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A remote sensing-based agricultural drought indicator and its implementation over a semi-arid region, Jordan

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Abstract

The objective of the study was to develop a remote sensing (i.e., Landsat-8 and MODIS)-based agricultural drought indicator (ADI) at 30-m spatial resolution and 8-day temporal resolution and also to evaluate its performance over a heterogeneous agriculture dominant semi-arid region in Jordan. Firstly, we used principal component analysis (PCA) to evaluate the correlations among six commonly used remote sensing-derived agricultural drought related variables. The variables included normalized difference water index (NDWI), normalized difference vegetation index (NDVI), visible and shortwave drought index (VSDI), normalized multiband drought index (NMDI), moisture stress index (MSI), and land surface temperature (LST). Secondly, we integrated the relatively less correlated variables (that were found to be NDWI, VSDI, and LST) to generate four agricultural drought categories/conditions (i.e., wet, mild drought, moderate drought, and severe drought). Finally, we evaluated the ADI maps against a set of 8-day ground-based standardized precipitation index values (i.e., SPI-1, SPI-2, …, SPI-8) by use of confusion matrices and observed the best results for SPI-4 (i.e., overall accuracy and Kappa-values were 83% and 76%, respectively) and SPI-5 (i.e., overall accuracy and Kappa-values were 85% and 78%, respectively). The results demonstrated that the method would be valuable for monitoring agricultural drought conditions in semi-arid regions at both a reasonably high spatial resolution (i.e., 30-m) and a short time period (i.e., 8-day).

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References

  • AghaKouchak A, Farahmand A, Melton F S, et al. 2015. Remote sensing of drought: progress, challenges and opportunities. Reviews of Geophysics, 53(2): 452–480.

    Article  Google Scholar 

  • Akbar T A, Hassan Q K, Achari G. 2011. A methodology for clustering lakes in Alberta on the basis of water quality parameters. Clean-Soil, Air, Water, 39(10): 916–924.

    Article  Google Scholar 

  • Akther M S, Hassan Q K. 2011a. Remote sensing based estimates of surface wetness conditions and growing degree days over northern Alberta, Canada. Boreal Environment Research, 16(5): 407–416.

    Google Scholar 

  • Akther M S, Hassan Q K. 2011b. Remote sensing-based assessment of fire danger conditions over boreal forest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(4): 992–999.

    Article  Google Scholar 

  • Al-Qinna M I, Hammouri N A, Obeidat M M, et al. 2010. Drought analysis in Jordan under current and future climates. Climatic Change, 106(3): 421–440.

    Article  Google Scholar 

  • Anderson M C, Hain C, Wardlow B, et al. 2011. Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the Continental United States. Journal of Climate, 24(8): 2025–2044.

    Article  Google Scholar 

  • Anjum S A, Xie X, Wang L, et al. 2011. Morphological, physiological and biochemical responses of plants to drought stress. African Journal of Agricultural Research, 6(9): 2026–2032.

    Google Scholar 

  • Benmecheta A, Abdellaoui A, Hamou A. 2013. A comparative study of land surface temperature retrieval methods from remote sensing data. Canadian Journal of Remote Sensing, 39(1): 59–73.

    Article  Google Scholar 

  • Boken V K, Cracknell A P, Heathcote R L. 2005. Monitoring and Predicting Agricultural Drought: A Global Study. New York: Oxford University Press, 472.

    Google Scholar 

  • Brown J F, Wardlow B D, Tadesse T, et al. 2013. The vegetation drought response index (VegDRI): a new integrated approach for monitoring drought stress in vegetation. GIScience & Remote Sensing, 45(1): 16–46.

    Article  Google Scholar 

  • Brown L, Chen J M, Leblanc S G, et al. 2000. A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: an image and model analysis. Remote Sensing of Environment, 71(1): 16–25.

    Article  Google Scholar 

  • Brown M E. 2008. Derived agricultural and climate monitoring products. In: Brown M E, ed. Famine Early Warning Systems and Remote Sensing Data. Berlin: Springer-Verlag, 83–96.

    Chapter  Google Scholar 

  • Chakraborty A, Sehgal V K. 2010. Assessment of agricultural drought using MODIS derived normalized difference water index. Journal of Agricultural Physics, 10: 28–36.

    Google Scholar 

  • Chowdhury E H, Hassan Q K. 2013. Use of remote sensing-derived variables in developing a forest fire danger forecasting system. Natural Hazards, 67(2): 321–334.

    Article  Google Scholar 

  • Chowdhury E H, Hassan Q K. 2015. Development of a new daily-scale forest fire danger forecasting system using remote sensing data. Remote Sensing, 7(3): 2431–2448.

    Article  Google Scholar 

  • FAO/WFP. 1999. FAO/WFP Crop and Food Supply Assessment Mission to the Kingdom of Jordan. Rome: FAO. [2015-12-16]. http://www.fao.org/docrep/004/x2207e/x2207e00.htm#P176_17001.

    Google Scholar 

  • Gao B C. 1996. NDWI-a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3): 257–266.

