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2.2. Crop survey, area assessment and their pre-operational 
validation 
The method that had been developed by FÓMI RSC, used 
Landsat data and applied digital image analysis for the crop 
identification and area estimation (Csornai et al., 1983). This 
approach gradually expanded to 3 counties areas by 1990 
(Csornai et. al., 1990). It was found that the provision of really 
more accurate county level data than those, that had been 
provided by the traditional non-remote sensing systems in 
Hungary, was only viable through advanced digital image 
analysis based crop area assessment. This approach also 
provides reliable crop maps, which are necessary to the crop 
development monitoring models. : 
As a result of the major final validation survey in NCMP (1993- 
96) it was clearly found (Csornai et. al, 1997) that the 
application and results of digital image analysis compares well 
with the data of the Central Statistical Office, Hungary (CSOH) 
for a 5 years, 6 counties data set (Figs.l.a.b.) The strong 
relationship in the Landsat TM derived (FÓMI RSC) and CSOH 
data for the major crops is promising to the further applications 
of satellite data in the inventories. 
2.3. Crop monitoring and yield estimation 
The most promising results of the NCMP are those related to 
the crop monitoring and yield forecast models. The models were 
developed by FÓMI RSC. They integrated NOAA AVHRR and 
Landsat or other high-resolution satellite data. This approach 
essentially combines the benefits of both data sources: the 
temporal resolution through NOAA AVHRR and spatial 
resolution by Landsat TM or other high resolution images (e.g. 
IRS-1C, SPOT) This approach requires fairly good 
classification for the performance with the high-resolution 
images. With the adaptation of a linear unmixing model (Puyou 
Lascassies et. al., 1994) to NOAA AVHRR series and Landsat 
TM, fairly good results were achieved for the two major crops - 
wheat and maize- for the same study area and period. The first 
results concerning the drought indication within the monitoring 
are good. The county wheat and maizc yields predicted by the 
model compared favourably to the official data (Figs.2.a.b.). It 
was also found that the timeliness requirement can be met by 
the yield forecast model. 
Both the crops areas and the major crops development and 
yields were estimated by remote sensing methods. This 
validation provided a firm basis for the first operative crop 
monitoring campaign in 1997. 
3. OPERATIONAL CROP AREA ASSESSMENT 
AND YIELD FORECAST IN 1997 
The thorough previous validation created a firm basis to move 
-forward an operational campaign in 1997. The crop data- 
reporting calendar was set by the customer, the Ministry of 
Agriculture. 
It consisted of five dates from June 30 to October 1. The area 
covered directly was a characteristic subsample (6) of all the 
counties (19), so that 40 % of the total cropland in Hungary was 
directly monitored. Beyond the counties level crop area and 
predicted yield data these had to be expanded to the entire area 
of Hungary. This expansion used a subregional temporal 
correlation analysis plus a direct robust method (see 4.). The 
eight main crops monitored were winter wheat, winter and 
spring barley, maize, sugar beet, sunflower, alfalfa and maize to 
ensilage. These crops together represent the 78-82 % of the 
entire Hungarian cropland. 
The crops area assessment was based on the multitemporal 
image analysis of Landsat TM and IRS-1C LISS III. data from 
the early May-August period, to compensate for the cloudiness 
in 1997. Cloud cover was some 30 % bigger than the average in 
the 1991-96 period. The comparison of the remote sensing 
results with CSOH data is obviously an indication only and the 
differences cannot, by any means be interpreted as the errors of 
the remote sensing technology. A thorough study is under way 
that will produce confidence values attached to the area 
estimates. The difference of crop areas estimates of FOMI RSC 
and the Central Statistical Office, Hungary (CSOH) ranged in 
the 0.8-3.7 % (Fig.5.a.) for the entire cropland in Hungary. The 
county crop area differences occurred in the interval of 1.5-21 
% depending on the crop and county. However the area 
weighted average difference was 4.08 %. 
This partially can be explained by the main differences in 
definitions, that is the ownership based sampling of CSOH and 
the administrative, topographic boundary based total coverage 
of cropland by the satellite images (FOMI RSC). The actual 
standard crop maps derived were also provided to MoA (Fig.3.). 
The crop yield forecast was accomplished by the application of 
FOMI RSC developed model which combines high-resolution 
satellite (Landsat TM and IRS-1C LISS III.) data and NOAA 
AVHRR time series. The reporting dates corresponded to those 
of the operative Production Forecast System of the Ministry of 
Agriculture. Both appeared prior to the beginning of harvest. 
The final official data are available after the harvest: by the end 
of August for wheat and barley and in December (January) for 
the rest. FOMI RSC provided yield estimates for the counties 
and expanded to Hungary. The yield data compared favourably 
with CSOH values, appeared six weeks later (Fig.5.b.). The 
differences were less than 1 % for wheat and 4.5 % for maize 
average yields in Hungary. The differences at county level 
averages are certainly bigger. Because of the method applied, 
yield spatial distribution maps could also be reported (Fig.4.) 
for the major crops. 
Intemational Archives of Photogrammetry and Remote Sensing. Vol. XXXII, Part 7, Budapest, 1998 109