A combined satellite-derived drought indicator to support humanitarian aid organizations

Remote Sensing

Volumn 8, Issue 4, 2016, Pages

  Enenkel, Markus ^a   Steiner, Caroline ^a   Mistelbauer, Thomas ^a   Dorigo, Wouter ^a   Wagner, Wolfgang ^a   See, Linda ^b   Atzberger, Clement ^c   Schneider, Stefan ^d   Rogenhofer, Edith ^e  

^a VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

^b INTERNATIONAL INSTITUTE FOR APPLIED SYSTEMS ANALYSIS   (Austria)

^c UNIVERSITY OF NATURAL RESOURCES AND LIFE SCIENCES   (Austria)

^d ZAMG Zentralanstalt fuer Meteorologie und Geodynamik   (Austria)

^e MÉDECINS SANS FRONTIÈRES   (Switzerland)

Author keywords

Drought index; Drought monitoring; Food security; Remote sensing

Indexed keywords

ATMOSPHERIC TEMPERATURE; DECISION SUPPORT SYSTEMS; FOOD SUPPLY; REMOTE SENSING; SATELLITES; SOIL MOISTURE;

ADDED VALUES; DECISION SUPPORT FRAMEWORK; DROUGHT INDICES; DROUGHT MONITORING; EARLY WARNING; FOOD SECURITY; LAND SURFACE TEMPERATURE; SPATIAL RESOLUTION;

DROUGHT;

EID: 84971657404     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8040340     Document Type: Article

References (89)

