Remote sensing-based monitoring of the spatiotemporal characteristics of drought using hydro-meteorological indices

References 
 
Abbaspour M,  Sabetraftar A. 2005. Review of cycles and indices of drought and their effect on water       resources, ecological, biological, agricultural, social and economic issues in Iran. Int J Environ Stud       62: 709–724. https://doi.org/10.1080/00207230500288968. AghaKouchak A, Farahmand A, Melton FS, Teixeira J, Anderson MC, Wardlow BD, Hain CR. 2015.       Remote sensing of drought: progress, challenges and opportunities. Geophys Rev.       https://doi.org/10.1002/2014RG000456. Almamalachy YS, Al-Quraishi AMF, Moradkhani H. 2020. Agricultural drought monitoring over Iraq       utilizing MODIS products. Environ. Remote Sens GIS Iraq 10: 253–278,       https://doi.org/10.1007/978-3-030-21344-2_11. Anderson MC, Hain C, Wardlow B, Pimstein A, Mecikalski JR, Kustas WP. 2011. Evaluation of drought       indices based on thermal remote sensing of evapotranspiration over the continental United States. J       Clim 24: 2025-2044. https://doi.org/10.1175/2010JCLI3812.1. Anyamba A, Tucker CJ, Eastman JR. 2001. NDVI anomaly patterns over Africa during the 1997/98       ENSO warm event. Int J Remote Sens 22: 1847–1860. https://doi.org/10.1080/01431160010029156. Artis DA. 1982. Carnahan WH Survey of emissivity variability in thermography of urban areas. Remote       Sens Environ 12: 313–329. https://doi.org/10.1016/0034-4257 (82)90043-8. Bazrafshan J, Khalili A. 2013. Spatial Analysis of Meteorological Drought in Iran from 1965 to 2003.       Desert 18: 63-71. Below R, Grover-Kopec E, Dilley M. 2007. Documenting drought-related disasters: A global       reassessment. J Environ. Dev 16: 328–344. https://doi.org/10.1177%2F1070496507306222. Bento VA, Gouveia CM, DaCamara CC, Trigo IF. 2018. A climatological assessment of drought impact       on vegetation health index. Agric. for Meteorol 259: 286–295.       https://doi.org/10.1016/j.agrformet.2018.05.014. Bhuiyan C. 2004. Various drought indices for monitoring drought condition in Aravalli terrain of India.       Proc XXth ISPRS Congr, Istanbul, Turkey, pp 12–23. Chezgi J, Pourghasemi HR, Naghibi SA, Moradi HR, KheirkhahZarkesh M. 2016. Assessment of a       spatial multi-criteria evaluation to site selection underground dams in the Alborz Province, Iran.       Geocarto Int 31: 628–646. https://doi.org/10.1080/10106049.2015.1073366. Donia N. 2019. NDWI Based Change Detection Analysis of Qarun Lake Coastal Area, El-Fayoum,       Egypt. Adv Remote Sens Geo Informatics Appl 25: 121–124.  https://doi.org/10.1007/978-3-030-      01440-7_29. Du L, Tian Q, Yu T, Meng Q, Jancso T, Udvardy P, Huang Y. 2013. A comprehensive drought       monitoring method integrating MODIS and TRMM data. J Appl Earth Obs Geoinf Int 23: 245-253.       https://doi.org/10.1016/j.jag.2012.09.010. Duan T, Chapman SC, Guo Y, Zheng B. 2017. Dynamic monitoring of NDVI in wheat agronomy and       breeding trials using an unmanned aerial vehicle. F Crop Res 210: 71–80.       https://doi.org/10.1016/j.fcr.2017.05.025. Gao B-C. 1996. NDWI-A normalized difference water index for remote sensing of vegetation liquid       water from space. Remote Sens Environ 58: 257–266. Guo H, Bao A, Liu T, Jiapaer G, Ndayisaba F, Jiang L, Kurban A, De Maeyer P. 2018. Spatial and       temporal characteristics of droughts in Central Asia during 1966–2015. Sci Total Environ 624, 1523–      1538. https://doi.org/10.1016/j.scitotenv.2017.12.120. Guhathakurta P, Menon P, Inkane PM, Krishnan U, Sable ST. 2017. Trends and variability of       meteorological drought over the districts of India using standardized precipitation index. J Earth Syst       Sci 126: 120-140. https://doi.org/10.1007/s12040-017-0896-x. Halder S, Roy MB, Roy PK. 2020. Analysis of groundwater level trend and groundwater drought using        Standard Groundwater Level Index: a case study of an eastern river basin of West Bengal, India,       SN. Appl Sci 2: 1–24. https://doi.org/10.1007/s42452-020-2302-6. Hao Z, Singh VP. 2015. Drought characterization from a multivariate perspective: A review. J Hydrol       527: 668–678. https://doi.org/10.1016/j.jhydrol.2015.05.031. Hao C, Zhang J, Yao F. 2015. Combination of multi-sensor remote sensing data for drought monitoring       over Southwest China. J Appl Earth Obs Geoinf Int 35: 270-283.        https://doi.org/10.1016/j.jag.2014.09.011. 
