Livestock plays an important economic role in Niger – especially in the semi-arid regions – while being highly vulnerable due to the large inter-annual variability of precipitation and hence rangeland production. This study aims to support effective rangeland management by developing an approach for mapping rangeland biomass production. The observed spatiotemporal variability of biomass production is utilised to build a predictive model based on ground and remote sensing data for the period 2001 to 2015. The phenology-tuned seasonal cumulative Normalised Difference Vegetation Index (cNDVI), computed from 10-day image composites of the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI data, was used as proxy of biomass production. A linear regression model was tuned with multi-annual field measurements of herbaceous biomass at the end of the growing season. Besides a general model utilizing all available sites for the calibration, different aggregation schemes of the study area with a varying number of calibration units and different biophysical meaning were tested. Sampling sites belonging to a certain calibration unit of a selected scheme were aggregated to compute the regression. The different aggregation schemes were evaluated with respect to their predictive power. Results gathered at the different aggregation levels were subjected to a cross-validation (cv) applying a jackknife technique (leaving one year out at a time). In general, the model performance increased with increasing model parameterization indicating the importance of additional unobserved and spatially heterogeneous agro-ecological effects (which might relate to grazing, species composition, optical soil properties, etc.) in modifying the relationship between cNDVI and herbaceous biomass at the end of the season. The biophysical aggregation scheme, whose calibration units were derived from an ISODATA classification utilizing 10-day NDVI images from January 2001 to December 2015, showed the best performance in respect of the predictive power (R2cv = 0.47) and the RMSEcv (398 kg ha-1) although not being the model with the highest number of calibration units. The proposed approach can be applied for the timely production of maps of estimated biomass at the end of the growing season before field measurements are made available.