Global mapping of forest aboveground biomass (AGB) is a challenging task and crucial for forest management and planning. Lidar data provides 3D information of forest stands and has been consistently identified as the state-of-the-art technology for monitoring forests. However, lidar data are expensive and their acquisition is spatially restricted to some forest areas. A reasonable solution for overcoming these restrictions is the use of spaceborne data. In recent years, the number of multispectral sensor based satellites have consistently increased, including open data sources like Sentinel-2 and small satellite sources like RapidEye and Dove constellations. In this work, we compared and evaluated different multispectral satellite data like Sentinel-2, RapidEye and Dove on the basis of different available spectral, spatial and temporal information for modelling AGB. We also used airborne lidar data as the state-of-the-art to compare results from multispectral data models. The experiments were performed under a common framework of variable elimination based on autocorrelation analysis and variable selection using stepAIC (Akaike Information Criterion) algorithm. A multiple linear regression with leave-one-out cross validation (LOOCV) was used to perform p-value quartiling for spectral information analysis, generate LOOCV metrics for temporal information analysis and modelling at spectral parity for spatial information analysis. Results demonstrate a clear and extensive influence of spectral information from specific channels like red-edge and SWIR for modelling AGB. Also, the addition of temporal information increases the precision and agreement of multispectral AGB models. Differently, the spatial information may not be relevant unless datasets are at spectral parity.