal, acceptable and 
harmful conditions for each phenological stage of durum wheat, for example, 
were received and generally published. 
Historical meteorological data were obtained for stations that had kept 
records for a fairly long time (25 to 30 years). The data collected were those 
usually available through the GTS (Global Telecommunications Systems) 
network and were used as input for the various models and sub-models. They 
include daily data on: 
- rainfall; 
- maximum and minimum temperatures; 
- water vapour pressure or relative humidity; 
- average wind speed; 
- hours of sunshine or cloud cover. 
Such data were obtained for 350 European weather stations.The real­
time data are identical but are obtained for a different group of ground stations, 
comprising some 700 stations. 
The historical yield data were obtained from Eurostat's Regio database. 
The sub-models 
Potential évapotranspiration sub-model 
The various meteorological services use different formulae. In preference 
to measurements in lysimeters, the working party adopted the proposal by 
Choisnel et al. (1992), i.e the use of the original Penman formula, and not the 
often proposed Penman-Monteith formula. The adjustments are very good for 
the most important period March-October. Daily data are required. 
Global radiation sub-model 
The preceding sub-model requires figures on hours of sunshine, however, 
the data recorded by the ground stations often include a cloud cover indicator at 
various periods and not the hours of sunshine. A conversion formula is 
therefore required. This was tested in some 20 stations which made both types 
of observation. 
22 
Satellite interpolation of meteorological data sub-model 
Since the historical data were not obtained for the same group as the 
real-time data, and could differ for European or regional applications, notional 
stations have to be created by interpolation. 
A grid of points 50 km apart represents the notional stations. The 
parameters are obtained from the surrounding stations. Between one and four 
stations are selected out of an initial total of seven by eliminating those which 
present dissimilarities with respect to the core defined by all seven. 
For example, stations close to the sea or at very, high altitude, which are 
dissimilar, would be eliminated. 
The Agrometeorology Support Group (SuGrAm)t created in 1990, has validated 
the network of meteorological stations and the interpolation method, and the 
sub-models to calculate the potential évapotranspiration and the total 
insolation. This group is coordinated by the World Meteorological 
Organisation. 
The parameters are then obtained by weighting the values from the 
selected stations, except for rainfall. The rainfall parameter is obtained only 
from a station selected in the same manner: the one with thé smallest 
difference with respect to the core defined above. 
The development model or qualitative model 
This is based on the WOFOST model (van Keulen and Wolf, 1986; van 
Diepen et al., 1989), developed by the University of Wageningen, which is a 
deterministic model based on the energy balance and the water balance. It was 
adapted to cope with the input data defined above obtained from the sub­
models. Its output is in the form of the following data: 
abnormal weather conditions; 
development indicators: 
biomass 
grain production; 
actual water reserve; 
phenological stage; 
water balance; 
excess water; 
etc. 
for the decade and since the beginning of the cycle. 
related maps. 
This model depends on agronomic parameters relating to each crop. In 
mid-1993 it functions for one typical crop: winter wheat. 
The output is quantitative, but as the parameter sought is not primarily 
yield, it provides an initial qualitative approach to this. 
The yield prediction or quantitative model 
The input for this model will be a variable linked to the trend drawn 
from historical yield data and a variable drawn from the development model. 
Since various soil types and crop varieties coexist in a 50x50 km grid, yielding 
different results, the results for a basic square are produced by weighting the 
basic results of the model. 
23 
(4) 
This component is in the process of development, but is not yet operational. 
Computer systems 
A series of programs or computer applications had to be developed. 
On the recommendation of the Commission, the geographical information 
system used is Arc-info. As Arc-info's database system is not particularly good, 
only the Arc portion was used. 
In particular we can mention the AMDaC program for the receiving, decoding, 
quality checking, replacing of missing values and filing of synoptic 
meteorological data. This program is the first of its kind to be applicable outside 
national frontiers and can be transported to other countries or regions within 
theE.C. 
The data are stored in the Oracle database. The software was developed 
in FORTRAN or C and runs under UNIX. As it is still at the development 
stage, it will have to be industrialized to make it portable and easy to maintain, 
which would be difficult to achieve at present. The various sub-models and 
models were developed separately by different organizations, but are all 
incorporated into the project, at which level the statistical and cartographical 
output is generated. 
4.2.2. Pollen count model 
As this method was already used by groups of wine producers for then-
own forecasts, the work consisted in setting up a network of pollen sensors in 
the main wine-producing countries. 
The difficulty lay in recruiting a network of people to collect the samples, 
maintain the filters and the station, etc. while ensuring a fairly objective choice 
of sites. This was coordinated by CEMAGREF of Bordeaux in cooperation with 
the CNRS's pollen research centre in Montpellier. 
Calibrating pollen count to regional wine production requires about five 
years. However, after the first two or three years, the data can be used 
qualitatively. A higher pollen count will indicate a more abundant harvest, 
though it is impossible to quantify. 
An important element in the results is the date of flowering, which is 
indicated by the pollen count curve. This provides a basis for establishing a 
phenological parameter for use in a future agrometeorological model. It also 
makes it possible to monitor the weather conditions at the time of flowering, in 
itself an important factor. 
An exchange of results has been organized with organizations that set up 
such networks earlier, so that the method could be tested locally. There is a 
problem with the capacity for rapid processing of the pollen filters, which all 
come in at the same time. 
The current state of the network is shown on Figure 10. 
24 
• FINANCEMENT CCR 
• FINANCEMENT EXTERIEUR CCR 
• FINANCEMENT PARTAGE 
Figure 10: The pollen capture network of the Agriculture Project 
4.2.3. Agromet model extended to perennial crops 
The Agromet model for annual crops currently used by EUROSTAT was 
extended to cover perennial crops: vines, olives and citrus fruit. The only 
change to the yield prediction model for the current year is the introduction, as 
an explanatory variable, of the previous year's yield. 
2S~ 
4.3 Results 
4.3.1 Agrometeorological model 
A single database was developed for all the agrometeorological 
applications.lt contains not only daily meteorlogical data but also knowledge 
bases on the main crops, updated E.C. soils maps and estimates of useful land 
reserves in the different countries. It is fully harmonized throughout the 
Community's twelve Member States. It is complementary to the Corine 
database, which, for example, contains records from more weather stations, but 
on a monthly basis, and only contains interannual temperature and rainfall 
averages. 
The water balance, phenological and yield prediction models have been 
conceived. For the non crop-specific part of the system, qualitative outputs are 
produced every 10 days. The software for the real-time processing of 
meteorological data is also operational (see figures 11,12,13 and 14). 
These products can already be used by experts in conjunction with other 
information . The maps clearly highlight the problems encountered at the 
beginning of the 1992 season in Portugal and at the end of the season in 
Denmark and eastern Germany. The time curves for one of the grid squares in 
Denmark show the growth of vegetation in 1992 in comparison with a normal 
year, and also the deficit in biomass and grain weight. 
The crop-specific yield prediction components have still to be 
implemented, but all the elements are there to do so. 
