International Archives of Photogrammetry and Remote Sensing. Vol. XXXII Part 7C2, UN1SPACE III, Vienna, 1999 
55 
/”S32&\ 
I5PRS 
UNISPACE III - ISPRS Workshop on 
“Resource Mapping from Space” 
9:00 am -12:00 pm, 22 July 1999, VIC Room B 
Vienna, Austria 
ISPRS 
Presently the robust-type methods are ready to practical 
applications in service level. Our robust basic model uses only low 
resolution NOAA AVHRR data and in the calibration phase the 
known statistical (i.e. classical) yield data of counties and the 
country (or other regions); □ in Hungary the yield data of the 
Central Statistical Office. (It is necessary to remark here that 
nobody has any criteria to decide wether the classical regional data 
or the RS data are closer to the reality. The long time application 
Table 4 presents our results in the yield forecasting of com. 
Naturally, the effect of stresses which appear after the moment 
(DOY) of prediction cannot be incorporated into the forecasting. So 
the differences of the forecasted yields and the estimated yields in 
tire draughting years 1992 and 1993 (see Table 2 and Table 4 
together) characterise the yield loss generated by the drought after 
the moment of forecasting. 
The results presented in Fig. 7. confirm the wide applicability of the 
satellite remote sensing in yield forecasting and estimation. 
These results demonstrate the practical usefulness of satellite 
remote sensing methods in agricultural applications. This is an 
effective tool in agriculture and in its economic management from 
farmers to state administration. These methods are not only 
effective, but relatively simple and cheap, and therefore they are 
important for developing countries too. The application of these 
methods is an important step to stabilize the food supply and 
market, and after these to assist the increasing of yields. 
will produce the real probability of the new methods.) The results 
of this method in tire counties investigated with liigher resolution 
method too (see Table 1) are presented in Table 2. The results of 
this robust method are presented in the cases of six plants on Fig. 
6., presenting the county and countiy averages of yields. Table 3 
presents the results of yield-average determination of ten plants on 
countiy level in Hungary in the years 1991~93. 
REFERENCES 
Avenosav G.A. (1996): Fundamental problems of Earth research 
from space; Russian Space Bulletin, 3(4), 9-12. 
Ferencz Cs., Ferencz-Arkos I., Hamar D., Lichtenberger J. and 
Tarcsai Gy. (1987): Surface models including direct 
cross-radiation: a simple model of furrowed surfaces; Int. 
J. Remote Sensing, 8, 449-465 and 1850. 
Ferencz Cs., Tarcsai Gy. and Lichtenberger J. (1993): Correction of 
atmospheric effects of satellite remote sensing data 
(Landsat MSS - NOAA AVHRR) for surface canopy 
investigations; Int. J. Remote Sensing, 14, 3417-3431. 
Hamar D., Ferencz Cs., Lichtenberger J., Tarcsai Gy. and Ferencz 
Arkos I. (1996): Yield estimation for com and wheat in 
the Hungarian Great Plain using Landsat MSS data; Int. 
J. Remote Sensing, 17,1689-1699. 
Lichtenberger J., Ferencz Cs., Tarcsai Gy. and Timar G. (1995): 
New atmospheric correction method for Landsat TM 
data: the ACABA algorithm; Annales Unfv. Sci. Bp. 
Rolando Eotvos Nom., Sec. Geophys. et Meteor., XI, 
137-164. 
Myneni R.B., Keeling C.D., Tucker C.J., AsrarG. andNemaniR.R. 
(1997): Increased plant growth in the northern latitudes 
from 1981 to 1991; Nature, 386, 698-702.