Advertisement

Theoretical and Applied Climatology

, Volume 124, Issue 3–4, pp 703–721 | Cite as

A spatiotemporal analysis of droughts and the influence of North Atlantic Oscillation in the Iberian Peninsula based on MODIS imagery

  • Stefan Mühlbauer
  • Ana Cristina Costa
  • Mário Caetano
Original Paper

Abstract

Drought is among the least understood natural hazards and requires particular notice in the context of climate change. While the Mediterranean climate is by itself prone to droughts, a rise of temperatures and alteration of rainfall patterns already render the southern parts of continental Portugal and Spain highly susceptible to desertification. Precipitation in the Iberian Peninsula is mainly controlled by the large-scale mode of North Atlantic Oscillation (NAO) and is distributed with elevated variability over the cold months. Most drought studies of this region rely on meteorological data or apply information on vegetation dynamics, such as the Normalised Differenced Vegetation Index (NDVI), to indirectly investigate droughts. This paper evaluates the influence of the NAO winter index on the spatiotemporal occurrence of droughts in the Iberian Peninsula during the spring and summer seasons (March to August) for the years 2001–2005, 2007 and 2010. We applied the Vegetation Temperature Condition Index (VTCI) to identify local droughts. VTCI is a remote sensing drought index developed for reflecting soil moisture conditions in agricultural areas and combines information on land surface temperature (LST) and NDVI. As such, VTCI overcomes the shortcomings of NDVI in terms of drought monitoring. We derived biweekly information on LST and NDVI from MODIS/Terra and produced VTCI–NAO correlation maps at a confidence level of at least 90 % based on the VTCI time series. The results reflect a typical Mediterranean pattern in most parts of Iberia that is highly influenced by relief. Spring seasons are marked by great variability of precipitation, while summers persistently become dry, particularly in the south. NAO exerts its greatest influence in April and June, clearly delineating high correlation areas in the northwest and southeast with reverse patterns between the spring and early summer months. Due to the impact on water availability, the spring months are important for plant growth. At the same time, agricultural lands were found with types of land cover less resilient to droughts. The knowledge acquired in studies like the one reported here is therefore likely to be used in drought warning models for agriculture in spring.

Keywords

Normalise Difference Vegetation Index Iberian Peninsula Land Surface Temperature Land Cover Type North Atlantic Oscillation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

704_2015_1451_Fig11_ESM.gif (417 kb)
ESM 1

(GIF 417 kb)

704_2015_1451_MOESM1_ESM.tif (6.7 mb)
High Resolution image (TIFF 6814 kb)
704_2015_1451_Fig12_ESM.gif (344 kb)
ESM 2

(GIF 343 kb)

704_2015_1451_MOESM2_ESM.tif (5.6 mb)
High Resolution image (TIFF 5758 kb)
704_2015_1451_Fig13_ESM.gif (416 kb)
ESM 3

(GIF 415 kb)

704_2015_1451_MOESM3_ESM.tif (6.7 mb)
High Resolution image (TIFF 6812 kb)
704_2015_1451_Fig14_ESM.gif (399 kb)
ESM 4

(GIF 398 kb)

704_2015_1451_MOESM4_ESM.tif (6.1 mb)
High Resolution image (TIFF 6265 kb)
704_2015_1451_Fig15_ESM.gif (423 kb)
ESM 4

(GIF 423 kb)

704_2015_1451_MOESM5_ESM.tif (6.8 mb)
High Resolution image (TIFF 6915 kb)
704_2015_1451_Fig16_ESM.gif (401 kb)
ESM 6

(GIF 400 kb)

704_2015_1451_MOESM6_ESM.tif (6.1 mb)
High Resolution image (TIFF 6290 kb)
704_2015_1451_Fig17_ESM.gif (421 kb)
ESM 7

(GIF 421 kb)

704_2015_1451_MOESM7_ESM.tif (6.7 mb)
High Resolution image (TIFF 6892 kb)
704_2015_1451_Fig18_ESM.gif (401 kb)
ESM 8

(GIF 401 kb)

704_2015_1451_MOESM8_ESM.tif (6.2 mb)
High Resolution image (TIFF 6386 kb)
704_2015_1451_Fig19_ESM.gif (426 kb)
ESM 9

(GIF 425 kb)

704_2015_1451_MOESM9_ESM.tif (6.8 mb)
High Resolution image (TIFF 6951 kb)
704_2015_1451_Fig20_ESM.gif (401 kb)
ESM 10

(GIF 401 kb)