356                                                                                                                       DESERT, 27-2, 2022 
 
He Z, Vorogushyn S, Unger-Shayesteh K, Gafurov A, Kalashnikova O, Hagenlocher M. 2018.       Attribution of vegetation health index (VHI) changes to runoff changes in headwater catchments in       the Chu river basin, Central Asia. EGU Gen Assem Conf Abstr, pp 86-100. Heydari H, ValadanZoe, M, Maghsoudi Y, Dehnavi S. 2018. An investigation of drought prediction       using various remote-sensing vegetation indices for different time spans. Int J Remote Sens 39:       1871–1889. https://doi.org/10.1080/01431161.2017.1416696. Hu T, Renzullo LJ, VanDijk AIJM, He J, Tian S, Xu Z. 2020. Monitoring agricultural drought in       Australia using MTSAT-2 land surface temperature retrievals. Remote Sens Environ 236: 111-119.       https://doi.org/10.1016/j.rse.2019.111419. Ji L, Peters AJ. 2003. Assessing vegetation response to drought in the northern Great Plains using       vegetation and drought indices. Remote Sens Environ 87: 85–98. https://doi.org/10.1016/S0034-      4257(03)00174-3. Kaisermann A, de Vries FT, Griffiths RI, Bardgett RD. 2017. Legacy effects of drought on plant–soil       feedbacks and plant–plant interactions. New Phytol 215: 1413–1424.       https://doi.org/10.1111/nph.14661. Kleijnen JPC. 2017. Regression and Kriging metamodels with their experimental designs in simulation:       a review. Eur J Oper Res 256: 1–16. https://doi.org/10.1016/j.ejor.2016.06.041. Kogan FN. 1995. Application of vegetation index and brightness temperature for drought detection. Adv       Sp Res 15: 91–100. https://doi.org/10.1016/0273-1177(95)00079-T. Kogan FN. 1997. Global drought watch from space. Bull Am Meteorol Soc 78: 621–636.       https://doi.org/10.1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2. Kogan FN. 2001. Operational space technology for global vegetation assessment. Bull Am Meteorol       Soc 82:1949–1964.https://doi.org/10.1175/1520-0477(2001)082%3C1949:OSTFGV%3E2.3.CO;2. Kong D, Zhang Q, Singh VP, Shi P. 2017. Seasonal vegetation response to climate change in the       Northern Hemisphere (1982–2013). Glob Planet Change 148: 1–8.       https://doi.org/10.1016/j.gloplacha.2016.10.020. Li R, Zhao T, Shi J. 2016. Index-based evaluation of vegetation response to meteorological drought in       Northern China. Nat Hazards 84: 2179–2193. https://doi.org/10.1007/s11069-016-2542-3. Liu X, Zhu X, Pan Y, Li S, Liu Y, Ma Y. 2016. Agricultural drought monitoring: Progress, challenges,       and prospects. J Geogr Sci 26: 750–767. https://doi.org/10.1007/s11442-016-1297-9. Liu Q, Zhang Sh, Zhang H, Bai Y, Zhang J. 2020. Monitoring drought using composite drought indices       based on remote sensing. Science of the Total Environment 711: 1-10.       https://doi.org/10.1016/j.scitotenv.2019.134585 Mallick K, Bhattacharya BK, Patel NK. 2009. Estimating volumetric surface moisture content for       cropped soils using a soil wetness index based on surface temperature and NDVI. Agric for Meteorol       149: 1327–1342. https://doi.org/10.1016/j.agrformet.2009.03.004. Martínez-Fernández J, González-Zamora A, Sánchez N, Gumuzzio A, Herrero-Jiménez CM. 2016.       Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil       Water Deficit Index. Remote Sens Environ 177: 277–286. https://doi.org/10.1016/j.rse.2016.02.064. McKee TB, Doesken NJ, Kleist J. 1993. The relationship of drought frequency and duration to time       scales. Proc 8th Conf Appl Climatol Boston, pp 179–183. Mesgaran MB, Madani K, Hashemi H, Azadi P. 2017. Iran’s land suitability for agriculture. Sci Rep 7:       1–12. https://doi.org/10.1038/s41598-017-08066-y. Mishra A, Vu T, Veettil AV, Entekhabi D. 2017. Drought monitoring with soil moisture active passive       (SMAP) measurements. J Hydrol 552: 620–632. https://doi.org/10.1016/j.jhydrol.2017.07.033. Mishra AK, Singh VP. 2010. A review of drought concepts. J Hydrol 391: 202–216.       https://doi.org/10.1016/j.jhydrol.2010.07.012. Moazami N. 2016. Drought monitoring and analysis using remote sensing indices (Case Study: Alborz       Province). MSc Dissertation, International Desert Research Center (IDRC), University of Tehran,       203p. Nouri H, Anderson S, Sutton P, Beecham S, Nagler P, Jarchow CJ. 2017. NDVI, scale invariance and       the modifiable areal unit problem: An assessment of vegetation in the Adelaide Parklands. Sci Total       Environ 584: 11–18. https://doi.org/10.1016/j.scitotenv.2017.01.130. Omid M, Khanali M, Zand S. 2018. Energy analysis and greenhouse gas emission in broiler farms: A       case study in Alborz Province, Iran. Agric Eng Int CIGR J 19: 83–90. 

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Palmer WC. 1965. Meteorological drought. Research Paper No.45, U.S. Department of Commerce,       Weather Bureau, Washington D.C. Park S, Im J, Park S, Rhee J. 2017. Drought monitoring using high resolution soil moisture through       multi-sensor satellite data fusion over the Korean peninsula. Agric for Meteorol 237: 257–269.       https://doi.org/10.1016/j.agrformet.2017.02.022. Pishgar-Komleh SH, Akram A, Keyhani A, VanZelm R. 2017. Life cycle energy use, costs, and       greenhouse gas emission of broiler farms in different production systems in Iran-a case study of       Alborz province. Environ Sci Pollut Res 24: 41–49. https://doi.org/10.1007/s11356-017-9255-3. Rahimzadeh Bajgiran P, Darvishsefat AA, Khalili A, Makhdoum MF. 2008. Using AVHRR-based       vegetation indices for drought monitoring in the Northwest of Iran. J Arid Environ 72: 1086–1096.       https://doi.org/10.1016/j.jaridenv.2007.12.004. Rhee J, Im J, Carbone GJ. 2010. Monitoring agricultural drought for arid and humid regions using multi-      sensor remote sensing data. Environ Remote Sens 114: 2875-2887.       https://doi.org/10.1016/j.rse.2010.07.005. Sánchez N, González-Zamora Á, Martínez-Fernández J, Piles M, Pablos M. 2018. Integrated remote       sensing approach to global agricultural drought monitoring. Agric for Meteorol 259: 141–153.       https://doi.org/10.1016/j.agrformet.2018.04.022. 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: 213–224. https://doi.org/10.1016/S0034-4257(01)00274-7. Sayago S, Ovando G, Bocco M. 2017. Landsat images and crop model for evaluating water stress of       rainfed soybean. Remote Sens Environ 198: 30–39. https://doi.org/10.1016/j.rse.2017.05.008. Shao-E Y, Bing-fang W. 2010. Calculation of monthly precipitation anomaly percentage using web-      serviced remote sensing data. 2nd Int Conf Adv Computer Control 5: 621–625.       