Remote sensing of soil moisture in desert regions is possible with microwave sensors (Schmugge 1981) and by 
termal techniques (Wetzel & Atlas 1981; England et al. 1983). However, no satellite-based soil moisture 
remote sensing system, using microwave techniques, is currently available on an operational or semi-operational 
basis for an area the size of the desert locust recession area; thermal remote sensing techniques that use 
Meteosat data are still in the research phase. 
Remote sensing of meteorological events that cause soil moisture changes relevant for agricultural crop 
production and locust population development, can be undertaken from geostationary environmental satellites, 
e.g. Meteosat for Africa and the Near-East The high temporal frequency of this type of data in the visible 
and thermal infrared wavelengths permits detailed monitoring of weather systems likely to produce the rainfall 
necessary to support crop development and initiate locust breeding. 
Various schools have developed and tested remote sensing techniques for estimating rainfall quantitatively from 
Meteosat data. FAO has been closely involved in these developments as undertaken by the Universities of 
Bristol (Barrett 1977,1980; Hielkema 1980; Barrett & Harrison 1986) and Reading (Milford & Dugdale 1987). 
The technique developed by the University of Reading, based exclusively on the use of Meteosat thermal 
infrared data, which was selected by FAO for implementation on the ARTEMIS system. The technique is 
based on the observed linear relationship between the vertical extent of a cloud in the atmosphere, as 
evidenced by the temperature of its top, which can be measured by the satellite at frequent intervals, and the 
amount of rain which falls from the bottom of the cloud. This technique is particularly suitable for application 
in tropical and arid environments where generally over 90% of the annual rainfall comes from strong convective 
activity, i.e. strong vertical development of clouds. 
Remote sensing of green vegetation biomass is a well developed technique for which several satellites 
frequently collect data on a worldwide scale. The technique involves measuring reflected spectral radiance that 
results from the interaction between the green-leaf vegetation cover and incident solar spectral irradiance. 
These measurements can be made from any altitude above the surface (1 m, 10,000 m, 1,000 km) depending 
on the remote sensing system in question (ground-based, aircraft or satellite, respectively). Spectral estimation 
of green-leaf biomass generally involves the use of two wavelength regions, the red (0.6-0.7 pm) and near 
infrared (0.75-1.1 pm), although the specific wavelengths used often vary slightly between instruments. The 
0.6-0.7 pm region correspond to the in vivo red region of chlorophyll absorption and is inversely related to 
chlorophyll density. In the 0.75-1.1 pm region, reflectance is proportional to green-leaf density. Ratio 
combinations of these two wavelength regions thus contain information related to the chlorophyll-green-leaf 
density (Tucker 1979). Use of these two bands for making plant canopy inferences by non-destructive 
techniques is facilitated by the proximity of the two bands in the electromagnetic spectrum. This proximity 
enables simple band ratios or other combinations to be used to compensate for differences in solar flux 
intensities. Linear or ratio combinations of red/near infrared spectral data have been used to quantify a variety 
of vegetation types (Tucker 1980; Curran 1983).