704_2015_1451_MOESM10_ESM.tif (6.1 mb)
High Resolution image (TIFF 6271 kb)
704_2015_1451_Fig21_ESM.gif (420 kb)
ESM 11

(GIF 420 kb)

704_2015_1451_MOESM11_ESM.tif (6.6 mb)
High Resolution image (TIFF 6762 kb)
704_2015_1451_Fig22_ESM.gif (396 kb)
ESM 12

(GIF 395 kb)

704_2015_1451_MOESM12_ESM.tif (6.3 mb)
High Resolution image (TIFF 6425 kb)
704_2015_1451_Fig23_ESM.gif (427 kb)
ESM 13

(GIF 427 kb)

704_2015_1451_MOESM13_ESM.tif (6.7 mb)
High Resolution image (TIFF 6890 kb)
704_2015_1451_Fig24_ESM.gif (403 kb)
ESM 14

(GIF 402 kb)

704_2015_1451_MOESM14_ESM.tif (6.2 mb)
High Resolution image (TIFF 6383 kb)
704_2015_1451_Fig25_ESM.gif (375 kb)
ESM 15

(GIF 375 kb)

704_2015_1451_MOESM15_ESM.tif (6.1 mb)
High Resolution image (TIFF 6282 kb)
704_2015_1451_Fig26_ESM.gif (372 kb)
ESM 16

(GIF 371 kb)

704_2015_1451_MOESM16_ESM.tif (5.9 mb)
High Resolution image (TIFF 6067 kb)
704_2015_1451_MOESM17_ESM.pdf (201 kb)
ESM 17 (PDF 200 kb)

