Remote-sensing estimation of the water stress coefficient and comparison with drought evidence

Nesrine Abid (Corresponding Author), Zoubeida Bargaoui, Chris M. Mannaerts

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
1 Downloads (Pure)

Abstract

Drought assessment of croplands and sylvo-pastoral areas is crucial in semi-arid regions. Satellite remote sensing offers an opportunity for such assessment. This study presents a method of spatial and temporal estimation of drought index in Medjerda basin (23,700 km2) using satellite data and its validation with in situ investigation of areas with crop damage realized by the ministry of agriculture. To estimate drought index, potential evapotranspiration (PET) is calculated using Penman–Monteith equation and modified FAO-56 crop coefficient (Kc) approach combined with remote-sensing data and actual evapotranspiration is derived from the Meteosat Second Generation platforms. The period of study is the 2010 water year. PET estimations show good accuracy with corrected pan evaporation observations up to 0.9. In comparison, the water stress coefficient (Ks) aggregated by land-cover type shows the coefficient of determination with the fraction of drought damage areas of 0.5 for the third decade of March and first decade of April in croplands areas and 0.8 for the second and third decades of May in croplands and sylvo-pastoral areas. This study showed that satellite data approaches could successfully be used to monitor drought in river basins in the Northern Africa and Mediterranean region.
Original languageEnglish
Pages (from-to)4616-4639
Number of pages24
JournalInternational journal of remote sensing
Volume39
Issue number14
Early online date26 Jan 2018
DOIs
Publication statusPublished - 1 Jun 2018

Keywords

  • UT-Hybrid-D
  • ITC-ISI-JOURNAL-ARTICLE
  • Stress coefficient (K ) actual evapotranspiration (AET)
  • Satellite Application Facility (LSA SAF)
  • FVC
  • Crop coefficient (K )
  • Drought
  • NDVI
  • Water stress
  • Tunisia
  • Remote sensing

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