    Article  Google Scholar 

  • Ghulam A, Li Z L, Qin Q M, et al. 2008. Estimating crop water stress with ETM+ NIR and SWIR data. Agricultural and Forest Meteorology, 148(11): 1679–1695.

    Article  Google Scholar 

  • Gu Y, Hunt E, Wardlow B, et al. 2008. Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data. Geophysical Research Letters, 35(22): L22401, doi: 10.1029/2008GL035772.

    Article  Google Scholar 

  • Hao Z C, AghaKouchak A, Nakhjiri N, et al. 2014. Global integrated drought monitoring and prediction system. Scientific Data, 1: 140001.

    Article  Google Scholar 

  • Hao Z H, AghaKouchak A. 2013. Multivariate standardized drought index: a parametric multi-index model. Advances in Water Resources, 57: 12–18.

    Article  Google Scholar 

  • Hazaymeh K, Hassan Q K. 2015a. Fusion of MODIS and Landsat-8 surface temperature images: a new approach. PLoS One, 10(3): e0117755.

    Article  Google Scholar 

  • Hazaymeh K, Hassan Q K. 2015b. Spatiotemporal image-fusion model for enhancing the temporal resolution of Landsat-8 surface reflectance images using MODIS images. Journal of Applied Remote Sensing, 9(1): 096095.

    Article  Google Scholar 

  • Hazaymeh K, Hassan Q K. 2016. Remote sensing of agricultural drought monitoring: a state of art review. Aims Environmental Science, 3(4): 604–630.

    Article  Google Scholar 

  • Hunt E R Jr, Rock B N. 1989. Detection of changes in leaf water content using near-and middle-infrared reflectances. Remote Sensing of Environment, 30(1): 43–54.

    Article  Google Scholar 

  • Jackson R D, Idso S B, Reginato R J, et al. 1981. Canopy temperature as a crop water stress indicator. Water Resources Research, 17(4): 1133–1138.

    Article  Google Scholar 

  • Jang J D, Viau A A, Anctil F. 2006. Thermal-water stress index from satellite images. International Journal of Remote Sensing, 27(8): 1619–1639.

    Article  Google Scholar 

  • Jensen J R. 2005. Introductory Digital Image Processing: A Remote Sensing Perspective (3rd ed.). New Jersey: Prentice Hall, 526.

    Google Scholar 

  • JMWI (Jordanian Ministry of Water and Irrigation). 2014. [2015-12-16]. http://mwi.gov.jo/sites/ar-jo/Lists/List1/DispForm.aspx?ID=196. (in Arabic)

  • Kogan F. 2002. World droughts in the new millennium from AVHRR-based vegetation health indices. EOS, Transactions American Geophysical Union, 83(48): 557–563.

    Article  Google Scholar 

  • Lambin E F, Ehrlich D. 1996. The surface temperature-vegetation index space for land cover and land-cover change analysis. International Journal of Remote Sensing, 17(3): 463–487.

    Article  Google Scholar 

  • Li J, Heap A D. 2014. Spatial interpolation methods applied in the environmental sciences: a review. Environmental Modelling & Software, 53: 173–189.

    Article  Google Scholar 

  • Lloyd-Hughes B, Saunders M A. 2002. A drought climatology for Europe. International Journal of Climatology, 22(13): 1571–1592.

    Article  Google Scholar 

  • Logan K E, Brunsell N A, Jones A R, et al. 2010. Assessing spatiotemporal variability of drought in the U.S. central plains. Journal of Arid Environments, 74(2): 247–255.

    Article  Google Scholar 

  • McKee T B, Doesken N J, Kleist J. 1993. The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology. Anaheim, California: Conference on Applied Climatology, 179–184.

    Google Scholar 

  • McVicar T R, Jupp D L B. 1998. The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: a review. Agricultural Systems, 57(3): 399–468.

    Article  Google Scholar 

  • Milton-Edwards B, Hinchcliffe P. 2009. Jordan: A Hashemite Legacy (2nd ed.). New York: Routledge, 147.

    Google Scholar 

  • Palmer W C. 1965. Meteorological Drought. Washington D.C.: U.S. Weather Bureau, Research Paper No. 45: 58.

    Google Scholar 

  • Palmer W C. 1968. Keeping track of crop moisture conditions, nationwide: the new crop moisture index. Weatherwise, 21(4): 156–161.

    Article  Google Scholar 

  • Ranjan R, Sahoo R N, Chopra U K, et al. 2015. Assessment of water status in wheat (Triticum aestivum L.) using ground based hyperspectral reflectance. In: Proceedings of the National Academy of Sciences, India Section B: Biological Sciences. India: Springer, 1–12, doi: 10.1007/s40011-015-0618-6.

    Google Scholar 

  • Saba M, Al-Naber G. 2010. Analysis of Jordan vegetation cover dynamics using MODIS/NDVI from 2000 to 2009. In: Proceedings of the International Conference of Food Security and Climate Change in Dry Areas. Amman: International Center for Agricultural Research in the Dry Areas (ICARDA).