References

  1. AEMET (2012) Website of the Spanish Meteorological Agency. www.aemet.es. Accessed February 2013
  2. Bayarjargal Y, Karnieli A, Bayasgalan M, Khudulmur S, Gandush C, Tucker CJ (2006) A comparative study of NOAA-AVHRR derived drought indices using change vector analysis. Remote Sens Environ 105:9–22CrossRefGoogle Scholar
  3. Bennie ATP, Hensley M (2001) Maximizing precipitation utilization in dry-land agriculture in South Africa—a review. J Hydrol 241:124–139CrossRefGoogle Scholar
  4. Bonifacio R, Dugdale G, Milford JR (1993) Sahelian rangeland production in relation to rainfall estimates from Meteosat. Int J Remote Sens 14:2695–2711CrossRefGoogle Scholar
  5. Caccamo G, Chisholm LA, Bradstock RA, Puotinen ML (2011) Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems. Remote Sens Environ 115:2626–2639CrossRefGoogle Scholar
  6. Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover and leaf area index. Remote Sens Environ 62:241–252CrossRefGoogle Scholar
  7. Carlson TN, Perry EM, Schmugge TJ (1990) Remote estimation of soil moisture availability and fractional vegetation cover for agricultural fields. Agric For Meteorol 52:45–69CrossRefGoogle Scholar
  8. Ceballos A, Martínez-Fernández J, Luengo-Ugidos MA (2004) Analysis of rainfall trend and dry periods on a pluviometric gradient representative of Mediterranean climate in Duero Basin, Spain. J Arid Environ 58:215–233CrossRefGoogle Scholar
  9. Ceballos-Barbancho A, Morán-Tejeda E, Luengo-Ugidos MA, Llorente-Pinto JM (2008) Water resources and environmental change in a Mediterranean environment: the south-west sector of the Duero river basin (Spain). J Hydrol 351:126–138CrossRefGoogle Scholar
  10. Choi M, Jacobs JM, Anderson MC, Bosch DD (2013) Evaluation of drought indices via remotely sensed data with hydrological variables. J Hydrol 476:265–273CrossRefGoogle Scholar
  11. Choudhury BJ, Ahmed NU, Idso SB, Reginato RJ, Daughtry CTS (1994) Relations between evaporation coefficients and vegetation indices studied by model simulations. Remote Sens Environ 50:1–17CrossRefGoogle Scholar
  12. Costa AC, Soares A (2009) Trends in extreme precipitation indices derived from a daily rainfall database for the South of Portugal. Int J Climatol 9(13):1956–1975CrossRefGoogle Scholar
  13. Costa AC, Soares A (2012) Local spatiotemporal dynamics of a simple aridity index in a region susceptible to desertification. J Arid Environ 87:8–18CrossRefGoogle Scholar
  14. Costa AC, Santos JA, Pinto JG (2012) Climate change scenarios for precipitation extremes in Portugal. Theor Appl Climatol 108:217–234CrossRefGoogle Scholar
  15. Dall’Olmo G, Karnieli A (2002) Monitoring phonological cycles of desert ecosystems using NDVI and LST data derived from NOAA-AVHRR imagery. Int J Remote Sens 23:4055–4071CrossRefGoogle Scholar
  16. Davenport ML, Nicholson SE (1993) On the relation between rainfall and the Normalized Difference Vegetation Index for diverse vegetation types in East Africa. Int J Remote Sens 14:2369–2389CrossRefGoogle Scholar
  17. EEA (2013) European Environmental Agency—CORINE Land Cover map 2006. http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-clc2006-100-m-version-12-2009. Accessed March 2013
  18. EUROSTAT (2013) Website of Eurostat, a Directorate-General of the European Commission about European Statistics. http://epp.eurostat.ec.europa.eu/portal/page/portal/eurostat/home/. Accessed March 2013
  19. Gao BC (1996) NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266CrossRefGoogle Scholar
  20. Ghulam A, Qin Q, Zhan Z (2007) Designing of the perpendicular drought index. Environ Geol 52:1045–1052. doi: 10.1007/s00254-006-0544-2 CrossRefGoogle Scholar
  21. Gillies RR, Carlson TN, Cui J, Kustas WP, Humes KS (1997) A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurement of the Normalized Difference Vegetation Index (NDVI) and surface radiant temperature. Int J Remote Sens 18:3145–3166CrossRefGoogle Scholar
  22. Gonzalez-Alonso F, Calle A, Casanova JL, Vazquez A, Cuevas JM (2000) Operational monitoring of drought in Spain using NOAA–AVHRR satellite images. Proceedings of 28th International Symposium on Remote Sensing of Environment 27–31 March, Cape Town, South AfricaGoogle Scholar
  23. González-Hidalgo JC, Brunetti M, De Luis M (2010) Precipitation trends in Spanish hydrological divisions, 1946–2005. Clim Res 43:215–228CrossRefGoogle Scholar
  24. Goodess CM, Jones PD (2002) Links between circulation and changes in the characteristics of Iberian rainfall. Int J Climatol 22:1593–1615CrossRefGoogle Scholar