1. Belal, A.-A.; El-Ramady, H.R.; Mohamed, E.S.; Saleh, A.M. Drought risk assessment using remote sensing and GIS techniques. Arab. J. Geosci. 2012, 7, 35-53
2. Lloyd-Hughes, B. The impracticality of a universal drought definition. Theor. Appl. Climatol. 2014, 117, 607-611
3. Overseas Development Institute Poverty and Poverty Reduction in Sub-Saharan Africa: An Overview of the Issues. Available online: http://www.odi.org/sites/odi.org.uk/files/odi-assets/publicationsopinion-files/860.pdf (accessed on 14 October 2015)
4. UN Food and Agriculture Organization. The State of Food Insecurity in theWorld-Meeting the 2015 International Hunger Targets: Taking Stock of Uneven Progress; UN Food and Agriculture Organization: Rome, Italy, 2015
5. UN Food and Agriculture Organization of the United Nations. The State of Food Insecurity in the World. High Food Prices and Food Security-Threats and Opportunities; UN Food and Agriculture Organization: Rome, Italy, 2008
6. Brown, M.E.; Funk, C.C. Food security under climate change. Science 2008, 319, 580-581
7. Funk, C.C.; Brown, M.E. Declining global per capita agricultural production and warming oceans threaten food security. Food Secur. 2009, 1, 271-289
8. United Nations Office for Disaster Risk Reduction (UNISDR). United Nations General Assembly Sendai Framework for Disaster Risk Reduction 2015-2030. In Proceedings of the Third United Nations World Conference on Disaster Risk Reduction, Sendai, Japan, 14-18 March 2015
9. Space-based Information for Disaster-Risk Reduction. Available online: http://www.un-spider.org/sites/default/files/Two_Pager_WCDRR_0.pdf (accessed on 22 June 2015)
10. Verdin, J.; Funk, C.; Senay, G.; Choularton, R. Climate science and famine early warning. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2005, 360, 2155-2168
11. Rashid, A. Global Information and EarlyWarning System on Food and Agriculture: Appropriate Technology and Institutional Development Challenges. In Early Warning Systems for Natural Disaster Reduction; Zschau, P.D.J., Küppers, A., Eds.; Springer Berlin Heidelberg: Berlin, Germany; Heidelberg, Germany, 2003; pp. 337-344
12. Sheffield, J.;Wood, E.F.; Chaney, N.; Guan, K.; Sadri, S.; Yuan, X.; Olang, L.; Amani, A.; Ali, A.; Demuth, S.; et al. A Drought Monitoring and Forecasting System for Sub-Sahara African Water Resources and Food Security. Bull. Am. Meteorol. Soc. 2014, 95, 861-882
13. Brown, M. Famine Early Warning Systems and Remote Sensing Data; Springer Berlin Heidelberg: Berlin, Germany; Heidelberg, Germany, 2008
14. Coughlan de Perez, E.; van den Hurk, B.; van Aalst, M.; Jongman, B.; Klose, T.; Suarez, P. Forecast-based financing: An approach for catalyzing humanitarian action based on extreme weather and climate forecasts. Nat. Hazards Earth Syst. Sci. Discuss 2014, 2, 3193-3218
15. Kull, D.; Mechler, R.; Hochrainer-Stigler, S. Probabilistic cost-benefit analysis of disaster risk management in a development context. Disasters 2013, 37, 374-400
16. Kellett, J.; Caravani, A. Financing Disaster Risk Reduction-A 20 Year Story of International Aid. Available online: http://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/8574.pdf (accessed on 14 October 2015)
17. Bachmair, S.; Kohn, I.; Stahl, K. Exploring the link between drought indicators and impacts. Nat. Hazards Earth Syst. Sci. 2015, 15, 1381-1397
18. Wilhite, D.A.; Svoboda, M.D.; Hayes, M.J. Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness. Water Resour. Manag. 2007, 21, 763-774
19. United Nations Statistics Division UN Data Country Profiles 2014. Available online: http://data.un.org/CountryProfile.aspx (accessed on 14 October 2015)
20. US Agency for International Development Ethiopia Agriculture and Food Security 2015. Available online: https://www.usaid.gov/ethiopia/agriculture-and-food-security (accessed on 14 October 2015)
21. Peel, M.C.; Finlayson, B.L.; McMahon, T.A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 2007, 11, 1633-1644
22. Alliance Development Works and United Nations University. World Risk Report 2014; Alliance Development Works: Berlin, Germany; United Nations University: Bonn, Germany, 2014
23. Naumann, G.; Barbosa, P.; Garrote, L.; Iglesias, A.; Vogt, J. Exploring drought vulnerability in Africa: An indicator based analysis to be used in early warning systems. Hydrol. Earth Syst. Sci. 2014, 18, 1591-1604
