Number range CHAPTERUtilization of Remotely Sensed Data and Geographic
Information Systems (GIS) for Agricultural Statistics in the United
States and the European Union

**By**

George Hanuschak

Director, Research and Development Division, United States Department of
Agriculture (USDA), National Agricultural Statistics Service (NASS), USA

**And**

**Jacques Delincé**

**Unit Head, AGRIFISH Unit, MARS Project, Joint Research Center (JRC),
European Commission.**

**Abstract:** The paper will cover the activities in the United States
and the European Union with regard to the utilization of remotely sensed
data for input to official agricultural statistics and for crop
condition and production forecasting, estimation and mapping. Both the
U.S. and the EU programs in this topic area are longstanding programs
and recent developments since the International Conference on
Agricultural and Environmental Statistical Applications in Rome, Italy
in 2001. Among the developments in the U.S. are research on the use of
administrative data from a GIS to supplement conventional statistical
data, research on the use of very high-resolution imagery for citrus
tree counting purposes, and the expansion of a crop mapping program to
more States with resource leveraging partnerships. Among the
developments in the EU are the expansion of the Monitoring Agriculture
with Remote Sensing (MARS) project into more countries, the continual
improvements in their data dissemination bulletin and their use of the
French SPOT/vegetation sensor data for crop condition monitoring. Both
programs are also evaluating the cost/benefits of their outputs to the
statistical agencies and the general public to improve the relevance,
timeliness, accuracy and cost effectiveness of their outputs and
programs.

Keywords: Remotely sensed data, geographic information systems, crop
production estimation, cropland data layer

1\. USDA NASS experience with acreage estimation and cropland data layer
public use GIS files and maps

Since the launch of Landsat I in 1972, NASS staff have been involved in
information extraction techniques for agricultural monitoring and
statistics purposes. NASS has used Landsat as an operational input to
building new area sampling frames since 1976. NASS staff chose to
develop a regression estimator for crop area estimates with Landsat as
the auxiliary variable and ground gathered data from an area sampling
frame as the primary variable \[Wigton, 1982\]. This approach has served
NASS well as an input to State, district and county levels, but it is
only available at the end of the crop season. NASS considers these
inputs along with its traditional area sample based and multiple frame
sample based crop area survey indicators. The regression estimator is
currently available as an input in eight major crop producing states.
However, in the last several years, the development of statewide crop
specific classification, called the cropland data layer, of Landsat
pixels released to the public. An example of the cropland data layer and
crop rotation across years is provided in Figure 1. It is released to
the public as a geographic information system data layer and has become
quite popular with a variety of data users in the agricultural sector of
the economy. Some of the data uses are: watershed monitoring, crop
rotation mapping and analysis, agribusiness uses such as planning a site
for a new plant, soil and crop interaction analysis, deep stratification
for area sampling frames, water quality monitoring, animal habitat
assessments, etc. The program has grown over the last decade by
leveraging resources among other federal and state government agencies
and universities \[Craig, 2001\].

Figure 1 -- Cropland Data Layer with Crop Rotation Across
Years![](media/image1.png){width="2.8055555555555554in"
height="1.8465277777777778in"}![](media/image2.png){width="2.8055555555555554in"
height="1.8465277777777778in"}![](media/image3.png){width="2.7909722222222224in"
height="1.9090277777777778in"}

There are new concerns about the Landsat data though as Landsat 5 is now
18 years old and Landsat 7 is not applicable for wall-to-wall projects,
such as the cropland data layer. The Landsat Data Continuity mission
schedule is slipping and there is a strong possibility of a rather long
(several years) data gap from Landsat. Therefore alternative sensors and
systems will be evaluated, such as French SPOT, Indian LISS and AWIFS,
and German RapidEye for the cropland data layer over the next few years.

# 

# 2. USDA NASS experience with wide area crop condition (vegetative index) mapping

The history and experience of USDA NASS staff with wide area (national)
crop condition monitoring is shorter but still extensive. NASS partnered
with the USDA Agricultural Research Service (ARS) starting in 1990 with
the goal of wide area crop condition monitoring using AVHRR data. After
several years of research and software development, NASS began putting
out an operational national crop condition (vegetative index) mapping
program in 1994. When both the current period and any historic period of
comparison are relatively free from serious atmospheric interference,
the images are quite informative on vegetative condition. However, when
there is serious atmospheric interference, the images are less valuable
and users must view the metadata cautions about these occurrences and
how to recognize them. Just viewing the images with atmospheric
interference without recognizing that, can cause problems with map
interpretation.