  25. Gouveia C, Trigo RM (2011) The Impacts of the NAO on the Vegetation Activity in Iberia. In: Vicente-Serrano SM, Trigo RM (eds) Hydrological, socioeconomic and ecological impacts of the North Atlantic Oscillation in the Mediterranean Basin. Springer Verlag, Berlin-Heidelberg-New York, pp 113–128. doi: 10.1007/978-94-007-1372-7 CrossRefGoogle Scholar
  26. Gouveia C, Trigo RM, DaCamara CC, Libonati R, Pereira JMC (2008) The North Atlantic Oscillation and European vegetation dynamics. Int J Climatol 14:1835–1847. doi: 10.1002/joc.1682 CrossRefGoogle Scholar
  27. Gouveia C, Trigo RM, DaCamara CC (2009) Drought and vegetation stress monitoring in Portugal using satellite data. Nat Hazards Earth Syst Sci 9:185–195. doi: 10.5194/nhess-9-185-2009 CrossRefGoogle Scholar
  28. Goward SN, Xue Y, Czajkowski KP (2002) Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: an exploration with the simplified simple biosphere model. Remote Sens Environ 79:225–242CrossRefGoogle Scholar
  29. Hurrell JW (1995) Decadal trends in the North Atlantic Oscillation and relationships to regional temperature and precipitation. Science 269:676–679CrossRefGoogle Scholar
  30. Jang JD, Viau AA, Anctil F (2006) Thermal-water stress index from satellite images. Int J Remote Sens 27:1619–1639CrossRefGoogle Scholar
  31. Ji L, Peters AJ (2003) Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sens Environ 87:85–98CrossRefGoogle Scholar
  32. Jones PD, Jonsson T, Wheeler D (1997) Extension to the North Atlantic Oscillation using early instrumental pressure observations from Gibraltar and South-West Iceland. Int J Climatol 17:1433–1450CrossRefGoogle Scholar
  33. Julien Y, Sobrino JA, Verhoef W (2006) Changes in land surface temperature and NDVI values over Europe between 1982 and 1999. Remote Sens Environ 103:43–55CrossRefGoogle Scholar
  34. Karnieli A, Dall’Olmo G (2003) Remote sensing monitoring of desertification, phenology and droughts. Manag Environ Qual 14:22–38CrossRefGoogle Scholar
  35. Karnieli A, Agam N, Pinker RT, Anderson M, Imhoff ML, Gutman GG, Panov N, Goldberg A (2010) Use of NDVI and land surface temperature for drought assessment: merits and limitations. J Clim 23:618–633CrossRefGoogle Scholar
  36. Kogan FN (1990) Remote sensing of weather impacts on vegetation in non-homogeneous areas. Int J Remote Sens 11:1405–1419CrossRefGoogle Scholar
  37. Kogan FN (1995) Application of vegetation index and brightness temperature for drought detection. Adv Space Res 15:91–100CrossRefGoogle Scholar
  38. Kogan FN (1997) Global drought watch from space. Bull Am Meteorol Soc 78:621–636CrossRefGoogle Scholar
  39. Kogan FN (2000) Contribution of remote sensing to drought early warning. In: Wilhite DA, Sivakumar MVK, Wood DA (eds) Early warning systems for drought preparedness and drought management. Proc. Expert Group Meeting, 5–7 September, Lisbon (Portugal). World Meteorological Organization, Geneve, pp 86–100Google Scholar
  40. Lasanta T, Vicente-Serrano SM (2012) Complex land cover change processes in semiarid Mediterranean regions: an approach using Landsat images in northeast Spain. Remote Sens Environ 124:1–14CrossRefGoogle Scholar
  41. Lazzarini M, Marpu PR, Ghedira H (2013) Temperature-land cover interactions: the inversion of urban heat island phenomenon in desert city areas. Remote Sens Environ 130:136–152CrossRefGoogle Scholar
  42. Le Houerou HN (1996) Climate change, drought and desertification. J Arid Environ 34:133–185CrossRefGoogle Scholar
  43. Liu WT, Negron-Juarez RI (2001) ENSO drought onset prediction in northeast Brazil using NDVI. Int J Remote Sens 22:3483–3501CrossRefGoogle Scholar
  44. Lobo A, Ibanez Marti JJ, Carrera Giménez-Cassina C (1997) Regional scale hierarchical classification of temporal series of AVHRR vegetation index. Int J Remote Sens 18(15):3167–3193CrossRefGoogle Scholar
  45. Martín-Rosales W, Pulido-Bosch A, Vallejos A, Gisbert J, Andreu JM, Sánchez-Martos F (2007) Hydrological implications of desertification in southeastern Spain. Hydrol Sci J 52(6):1146–1161CrossRefGoogle Scholar
  46. Martins DS, Raziei T, Paulo AA, Pereira LS (2012) Spatial and temporal variability of precipitation and drought in Portugal. Nat Hazards Earth Syst Sci 12:1493–1501CrossRefGoogle Scholar
  47. Martín-Vide J, Fernández D (2001) El ídice NAO y la precipitación mensual en la España peninsular. Invest Geogr 26:41–58Google Scholar
  48. Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391:202–216. doi: 10.1016/j.jhydrol.2010.07.012 CrossRefGoogle Scholar