Table 3 shows the first yield estimates for several crops, prepared by the 
Agriculture Project at the end of October 1993, compared with the 1992 yields 
(Eurostat figures). This table is an extract from the Y.S.P.R.A.('Yield Surface 
Production Report on Agriculture') newsletter, produced monthly since 
January 1993 by the Agriculture Project. This newsletter is an experimental 
product, with very limited distribution in DG VI and EUROSTAT. It is 
produced mainly for method validation and does not yet have the value of an 
operational product. Both estimates and the method, still need to be refined and 
validated. 
2$ 
(E) in. 
(E) 
£0. Crop 
Wheat 
Common 
wheat 
(E) Durum 
wheat 
(E) Barley 
(E) Grain 
maize May 
5.56 
5.35 
6.22 
5.94 
3.02 
2.78 
4.13 
4.10 
7.67 
7.54 June 
5.46 
5.36 
6.08 
5.94 
3.02 
2.79 
3.96 
4.10 
7.68 
7.55 July 
5.47 
5.35 
6.10 
5.94 
3.02 
2.79 
3.97 
4.11 
7.68 
7.51 August 
5.46 
5.29 
6.10 
5.87 
2.95 
2.78 
4.14 
4.08 
7.73 
7.65 Sept. 
5.40 
5.27 
6.04 
5.85 
2.95 
2.75 
4.13 
4.04 
7.79 
7.66 5.26 
60.1 
2.41 
4.19 
7.73 
(E) Sunflower 1.79 
1.63 1.66 
1.63 1.66 
1.61 1.54 
1.50 1.52 
1.49 1.15 
Table 3: Summary of the 1993 monthly yield forecasts for selected crops, as 
elaborated by the MARS Project, (yield = T/ha; E = EUROSTAT figures; Y = Action 
5 estimates; M = EUROSTAT figures as updatesd on 9.12.93) 
Figure 11: general state of the summer crops, at the end of the month of 
September 1993 
If 
CUMULATTiD WEIGHT ABOVE GROUND DIOMASS 
PERCENT DEVIATION FROM LONG ItiRM MEAN 
1992 
Figure 12: Simulated dry matter of winter wheat: difference as compared to 
the interannual mean (situation in mid-june 1992) 
CUMULATED WEIGHT ABOVE GROUND UIOMASS 
PERCENT DEVIATION FROM LONG TiiRM MEAN 
1992 
second decade or September 
cumulated since the first decade of January 
• — CD . » 
• -U- H 
OB •». i 
tM i i 
ffljj i ii 
ta ... >• 
Figure 13:. Simulated dry matter of winter wheat: difference as compared to 
the interannual mean (situation in mid-september 1992) 
28~&3 
{Cumutalxi wgl. blomou Itta/mx» fr 
22*2888;; 
D«cad« CtanutaUd «g». Skxag* organ If 92/m«an| 
•000 
7000 
6000 
O «=° 
^ 4QU0 
•* 3000 
9000 
1000 
A fi««i.i..ii«i«i.i AAAAAA 
• *— WÏO 
* • - wsoh 
— ^t r« O « o « « & ci 
X — — — N <* <N n 
D«cad« 
t«d Arao Mac Ittl/iMonl 
1— loi 
— lain 
D«cad« jOavatopmani Stag* IfVI/nwonW 
2i0 
M 
•a» 
O ISO 
o 
È too o c 
Ï so 
a 
o •••gaaoAM 
0« cads • - dvj 
-A— dvjn 
Figure 14; Time profiles of simulated winter wheat dry matter in Denmark, 
1992.(wdm:1992 dry matter; wso:storage organ dry matter,; lai: 
leaf area index, dvs: development stage; n: inteeannual means) 
4.3.2 Pollen count models 
The raw annual results were obtained for each pollen station. The series 
does not cover enough years to calibrate the results and determine a reliable 
relationship between production and pollen count. However, the results 
analysed for certain stations are encouraging and the information obtained is 
already of great use to experts, who can collate them with other information. 
For example, in France, where the network is most developed and where the 
-stations are the oldest, a group of experts who were shown these results 
together with the results of the qualitative models in action 3 predicted wine 
production in 1992 of 65 million hectolitres, within 300 000 hectolitres of the 
actual yield at the end of 1992. 
In July 1993 the same group indicated a production prediction of 60 million 
hectolitres in 1993, while the official SCEES estimate, produced with a 
different and indépendant method, was also 60 million hectolitres. 
3* 
600 8000 
Figure 15: example of a pollinazation curve and of a relation between 
pollen/wine production 
4.4. The technical fallout for other programmes 
Through Action 3 anumber of new products have become available, wich 
are useful both for the scientific world and as support for planning and taking 
decisions: , . , , ^ 
-the agro-pedo-climatological knowledge bases on the mam crops (see also 
annex 3); . . r . . 
-the AMDaC software for the decoding, quality control, replacing oi missing 
values and filing of synoptic meteorological data; 
-an updated andcorrected 1:1,000,000 soil map of the E.C.; 
-a data base on "useful reserves of European soils"; 
-methods for standard calculation of potential évapotranspiration and total 
insolation from visual metereological observations. 
Action 3 has also supplied important scientific and technical support to 
the work of Directorate General VI, in updating the vine-growing areas of the 
E.C.. Algorithms to estimate the potential of ripening and phenological phases 
of the vine have been developed, validated and mapped. 
2>4 
At the end of 1993, some of the expertise acquired was also being transferred to 
a network of perispheral mediterranean regions, for the structural and 
combined monitoring of the water resources of these regions. 
4.5 Conclusions, future developments 
The results of the yield prediction models obtained so far can be used, 
though with a certain amount of caution as they have not been validated in 
detail. This will require another two or three years. 
The level of the results - by large region for yields, by 50x50 km grid for 
qualitative data - is not very satisfactory for certain regions or even countries, 
which would like to see a finer resolution. Work must be done on the sensitivity 
of the models to see what obstacles lie in the way of further precision. If 
precision is limited by the input data, which is probable, it will be up to the 
Member States or regions, which sometimes have more detailed data, to 
improve the model. The improvements wich could be introduced by the 
Agricultural Project and wich have already been envisaged include the 
introduction of the rising capillary water in the water balance and the taking 
into account of irrigated areas. 
The link with remote sensing has not been tackled, even though the 
output of models such as the leaf area index is a parameter that could be linked 
to vegetation indices derived from satellite data. 
Certain indicators such as global radiation, which are generated by a sub­
model, could be derived from meteosat-type satellite data, but this has not been 
tackled either. 
This type of link is necessary because it would make it possible to monitor the 
model with the use of independent objective data. 
It is therefore necessary to finalize full operational procedures from the 
instruments and methods developed. It is also necessary to start perfecting 
them as of now. The type of semi-deterministic model used and the possible 
links with remote sensing data give us the means to do so. 
The model developed is a minimal one, standard throughout the European 
Community. It can be enhanced locally where there are more reliable basic 
data. This can be done by transfer and local improvement of the model. At the 
same time, more general improvements can be made as part of the project. 
^ 
5. ACTION 4: EUROPEAN RAPID ESTIMATES OF ACREAGE AND 
POTENTIAL YIELDS 
5.1 Objectives and methods 
The principal objective of this action was to provide early information at 
Community level on changes in crop acreage each year with respect to the 
previous year, as well as indicators of potential yields. 
