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Abstract:The 'rain use efficiency' (RUE) may be defined as the ratio of above-ground net primary productivity (ANPP) to annual precipitation, and it is claimed to be a conservative property of the vegetation cover in drylands, if the vegetation cover is not subject to non-precipitation related land degradation. Consequently, RUE may be regarded as means of normalizing ANPP for the impact of annual precipitation, and as an indicator of non-precipitation related land degradation. Large scale and long term identification and monitoring of land degradation in drylands, such as the Sahel, can only be achieved by use of Earth Observation (EO) data. This paper demonstrates that the use of the standard EO-based proxy for ANPP, summed normalized difference vegetation index (NDVI) (National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies 3rd generation (GIMMS3g)) over the year (ΣNDVI), and the blended EO/rain gauge based data-set for annual precipitation (Climate Prediction Center Merged Analysis of OPEN ACCESSRemote Sens. 2013, 5 665 Precipitation, CMAP) results in RUE-estimates which are highly correlated with precipitation, rendering RUE useless as a means of normalizing for the impact of annual precipitation on ANPP. By replacing ΣNDVI by a 'small NDVI integral', covering only the rainy season and counting only the increase of NDVI relative to some reference level, this problem is solved. Using this approach, RUE is calculated for the period 1982-2010. The result is that positive RUE-trends dominate in most of the Sahel, indicating that non-precipitation related land degradation is not a widespread phenomenon. Furthermore, it is argued that two preconditions need to be fulfilled in order to obtain meaningful results from the RUE temporal trend analysis: First, there must be a significant positive linear correlation between annual precipitation and the ANPP proxy applied. Second, there must be a near-zero correlation between RUE and annual precipitation. Thirty-seven percent of the pixels in Sahel satisfy these requirements and the paper points to a range of different reasons why this may be the case.

Interannual changes of vegetation are crucial in understanding ecosystem dynamics under global change. However, there is no automated tool to extract these interannual changes from remote sensing time series. To fill this gap, the Ensemble Empirical Mode Decomposition (EEMD) framework was refined and implemented to decompose time series of Normalized Difference Vegetation Index (NDVI) and reconstruct their interannual components. The performance of EEMD-based interannual NDVI detection was assessed using simulated time series, and its sensitivity to model and data parameters was determined to provide a basis for remote sensing applications. The sensitivity analysis highlighted application limitations for time series with low interannual to annual amplitude ratios and high irregularity in timing of growing seasons, as these factors have the strongest effects on the overall performance. However, within these limitations, the detected interannual components correspond well to simulated input components with respect to timing of episodes and composition of time scales. The applicability on real world NDVI time series was demonstrated by mapping the coupling between precipitation variability, interannual vegetation changes, and the El Niño Southern Oscillation and Indian Ocean Dipole phenomena for ecoregions in East and Central Africa. In most areas where precipitation was found sensitive to oceanic forcing, the EEMD detected vegetation changes matched the predicted response, except in dense forest ecosystems.

Ecosystem sensitivity to climate variability varies across East Africa, and identifying the determinant factors of this sensitivity is crucial to assessing region‐wide vulnerability to climate change and variability. Such assessment critically relies on spatiotemporal data sets with inherent uncertainty, on new processing techniques to extract interannual variability at a priori unknown time scales and on adequate statistical models to test for biogeographical effects on vegetation‐precipitation relationships. In this study, interannual variability in long‐term records of normalized difference vegetation index and satellite‐based precipitation estimates was detected using ensemble empirical mode decomposition and standardized precipitation index with varying accumulation periods. Environmental effect modeling using additive models with spatially correlated effects showed that ecosystem sensitivity is primarily predicted by biogeographical factors such as annual precipitation distribution (reaching maximum sensitivity at 500 mm yr−1), vegetation type and structure, ocean‐climate coupling, and elevation. The threat of increasing climate variability and extremes impacting productivity and stability of ecosystems is most imminent in semiarid grassland and mixed cropland ecosystems. The influence of oceanic phenomena such as El Niño–Southern Oscillation and Indian Ocean Dipole is foremost reflected in precipitation variability, but prolonged episodes also pose risks for long‐term degradation of tree‐rich ecosystems in the East African Great Lakes region.