# 3. USDA NASS experience with crop yield modeling using remote sensing as an input

The history of the crop yield modeling research program coincides with
the crop condition mapping program. NASS partnered with USDA ARS to
develop and test crop yield models that used weather data, soils data,
crop specific categorized Landsat data and AVHRR or MODIS data as
inputs. The results of this research program have been mixed. At the
state level, NASS' conventional yield program that uses farmer reported
yields and objective yield inputs, such as fruit counts and weights, are
the primary inputs to the state level yield forecasting and estimation.
However, through a new project emphasis and joint partnership with ARS
and NASA, NASS will explore a new small area estimation approach, which
seems promising at this stage. The approach is to take the official NASS
State crop forecast or estimate for yield and to dis-aggregate it to the
Agricultural Statistics District level (about 10 counties) and then
further to the county level. This combines the strengths of the
operational NASS yield program (National and State probability
surveys--objective and farmer reported) and the geospatial distribution
strengths of the remotely sensed data, weather data and soils data
\[Doraiswamy, 2003\].

**4. USDA NASS experience with the utilization of geographic information
systems**

USDA NASS research staff began using geographic information systems
around 1990 as well. The original applications were area sampling frame
construction and geospatial data displays of NASS data at the county
level. Those applications went quite well and have now expanded to the
Census of Agriculture Agricultural Atlas (of different types of maps),
visualization of a crop season using survey data \[Wade, 1997\],
remotely sensed data and weekly agricultural expert opinion at the
county level, the beginning of fruit frames as GIS based sampling
frames, displays of the Cropland Data Layer and AVHRR national
vegetative index maps and the beginning of using administrative data
from the USDA Farm Service Agency in a GIS to complement NASS data
collection and alleviating some response burden. GIS applications in
NASS are still growing and some need to transfer from applied research
to operational. An example from the Agricultural Atlas follows in Figure
2. NASS remote sensing and GIS activities and products can be viewed at
<http://www.nass.usda.gov/research>.

![](media/image4.png){width="5.707638888888889in"
height="4.311805555555556in"}

**Figure 2 -- Agricultural Atlas**

**5. USDA NASS experience with citrus tree counting using very high
resolution satellite imagery**

In the summer of 2002, NASS research staff were approached by the
Florida Department of Citrus (FDOC) to evaluate the use of very high
resolution satellite imagery, QuickBird, for citrus tree counting. An
example of a QuickBird in a Florida citrus grove follows in Figure 3.
NASS conducted a small pilot level project in Florida to determine the
technical feasibility and reliability of tree counting from very high
resolution imagery. The pilot had mixed results \[Mueller, 2003\].
Mature citrus blocks without substantial disease were the easiest to get
accurate tree counts from. However, mixed blocks of some mature and some
replants and some diseased trees in a block caused accuracy issues with
any of the manual or automated techniques tried to date. There are two
promising automated techniques or algorithms. The first is software from
the University of Singapore called CRISP. The second is software called
OLICOUNT and POLYCOUNT from the European Union MARS project. This is a
lead in to the second portion of this paper, which gives an update of
activities in the European Union.

![](media/image5.png){width="2.0256944444444445in"
height="4.638888888888889in"}![](media/image6.png){width="1.8840277777777779in"
height="4.629861111111111in"}![](media/image7.png){width="1.99375in"
height="4.649305555555555in"}

## Figure 3 -- QuickBird in a Florida Citrus Grove

**6. The EU uses of GIS and Remote Sensing in Agricultural Statistics**

Considering that the production of official agricultural statistics in
the EU25 is steered by EU regulations (Reg. n°837/1990, 959/1993) but
remains a national competence and official responsibility, several past
and current applications can be mentioned on the utilization of remote
sensing and GIS for improvement of the agricultural statistics:

-   At EU level, the JRC MARS project started, in 1988, different RS
    activities relating to the production of crop area regional
    statistics \[Taylor, 1997\], to the timely estimation of EU level
    crop acreages \[Genovese and Meyer-Roux, 1998\], and to the use of
    agro meteorological models for yield estimation \[Genovese, 1998\].
    This activity is on going and has even been extended to regions
    outside the European Union \[Nègre, 2003\].

-   Through the TAPAS program and the Land Use land cover activities,
    EUROSTAT initiated in house studies and financed programs in Finland
    \[Tuikkanen, 2003; EC, 2001; Duhamel, 1996\].