Another aim was to develop a forecasting method that could be applied to 
foreign harvests. 
The method consisted in defining a sample of representative sites - about 
50 for the Community (see Figure 16) - obtaining and interpreting high-
resolution satellite images (about three or four each year) of the sites and 
extracting the required information. 
For the European forecasts, the method uses no ground data for the year, but 
incorporates ground observations for the preceding year. This provides a basis 
for checking the validity of the method retrospectively. For non-Community 
countries, it is advisable to forgo any recourse to systematic surveying. 
Figure 16: The sample of 53 Action 4 sites (rapid estimates) 
33 
The quality of the selection proved essential to the quality of the results, 
both in statistical terms and in terms of the acquisition of images. 
Means of acquisition of images 
The JRC cooperated with the producers of the images, Spotimage and 
Eurimage, in order to fulfil two essential conditions: obtaining three to four 
images per site over a period of about five months and reducing the delay in 
obtaining the images to five or ten days between date of acquisition and 
delivery to the analysis location. The principle was to acquire the earliest 
available image, whether Landsat or Spot. 
Supplementary ground observations 
The plan was to conduct ground surveys of 16 segments of 50 hectares each, 
selected systematically from the sites. For sites having a significant area with 
little agriculture a stratification was introduced. The survey data for year 'n-1' 
were intended to assist photointerpretation in year fn\ The results of surveys in 
year 'n-11 were therefore to be available at the start of year 'n\ These were also 
to be used to monitor the remote sensing work retrospectively. They could also 
constitute an independent source of information, as the identification of the 
farmer was planned in 25 points per segment in order to collect information on 
the yields in year 'n' and crop projections for year 'n +1'. 
Computer-assisted photointerpretation method 
To achieve a rapid analysis of a site in three to four days, it was not possible to 
photointerpret the whole site of 1 600 km2. The proposed method consisted in 
photointerpreting some 30 segments selected at random, including the 16 used 
for the ground surveys. The site was. then classified automatically and the areas 
obtained extracted from the regression estimator method. This method was 
later abandoned in favour of the direct result extracted from the classification. 
Statistical model 
The variation n/n-1 was applied to the Eurostat statistics for year 'n-1', both at 
national level and at European level. The statistical model was later refined by 
the contractor to account for non-discernible crops and missing sites. 
5.2.2 Implementation of the method 
The analysis was carried out by a group of companies led by SCOT, a 
land observation consultancy company, including the French Ministry of 
Agriculture's central office for statistical surveys and studies (SCEES) and the 
French companies SGS and Geosys. The first phase of the work, which consisted 
in designing and writing the image-processing software, was completed in 
1989. On the basis of an analysis of a number of sites, the photointerpretation 
experts defined their method of computer-assisted photointerpretation. During 
1990, 20 sites were analysed. In this second phase, the aim was to bring the 
system into operational mode. However, final touches to the software and the 
photointerpretation method meant that the deadlines could not be met, and 
cannot be expected to until the end of the second phase and in preparation for 
the third phase. 
34 
The availability of images was satisfactory, even for areas in north-west 
Europe where there is frequent cloud cover. Delivery deadlines of about seven 
to ten days from the recording of the image were generally met. 
The results obtained for the sites analysed were very encouraging for the 
validity of the method. The crop situation reports on the main crops analysed in 
each site using only the satellite images provided information consistent with 
data obtained from other sources. 
The supplementary ground work does not call for particular comment, 
being identical in design to that described in action 1. It provided an 
opportunity to experiment with this type of survey in all the countries of the 
Community, which was not the case with the inventory actions. The statistical 
services or ministries of agriculture occasionally carried out the work directly 
in Portugal, France and Denmark. These surveys were particularly useful in 
cases where, for varidus reasons, such as the size of fields, remote sensing was 
at the limit of its operational viability. 
5.2.3 Full-scale testing of the method (1992-1993) 
Full-scale testing of the method was carried out under contract by a 
group of companies led by SCOT, including GAF, NRSC, Telespazio and CISI 
informatique. The test workshop was separated from the development sections 
by the creation of the company SOTEMA grouping together the French 
operators. 
This stage was marked by the development of a large crop situation 
function, as results were obtained for all 53 sites (three sites were added for the 
former East Germany). The statistical model also had to be improved. 
Changes had to be made to accommodate the new requirements of DG 
VI, such as the study of fallow land, which was not in the original plan. 
In 1992, it was possible to cover all the sites with satellite images; 
however, at 9 August, two sites had still not been covered for 1993. In such 
circumstances, the statistical model attributes to these sites conditions similar 
to those observed in neighbouring sites. 
The deadlines for obtaining the images were kept down to about four to 
five days and three to four days for analysis. The objective of incorporating all 
the data acquired, except that on the last ten days, into an information bulletin 
or fax has therefore been achieved (see fax in annex). 
Improvements had to be made to the processing software, especially for 
the geometrical adjustment of the images in order to keep costs down and meet 
deadlines. Previously, this pre-processing was done externally. 
In 1993, a more industrialized and portable software was produced in 
order to convert from the Vax/VMS system to UNIX. This was done to prepare 
for the transition to operational use.On the other hand, for financial reasons, it 
was not possible to do the field work in 1993. 
35 
This has no direct effect, since, in its final form, the method can operate without 
field work. However, an up-to-date information base is necessary for the 
photointerpretation experts and this matter must be looked into closely in order 
to maintain the quality of the system. 
The data on crop acreage at site level are obtained directly from an 
analysis of the images. For yields, remote sensing is only a rough guide. Since 
1993, the data from action 3 on each site have been incorporated and are a 
significant factor in the assessment. 
5.3 Results 
The results can be evaluated in more than one manner. The first is 
theoretical and consists in calculating the accuracy of the sample assuming 
that the estimate at site level is perfect, i.e. assuming that the 
photointerpretation method is perfect. For the main crops, the error in the 
trend due to sampling is in the region of 2%. That means that if there is a 5% 
change in acreage, the sample allows us to situate it between 3% and 7% (with 
90% probability). 
Another method consists in comparing the results at the end of the work 
with results from a source considered to be correct, i.e. with Eurostat statistics. 
As it is a rapid prediction, what is important is the trend over time and 
the information being produced earlier than with other sources. 
In practice, the Eurostat estimates can vary significantly six months 
after harvest and, for some ten faxes and ten crops, synthesis is not easy to 
achieve. 
The following curves,(figure 17), for durum wheat and common wheat, 
two crops that are difficult to tell apart in regions where they coexist, show that 
they were correctly estimated as early as the fax of 9 June 1992 in both acreage 
and production, even though their respective variations between 1991 and 1992 
were sharply contrasting. Once some hundred images had been analyzed, the 
variations were not subject to further adjustment. The estimates were 
significantly better than those of the professional organization, Coceral. 
A comparison of the results at 12/10/1992 on the acreage of the various 
crops appears to yield better results than the theoretical 2% tolerance 
mentioned above, except in the case of sunflowers(figure 18). A detailed 
analysis has demonstrated that, for this crop, areas designated as sunflower 
fields were interpreted as bare ground, probably owing to the very low yield, 
which explains the very good result achieved by the production forecast on the 
basis of a very poor acreage forecast. 