  49. Moran MS, Clarke TR, Inque U, Vidal A (1994) Estimating crop water deficit using the relation between surface air temperature and spectral vegetation index. Remote Sens Environ 49:246–263CrossRefGoogle Scholar
  50. Moratiel R, Snyder RL, Durán JM, Tarquis AM (2011) Trends in climatic variables and future reference evapotranspiration in Duero Valley (Spain). Nat Hazards Earth Syst Sci 11:1795–1805CrossRefGoogle Scholar
  51. Nemani R, Pierce L, Running S, Goward S (1993) Developing satellite-derived estimates of surface moisture status. J Appl Meteorol 32:548–557CrossRefGoogle Scholar
  52. Nicholson SE, Tucker CJ, Ba MB (1998) Desertification, drought and surface vegetation: an example from the West African Sahel. Bull Am Meteorol Soc 79:815–829CrossRefGoogle Scholar
  53. Osborn TJ, Briffa KR, Tett SFB, Jones PD, Trigo RM (1999) Evaluation of the North Atlantic Oscillation as simulated by a climate model. Clim Dyn 15:685–702CrossRefGoogle Scholar
  54. Paolo AA, Rosa RD, Pereira LS (2012) Climate trends and behaviour of drought indices based on precipitation and evapotranspiration in Portugal. Nat Hazards Earth Syst Sci 12:1481–1491CrossRefGoogle Scholar
  55. Paredes D, Trigo RM, Garcia-Herrera R, Franco Trigo I (2006) Understanding precipitation changes in Iberia in early spring: weather typing and storm-tracking approaches. J Hydrometeorol 7:101–113CrossRefGoogle Scholar
  56. Parida BR, Oinam B, Patel NR, Sharma N, Kandwal R, Hazarika MK (2008) Land surface temperature variation in relation to vegetation type using MODIS satellite data in Gujarat state of India. Int J Remote Sens 29(14):4219–4235CrossRefGoogle Scholar
  57. Park S, Feddema JJ, Egbert SL (2004) Impacts of hydrologic soil properties on drought detection with MODIS thermal data. Remote Sens Environ 89:53–62CrossRefGoogle Scholar
  58. Patel NR, Barida BR, Venus V, Saha SK, Dadhwal VK (2011) Analysis of agricultural drought using vegetation temperature condition index from terra/MODIS satellite data. Environ Monit Assess 184(12):7153–7163. doi: 10.1007/s10661-011-2487-7 CrossRefGoogle Scholar
  59. Pereira LS, Paulo AA (2004) Droughts: concepts indices and prediction. In: Hamdy A, Trisorio-Liuzzi G (eds) Options méditerranéennes, series B, studies and research, water management for draught mitigation in the Mediterranean, vol 471. International Centre for Advanced Mediterranean Agronomic Studies, Bari, pp 13–144Google Scholar
  60. Peters AJ, Reed BC, Eva MD, Havstad KM (1993) Satellite assessment of drought impact on native plant communities of southeastern New Mexico, U.S.A. J Arid Environ 24:305–319CrossRefGoogle Scholar
  61. Prihodko L, Goward SN (1997) Estimation of air temperature from remotely sensed surface observations. Remote Sens Environ 60:335–346CrossRefGoogle Scholar
  62. Rhee J, Im J, Carbone GC (2010) Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sens Environ 114:2875–2887CrossRefGoogle Scholar
  63. Rodríguez-Puebla C, Nieto S (2010) Trends of precipitation over the Iberian Peninsula and the North Atlantic oscillation under climate change conditions. Int J Climatol 30(12):1807–1815Google Scholar
  64. Rodríguez-Puebla C, Encinas AH, Nieto S, Garmendia J (1998) Spatial and temporal patterns of annual precipitation variability over the Iberian Peninsula. Int J Climatol 18:299–316CrossRefGoogle Scholar
  65. Rojas O, Vrieling A, Rembold F (2011) Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sens Environ 115:343–352CrossRefGoogle Scholar
  66. Rouse JW, Hass RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the Great Plains with ERTS. In: Freden SC, Mercanti EP, Becker MA (eds) Proceedings of the 3rd Earth Resources Technology Satellite-1 symposium, Volume 1: Technical Presentations, p 309−317. Washington DC. NASA SP-351Google Scholar
  67. Salinas-Zavala CA, Douglas AV, Diaz HF (2002) Inter-annual variability of NDVI in northwest Mexico. Associated climatic mechanisms and ecological implications. Remote Sens Environ 82:417–430CrossRefGoogle Scholar
  68. Sandholt I, Rasmussen K, Andersen J (2002) A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sens Environ 79:213–224CrossRefGoogle Scholar
  69. Sannier CAD, Taylor JC (1998) Real-time vegetation monitoring with NOAA/AVHRR in Southern Africa for wildlife management and food security assessment. Int J Remote Sens 19:621–639CrossRefGoogle Scholar
  70. Santos J, Corte-Real J, Leite S (2007) Atmospheric large-scale dynamics during the 2004/2005 winter drought in Portugal. Int J Climatol 27:571–586. doi: 10.1002/joc.1425 CrossRefGoogle Scholar
