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
:1. Introduction
2. Study Area
3. Materials and Methods
3.1. MODIS Data and Calculation of NDVI and VCI
3.2. TRMM Data and Derivation of SPI
3.3. Ancillary Data
3.4. Extraction of Average Growing Season with TIMESAT
3.5. Detection of Agriculturally Relevant Droughts
4. Results
4.1. Timing of Average Growing Season
4.2. Spatio-Temporal Patterns of Agriculturally Relevant Droughts over Africa
4.3. Agricultural Droughts during El Niño-Southern Oscillation (ENSO) Events
4.4. Drought-Affected Cropland and Agricultural Production
5. Discussion
5.1. Monitoring Agricultural Droughts over Africa
5.2. Comparison of SPI-3 and VCI
5.3. Spatio-Temporal Variability of Agricultural Droughts during ENSO Events
5.4. Potential and Limitations
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Dataset | TRMM 3B43 (V7) | MODIS MOD09A1 (V6) | ESA CCI-LC 2010 |
---|---|---|---|
Variable | Precipitation rate (mm/h) | Surface reflectance | Land Cover classification |
Source | TRMM, gauge analysis | MODIS | MERIS and SPOT-Vegetation |
Temporal coverage | 1 January 1998 to present | 26 February 2000 to present | 2008–2012 |
Spatial coverage | 50 S to 50N | Global | Global |
Temporal resolution | 1 month | 8 days (composite) | no time series |
Spatial resolution | × | 500 m | 300 m |
Data format | netCDF | HDF | GeoTIFF |
Drought Category | SPI Range | VCI Range (%) |
---|---|---|
Extreme drought | SPI ≤ −2 | VCI < 10 |
Severe drought | −2 < SPI ≤ −1.5 | 10 ≤ VCI ≤ 20 |
Moderate drought | −1.5 < SPI ≤ −1 | 20 < VCI < 35 |
No drought | SPI > −1 | VCI ≥ 35 |
Region | Years | Countries * Affected by Drought Based on | |
---|---|---|---|
Rainfall Anomalies | Vegetation Condition | ||
(Relative Duration of SPI-3 <−1) | (Relative Duration of VCI < 35) | ||
Eastern Africa | 2000/2001 | ER ET KE SD SO SS | |
2001/2002 | ER ET SD | ER ET KE SD SS TA | |
2002/2003 | ER ET KE TA | ER ET SD SS KE TA | |
2003/2004 | BI ET SD TA | ER ET KE SD SO SS TA | |
2004/2005 | BI KE SD SO TA | ER ET KE SD SS TA | |
2005/2006 | KE TA | ER ET KE SD SO TA UG | |
2006/2007 | SO | ET SD | |
2007/2008 | ET KE TA | ER ET KE SO SD TA | |
ER ET KE SD SO SS UG TA | ER ET KE SD SO SS TA | ||
2009/2010 | BI ER RW SD SO SS UG TA | ER ET SD SO SS TA | |
ER ET KE SD SO SS UG TA | ER ET KE SD SO TA | ||
2011/2012 | ET SD SS UG TA | ER ET KE SD SO TA | |
2012/2013 | ER SD SS TA UG | ER ET SD SS TA | |
2013/2014 | KE RW UG TA | KE SD SO TA | |
2014/2015 | ER ET KE UG TA | ER ET KE SD SO TA | |
2015/2016 | ER ET KE RW SD SO UG | ER ET SD | |
Southern Africa | 2000/2001 | AO | AO NA ZA |
2001/2002 | AO BW SZ ZA ZW | BW MZ NA ZA ZM ZW | |
BW NA SZ ZA ZM ZW | BW NA SZ ZA ZM ZW | ||
2003/2004 | AO MW SZ | AO BW ZA | |
BW MW MZ ZM ZW | AO BW MW MZ NA ZA ZM ZW | ||
2005/2006 | BW MZ ZA | ||
2006/2007 | BW LS ZA | BW LS MZ NA ZA ZW | |
2007/2008 | MG MZ ZW | BW MW MZ ZM ZW | |
2008/2009 | MG MZ | MZ ZA ZW | |
2009/2010 | AO MZ NA ZA | AO ZA | |
2010/2011 | MG MW MZ ZA | MZ ZW | |
2011/2012 | AO BW LS MZ ZA ZW | AO BW MZ ZW | |
2012/2013 | AO BW MG NA ZM ZW | AO BW MW NA ZA ZM ZW | |
2013/2014 | MG MZ ZA ZM | MW MZ ZM ZW | |
AO BW LS MW MZ NA ZA ZM ZW | AO BW LS MW MZ NA SZ ZA ZW | ||
AO BW LS MG MW MZ NA SZ ZA ZM ZW | BW LS MZ NA SZ ZA ZW |
Years | Countries * Affected by Drought | |
---|---|---|
Eastern Africa | Southern Africa | |
2000/2001 | MZ SZ ZW | |
2001/2002 | UG | LS MW |
2002/2003 | RW | MG MZ |
2003/2004 | RW | |
2004/2005 | RW UG | |
2005/2006 | MG MW | |
2006/2007 | RW | SZ |
2007/2008 | UG | LS MW |
2008/2009 | BI | MZ |
2009/2010 | MG ZW | |
2010/2011 | BI | |
2011/2012 | KE TA | MW |
2012/2013 | ||
2013/2014 | NA | |
2014/2015 | MG | |
2015/2016 | SS |
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Winkler, K.; Gessner, U.; Hochschild, V. 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. Remote Sens. 2017, 9, 831. https://doi.org/10.3390/rs9080831
Winkler K, Gessner U, Hochschild V. 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. Remote Sensing. 2017; 9(8):831. https://doi.org/10.3390/rs9080831
Chicago/Turabian StyleWinkler, Karina, Ursula Gessner, and Volker Hochschild. 2017. "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" Remote Sensing 9, no. 8: 831. https://doi.org/10.3390/rs9080831