Where the results were very different, as for durum wheat production, 
which showed a gap of about 10%, it was not necessarily because of error in 
action 4. Here, a subsequent adjustment by Eurostat brought the final result to 
within a few percent. 
Certain crops that are too localized, such as potatoes and sugar beet, are 
poorly estimated, however. 
36 
The presentation of the results and the related comments - two important 
features of the crop situation analysis - satisfy DG VI, which is the target user 
for this action. The lack of prediction data at national level is inherent in the 
sampling method. For countries that have ten or so sites, the prediction for two 
or three crops appears to be roughly valid. The cartographical representation of 
individual sites provides some information, e.g. on sunflowers in 1993, where 
the trend in the Iberian peninsula is contrasted with that in the rest of Europe 
(Figure 18). Such information at sub-European level, however, is very limited 
and must be viewed with caution, as the sample is not designed for this purpose. 
BLE DUR BLETENDRE 
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ta.04 ii.os oaos os,c7 axa» 
zt.at nos 1208 SQ07 17.0 
Date (1992) E*tun*ban «able 
|iJ Sus. f* bucc* 
/ 
i 2*08 
14.09 1110 l.OS O0.M 08.07 03 08 31.01 28.CB 
37.04 jaOS 22.08 20.07 17.01 14.0» 1110 
AeeonK/ 
AGROMET 
X 
ELMOSTAT 14.10.a2 
COCEHAC Date (1992) 
Evolution de la production Evolution de la production 
EUROSTAT wt 
17-JL EUROSTAT UMim 
iMMab | ^"-S EUROSTAT 1»1 
EUROSTATOtUJtn 
(5JS.ua. lMImifta 
^ 
r moi 
20,07 17.01 I«.OP 12.10 13.04 «1.09 OB.O8 00.07 0308 31.0 
37.04 SOS 22.08 7307 17.08 14.0B ' 1110 
Date (1992) Date (1992) 
Figure 17: Evolution of the wheat average and production during the 1992 
season: comparison of the Action 4 with the EUROSTAT 
estimates. 
37 
The validity of the 1993 results can only be verified when the official figures 
will have become available (in early 1994). A first evaluation indicates that the 
Action 4 estimates of acreage and production are, for most crops, in agreement 
with the EUROSTAT estimates. However, the total cereal production, and 
mainly of soft wheat, appeared to be difficult to asses in 1993. This is probably 
due to the new Common Agricultural Policy, particulary is related to the set-
aside regulations. If this analysis is confirmed, it would mean that the lands 
put into fallow during this first year of C \P, were generally the lest fertile 
ones. The common models used to estimate yield, without taking into account 
this shift, were therefore difficult to apply. 
£ 
w EVOLUTIONS DES SUPERFICIES 
Action 4 et Eurostat, 1992 sur 1991 
Blé dur Céréales total Pomme de terre Tournesol 
Blé tendre Orge Colza Mats grain Betterave à sucre 
Action 4 I I Eurostat 
EVOLUTIONS DES PRODUCTIONS 
Action 4 et Eurostat, 1992 sur 1991 
Blé dur Céréales total Pomme de terre Tournesol 
Blé tendre Orge Colza Niais grain Betterave à sucre 
Action 4 1 1 Eurostat 
Figurel8: Evolution of the average and production of the principal crops in 
1992: comparison of the Action 4 estimates with the EUROSTAT 
estimates 
3R 
SITE SAMPLES OF MARS PROJECT (Action 4) 
1993 MARKETING YEARS 
Crop situation in 9/8/93 
Sunflower seed 
28000 Ha 
Ç_) 21000 Ha 
O 14000 Ha 
O 7000 Ha Evolution : 
-SO* -35* -25» -15% -5% 
= 10* 20* 30* 40* > SO* 
Figure 19: Map for sunfiower, as published in the Crop Status Fax of 9 
January 1993. 
39 
5.4 Conclusions, future developments 
If the quality of the 1993 results is as high as that of the 1992 results, the 
method developed may be considered operational throughout the Community 
without major changes. 
Further improvements must be incorporated, e.g. the introduction of the 
analysis of ERS.l radar images in order to enhance accuracy and reliability 
over time. 
The links with other sources must be improved, particularly for the yield 
predictions. 
More varied applications will probably be introduced, as was the case with 
fallow land ana the setting-aside of arable land, which were not planned 
initially. 
In the territory of the European Community, the role of the ground surveys 
should be reassessed to determine whether they should be continued. Their 
function of monitoring the quality of the photointerpretation work is no longer 
essential in the operational phase. 
In the studies and discussions prior to the conception of the project, the 
importance of predicting foreign harvests was taken into consideration. 
However, for technical reasons, it seemed preferable to postpone this action to 
the second part of the ten-year programme. 
For the application to foreign countries, which was an essential objective of 
action 4, it would be advisable to examine which information base could take 
the place of observations on the ground. A better balance should be established 
between the contribution of actions 2 and 3, which are less costly in operational 
use, and action 4, for which photointerpretation work will remain an essential, 
high-cost feature. 
40 
6. BACK-UP AND SYNTHESIS ACTIONS 
Three back-up and synthesis actions were defined in the pilot project: 
- action 5: advanced agricultural information system; 
- action 6: area survey system; 
- action 7: long-term studies. 
Moreover, the large number of contacts and requests for information called for 
a huge effort in the area of information and communication. 
6.1 Action 5: Advanced agricultural information system 
The objective of this action was to integrate the various actions and also 
incorporate conventional surveys in order to create a complete information 
system including the new methods described above. It therefore lies 
downstream of the preceding actions. 
Work mainly covered the link between area-type ground surveys and the 
analysis of high-resolution images from actions 1 and 4 (see paragraph 6.2: 
Area surveys). 
Synthesis work, such as the Y.S.PRA Bulletin, was another way of 
taking advantage of the various individual actions in order to integrate the 
results (see copy of bulletin in annex). This makes use of the results of action 4 
for acreage and actions 2 and 3 for yields. In contrast to the SOTEMS reports, 
the information supplied is aimed at national level. 
6.2 Action 6: Area survey systems 
The aim of this action was to design a type of survey to be used in 
conjunction with remote sensing and therefore suitable for the ground work 
required for the project. Another aim was to develop a comprehensive system of 
agricultural statistics on an area basis. 
All the ground surveys conducted for the project were area-type surveys. 
In such surveys, the statistical survey units are not economic entities, sucn as 
agricultural holdings, but small geographical units called "segments". These 
segments, or small units of area are obtained by dividing up the territory. In 
the project the basic unit is a 50 ha square. After three years of studies, this 
shape and size proved on average to be fairly close to optimal in most of the 
regions studied. Non-square segments with physical boundaries might be 
easier for the enumerators on the ground; however, the creation of all these 
detailed boundaries would entail an extremely large investment. The Italian 
project, Agrit, made such an investment in Emilia-Romagna forts own 
purposes, and since 1990 we have used such segments with physical 
boundaries. The results do not appear to show any improvement with respect to 
square segments and therefore do not justify the cost. This was studied in 
Navarre, where the contractor reached the same conclusion. 
