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Fig 2: Access to shared water resources information from space by various actors resulting in new 
control mechanisms in management 
The scientific progress of satellite interpretations has undergone major improvements in the last 10 
years. Conversion algorithms between the spectral radiances at the one hand and bio-phyiscal or 
hydrological parameters at the other hand have been improved. Some review of progress can be found 
in (Engman and Gurney, 1991; Kite and Pietroni, 1996; Rango and Shalaby, 1998; Schultz and 
Engman, 2000; Bastiaanssen et al, 2000). Basic research is elementary for improving the accuracy of 
estimation procedures, because none of the surface features summarized in Table 2 can be directly 
measured. Remote sensing scientists have generally not grasped the need to investigate river basin 
processes and dedicated more efforts to climatology and ecology (e.g. Nemani et al., 1996). Driving 
the research community is an important asset for the future. 
  
  
  
  
  
  
  
  
Discipline Application 
Hydrology Snow cover, precipitation, soil moisture, evapotranspiration 
Agriculture Irrigated area, rainfed area, crop identification, biomass growth, crop yield, 
irrigation performance 
Environment Forest area, wetlands, rangelands, waterlogging, salinization, water quality 
Geography Digital elevation, land slope, land aspects, land cover, land use 
  
  
Table 2: Satellite measurements for possible applications in national and international river basins 
It should be recognized that lots of database building has been realized during the last 10 years. 
Several public domain databases on climate, river runoff, land cover and elevation have been 
established and are continuously updated. The US Geological Survey has prepared a digital elevation 
map of the world with a 1 km spatial resolution, GTOPO30, which is available at the world wide web. 
The global land cover map from Eidenshink and Faundeen (1998) has a global 1 km grid. Examples 
of finer resolution land cover databases are IGBP-DIS global land cover data set Discover (Loveland 
and Belward, 1997), Corine, Pelcom, Africover (FAO) and the Asian Association on Remote Sensing 
AARS-Global Land Cover Data Set (Chiba University). Discover only recognizes cropland and no 
further breakdown in crops or irrigated agriculture is made as it aims at support global change 
modelling. Corine, Africover and AARS are more suited for planning of agricultural development. 
  
444 International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000.