-   At Member States level, operational projects use remotely sensed
    data and GIS information for the production of official statistics.
    The AGRIT project in Italy \[Greco, 2001\] uses several complete
    coverages of Landsat imagery yearly. The Fömi activities in Hungary
    produce county, regional, plus national acreages and yields
    estimates based on Landsat, IRS, SPOT and NOAA AVHRR data \[Csornai,
    2003\]. In Belgium, the SAGRIWATEL project aims at the integration
    of agricultural control and statistical activities integrating
    modern techniques like remote sensing, GIS and crop growth modeling
    \[Tychon, 2003\]

-   The CORINE Land Cover mapping project \[EEA, 2001\], lead by the
    European Environmental Agency, offers a complete updated mapping of
    the EU25 at a 100.000 scale which, including the LANDSAT Image 2000
    coverage, offers useful tools for survey stratification or landscape
    characterization.

-   More recently the European Commission and the European Space Agency
    joined their effort on the GMES (Global Monitoring for Environment
    and Security) initiative. Of particular interest is the GMFS (Global
    Monitoring for Food Security) project whose objective is to favor
    access to the RS information needed in Africa for timely monitoring
    of crop productions \[GMFS, 2003\].

**7. JRC/MARS monthly European Yield Forecasts**

The knowledge in real time of crop conditions in the 25 EU Member States
is essential to DG Agriculture for the agricultural market management.
In complement to the information collected from Member States through
EUROSTAT, the MARS project has been entrusted to produce periodical
bulletins describing the current crop growing conditions and forecasting
the yields of the major European crops.

On the base of archive and real time satellite data (SPOT-Vegetation and
NOAA-AVHRR) and meteo data (30 daily parameters on 1500 stations), a
crop growth model (derived from the WOFOST model) is run daily and
results in four outputs:

-   Updates of the MARSOP website (http\\\\marsop.info), where
    pre-processed products can be freely accessed and where authorized
    users can obtain dedicated products;

-   Bi-weekly bulletins sent to DG Agriculture, commenting on the
    weather conditions as well as short term meteo forecasts;

-   Monthly input to the EUROSTAT AGROMET letter sent to Member States
    to support them in the elaboration of the national early production
    estimates that they provide monthly to EUROSTAT; and

-   Periodical bulletins (7 per year) presenting the expected yields at
    EU and Member states level.

This system allowed, in August 2003, a real time and independent
quantification of the influence on the crop yields of the extreme hot
and dry conditions faced in Europe and supported the EU decision-making
process in order to minimize the effects of the crisis.

**8. The European Area Estimates: LUCAS and associated research**

The early estimation of crop acreages is a more difficult exercise than
the yield prediction. To be useful, estimates have to be obtained early
(first data in February, thus 3-4 months before harvest), have to be
precise (for a fixed and stable potential area, changes are limited to
weather constraints and economical conditions) and should ideally be low
cost.

The actual EU strategy to get EU wide crop acreages consists of the
LUCAS survey \[Delincé, 2001\]. Realized every two years in EU15 since
2001, the survey is a point area frame survey composed of 100.000
systematic sample points clustered in 10.000 Primary Sampling Units.
Under EUROSTAT supervision, the June-July field survey usually provides
aggregated results by end of July.

The possible enhancements of the LUCAS survey are mainly:

-   The date of first availability of results: the LUCAS data are
    available on all MS by end of July (roughly at the date of harvest).
    Anyway, more early information is needed for market management.

-   The estimate's precision, as the observed variances are still too
    large.

-   The nomenclature (including definitions) has still to be worked so
    that inter annual evolution do not confound with methodological
    changes and so that the LUCAS limited nomenclature fits with the
    national ones.

In that spirit, the MARS project tested the integration of IACS data and
MODIS satellite data with the LUCAS 2001 survey \[ITA, 2002\].

-   Using GIS possibilities, the integration of administrative data from
    farmer declarations with LUCAS survey was tried in Italy on 2001
    data. Using a regression estimator approach, the 9,250 LUCAS
    sampling points were linked with the IACS (Integrated Administrative
    and Control System, Reg. 1782/2003) data through the cadastral
    references. This allowed the calculation at PSU level of the
    correlation between the LUCAS and the IACS land covers.