41 
Maintaining two survey systems - a list system and an area system - is 
expensive. In certain cases, e.g. countries in transition in eastern and central 
Europe, a list system is inoperable. This question was studied as part of the 
project to create a comprehensive area-based system. 
In action 1, the yield component was obtained by identifying and 
questioning the farmers. These were not selected on the basis of agricultural 
censuses, but by identification on the basis of the area survey. The same 
method was used in action 4 for monitoring the results of photointerpretation. 
This method has been shown to be feasible and the response rates are 
generally very satisfactory. However, they depend greatly on the backing, or 
lack of it, of official bodies. Where official bodies were directly involved, the 
response rates were close to 100%. 
The method was also used to obtain structural data, such as form of ownership 
or type of farming. In the example below of the Czech Republic, the following 
results were recorded in 1992, (figure 20). This survey will be repeated to 
monitor trends.Chart of the status of farmers in the Czech Republic 
It is important to keep the technical lead that has been achieved with area 
surveys, which can be used not only for agricultural statistics but also for 
environmental or structural statistics. The link with remote sensing 
techniques means that they are susceptible to constant improvement. However, 
in the short term, the use of remote sensing is mainly confined to the 
stratification and allocation aspects of the surveys and does not extend to the 
automatic classifications which were the main subject initially studied. 
privé (individu) (4%) 
privé (entreprise) (6%) -état (31%) 
coopérative (59%) publique (0%) 
Figure 20: Diagramme related to the agricultural farms in the Czech Republic 
42 
6.3 Action 7: Long-term studies 
The objective was to allocate part of the resources of the project to work 
that would probably not generate operational applications during the life of the 
project, but would deal with prospective uses in the medium and long term. 
In 1989 and 1990, this work focused mainly on basic research into radar 
techniques. Following the launching of the ERS 1 satellite in 1991, work 
continued using real data for which there is expected to be continuity of service. 
By mid-1993, it was apparent that radar images are of potential use to the 
project, for both action 1 and 4. However, this technique has yet to be tested on 
larger areas and, above all, the actual image acquisition capacity will have to 
be ascertained, as ERS 1 is a multi-mission satellite mainly intended for 
marine applications (see radar image in annex). 
Work also went into developing the Space software on parallel 
computers in support of action 2. The operational processing of a database of 10 
years of satellite images for the whole of Europe, particularly in real time, is 
extremely burdensome. The use of parallel computers could lighten the load, as 
the time savings achieved with such computers have demonstrated. 
However, more work is needed to ascertain whether these savings are not 
partly or fully cancelled out by the handling, inputting and outputting time for 
the data storage media. 
6.4 Information and Communication 
The large number of participants in the project meant that one person 
had to be assigned exclusively to information and communication activities. 
Four activities were involved: 
- editing the Teleagrine ws bulletins ; 
- organizing conferences, meetings and visits; 
- organizing training courses; 
- setting up scientific working parties. 
Five Teleagrinews bulletins were produced, providing information on 
the main results achieved by the project and the scientific choices made. The 
frequency and regularity of the bulletins left something to be desired, but this 
constituted only one means of communication. 
A conference was organized every two years. The first was held in 
Varese, Italy, on 10 and 11 October 1989 and the second at Belgirate, Italy, on 
26 and 27 November 1991: A third one is planned for 17 and 18 November 1993, 
again in Belgirate. They provided an opportunity for a detailed presentation of 
the project's progress and comparison with other methods. They also allowed 
national agricultural statisticians to communicate with scientists working in 
the field of remote sensing. 
43 
Several training courses in connection with action 1 on regional 
inventories were held in Ispra, Lisbon and Athens. Some regional and national 
agricultural statisticians took part, but the content of the courses was aimed at 
a fairly wide public. 
Several scientific working parties were set up to provide back-up for the 
project. A short-lived technical group studied problems of classification and the 
processing of satellite data for Mediterranean regions. The soils and 
geographical information systems support group was set up to validate the 
work on soil cartography done as part of the project. The agrometeorology 
support group monitored certain aspects of the yield prediction model. 
The Agricultural project is also represented in several international 
organisations and working groups such as, PASSAGER VEGETATION, GISIG 
(Geopgraphical Information Systems International Group), International 
Society for Photogrammetry and Remote Sensing, etc.. 
The project has established close contact with groups such as the 
European Space Agency, F.A.O., the World Meteorological Organisation, the 
European Environment Agency (task force), United Nations Programme for 
the Environment, United States Department of Agriculture, United States 
Department for Agricultural Research, etc.. 
7. INTERNAL STUDIES RELATING TO THE PROJECT 
Most of the scientific work for the project consisted in assessing the 
various alternative methods, choosing one or more and having the 
corresponding studies carried out under contract. The monitoring and 
evaluation of the contracts led in turn to the preparation of the next stage. 
Apart from this, studies were carried out by thé project's own teams, 
particularly in the area of computers. 
A complete computer system called MARS-PED was set up for processing 
•high-resolution data for action 1. This was derived from and adopted all the 
formats of an application called PEDITOR used by the US Department of 
Agriculture. The version currently available was developed on VAX 
microprocessors. We were able to take advantage of earlier development work 
by the JRC, which was all done on VAX, system VMS. A system for digitalizing 
the ground surveys using video camera was developed to shorten data inputting 
times for these surveys. For one region and some 400 observations, processing 
which originally took about one and a half months was completed in a week. 
This method is now used by about ten services companies in their work. 
The aim of these actions is to assist the companies that produce the 
inventories and to harmonize computer systems. They enable the project team 
to master the techniques in order to be able to assist regional and national 
organizations that decide to equip themselves with similar systems. The 
emphasis was placed on action 1, because, for the other actions, processing is to 
be done centrally and the problems of coordination are less acute and on the 
production of maps based on satellite images to support activities in the field 
(Action 6). 
44 
The centralized developments for action 2 and 3 were done on SUN 
hardware using UNIX operating software.Equipment suitable for processing 
NOAA-AVHRR data in near real time has been installed, it is copied from the 
ESA's equipment in this field, i.e. SUN 3 plus optical disks.The geographical 
information system and the agrometeorological models for yield prediction 
have also been installed on SUN. The installation of this equipment was done 
gradually and this has allowed the work done externally to be progressively 
integrated. 
8. BUDGETARY MATTERS 
The table in Annex 2 giving the cost of the project action by action shows 
very different trends in the various actions. 
The cost of action 1 - regional inventories - was very high at the 
beginning of the project. This is because it started earlier. Five regions were 
monitored, instead of the three or four originally planned. The passage to the 
operational stage was accelerated by the higher outlay, which led to savings at 
the end of the programme. 
Similarly, the cost of action 4 appears very high. This is because of a 
much greater than expected increase in the cost of high-resolution satellite 
images. It is also because part of the work planned for action 5 was carried out 
for monitoring purposes as part of action 4 and is therefore included in this 
action. 