https://doi.org/10.1109/ICACC.2010.5486796. Thornthwaite CW, Mather JA, Thornthwaite W. 1955. The water balance. Publications in Climatology,       Laboratory of Climatology, Vol. 8. Tian L, Yuan S, Quiring SM. 2018. Evaluation of six indices for monitoring agricultural drought in the       south-central United States. Agric for Meteorol 249: 107–119.       https://doi.org/10.1016/j.agrformet.2017.11.024. Toth C, Jóźków G. 2016. Remote sensing platforms and sensors: A survey. ISPRS J Photogramm       Remote Sens 115: 22–36. https://doi.org/10.1016/j.isprsjprs.2015.10.004. Van Loon AF, Gleeson T, Clark J, Van Dijk AIJM, Stahl K, Hannaford J. 2016. Drought in the       Anthropocene. Nat Geosci 9: 89-91. https://doi.org/10.1038/ngeo2646. Verstraeten W. 2006. Integration of remotely sensed hydrological data into an ecosystem carbon flux       model. PhD Dissertation, Katholieke University Leuven, 223p. Vicente-Serrano S, Beguería S, López-Moreno JI. 2010. A multiscalar drought index sensitive to global       warming: the standardized precipitation evapotranspiration index. J Clim 23: 1696–1718.       https://doi.org/10.1175/2009JCLI2909.1. Vurukonda SSKP, Vardharajula S, Shrivastava M, Skz A. 2016. Enhancement of drought stress       tolerance in crops by plant growth promoting rhizobacteria. Microbiol Res 184: 13–24.       https://doi.org/10.1016/j.micres.2015.12.003. Xu P, Zhou T, Zhao X, Luo H, Gao S, Li Z, Cao L. 2018. Diverse responses of different structured       forest to drought in Southwest China through remotely sensed data. J Appl Earth Obs Geoinf Int 69:       217-225. https://doi.org/10.1016/j.jag.2018.03.009. Xu Y, Wang L, Ross KW, Liu C, Berry K. 2018. Standardized soil moisture index for drought       monitoring based on soil moisture active passive observations and 36 years of north American land       data assimilation system data: A case study in the southeast United States. Remote Sens 10: 1-13.       https://doi.org/10.3390/rs10020301. Zambrano F, Lillo-Saavedra M, Verbist K, Lagos O. 2016. Sixteen years of agricultural drought       assessment of the BioBío region in Chile using a 250 m resolution Vegetation Condition Index (VCI).       Remote Sens 8: 1-20. https://doi.org/10.3390/rs8060530. Zare M, Drastig K, Zude-Sasse M. 2020. Tree Water Status in Apple Orchards Measured by Means of       Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from       Landsat 8 Satellite Images. Sustainability 12: 1-19. https://doi.org/10.3390/su12010070. 
358                                                                                                                       DESERT, 27-2, 2022 
 
Zargar A, Sadiq R, Naser B, Khan FI. 2011. A review of drought indices. Environ Rev 19: 333-349.       https://doi.org/10.1139/a11-013. Zhang A, Jia G. 2013. Monitoring meteorological drought in semiarid regions using multi-sensor       microwave remote sensing data. Environ Remote Sens 134: 12-23.       https://doi.org/10.1016/j.rse.2013.02.023. Zhang X, Chen N, Li J, Chen Z, Niyogi D. 2017. Multi-sensor integrated framework and index for       agricultural drought monitoring. Remote Sens Environ 188: 141–163.      https://doi.org/10.1016/j.rse.2016.10.045.