> Table 1 -- Comparison between the LUCAS estimate (systematic sampling)
> and the regression estimator, using the IACS declarations as auxiliary
> variable (case of Italy)

  ------------- --------- -------- ------ -------------- ------- ------ --------------- --------------- -------
                **\[a\]                   **\[b\]                       **Rel. Eff.     **Diff.         
                LUCAS**                   Regression**                  \[b\]/\[a\]**   \[b\]-\[a\]**   

                Sup (ha)  s.e.     Cv (%) Sup (ha)       s.e.    Cv (%)                 Sup(ha)         \%
                          (ha)                           (ha)                                           

  Common wheat  603558    55489    9.2    629137         45543   7.2    1.48            25579           4.24

  Durum Wheat   1541286   104388   6.8    1566513        79554   5.1    1.72            25227           1.64

  Maize         1501052   99291    6.6    1566717        53744   3.4    3.41            65664           4.38

  Rice          208554    55316    26.5   215136         15212   7.1    13.22           6582            3.16

  Sugar beet    202493    37536    18.5   216208         25747   11.9   2.13            13715           6.84

  Sunflower     205345    32307    15.7   218155         23709   10.9   1.86            12810           6.24

  Soya          192511    35229    18.3   265926         22953   8.6    2.36            73415           38.20
  ------------- --------- -------- ------ -------------- ------- ------ --------------- --------------- -------

> The use of administrative data as an auxiliary variable generally
> improves the accuracy of the estimates, at times with significant
> increments in efficiency (ex. Maize). On the whole, the variation
> coefficient for the cereals aggregate is reduced from 3.55 to 2.77.
>
> But the difference in the surface area estimates cannot be
> disregarded: the use of regression always leads to a systematic
> increment in the estimate. It is highly probable that the declarations
> "lost" on a sampling unit level due to errors in the codification of
> the cadastral parcel identification explain the discrepancy. In
> conclusion, even if the administrative data potentially constitute a
> useful source of auxiliary information for improving the efficiency of
> the LUCAS survey, the analysis of the Italian data showed that a
> reliable and complete geo-referencing system is a condition for
> operational use.

-   Medium resolution satellite data (MODIS or MERIS) present interest
    to get crop acreage statistics early in the season. The limitations
    faced with high resolution SPOT or LANDSAT satellite (high costs,
    low probability of availability due to cloud coverage, limited
    radiometric information) could be overcome due to a daily coverage
    and to an increased number of channels (15 for MERIS). In that
    context, a study was done on two Italian regions (Marche and Sicily)
    on November 2000 and April 2001 MODIS images. In the Marche region,
    the estimated acreages of winter cereals on the MODIS data amounted
    to 210.079 ha, in good agreement with the 218.587 ha estimated in
    the AGRIT 2001 Project. Further analysis of the quality of the
    classification was performed, by comparing the MODIS classification
    results with LANDSAT classification results at MODIS pixel level
    (1km²)

> Figure 4 -- Relation between the multi-temporal MODIS and LANDSAT
> image classifications of winter cereals in the Marches region in 2001.
>
> ![](media/image8.wmf){width="5.1930555555555555in"
> height="3.9055555555555554in"}

**9. MARS tree counting software: POLYCOUNT**

During the 1998 OLISTAT survey (Estimation of number and areas of Olives
trees at Member States level), the "OLICOUNT" software was developed in
order to automatically count, locate and measure areas of olive
trees/groves. In the context of the Olives GIS (Reg. 2366/1998), all
Member States adapted and adopted the MARS/JRC algorithm, allowing a
uniform definition of these parameters in the EU administrative data.
More recently, studies were conducted in order to open the software to
multi-spectral digital data (QuickBird, ADS40) and to cover
classification of other species (citrus, nuts).

**10. JRC/MARS activities in Food Security**

MARS-FOOD's main focus is to develop and operate improved methods for
crop forecasting in regions outside of Europe, in particular in regions
stricken by recurrent food shortages. Global data (satellite, meteo) are
received daily and ten-daily. Global meteorological and
agrometeorological data are derived from the ECMWF (European Centre for
medium-Range Weather Forecast) global model, at one-degree spatial
resolution. Global ten-day NDVI satellite syntheses from the
SPOT-Vegetation instrument are used too. Different agrometeorological
models are used according to the geographical area. A modified CGMS
model is used for Russia, Central Asia and the Mediterranean Basin. For
the rest of the world, a more simple approach based on the FAO Crop
Specific Water Balance is being put into place. Dry matter productivity
is also calculated on a ten-day basis using a Monteith model approach
with radiation derived from the ECMWF and interception efficiency from
the SPOT-Vegetation data. Based on these methods, analysis is done on a
regular basis and in real-time at regional level on Russia and Central
Asia, the Mediterranean Basin, the Horn of Africa and at national level
for Somalia. Prospects are to soon extend the bulletin production also
to South America and to Sudan. Close collaboration exists with FAO, both
on scientific and organizational matters.