    Google Scholar 

  • Samanta A, Ganguly S, Myneni R B. 2011. MODIS enhanced vegetation index data do not show greening of amazon forests during the 2005 drought. New Phytologist, 189(1): 11–15, doi: 10.1111/j.1469-8137.2010.03516.x.

    Article  Google Scholar 

  • Sandholt I, Rasmussen K, Andersen J. 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79(2–3): 213–224.

    Article  Google Scholar 

  • Sha Z Y, Zhong J L, Bai Y F, et al. 2016. Spatio-temporal patterns of satellite-derived grassland vegetation phenology from 1998 to 2012 in Inner Mongolia, China. Journal of Arid Land, 8(3): 462–477.

    Article  Google Scholar 

  • Shahabfar A, Ghulam A, Eitzinger J. 2012. Drought monitoring in Iran using the perpendicular drought indices. International Journal of Applied Earth Observation and Geoinformation, 18: 119–127.

    Article  Google Scholar 

  • Sims A P, Niyogi D D S, Raman S. 2002. Adopting drought indices for estimating soil moisture: a North Carolina case study. Geophysical Research Letters, 29(8): 1183.

    Article  Google Scholar 

  • Svoboda M, LeComte D, Hayes M, et al. 2002. The drought monitor. Bulletin of the American Meteorological Society, 83: 1181–1190.

    Article  Google Scholar 

  • Tucker C J, Choudhury B J. 1987. Satellite remote sensing of drought conditions. Remote Sensing of Environment, 23(2): 243–251.

    Article  Google Scholar 

  • USGS. 2013. Using the USGS Landsat 8 product. USA: USGS. [2015-12-16]. http://landsat.usgs.gov/Landsat8_Using_Product.php.

    Google Scholar 

  • van Wesemael B, Cammeraat E, Mulligan M, et al. 2003. The impact of soil properties and topography on drought vulnerability of rainfed cropping systems in southern Spain. Agriculture, Ecosystems & Environment, 94(1): 1–15.

    Article  Google Scholar 

  • Wang W, Huang D, Wang X G, et al. 2011. Estimation of soil moisture using trapezoidal relationship between remotely sensed land surface temperature and vegetation index. Hydrology and Earth System Sciences, 15(5): 1699–1712.

    Article  Google Scholar 

  • Wilhite D A, Svoboda M D, Hayes M J. 2007. Understanding the complex impacts of drought: a key to enhancing drought mitigation and preparedness. Water Resources Management, 21(5): 763–774.

    Article  Google Scholar 

  • Wu H, Wilhite D A. 2004. An operational agricultural drought risk assessment model for Nebraska, USA. Natural Hazards, 33(1): 1–21.

    Article  Google Scholar 

  • Wu H, Svoboda M D, Hayes M J, et al. 2007. Appropriate application of the standardized precipitation index in arid locations and dry seasons. International Journal of Climatology, 27(1): 65–79.

    Article  Google Scholar 

  • Wu J J, Zhou L, Liu M, et al. 2013. Establishing and assessing the integrated surface drought index (ISDI) for agricultural drought monitoring in mid-eastern China. International Journal of Applied Earth Observation and Geoinformation, 23: 397–410.

    Article  Google Scholar 

  • Zhang N, Hong Y, Qin Q M, et al. 2013. VSDI: a visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing. International Journal of Remote Sensing, 34(13): 4585–4609.

    Article  Google Scholar 

  • Zhou L, Lyu A. 2016. Investigating natural drivers of vegetation coverage variation using MODIS imagery in Qinghai, China. Journal of Arid Land, 8(1): 109–124.

    Article  Google Scholar 

  • Zhu G L, Ju W M, Chen J M, et al. 2014. A novel moisture adjusted vegetation index (MAVI) to reduce background reflectance and topographical effects on LAI retrieval. PLoS ONE, 9(7): e102560.

    Article  Google Scholar 

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Acknowledgements

We would like to thank the University of Calgary, Canada and Yarmouk University, Jordan for providing partial financial support in the form of awards to Mr. Khaled HAZAYMEH and the National Sciences and Engineering Research Council (NSERC), Canada for a Discovery grant to Dr. Quazi HASSAN. We would also like to thank USGS, NASA, and the Jordanian Ministry of Water and Irrigation for providing the required data at no cost.

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Authors and Affiliations

  1. Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada

    Khaled Hazaymeh & Quazi K. Hassan

  2. Department of Geography, Faculty of Arts, Yarmouk University, Irbid, 21163, Jordan

    Khaled Hazaymeh

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  1. Khaled Hazaymeh
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  2. Quazi K. Hassan
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Correspondence to Quazi K. Hassan.

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Hazaymeh, K., Hassan, Q.K. A remote sensing-based agricultural drought indicator and its implementation over a semi-arid region, Jordan. J. Arid Land 9, 319–330 (2017). https://doi.org/10.1007/s40333-017-0014-6

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  • DOI: https://doi.org/10.1007/s40333-017-0014-6

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