Identifying Droughts Affecting Agriculture in Africa Based on Remote Sensing Time Series between 2000–2016: Rainfall Anomalies and Vegetation Condition in the Context of ENSO

Abstract

Droughts are amongst the most destructive natural disasters in the world. In large regions of Africa, where water is a limiting factor and people strongly rely on rain-fed agriculture, droughts have frequently led to crop failure, food shortages and even humanitarian crises. In eastern and southern Africa, major drought episodes have been linked to El Niño-Southern Oscillation (ENSO) events. In this context and with limited in-situ data available, remote sensing provides valuable opportunities for continent-wide assessment of droughts with high spatial and temporal resolutions. This study aimed to monitor agriculturally relevant droughts over Africa between 2000–2016 with a specific focus on growing seasons using remote sensing-based drought indices. Special attention was paid to the observation of drought dynamics during major ENSO episodes to illuminate the connection between ENSO and droughts in eastern and southern Africa. We utilized Tropical Rainfall Measuring Mission (TRMM)-based Standardized Precipitation Index (SPI) with 0 . 25 ∘ resolution and Moderate-resolution Imaging Spectroradiometer (MODIS)-derived Vegetation Condition Index (VCI) with 500 m resolution as indices for analysing the spatio-temporal patterns of droughts. We combined the drought indices with information on the timing of site-specific growing seasons derived from MODIS-based multi-annual average of Normalized Difference Vegetation Index (NDVI). We proved the applicability of SPI-3 and VCI as indices for a comprehensive continental-scale monitoring of agriculturally relevant droughts. The years 2009 and 2011 could be revealed as major drought years in eastern Africa, whereas southern Africa was affected by severe droughts in 2003 and 2015/2016. Drought episodes occurred over large parts of southern Africa during strong El Niño events. We observed a mixed drought pattern in eastern Africa, where areas with two growing seasons were frequently affected by droughts during La Niña and zones of unimodal rainfall regimes showed droughts during the onset of El Niño. During La Niña 2010/2011, large parts of cropland areas in Somalia (88%), Sudan (64%) and South Sudan (51%) were affected by severe to extreme droughts during the growing seasons. However, no universal El Niño- or La Niña-related response pattern of droughts could be deduced for the observation period of 16 years. In this regard, we discussed multi-year atmospheric fluctuations and characteristics of ENSO variants as further influences on the interconnection between ENSO and droughts. By utilizing remote sensing-based drought indices focussed on agricultural zones and periods, this study attempts to contribute to a better understanding of spatio-temporal patterns of droughts affecting agriculture in Africa, which can be essential for implementing strategies of drought hazard mitigation.

Spatio-Temporal Analysis of Vegetation Dynamics as a Response to Climate Variability and Drought Patterns in the Semiarid Region, Eritrea

“…VCI is calculated based on inputs like NDVI and EVI, and different authors have used either NDVI or EVI as a base for calculating VCI in Equation 3 [32][33][34][35]. VCI is a pixel-based analysis for finding vegetation conditions at a specific location of the pixel by considering the mean of the multi-annual variability and the minimum and maximum variability of the Vegetation Index.…”

Spatio–temporal Assessment of Drought in Ethiopia and the Impact of Recent Intense Droughts

“…Besides the common SAI, another method to compare the current NDVI with historical values is the Vegetation Condition Index [52]. The VCI has been extensively used to monitor vegetation conditions [53]. It normalizes NDVI on a pixel-by-pixel basis, scaling between the minimum and maximum values of NDVI:…”

The Effect of Droughts on Vegetation Condition in Germany: An Analysis Based on Two Decades of Satellite Earth Observation Time Series and Crop Yield Statistics

“…It is very likely that heat waves will be more intense, more frequent, and last longer, and that risk levels regarding water restrictions and damages from extreme temperatures for Europe among each other. Additionally, most of these studies used meteorological parameters as indicators for drought, like Standardized Precipitation Index (SPI) [17,18,23], Standardized Precipitation Evaporation Index (SPEI) [21], or soil moisture [22,27], and investigated the reaction of the vegetation during their previously detected climate extremes. However, extreme vegetation reactions and climate extremes often have a non-linear relationship, as extreme reactions within the vegetation are possibly induced by an accumulation or specific combination of climatic conditions that would not be extreme when considered separately [28,29].…”

“…In particular, with regard to agricultural droughts, the examination of satellite-based vegetation indices has become an important method as no field measurements, interpolation, or large-scale modelling are required. For investigating vegetation status under extreme conditions, several medium-resolution satellite data sets (e.g., AVHRR, Spot-VEGETATION, MERIS, MODIS, and SeaWIFS) were used in previous studies [16][17][18][19][20][21][22][23][24]. When investigating the influence of climate extremes on agriculture, the focus lies on the assessment of the reaction of plants toward extreme climate conditions.…”

“…In previous studies, extreme conditions of plant properties were already investigated using satellite-based proxies of vegetation vitality and/or productivity. The most prominent investigated parameters in this regard are vegetation indices, such as NDVI [16][17][18]21,23], and modelled productivity estimates, such as fraction of absorbed photosynthetically active radiation (FAPAR) [19,25], net primary productivity (NPP) [10], and gross primary productivity (GPP) [20,22,26]. Previous studies analyzing the reaction of plants toward climate extremes found temperate regions to show rapid responses of vegetation vitality and productivity [21,23].…”