As for MARS-STAT, links with the national services are strong. A
particular effort has been done, jointly with FAO, to develop regional
(sub-continental), thematic networks on crop monitoring for food
security. Two networks were initiated for South-America (November 2002)
and Eastern Africa (January 2003), with the organization of two
dedicated workshops in Cordoba (Argentina) and Nairobi (Kenya). Access
to information is opened to national services interested, either for
bulletins such as Table 2 (at
<http://agrifish.jrc.it/marsfood/Bulletins/>) or for real-time maps (at
[*http://www.marsop.info*](http://www.marsop.info/)).

Table 2 - 2004 Winter Wheat Yield expectation in Russia and Central Asia

(from Mars-Food bulletin end-June 2004)

+---------------+--------------+---------+----------------------------+
| ***Country*** | ***Comparing |         | ***Winter Wheat Yield      |
|               | with         |         | 2004***                    |
|               | previous     |         |                            |
|               | year***      |         | ***expectation***          |
|               |              |         |                            |
|               |              |         | ***(t/ha)***               |
+---------------+--------------+---------+----------------------------+
|               | ***Me        | **      |                            |
|               | teorological | *Winter |                            |
|               | conditions   | crop    |                            |
|               | during       | status  |                            |
|               | May-June***  | at the  |                            |
|               |              | end of  |                            |
|               |              | June*** |                            |
+---------------+--------------+---------+----------------------------+
| Russia        | **+**        | **+**   | **2,2-2,5**                |
+---------------+--------------+---------+----------------------------+
| Armenia       | **+**        | **+**   | **2,3-2,7**                |
+---------------+--------------+---------+----------------------------+
| Azerbaijan    | **-**        | **=**   | **2,2-2,5**                |
+---------------+--------------+---------+----------------------------+
| Georgia       | **+**        | **+**   | **2,0-2,3**                |
+---------------+--------------+---------+----------------------------+
| Kazakhstan    | **-**        | **-**   | **0,8-1,2**                |
+---------------+--------------+---------+----------------------------+
| Kyrgyzstan    | **-**        | **-**   | **2,1-2,3**                |
+---------------+--------------+---------+----------------------------+
| Tajikistan    | **-**        | **-**   | **1,5-1,8**                |
+---------------+--------------+---------+----------------------------+
| Turkmenistan  | **-**        | **-**   | **2,6-2,8**                |
+---------------+--------------+---------+----------------------------+
| Uzbekistan    | **-**        | **-**   | **2,7-3,0**                |
+---------------+--------------+---------+----------------------------+
| Afghanistan   | **-**        | **-**   | **n/d**                    |
+---------------+--------------+---------+----------------------------+
| Iraq          | **=**        | **=**   | **0,7-0,9**                |
+---------------+--------------+---------+----------------------------+
| Iran          | **+**        | **=**   | **1,8-2,0**                |
+---------------+--------------+---------+----------------------------+
| Kuwait        | **=**        | **=**   | **2,3-2,5**                |
+---------------+--------------+---------+----------------------------+
| Northern      | **-**        | **=**   | **n/d**                    |
| India         |              |         |                            |
+---------------+--------------+---------+----------------------------+
| Northern      | **=**        | **=**   | **n/d**                    |
| Nepal         |              |         |                            |
+---------------+--------------+---------+----------------------------+
| Northern      | **=**        | **=**   | **n/d**                    |
| Pakistan      |              |         |                            |
+---------------+--------------+---------+----------------------------+
| Western China | **-**        | **=**   | **n/d**                    |
+---------------+--------------+---------+----------------------------+
| *+ indicates  |              |         |                            |
| figures which |              |         |                            |
| are higher    |              |         |                            |
| than          |              |         |                            |
| normal, -     |              |         |                            |
| indicates     |              |         |                            |
| figures which |              |         |                            |
| are lower     |              |         |                            |
| than normal,  |              |         |                            |
| and =         |              |         |                            |
| indicates     |              |         |                            |
| figures,      |              |         |                            |
| which are     |              |         |                            |
| close to      |              |         |                            |
| normal.*      |              |         |                            |
+---------------+--------------+---------+----------------------------+

Bibliography

Craig, M. (2001), "A Resource Sharing Approach to Crop Identification
and Estimation," 2001 ASPRS/ACSM Annual Convention Technical Papers,
available on CD, Proceedings of the ASPRS 2001 Conference, Bethesda, MD
-- USA.