The cost of actions 2 and 3 was much lower than expected. This is partly 
because they were delayed by about one year; indeed, they could not be started 
right at the beginning owing to staff shortages. The lost time was made up 
without extra cost owing to the low cost of NOAA images. Moreover, processing 
is done internally, which led to savings. It is difficult to separate the cost of 
these two actions because the same equipment and maintenance is used for 
both. 
For the three fairly centralized actions, 2,3 and 4, the operating costs are 
in the region of ECU 500 000, ECU 500 000 and ECU 2 500 000 respectively. 
One-third of the cost of each project is attributable to the purchase of input 
data, meteorological data or satellite images. However, the cost of analysts is 
not included in these amounts for actions 2 and 3, whereas it is included in the 
cost of action 4. Analysis is necessarily decentralized at each level for actions 2 
and 3. 
9. RELATIONS WITH REPRESENTATIVES OF THE MEMBER 
STATES 
The representatives of the Member States were kept informed of the 
progress of the project via the Standing Committee on Agricultural Statistics 
(see Articles 2 and 4 of Council Decision 88/503/EEC) and the working parties 
on crop production statistics, chaired by the Statistical Office. One of the 
working parties met at Ispra on 26, 27 and 28 March 1991 and has taken a 
particular interest in remote sensing. 
45 
The national statistical services will play an active part in putting the methods 
developed by the pilot project into operational use. This is already the case with 
action 1 on regional inventories. In this connection, attention should be drawn 
to the implementation of the plan for restructuring the agricultural survey 
system in Greece following Council Decision 90/386/EEC of 16 July 1990. 
Other operations aimed at introducing remote sensing methods into 
national systems have also been carried out in Portugal and Spain. 
An informal working party has been proposed to monitor these 
operations and give advice on the process of putting the methods into 
operational use. It will be coordinated by Eurostat. 
10. CONCLUSIONS 
During the first phase 1989-1993, the progress on the pilot project on 
remote sensing applied to agricultural statistics was in line with the 
programme established and approved by Council Decision 88/503/EEC. 
Action 1 on regional inventories was put into operational use as early as 
1991, even though this was originally planned to take three to five years. 
Programmes for this have been developed with the countries concerned. 
For actions 2 and 3 on the monitoring of plant growth and 
agrometeorological yield prediction, a georeferenced database has been 
established. It is complementary to the Corine database and could have many 
applications in the fields of both agriculture and the environment. 
Cartographical products and trend indicators concerning the state of 
crops were generated from these models and are already being used for action 4. 
However, action 4 is entering the validation stage, which will probably lead to 
certain changes. These products must be adapted to suit national and especially 
regional needs. Links have not been established between the models of action 3 
and remote sensing data, either to provide input data for the model or for 
monitoring purposes at certain stages. 
Action 4 on European rapid estimates of acreage and potential yields led 
to the production, as early as 1992, of forecasts that have proved fairly accurate 
at an early date. It is ready to be put into operation at the end of the first phase 
without major changes, with the possible exception of ground monitoring. 
Some work involving non-Community countries was done following the 
recommendations of the 1991 report to the Council . This was confined to 
limited technical cooperation. 
At the end of the first five-year period, all the basic tools planned have 
more or less been developed. The potential of remote sensing as a new source of 
information for agricultural statistics has been fully explored. Certain 
activities, such as action 4, have produced results fully in line with initial 
expectations. In other cases, such as action 1, remote sensing has proved to be 
more effective in the area of stratification than in automatic classification, 
though the project had placed the emphasis on the latter. 
46 
For the qualitative and quantitative crop yield prediction models, results 
were expected first from action 2 and only in the second phase of the project 
from action 3. The basic products were obtained as early as the fourth and fifth 
years. However, they have not been validated and are not yet in a form that can 
be put into operational use. Nevertheless, they can begin to be used, even 
though the final development of stable products will take another two or three 
years. 
The bulk of future work will involve the integration of the various 
components, which are mutually reinforcing. The products of actions 2 and 3 
must be better integrated. By allowing for close observation of certain sites, 
action 4 should provide an opportunity to improve these models. Conversely, 
the models will provide opportunities to improve the yield component of action 
4. 
The interaction and complementarity of the various actions should also 
be essential in the development of prediction methods for foreign harvests, 
which was one of the initial priorities but was postponed until the second phase 
for technical reasons. 
As and when the actions come into operational use, it is important to 
maintain effective coordination between large numbers of users. The 
continuation of action 1 depends on Eurostat making efforts in this field, as 
does the employment by the Member States of methods developed in actions 2 
and 3. 
While the JRC has an important role to play in the actions that are still 
being developed, i.e. actions 2 and 3 and the prediction of foreign harvests, for 
the actions that are being put into operational use its role should be confined to 
providing technical support, the details of which remain to be defined. Here, it 
is the user and the coordinator who play the essential role and direct this 
technical support. 
47 
ANNEXES 
Annex 1: Main project contractors in 1990 and 1991 
Annex 2: Cost of the pilot project 
Annex 3: Project publications 
ns 
Annex 1: Main Contractants of the Project 
Action 1 : Regional Inventories 
Germany 
Spain 
France 
Greece 
Italy 
Informatics: Contractant 
GAF (F.R.G.) 
Hunting (U.K.) 
Sodeteg/Sysame (F) 
BDPA/Sysame (F) 
Aquater (I) 
R.S.D.E. (I) Sub-contractant 
Aurenza (E) 
Aquater (I) 
ADK (G), BRGM (F) 
Sodeteg/Sysame (F) 
Action 2: Vegetation monitoring 
Thematic studies 
Informatics 
1990-1991 
1992-1993 DHV(NL) 
Hunting (U.K.) 
I.N.M.G. (P) 
INRA(F) 
Logica (U.K.) 
Silsoe College (U.K.) 
Telespazio (I) 
Tecnodata (I) 
EOS. (R.U.) LERTS, CEMAGREF (F) 
Reading Univ (U.K.) 
Action3 : Agrometeorological models 
Model developement and 
statistical validation 
Informatics 
Potential évapotranspiration Staring Centre (NL) CABO (NL) 
Fac. Sciences Agron. 
de Gembloux (B) 
Hunting TS (U.K.) 
Q-RAY (NL) 
Metea Consult (NL) 
Univ. East Anglia (U.K.) 
SGS-Qualitest (F) Météo-France (F) 
INRA(F) 
H9 
Agrometeorological crop inventories Fac. Sciences Agron. Gembloux (B) 
INRA(F) 
Scottish Crop Research Institute (U.K.) 
Univ. Edinburgh (U.K.) 
Meteorological Office (U.K.) 
A.G.P.M. (F) 
Aquater (I) 
Processing and interpolation 
of meteorological data Meteo Consult (NL) 
Staring Centre (NL) 
Global solar radiation The Meteorological Office (U.K.) 
World Meteorological Organization 
Grapevine production 
prediction - pollen CEMAGREF (F) CNRS (F) 
Statistical model, yield 
prediction of fruits 
and grapevine Fac. Sciences Agron. Gembloux (B) 
Action 4: Rapid Euroepen Estimates 
Image analysis 
1988-1991 
1992-1993 SCOT Conseil (F) 
SCOT Conseil (F) SCEES/SGS/GEOSYS (F) 
SOTEMA, CISI (F) 
Telespazio (I) 
GAF (F.R.G.) 