24. Funk, C.; Dettinger, M.D.; Michaelsen, J.C.; Verdin, J.P.; Brown, M.E.; Barlow, M.; Hoell, A. Warming of the Indian Ocean threatens eastern and southern African food security but could be mitigated by agricultural development. Proc. Natl. Acad. Sci. USA 2008, 105, 11081-11086
25. Clay, J.W.; Holcomb, B.K. Politics and the Ethiopian Famine: 1984-1985; Transaction Publishers: Cambridge, MA, USA, 1985
26. Hillbruner, C.; Moloney, G. When early warning is not enough-Lessons learned from the 2011 Somalia Famine. Glob. Food Secur. 2012, 1, 20-28
27. Funk, C. We thought trouble was coming. Nat. News 2011, 476
28. Maidment, R.I.; Grimes, D.; Allan, R.P.; Tarnavsky, E.; Stringer, M.; Hewison, T.; Roebeling, R.; Black, E. The 30 year TAMSAT African Rainfall Climatology and Time series (TARCAT) data set. J. Geophys. Res. Atmos. 2014, 119
29. Tarnavsky, E.; Grimes, D.; Maidment, R.; Black, E.; Allan, R.P.; Stringer, M.; Chadwick, R.; Kayitakire, F. Extension of the TAMSAT Satellite-Based Rainfall Monitoring over Africa and from 1983 to Present. J. Appl. Meteorol. Climatol. 2014, 53, 2805-2822
30. Wan, Z. New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products. Remote Sens. Environ. 2008, 112, 59-74
31. Balint, Z.; Mutua, F.M.; Muchiri, P. Drought Monitoring with the Combined Drought Index; FAO-SWALIM: Nairobi, Kenya, 2011
32. Palmer, W.C. Meteorological Drought; US Department of Commerce: Washington, DC, USA, 1965
33. Wells, N.; Goddard, S.; Hayes, M.J. A Self-Calibrating Palmer Drought Severity Index. J. Clim. 2004, 17, 2335-2351
34. Vicente-Serrano, S.M.; Beguería, S.; López-Moreno, J.I. A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. J. Clim. 2010, 23, 1696-1718
35. AghaKouchak, A. A baseline probabilistic drought forecasting framework using Standardized Soil Moisture Index: Application to the 2012 United States drought. Hydrol. Earth Syst. Sci. 2014, 18, 2485-2492
36. Qiu, J.; Crow, W.T.; Nearing, G.S.; Mo, X.; Liu, S. The impact of vertical measurement depth on the information content of soil moisture times series data. Geophys. Res. Lett. 2014
37. El Sharif, H.; Wang, J.; Georgakakos, A.P. Modeling Regional Crop Yield and Irrigation Demand Using SMAP Type of Soil Moisture Data. J. Hydrometeorol. 2015, 16, 904-916
38. Kumar, S.V.; Harrison, K.W.; Peters-Lidard, C.D.; Santanello, J.A.; Kirschbaum, D. Assessing the Impact of L-Band Observations on Drought and Flood Risk Estimation: A Decision-Theoretic Approach in an OSSE Environment. J. Hydrometeorol. 2014, 15, 2140-2156
39. McKee, T.B.; Doesken, N.J.; Kleist, J. The Relationship of Drought Frequency and Duration to time Scales. In Proceedings of the Eighth Conference on Applied Climatology, Anaheim, CA, USA, 17-22 January 1993
40. Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127-150
41. Dai, A. Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900-2008. J. Geophys. Res. 2011, 116
42. Otkin, J.A.; Anderson, M.C.; Hain, C.; Mladenova, I.E.; Basara, J.B.; Svoboda, M. Examining Rapid Onset Drought Development Using the Thermal Infrared-Based Evaporative Stress Index. J. Hydrometeorol. 2013, 14, 1057-1074
43. Anderson, M.C.; Zolin, C.A.; Sentelhas, P.C.; Hain, C.R.; Semmens, K.; Tugrul Yilmaz, M.; Gao, F.; Otkin, J.A.; Tetrault, R. The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts. Remote Sens. Environ. 2016, 174, 82-99
44. Dinku, T.; Ceccato, P.; Grover-Kopec, E.; Lemma, M.; Connor, S.J.; Ropelewski, C.F. Validation of satellite rainfall products over East Africa's complex topography. Int. J. Remote Sens. 2007, 28, 1503-1526
45. Thiemig, V.; Rojas, R.; Zambrano-Bigiarini, M.; Levizzani, V.; De Roo, A. Validation of Satellite-Based Precipitation Products over Sparsely Gauged African River Basins. J. Hydrometeorol. 2012, 13, 1760-1783
46. The World Bank Turn down the Heat. Why a 4 °C Warmer World Must Be Avoided; The World Bank: Washington, DC, USA, 2012
47. Liu, Y.Y.; Dorigo, W.A.; Parinussa, R.M.; de Jeu, R.A.M.; Wagner, W.; McCabe, M.F.; Evans, J.P.; van Dijk, A.I.J.M. Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sens. Environ. 2012, 123, 280-297