Doraiswamy, P. (2002), "Remote Sensing Applications in Agrometeorology,"
CagM-XIII Open Forum Sessions, Ljubljana, Slovenia.

Mueller, R., Boryan, C., Craig, M., Fleming, M. and Hanuschak, G.
(2003), "Pilot Research Project: Investigation of Very High Resolution
Spaceborne Imagery for Citrus Tree Counting," Report for FDOC Contract
No. 02-17.

Wade, L., Craig, M. and Hanuschak, G. (1997), "Using Geographical
Information Systems (GIS) For Dissemination of U.S. Agricultural
Statistics," Geographic Information Systems Work Session sponsored by
the United Nations Economic Commission for Europe (UN-ECE), Brighton,
United Kingdom.

Wigton, W., Allen, R. and Hanuschak, G. (1982), "Integration of Landsat
Data Into The Crop Estimation Program of USDA's Statistical Reporting
Service," Proceedings of the 1982 Machine Processing of Remotely Sensed
Data Symposium, Eighth International Symposium, LARS, Purdue University,
West Lafayette, IN -- USA, pp. 45-56.

Csornai G., Wirnhardt Cs., Suba Zs., Nador G., Martinovitch L., Tikasz
L., Lelkes M., Kocsis A., Zelei Gy. (2003) The Operational Crop
Monitoring and Production Forecast Program (CROPMON 1997) and others RS
based Applications. Geoinformation for European wide Integration.
Proceeding of the 22^nd^ Symposium of the European Association of
Rempote Sensing Laboratories, Prague, Czech Republic, 4-5 June 2002,
Millpress Rotterdam Ntherlands, p561-566.

Delincé J. (2001) A European Approach to Area Frame Surveys. In CAESAR,
vol II, pp463-472.

De Longueville F., Pekel J.F., Quintin L., Oger R., Tychon B., Defourny
P., Debouche C., Vanhalle L., Leteinturier B. (2004) Monitoring of the
Walloon Agriculture State with Remote Sensing -- SAGRIWATEL. Poster
Sesion at Workshop Stereo-Vegetation, 3^rd^ Belgian EO day, Brussels,
May 6 2004.

Duhamel C. (1996) Télédétection et Statistique. In : Statistique et
Géographie, SIGMA 1996, EUROSTAT CA-AB-96-002-FR-C, pp 29-33.

European Commission (2001) Manual of Concepts on land Cover and Land Use
Information Systems. Theme 5: Agriculture and fisheries, Collection:
Methods and Nomenclatures, EUROSTAT, Luxembourg, 106pp.

European Environment Agency (2001) Towards Agri Environmental
Indicators: Integrating Statistical and Administrative Data with Land
Cover Information. Topic Report 6/2001, Copenhagen, 131pp.

Genovese G. (1998) The methodology, the results and the evaluation of
the MARS crop yield forecasting system. In: Agrometeorological
Applications for Regional Crop Monitoring and Production Assesment.
Riks, Terres, Vossen Eds. EUR Publication 17735 EN, Ispra, Italy 100p.

Genovese G. Meyer-Roux J. (1998) Rapid Estimates of Area Changes of the
Main Crops at European Level, overview of the Methodology, Results and
Orientations. Special Issue pf GEO-Observateur, n°9, CRTS, ISSN
113-4410, pp91-99.

GMFS (2003) Global Monitoring for Food Security: Satellite data support
service for existing early warning Systems. ESA/VITO Brochure, 6pp.

Greco M. and Martino L (2001) The Agricultural Satistical System in
Italy. In CAESAR, vol II pp 451-462.

ITA (2002) ICARE: Integrated Crop Area Estimates, Final MARS/JRC Report,
71pp.

NEGRE T., REMBOLD F., ROJAS O. (2003)- Integrated agricultural
monitoring and yield forecasting for Eastern Africa: the JRC approach.
In: Proceedings International Workshop on Crop monitoring and Early
Warning for Food Security, 28-30 January 2003, Nairobi (Ke). EUR 20869
EN

Taylor C., Sannier C., Delincé J., Gallego F.J. (1997) Regional Crop
Inventories in Europe assisted by Remote Sensing: 1988-1993, Synthesis
report of the MARS Project-Action 1. JRC Technical report EUR 17319,
European Commission, Italy, 71pp.

Tuikkanen M. (2003) TAPAS report: Development and improvement of early
estimations of areas and production of most important crops in Finland.
EUROSTAT report, Luxembourg.