Full text: Special UNISPACE III volume

International Archives of Photogrammetry 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 
temporal profiles for wheat in different counties of Hungaiy in 
different years can be seen. 
□ General crop canopy monitoring □ (l.r. data). 
□ Local crop canopy monitoring □ (h.r. data). 
□ Crop yield estimation and forecasting □ (l.r. data; or l.r.+kr. 
□ Investigation and monitoring of land cover (forests, meadows, 
crops, man-made areas) □ (h.r. data; or h.r.+l.r. data). The 
CORINE international land cover project produces good examples 
of this application 
□ Monitoring of forests and meadows □ (l.r.+h.r. data). 
□ Monitoring of natural stress and man-made effects on the whole 
canopy □ (l.r. data; h.r. data). 
c) Soil investigations; 
□ Soil moisture monitoring, irrigation assistance □ (kr data in 
most cases). 
D Desertification, waste land mapping □ (l.r. data; h.r. data). 
□ Monitoring of sodification □ (kr. and hyperspectral data). 
□ Monitoring of soil degradation and destruction □ (h.r. data; or 
h.r.+l.r. data). 
d) Agricultural water monitoring: 
□ Flood monitoring □ (h.r. data in most cases). 
□ Surface water monitoring (irrigation reserves etc.) □ (kr. data in 
most cases). 
At the present time the satellite data bases used in these tasks are 
mainly the data measured by optical (visible and infrared) sensors. 
The first cause of this is that at the beginning of satellite remote 
sensing (meteorology, reconnaissance and the first Landsat 
satellites) optical sensors (scanners. TV cameras) were used 
onboard. Therefore the development of the application methods 
preprocessing of raw satellite RS data: geometric and atmospherio- 
correction (e.g. Ferencz et al, 1993, Lichtenberger et al, 1995) 
incorporating ground reference data is some cases; 
development of calibration functions (i.e. crop filters) only one 
times for a given crop using ground reference data and satellite RS- 
data together; 
derivation of tire temporal profiles of remote sensed parameters (at 
the given moment) of the investigated crops on the interesting 
territory, calculation of special agricultural RS indeces for the given 
application, stress (e.g. drought) and disaster (e.g. flood, fire) 
yield forecasting (at the given moment); 
refreshing of producting areas (acreage); 
final estimation of crop yields at the harvesting. 
From the point of view of the final accuracy of the whole process 
the preprocessing of the RS data lias a key role. The main phases of 
the preprocessing (Ferencz et al, 1993, Lichtenberger et al, 1995) 
are the following independently from the resolution of the data; 
data) □ see in the next chapter. 
□ Analysis of changing in the whole agricultural production-system 
on country, regional and global level. 
b) Monitoring of the canopy in general: 
was optical-band oriented. The second cause is the radiation 
characteristic of the canopy, especially tire radiation characteristic 
of the chlorophyll. The living plant absorbs the light in the visible 
band, especially in the red band. So the visible reflectance of a 
living canopy is small, but the reflectance of a living canopy in the 
near infrared band is high. This is an important characteristic of the 
plant canopy. Therefore the importance of optical RS data will not 
decrease in the future in agricultural applications. 
However, in the near future the role of microwave (MW) remote 
sensed data will increase for two reasons. First, the atmospheric 
attenuation of the MW radiation is much smaller than the optical 
one. so the MW radiation of Earth's surface remain detectable by 
satellite instruments even if the whole surface is covered by clouds. 
Second, the surface MW radiation (the radiation temperature of tire 
surface) and the surface MW reflectance is sensible to soil moishtre 
and water content of plant. (See e.g. the Radarsat or ERS results.) 
The agricultural application of satellite remote sensing means an 
accurate quantitative processing of remote sensed data. The values 
and the geographical positions of the processed data □ i,e. the pixel 
values and the spacial coordinates of the pixels □ must have an 
exact and accurate relation to the physical characteristics and the 
geodetical coordinates of the investigated surface. Therefore the 
methods used in this field have the following main phases of data 
The original digital numbers transmitted from satellite must be 
recalculated into physical values using the calibrational curves of 
satellite instruments before applying remote sensing radiation 
A strict filtering of cloudy pixels from data set. Correction of 
atmospherical effects on remaining (i.e. "non-cloudv") pixels and 
derivation of surface characteristic values (spectral surface 
reflectances, spectral surface radiances etc.). The full correction of 
atmospherical effects in satellite remote sensed data is essentially 
important. We use a high accuracy pixel-by-pixel atmospherical 
correction (the ACAB A algorithm □ Lichtenberger et al, 1995). □ 
At the end of this process the calculation of canopy characteristics 
(vegetation indices, such as the tasseled cap greenness, the 
normalized difference index etc. □ e.g. Ferencz et al, 1993). 
The next step is the geometrical transformation of the satellite data 
sets (images) into a commonly used map-projection. After this the 
data set is compatible to GIS. the geographical data and contours 
can be used to remove the pixels of the non-agricultural or non- 
investigated territories decreasing the whole data mass in the next

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