48. Liu, Y.; Parinussa, R.M.; Dorigo, W.A.; De Jeu, R.A.M.;Wagner, W.; van Dijk, A.I.J.M.; McCabe, M.F.; Evans, J.P. Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrol. Earth Syst. Sci. 2011, 15, 425-436
49. Wagner, W.; Dorigo, W.; De Jeu, R.A.M.; Fernandez, D.; Benveniste, J.; Haas, E.; Ertl, M. Fusion of Active and Passive Microwave Observations to create an Essential Climate Variable Data Record on Soil Moisture. In ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences; ISPRS: Melbourne, Australia, 2012; Volume I-7
50. McNally, A.; Shukla, S.; Arsenault, K.R.; Wang, S.; Peters-Lidard, C.D.; Verdin, J.P. Evaluating ESA CCI soil moisture in East Africa. Int. J. Appl. Earth Obs. Geoinf. 2016, 48, 96-109
51. Li, X.; Zhang, L.; Weihermuller, L.; Jiang, L.; Vereecken, H. Measurement and Simulation of Topographic Effects on Passive Microwave Remote Sensing Over Mountain Areas: A Case Study From the Tibetan Plateau. IEEE Trans. Geosci. Remote Sens. 2014, 52, 1489-1501
52. Wagner, W.; Hahn, S.; Kidd, R.; Melzer, T.; Bartalis, Z.; Hasenauer, S.; Figa-Saldaña, J.; de Rosnay, P.; Jann, A.; Schneider, S.; et al. The ASCAT Soil Moisture Product: A Review of its Specifications, Validation Results, and Emerging Applications. Meteorol. Z. 2013, 22, 5-33
53. Anyamba, A.; Tucker, C.J. Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981-2003. J. Arid Environ. 2005, 63, 596-614
54. Rembold, F.; Atzberger, C.; Savin, I.; Rojas, O. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sens. 2013, 5, 1704-1733
55. Atzberger, C. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs. Remote Sens. 2013, 5, 949-981
56. De Leeuw, J.; Vrieling, A.; Shee, A.; Atzberger, C.; Hadgu, K.M.; Biradar, C.M.; Keah, H.; Turvey, C. The Potential and Uptake of Remote Sensing in Insurance: A Review. Remote Sens. 2014, 6, 10888-10912
57. Tadesse, T.; Demisse, G.B.; Zaitchik, B.; Dinku, T. Satellite-based hybrid drought monitoring tool for prediction of vegetation condition in Eastern Africa: A case study for Ethiopia. Water Resour. Res. 2014, 50, 2176-2190
58. Nicholson, S.E.; Davenport, M.L.; Malo, A.R. A comparison of the vegetation response to rainfall in the Sahel and East Africa, using normalized difference vegetation index from NOAA AVHRR. Clim. Chang. 1990, 17, 209-241
59. Herrmann, S.M.; Anyamba, A.; Tucker, C.J. Recent trends in vegetation dynamics in the African Sahel and their relationship to climate. Glob. Environ. Chang. 2005, 15, 394-404
60. D'Odorico, P.; Caylor, K.; Okin, G.S.; Scanlon, T.M. On soil moisture-vegetation feedbacks and their possible effects on the dynamics of dryland ecosystems. J. Geophys. Res. Biogeosci. 2007, 112
61. Jamali, S. Investigating Temporal Relationships between Rainfall, Soil Moisture and MODIS-Derived NDVI and EVI for Six Sites in Africa. Available online: http://www.isprs.org/proceedings/2011/ISRSE-34/211104015Final00443.pdf (accessed on 14 October 2015)
62. Wang, J.; Rich, P.M.; Price, K.P. Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. Int. J. Remote Sens. 2003, 24, 2345-2364
63. Schnur, M.T.; Xie, H.;Wang, X. Estimating root zone soil moisture at distant sites using MODIS NDVI and EVI in a semi-arid region of southwestern USA. Ecol. Inform. 2010, 5, 400-409
64. Ji, L.; Peters, A.J. Lag and seasonality considerations in evaluating AVHRR NDVI response to precipitation. Photogramm. Eng. Remote Sens. 2005, 71, 1053-1061
65. Nandintsetseg, B.; Shinoda, M.; Kimura, R.; Ibaraki, Y. Relationship between Soil Moisture and Vegetation Activity in the Mongolian Steppe. Sci. Online Lett. Atmos. SOLA 2010, 6, 29-32
66. Zribi, M.; Paris Anguela, T.; Duchemin, B.; Lili, Z.;Wagner, W.; Hasenauer, S.; Chehbouni, A. Relationship between soil moisture and vegetation in the Kairouan plain region of Tunisia using low spatial resolution satellite data. Water Resour. Res. 2010, 46, 823-835
67. Atkinson, P.M.; Jeganathan, C.; Dash, J.; Atzberger, C. Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sens. Environ. 2012, 123, 400-417
68. Rembold, F.; Meroni, M.; Urbano, F.; Royer, A.; Atzberger, C.; Lemoine, G.; Eerens, H.; Haesen, D. Remote sensing time series analysis for crop monitoring with the SPIRITS software: New functionalities and use examples. Environ. Inform. 2015, 3, 1-11
