SC
CSOH
4. ROBUST YIELD PREDICTION BY
NOAA AVHRR SERIES
The primary yield forecast model (see 3.) performed well. There
were two reasons to develop 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 (e.g. CORINE Land Cover data base -
Büttner et. al., 1995) beyond the NOAA AVHRR series
and directly be applied to all the individual counties and
also for national crop production forecast.
The pre-processed and normalised NOAA AVHRR data set was
temporally filtered. The average reflectance profile and the
NDVI could be decomposed in time by a thorough spectral-
temporal correlation analysis. This substantial analysis showed
an extremely strong relationship between the predicted county
yields by this decomposition method and the CSOH data (Figs.
6.a.-d.). The county data set comprises a 5 years period in which
the low and high ends of yields occurred. The model seems to
be strong, independent from the year and area. Some hilly,
mountainous counties or those that were covered very sparsely
by the given crop had to be omitted from the analysis. Having
the performance of this model by county (r’=0,85-0,96) the
country level yield prediction seems to be very reliable (r^ =
0,93-0,99). These preliminary results suggest that a reliable
yield prediction model can be set up.
5. CONCLUSION
Both the validation of the developed remote sensing based crop
area assessment and yield forecast methods plus the first
operational monitoring and crop production forecast campaign
(1997) in Hungary clearly demonstrated that these methods can
be efficiently applied. Substantial background and investment is
certainly needed. About 300 man/year was invested by FOMI
RSC in the framework of the Hungarian Agricultural Remote
Sensing Program (1980 to date). The first operational
monitoring was designed very strictly by the Ministry of
Agriculture, Hungary, according to its existing operational
production forecast and monitoring system.
Remote sensing could be very efficiently used for precise crop
area estimation and provision of crop maps. The results suggest
that the necessary classification performance can be obtained in
most of the cases, therefore the analysis could be cost effective.
The investment to achieve this seems to be worthwhile.
The new combined AVHRR and Landsat TM or IRS-1C LISS-
III. or SPOT based crop monitoring and yield prediction models
and the approach performed properly and efficiently in a more
counties' area application and also for the entire country. The
second, the county level AVHRR based crop yield prediction
model worked very well and seems to have a real potential on
areas, having quite different cropping pattern.
After the first year, further assessment and gradual extension of
remote sensing into the information system of the Ministry of
Agriculture is under way. Together with the gradual expansion
of the direct target area from 6 counties to the whole country
more and even earlier reporting dates are planned. This system
- 1s supposed to operate parallel to the existing dynamic system of
MoA for monitoring area and crop development, plus yields of
the most important crops in Hungary.
6. ACKNOWLEDGEMENT
The whole HARSP (1980-) and in particular the recent NCMP
(1993-96) have been supported jointly by the National
Commuttee for Technological Development and the Ministry of
Agnculture, Hungary. Formerly, the Hungarian Academy of
Sciences, since 1992 the Hungarian Space Organisation have
also given both financial and scientific support to the program.
The major operational crop monitoring and production forecast
program from 1997, on is being supported by the Ministry of
Agriculture.
SAI, EC Joint Research Centre (Ispra) generously supported a
natural vegetation monitoring study by pre-processed NOAA
data for 1991-95.
REFERENCES
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International Archives of Photogrammetry and Remote Sensing. Vol. XXXII, Part 7, Budapest, 1998 113
