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Special UNISPACE III volume
Marsteller, Deborah

International Archives of Photograminetry and Remote Sensing. Vol. XXXII Part 7C2, UNISPACE III, Vienna, 1999
“Resource Mapping from Space”
9:00 am -12:00 pm, 22 July 1999, VIC Room B
Vienna, Austria
The performance of this approach in crop area assessment
proved to meet the strict requirements (Figs, l.a.b.) both for the
validation period (1991-96) and in the operational one. These
two comprises an 7 years, 33 cases (county/year) sample. The
strong relationship in the Landsat TM derived (FOMI RSC) and
Central Statistical Office, Hungary (CSOH) data for the major
crops proved, that this method was independent from the given
year or the area, the different terrain and complexity of the
2.2. Crop monitoring and yield estimation methods
The novel result of HARSP are the purely remote sensing crop
monitoring and yield forecast models. The models were
developed by FOMI RSC. They integrated NOAA AVHRR and
high-resolution satellite data (e.g. Landsat, IRS-1C/D, SPOT).
The models combine the benefits of both data sources: the
frequency of NOAA AVHRR data and spatial resolution of
high resolution images. This approach requires fairly accurate
crop maps. With the adaptation and improvement of a linear
unmixing model (Puyou Lascassies et. al., 1994) to NOAA
AVHRR series a crop development assessment and quantitative
yield forecast model was developed. The model was calibrated
at the spatial units level of 400-500 ha. That is the guarantee for
its good performance at the counties level (approx. 0,5 million
hectare each, in Hungary) and further. That is also why it can
produce a crop yield distribution map. The county wheat and
maize yields predicted by the model compared favourably to the
official data (Figs. 2.a.b.) both in the pre-validation period
(1991-96) and in the operational one (1997-99) as well. The
structure of the model is similar for different crops and it does
not depend on the area and the given year’s whether. It was also
found that the timeliness requirement can be met by the yield
forecast model.
The substantial R+D and validation created a firm basis to move
forward to an operational program: Crop Monitoring and
Production Forecast Program (CROPMON 1997-1999). The
crop data-reporting calendar was set by the customer, the
Ministry of Agriculture and Regional Development.
It consists of five dates from June 15 to October 1 in harmony
with the existing traditional production forecast system of
MoARD. The area covered directly have been a characteristic
subsample (6-9) of all the counties (19), so that 40-57 % of the
total cropland in Hungary have directly been monitored.
Beyond the crop area assessment and yield prediction for the
counties, these data are expanded to the entire area of Hungary.
This expansion uses a subregional temporal correlation analysis
plus a direct robust method (see 4.). The eight main crops
monitored are 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
The crops area assessment is based on the quantitative analysis
of multitemporal high resolution images (Landsat TM and IRS-
1C/1D LISS HI.) from early May through August, to
compensate for the cloudiness. The comparison of the remote
sensing results with CSOH data is obviously an indication only.
The differences cannot be interpreted, by any means, as 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) is in the
range of 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 MoARD
The crop yield forecast was accomplished by the application of
FOMI RSC developed model which combines high-resolution
satellite (Landsat TM and IRS-1C/1D LISS III. or SPOT) data
and NOAA AVHRR time series. The reporting dates
corresponded to those of the operative Production Forecast
System of the Ministry of Agriculture and Regional
Development. 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 them to Hungary using a regional-historical
correlation scheme. The country average yield data compare
favourably with CSOH preliminary values, that appear six
weeks later (Fig.5.b.). The differences are less than 1 % for
wheat and 4.5 % for maize average yields in Hungary. The
differences at county level averages are somewhat bigger.
Because of the method applied, yield spatial distribution maps
could also be reported (Fig.4.) for the major crops.
The primary yield forecast model (see 3.) performed well. There
were two reasons to develop a robust yield forecast model:
the need for a parallel, independent technique to control the
primary yield forecast model extrapolation (see 3.) from
the average yields of the directly monitored counties to the
entire cropland in Hungary and
the need for a stand-alone method that uses only very basic
land use information: the boundary of cropland in the
country. This was readily available from the CORINE
Land Cover data base in Hungary (Biittner et. al., 1995).
Beyond the land use NOAA AVHRR data series make up
the basis of the model. The model is directly applied to all
the individual counties and also for the national crop
production forecast.
The pre-processed and normalised NOAA AVHRR data set is
temporally filtered. The average reflectance profile and the
NDVI can be decomposed in time by a thorough spectral-
temporal correlation analysis. This substantial analysis shows
an extremely strong relationship between the predicted county
yields by this decomposition method and the CSOH data (Figs.