  71. Santos J, Andrade C, Corte-Real J, Leite S (2009) The role of large-scale eddies in the occurrence of winter precipitation deficits in Portugal. Int J Climatol 29:1493–1507CrossRefGoogle Scholar
  72. Santos JF, Pulido-Calvo I, Portela MM (2010) Spatial and temporal variability of droughts in Portugal. Water Resour Res 46, W03503. doi: 10.1029/2009WR008071 CrossRefGoogle Scholar
  73. Singh RP, Roy S, Kogan F (2003) Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. Int J Remote Sens 24(22):4393–4402CrossRefGoogle Scholar
  74. Solano R, Didan K, Jacobson A, Huete A (2010) MODIS Vegetation Index Users’ Guide (MOD13 Series). Collection 5, Vegetation Index and Phenology Lab, University of Arizona (http://vip.arizona.edu) 1 – 42
  75. Stisen S, Sandholt I, Nørgaard A, Fensholt R, Eklundh L (2007) Estimation of diurnal air temperature using MSG SEVIRI data in West Africa. Remote Sens Environ 110:262–274CrossRefGoogle Scholar
  76. Sun W, Wang PX, Zhang SY, Zhu DH, Liu JM, Chen JH, Yang HS (2008) Using the vegetation temperature condition index for time series drought occurrence monitoring in the Guanzhong Plain, PR China. Int J Remote Sens 29:5133–5144CrossRefGoogle Scholar
  77. Trigo RM, Osborn TJ, Corte-Real JM (2002) The North Atlantic Oscillation influence on Europe: climate impacts and associated physical mechanisms. Clim Res 20:9–17CrossRefGoogle Scholar
  78. Trigo RM, Pozo-Vázquez D, Osborn TJ, Castro-Díez Y, Gámiz-Fortis S, Esteban-Parra MJ (2004) North Atlantic Oscillation influence on precipitation, river flow and water resources in the Iberian Peninsula. Int J Climatol 24:925–944CrossRefGoogle Scholar
  79. USDA (1994) Major world crop areas and climatic profiles. World Agricultural Outlook Board. US Dep Agric Agric Handb 664:157–170Google Scholar
  80. Vicente-Serrano SM (2006) Spatial and temporal analysis of droughts in the Iberian Peninsula (1910-2000)/Analyse spatio-temporelle des sécheresses dans la Péninsule Ibérique (1910-2000) Résume. Hydrol Sci J 51:83–97CrossRefGoogle Scholar
  81. Vicente-Serrano SM (2007) Evaluating the impact of drought using remote sensing in a Mediterranean semi-arid region. Nat Hazards 40:173–208CrossRefGoogle Scholar
  82. Vicente-Serrano SM, Heredia-Laclaustra A (2004) NAO influence on NDVI trends in the Iberian Peninsula (1982–2000). Int J Remote Sens 25(14):2871–2879CrossRefGoogle Scholar
  83. Vicente-Serrano SM, Trigo RM (2011) Introduction. In: Vicente-Serrano SM, Trigo RM (eds) Hydrological. Socioeconomic and Ecological Impacts of the North Atlantic Oscillation in the Mediterranean Region. Springer-Verlag, Berlin-Heidelberg-New York, pp 1–8. doi: 10.1007/978-94-007-1372-7 Google Scholar
  84. Vicente-Serrano SM, Cuadrat JM, Romo A (2006) Aridity influence on vegetation patterns in the middle Ebro valley (Spain): evaluation by means of AVHRR images and climate interpolation techniques. J Arid Environ 66(2):353–375. doi: 10.1016/j.jaridenv.2005.10.021 CrossRefGoogle Scholar
  85. Wan Z (2007) MODIS Land Surface Temperature Products Users’ Guide, Collection 5. ICESS. University of California, Santa Barbara, pp 1–30Google Scholar
  86. Wan Z, Wang P, Li X (2004) Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index products for monitoring drought in the Southern Great Plains, USA. Int J Remote Sens 25(1):61–72CrossRefGoogle Scholar
  87. Wang PX, Li X, Gong JY, Song C (2001) Vegetation temperature condition index and its application for drought monitoring. IEEE 2001 International Geoscience And Remote Sensing Symposium (IGARSS), 9-13 July 2001. The University of New South Wales, SydneyGoogle Scholar
  88. Wilhite DA, Svoboda MD, Hayes MJ (2007) Understanding the complex impacts of drought: a key to enhancing drought mitigation and preparedness. Water Resour Manag 21(5):763–774. doi: 10.1007/s11269-006-9076-5 CrossRefGoogle Scholar
  89. Wu C, Chen JM, Desai AR, Hollinger DY, Arain MA, Margolis HA, Gough CM, Staebler RM (2012) Remote sensing of canopy light use efficiency in temperature and boreal forests of North America using MODIS imagery. Remote Sens Environ 118:60–72CrossRefGoogle Scholar
  90. Xoconostle-Cazares B, Ramirez-Ortega FA, Flores-Elenes L, Ruiz-Medrano R (2010) Drought tolerance in crop plants. Am J Plant Physiol 5(5):241–256. doi: 10.3923/ajpp.2010.241.256 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  • Stefan Mühlbauer
    • 1
  • Ana Cristina Costa
    • 1
  • Mário Caetano
    • 1
  1. 1.ISEGI, Universidade Nova de LisboaLisboaPortugal

Personalised recommendations