Monitoring vegetation cover across semi-arid regions: comparison of remoteobservations from various scales

Realistic parameterization of land surface processes must take into account heterogeneities in the land surface. In the case of a sparse canopy, inter pretation of remotely sensed measurements is very difficult and somewhat qu estionable in attempts to relate the vegetation indices (VIs) to fractional vegetation cover information. This paper provides an intercomparison of sa tellite observations at different scales for the purpose of assessing and m onitoring vegetation changes at a regional scale. It is designed (1) to eva luate the level of association that can be expected from a model relating b asic tools such as spectrally derived VIs from AVHRR and green biomass data for a set of heterogeneous surfaces in a representative semi-arid region a nd (2) to determine the best strategy for using satellite imagery in that c ontext. The quantitative relationships between radiation data collected in space and characteristics of land surfaces are investigated in the context of the HAPEX-Sahel study over the Niger. A north-south vegetation gradient was accurately located and documented. Corresponding SPOT data, acquired on the same day for the same test site, at 20 m spatial resolution were then resampled to the plate carree projection for comparison with National Ocean ic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) HRPT data (1 km spatial resolution). This processing helped in the description and full interpretation of the evolution of various vegeta tion indices derived from NOAA AVHRR data on these semiarid regions. One outcome of the data processing is that the resulting relationship betwe en spectral indices and the effective biomass is found to be nonlinear with in our low biomass range. When this scheme is applied to NOAA AVHRR data, t he Normalized Difference Vegetation Index (NDVI), the Modified Soil-Adjuste d Vegetation Index (MSAVI) and the Global Environment Monitoring Index (GEM I) appear to provide detailed information about biomass evolution. However, the accuracy is somewhat different depending on the fractional vegetation cover value. Strategies to estimate information on green biomass in semiari d regions are different depending on the vegetation index used. In order to use the NDVI or MSAVI properly at the surface level, we have no choice but to perform carefully prepared atmospheric corrections. This data preproces sing is not necessary for the GEMI, which is computed without the need for any atmospheric corrections.