NRSC (U.K.) 
Field documents IGN-Espace (F) 
Hunting TS (U.K.) 
Field observations ERA (I) 
Bureau of Land Data (DK) 
AGRAR (F.R.G.) 
DGPA (P) 
Organotechnica (G) 
K.U.L (B) 
Ibersat (E) 
SCEES (F) 
R.S.D.E. (I) 
Hunting TS (U.K.) 
Action 5: Advanced Agricultural Information System 
N.L.R. (NL) 
So 
Annex 2: Project costs (KEcus) 
Credits and Staff 
P: planned 
R: realized 
Specific Credits • 
Staff number P 
R 
P 
R 1989 
4825 
4825 
12 
9 1990 
5425 
5425 
12 
9 1991 
5725 
4600 
12 
8 1992 
6025 
5370(1) 
12 
12 1993 
6000 
4840 (2) 
12 
12 
(1) 4670 KEcus were planned in the beginning of the year, and a supplementary 700 KEcus were 
obained later on; 
(2) 3540 KEciis were planned in the beginning of the year, and a supplementary 1300 KEcus were 
available at the end of 1992. 
Costs per Action 
Action 1 
Action 2 & 3 
Action 4 
Research 
Others 
TOTAL 1989 
1300 
825 
1400 
600 
700 
4825 1990 
1400 
1225 
1500 
600 
700 
5425 1991 
600 
1100 
2100 
200 
600 
4600 1992 
550 
1370 
2600 
300 
550 
5370 1993 
250 
1590 
2300 
200 
500 
4840 Total 
4100 
6110 
9900 
1900 
3050 
25060 
S A 
Annex 3: A selection from the main publications of the 
J.R.C. Scientists within the Project or JRC Publications 
1. General 
The 10-Year Research and Development Plan, for the Application of Remote 
Sensing in Agricultural Statistics - JRC, Report SP.I.88.35/EN. 
Proceedings of the Conference on the Application of Remote Sensing to 
Agricultural Statistics - Varese (Italy) 10-11 October 1989 - June 1990 - JRC, Report 
EUR12581/EN. 
Proceedings of the Conference on the Application of Remote Sensing to 
Agricultural Statistics - Belgirate, Italy, 26-27 November 1991 - 1992 - JRC, Report 
14262 EN. 
J. Meyer-Roux, C. King, 1992. Agriculture and Forestry. International Journal of 
Remote Sensing, Special Issue: European Achèvements in Remote Sensing, Vol. 
13, No. 6 and 7, pp. 1329-1342, April/May 1992. 
J. Meyer-Roux, 1992. Remote Sensinq Applied to Agriculture: State of the Art and 
New Trends. Space in the Service of the Changing Earth, in Proc. of the Central 
Symposium of the "International Space Year" Conference, ESA SP-341, p. 647-649 
Munich (Germany), 30 March-4 April 1992. 
J. Meyer-Roux, M. Cornaert, 1991. Stato dell'Arte, Esempi dei Progetti MARS e 
CORINE. Atti del Convegno Europeo sui Sistemi Informativi per la Difesa del Suolo 
e la Tutela del Territorio, Roma 14-15 Maggio 1991. 
TeleAgriNewsno. 1-5. 
J. Meyer-Roux, 1991. La Télédétection Appliquée aux Statistiques Agricoles, 
Commission Economique pour l'Europe, 8-11 Juillet 1991, Document 
FAO/ECE:ESS(91)-7. 
A. Burrill, 1992. GIS Applications at the European Communities' Institute for 
Remote Sensing Applications. EUROCARTO X: Pan-European Environmental 
Policy: some relevant GIS applications, Oxford (GB) 16-18 Sept., 1992. 
st. 
2. Action 1 
J. Delincé, 1993. La Prévision et l'Estimation des Productions Végétales en Europe: 
Les Applications de la Télédétection dans le Projet MARS. In Proceedings of 
International Symposium MARISY 92, Rabat (Maroc), Oct.7-9,1992 
Géo Observateur MARISY 92, Numéro Spécial, pp. 37-42,1993. 
J. Gallego 1992. Applications of Remote Sensinq to Agricultural Statistics in the 
MARS Project of the EC. 2°Convegno Nazionàle ' Informatica e Agricoltura" 
Firenze, 17-18 Dicembre 1992. 
J.P. Le Gorgeu, J. Meyer-Roux, J.M. Terres1992. Area Frame Sampling Surveys in 
Romania and Czechoslovakia. 12th EARSeL Symposium, Eger (Hungary), 8-11 
September 1992. 
J. Gallego, C. Rueda,1991.. Area Estimates with Sampling Units Shared by 
Different Strata. 19th European Meeting of Statisticians, Barcelona (Spain), Sept. 
2-6,1991. 
FJ. Gallego 1992. Flàchenschàtzungen fur einjahrige Feldfrùchte mit Hilfe 
Fernerkundung: Neue Wege raumbezogener Statistik. Forum der Bundesstatstik 
No. 20, pp. 109-120, Wiesbaden: Statistisches Bundesampt. 
V. Perdigao, 1992. Stratification of Tras-os-Montes using integrated GIS and 
Landsat TM data. JRC EUR 14638 EN. 
D. Avenier, V. Perdigao, J.M. Terres, 1992. Méthodologies de stratification sur 
images satellitaires et urilisation d'un système d'information géographique. Mise 
en place du plan d'échantillonage, JRC EUR 14855 FR. 
V. Perdigao, 1992. Guide pratique à l'interprétation des images satellitaires pour 
la stratification, EurocoursesTélédét.. App.. Stat. Agr, Lisboa 1992. 
3. Action 2 
M. Sharman, I. Barnes, Ph. Bierlaire, A. Le Lerre, 1991. Software for Processing 
AVHRR Data for the European Communities: Algorithms, Benchmarks and 
Standards. 5th AVHRR Data Users Meeting, Tromso (Norway), 25th-28th June 
1991. 
M. Sharman, I. Barnes, Ph. Bierlaire 1991 Automatic Extraction from AVHRR of 
Data on Vegetation Condition for the European Communities. 5th AVHRR Data 
Users Meeting, Tromso (Norway), 25th-28th June 1991. 
^3> 
4. Action 3 
P. Vossen, 1992. Forecasting of National Crop Production: the Methodologies 
Developed in the Joint Research Centre in Support to the Commission of 
European Communities. EARSeL Workshop on Agrometeorology, Florence 
(ltaly)13-14 April 1992. 
P. Vossen, 1992. La Prévision du Potentiel de Production Viti- et Oléicole à l'Aide 
de la Méthode Aéropalynologique: Evaluation Primaire de la Méthode et des 
Investissements Faits par le Centre Commun de Recherche de la CE. In: Résumé 
de la Conférence Méthodes Aéropalynoloqiques, Phénologie de la Floraison et 
Prévision de Rendements, Bordeaux (France) 10-11 Mars 1992. 
P. Vossen, 1992. Timely forecasting of national sugar beet production in Europe: 
the CEC methodology and planned improvements using satellites. In: 
Proceedings of the 55tn Winter Congress of the International Institute for Sugar 
Beet Research, Brussels, Belgium, February 1992, pp. 261-281. 
