Reliable drought information is of utmost importance for efficient drought management. This paper presents a fully operational processing chain for mapping drought occurrence, extent and strength based on Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data at 250 m resolution. Illustrations are provided for the territory of Kenya. The processing chain was developed at BOKU (University of Natural Resources and Life Sciences, Vienna, Austria) and employs a modified Whittaker smoother providing consistent (de-noised) NDVI “Monday-images” in near real-time (NRT), with time lags between zero and thirteen weeks. At a regular seven-day updating interval, the algorithm constrains modeled NDVI values based on reasonable temporal NDVI paths derived from corresponding (multi-year) NDVI “climatologies”. Contrary to other competing approaches, an uncertainty range is produced for each pixel, time step and time lag. To quantify drought strength, the vegetation condition index (VCI) is calculated at pixel level from the de-noised NDVI data and is spatially aggregated to administrative units. Besides the original weekly temporal resolution, the indicator is also aggregated to one- and three-monthly intervals. During spatial and temporal aggregations, uncertainty information is taken into account to down-weight less reliable observations. Based on the provided VCI, Kenya’s National Drought Management Authority (NDMA) has been releasing disaster contingency funds (DCF) to sustain counties in drought conditions since 2014. The paper illustrates the successful application of the drought products within NDMA by providing a retrospective analysis applied to droughts reported by regular food security assessments. We also present comparisons with alternative products of the US Agency for International Development (USAID)’s Famine Early Warning Systems Network (FEWS NET). We found an overall good agreement (R² = 0.89) between the two datasets, but observed some persistent (seasonal and spatial) differences that should be assessed against external reference information. Full article

Time series of normalized difference vegetation index (NDVI) are important data sources for environmental monitoring. Continuous efforts are put into their production and updating. The recently released Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data set is a consistent time series with 1/12° spatial and bi-monthly temporal resolution. It covers the time period from 1981 to 2011. However, it is unclear if vegetation density and phenology derived from GIMMS are comparable to those obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI with 250 m ground resolution. To check the consistency between GIMMS and MODIS data sets, a comparative analysis was performed. For a large European window (40 × 40°), data distribution, spatial and temporal agreement were analyzed, as well as the timing of important phenological events. Overall, only a moderately good agreement of NDVI values was found. Large differences occurred during winter. Large discrepancies were also observed for phenological metrics, in particular the start of season. Information regarding the maximum of season was more consistent. Hence, both data sets should be well inter-calibrated before being used concurrently. Full article