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Rainfall Estimation fron passive microwave satellite data has been used widely over oceans but has been less successful over land. This is because over land surfaces, the high spatial and temporal variability in emissivity, coupled with relatively low contrast between surface and rain cloud microwave emissions, make the rainfall signal moo-e difficult to extract. The variability of emissivity is mainly due to variations in vegetation cover and soil moisture. Major improvements in reliability of rainfall estimates are possible if emissivities could be measured routinely at appropriate scales. The possibility of estimating emissivity at the frequencies relevant to rainfall from vegetation and soil moisture measurements is explored in this paper using data from airborne sensors over a semi-arid area in Spain. Results show a good correlation between vegetation cover (represented by Normalized Difference Vegetation Index) and emissivity in dry conditions. This relationship is not significantly affected by vegetation type. Under wet conditions, the correlation is greatly reduced possibly due to the difficulty in accounting for cloud effects at higher frequencies. Attempts to quantity the effect of soil moisture using the Antecedent Precipitation Index were partially successful but more accurate measurements would be needed for reliable retrieval of emissivities. The use of a soil-adjusted vegetation index produced a higher correlation with emissivity than did the nonadjusted Normalized Difference Vegetation Index. (C)Elsevier Science Inc., 2000.