J. Vogt, P. Vossen, 1993. Suivi des ressources hydriques de la zone 
méditerrannéenne par l'intégration des modèles agrométéorologiques et de 
données satellitaires: premiers résultats du projet hydre. In: Proceedings of the 
Conference Télédétection des Ressources en Eau, Tunis, Tunisie, Sept. 21-24, 
1993. 
A. Burrill,. P. Vossen, 1992. Development of an operational agrometeorological 
monitoring system. Proceedings of the Conference Application of Remote 
Sensing to Agricultural Statistics, Belgirate, Italy, 26th - 27th November 1991, pp. 
357-360. 
A. Burrill, P. Vossen, 1993. Agro-climatic zonation of grape sugar content in the 
E.C.,.Proceedings of the 4th European Conference on Geographical Information 
System (EGIS '93), 29 March - 1April, 1993, Genova. 
J.Meyer-Roux and A.Burrill, 1993. Remote Sensing and Geographic Information 
Systems Within the MARS Project, Geodetical Info Magazine, Vol. 7, no. 1, pp. 65-
69. 
A.Burrill, 1992. An AML-Driven Crop State Monitoring System, Proceedings of 
the Twelfth Annual ESRI User Conference. 8-12 June, 1992, Palm Springs. 
Ph. Bierlaire, A.Burrill and P.Vossen, 1992. Outline of an Operational Crop State 
Monitoring System. Paper presented at the Third European Conference on 
Geographical Information Systems (EGIS *92), April, 1992, Munich. 
*H 
5. Collection 
Un système d'Information Agronomique pour la Communauté Européenne / An 
Agricultural Information System for the European Community: 
G. Russell, 1990. Barley Knowledge Base. JRC, EUR 13040 EN. 
E. Choisnel, O. de Villele, F. Lacroze, 1992. Une Approche Uniformisée du Calcul 
de l'Evapotranspiration Potentielle pour l'Ensemble des Pays de la Communauté 
Européenne. JRC, EUR 14223 FR. 
A. Carbonneau, R. Riou, D. Guyon, J. Riom, C. Schneider, 1992. 
Agrométéorologie de la Vigne en France. JRC, EUR 13911 FR. 
A. Falisse, 1992. Aspects Agrométéorologiques du Développement des Cultures 
dans le Benelux et les Régions Voisines. JRC, EUR 13910 FR. 
G. Narciso, P. Ragni, A. Venturi, 1992. Agrometeorological Aspects of Crops in 
Italy, Spain and Greece. JRC, EUR 14124 EN. 
R. Palm, P. Dagnelie:, 1993. Tendance Générale et Effet du Climat dans la 
Prévision des Rendements Agricoles des Différents Pays des CE. JRC, EUR 15106. 
J. Bignon, 1990. Agrométéorologie et Physiologie du Mais Grain dans la 
Communauté EuropéenneJ. RC, EUR 13041. 
M.N. Hough, 1990, Agrometeorological Apsects of Crops in the United Kingdom 
and Ireland. A Review of Sugar Beet, Oliseed Rape, Peas, Wheat, Barley, Oats, 
Potatoes, Apples and Pears. JRC, EUR 13039. 
E.G/Beek, 1991.Spatial Interpolation of Daily Meteorological Data. The Winand 
Staring Centre for Integrated Land, Soil and Water Research, 2 Volumes. 
M.N. Hough and J. Parker, 1992. The Estimation, for the EC, of Global Solar 
Radiation and Sunshine Hours from Cloud Cover for 1-Day and 10-Day Periods. 
Meteorological Office. 
G.HJ. de Koning, M.J.W. Jansen, E.R. Boons-Prins, CA. van Diepen, F.W.T. 
Penning de Vries, 1993. Crop Growth Simulation and Statistical Validation for 
Regional Yield Forecasting Across the European Community. Simulation Reports 
CABO-TT, no. 31. JRC-CABO-DLO. 
E.Ri. Boons-Prins, G.H.J, de Koning, A. van Diepen, Penning de Vries, 1993 
Crop-specific Simulation Parameters for Yield Forecasting Across the European 
Community. Simulation Reports CABO-TT, no. 32. JRC-CABO-DLO, 250 pp. 
R. Palm, P. Dagnelie, 1993. Tendance Générale et Effects du Climat dans la 
Prévision des Rendements Agricoles des Différents Pays des CE. JRC, EUR 15106. 
I. Supit, 1993. Optimizing the Uses of European Solar Radiation Network. JRC-
EUR report. 
S^s 
6. Action 4 
M. Sharman, 1990. Visual Image Interpretation of High Resolution Imagery for 
Real Time Agricultural Monitoring in Europe. ISPRS Symposium, Victoria B.C., 
Sept. 17-21,1990. 
M. Sharman, H. de Boissezon, 1992. Action 4 - From image statistics: operational 
review after two years of rapid estimates of acreages and potential yields of 
major annual crops throughout the European Community. Proceedings of the 
European International Space Year Conference, Munich, 30th March - 4th April, 
1992 ESA Publication ISY1. 
M. Sharman, J. Meyer-Roux, 1991. The Use of High Resolution Imagery in an 
Advanced Agriculture Information System. 5ème Colloque International Masures 
Physiques et Signatures en Télédétection, Courchevel (France) 14-18/01/1991. 
SOTEMA, 1992. Estimation Rapides au Niveau Européenne .des Superficies et des 
Rendements Potentiels. Rapport final Phase 2. Centre Common de Recherche et 
SOTEMA, Toulouse, 130 pp. 
SOTEMA, 1933. Estimations Rapides au Niveau Européenne des Superficies et des 
Rendements Potentiels. Rapport final Phase 3. Centre Commun de Recherche et 
SOTEMA, Toulouse, 146 pp. 
SOTEMA, 1933. Rapid Estimates of Crop Acreages and Potential yields at 
•European Scale. Document de Synthèse. Centre Commun de Recherche et 
SOTEMA, Toulouse, 11 pp. 
7. Action 6 
V. Perdigao, J. Delincé, J.P. Cantou, V. Rodriguez, 1990. Utilisation des Images 
Numériques pour l'Organisation des Enquêtes Agricoles - Production dun 
Nouveau Type de Documents. In: Proc. of Symposium International de 
Cartographie Thématique Dérivée des Images Satellitaires, Saint-Mandé (France), 
1990, Comité Français de Cartographe Bull. 127-128. 
V. Perdigao, et al., 1991. Synthetic Cartographie Documents Based on High 
Resolution Satellite Imagery. In: Proc. 11th EARSeL Symposium, Graz, 1991, pp. 
406-417. 
V. Perdigao, 1991. Ground Survey Documents Based on High Resolution Satellite 
Imagery -1990 Campaign. JRC, S.P.I. 91.29. 
8. Support to DG XI EEA-FF 
V. Perdigao, A. Annoni, 1993. Integration of Remote Sensing and G.I.S. to Update 
a Multisources Land Cover Database. In: Proc. of Int. Symp. Operationalization of 
Remote Sensing, Vol. 6, pp. 53-60, Enschede. 
^6 
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