69. Atzberger, C.; Eilers, P.H.C. A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America. Int. J. Digit. Earth 2011, 4, 365-386
70. Eilers, P.H.C. A Perfect Smoother. Anal. Chem. 2003, 75, 3631-3636
71. Klisch, A.; Atzberger, C.; Luminari, L. Satellite-based drought monitoring in Kenya in an operational setting. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2015, XL-7/W3, 433-439
72. Vuolo, F.; Mattiuzzi, M.; Klisch, A.; Atzberger, C. Data Service Platform for MODIS Vegetation Indices Time Series Processing at BOKU Vienna: Current Status and Future Perspectives; Michel, U., Civco, D.L., Ehlers, M., Schulz, K., Nikolakopoulos, K.G., Habib, S., Messinger, D., Maltese, A., Eds.; SPIE: Vienna, Austria, 2012
73. Atzberger, C.; Eilers, P.H.C. Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements. Int. J. Remote Sens. 2011, 32, 3689-3709
74. Klisch, A.; Atzberger, C. Operational Drought Monitoring in Kenya Using MODIS NDVI Time Series. Remote Sens. 2016, 8
75. Mistelbauer, T.; Enenkel, M.; Wagner, W. POETS-Python Open Earth Observation Tools, Poster: GIScience 2014. Available online: http://pub-geo.tuwien.ac.at/showentry.php?ID=231873&lang=6&nohtml=1 (accessed on 14 October 2015)
76. Balint, Z.; Mutua, F.; Muchiri, P.; Omuto, C.T. Monitoring Drought with the Combined Drought Index in Kenya. In Developments in Earth Surface Processes; Elsevier B.V.: Amsterdam, The Netherlands, 2013; Volume 16, pp. 341-356
77. Berhan, G.; Tadesse, T.; Atnafu, S.; Hill, S. Drought Monitoring in Food-Insecure Areas of Ethiopia by Using Satellite Technologies. In Experiences of Climate Change Adaptation in Africa; Leal Filho, W., Ed.; Springer Berlin Heidelberg: Berlin, Germany; Heidelberg, Germany, 2011; pp. 183-200
78. UK Met Office How to Use Our Long-Range Predictions. Available online: http://www.metoffice.gov.uk/research/climate/seasonal-to-decadal/long-range/user-guide (accessed on 31 July 2014)
79. Molteni, F.; Stockdale, T.; Balmaseda, M.; Balsamo, G.; Buizza, R.; Ferranti, L.; Magnusson, L.; Mogensen, K.; Palmer, T.; Vitard, F. The New ECMWF Seasonal Forecasting System (System 4). In ECMWF Technical Memorandum (Technical Report), No. 656; European Centre for Medium-RangeWeather Forecasts: Reading, UK, 2011
80. Gneiting, T. Calibration of Medium-Range Weather Forecasts, ECMWF Technical Memorandum; European Centre for Medium-RangeWeather Forecasts: Reading, UK, 2014; Volume 719
81. Reliefweb Ethiopia Drought Fact Sheet #1 (FY200). Available online: http://reliefweb.int/report/ethiopia/ethiopia-drought-fact-sheet-1-fy-2000 (accessed on 14 October 2015)
82. UN Food and Agriculture Organization. Special Report FAO/FWP Crop and Food Supply Assessment Mission to Ethiopia 2001. Available online: http://www.fao.org/docrep/004/x9320e/x9320e00.HTM (accessed on 14 October 2015)
83. UN Food and Agriculture Organization Ethiopia. Drought-Hit Farmers Receive Emergency Aid-Pre-Famine Conditions in Pockets of the Country, 2003. Available online: http://www.fao.org/english/newsroom/news/2003/18548-en.html (accessed on 5 May 2015)
84. Meze-Hausken, E. Contrasting climate variability and meteorological drought with perceived drought and climate change in northern Ethiopia. Clim. Res. 2004, 27, 19-31
85. UN Food and Agriculture Organization Ethiopia. El Niño-Southern Oscillation (ENSO) and the Main Kiremt Rainy Season-An Assessment Using FAO's Agriculture Stress Index System (ASIS); UN Food and Agriculture Organization Ethiopia: Bole Subcity, Ethiopia, 2014
86. Zhao, H.; Gao, G.; An, W.; Zou, X.; Li, H.; Hou, M. Timescale differences between SC-PDSI and SPEI for drought monitoring in China. Phys. Chem. Earth Parts ABC 2015, in press
87. Hane, D.C.; Pumphrey, F.V. Crop Water Use Curves for Irrigation Scheduling; Agricultural Experiment, Station, Oregon State University: Corvallis, OR, USA, 1984
88. UN Emergencies Unit for Ethiopia. Livelihood disruption in cash crop and surplus producing areas-A situation analysis July 2002. Available online: http://reliefweb.int/sites/reliefweb.int/files/resources/FD6A0E2619E3ECBD85256C0C00642F45-undpeue-eth-31jul.pdf (accessed on 14 October 2015)
89. Tucker, C.; Pinzon, J.; Brown, M.; Slayback, D.; Pak, E.; Mahoney, R.; Vermote, E.; El Saleous, N. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 2005, 26, 4485-4498