Remote sensing of agricultural drought monitoring: A state of art review

Khaled Hazaymeh; Quazi K. Hassan; Khaled Hazaymeh; Quazi K. Hassan

doi:10.3934/environsci.2016.4.604

AIMS Environmental Science

2016, Volume 3, Issue 4: 604-630. doi: 10.3934/environsci.2016.4.604

Previous Article Next Article

Review Special Issues

Remote sensing of agricultural drought monitoring: A state of art review

Khaled Hazaymeh ^1,2,
Quazi K. Hassan ^{1
,
,}

1.
Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4
2.
Currently affiliated with the Department of Geography, Faculty of Arts, Yarmouk University, Irbid, Jordan

Received: 01 July 2016 Accepted: 26 September 2016 Published: 29 September 2016

Agricultural drought is a natural hazard that can be characterized by shortage of water supply. In the scope of this paper, we synthesized the importance of agricultural drought and methods commonly employed to monitor agricultural drought conditions. These include: (i) in-situ based methods, (ii) optical remote sensing methods, (iii) thermal remote sensing methods, (iv) microwave remote sensing methods, (v) combined remote sensing methods, and (vi) synergy between in-situ and remote sensing based methods. The in-situ indices can provide accurate results at the point of measurements; however, unable to provide spatial dynamics over large area. This can potentially be addressed by using remote sensing based methods because remote sensing platforms have the ability to view large area at a near continuous fashion. The remote sensing derived agricultural drought related indicators primarily depend on the characteristics of reflected/emitted energy from the earth surface, thus the results can be relatively less accurate in comparison to the in-situ derived outcomes. Despite a significant amount of research and development has been accomplished in particular to the area of remote sensing of agricultural drought, still there are several challenges. Those include: monitoring relatively small area, filling gaps in the data, developing consistent historical dataset, developing remote sensing-based agricultural drought forecasting system, integrating the recently launched and upcoming remote sensors, and developing standard validation schema, among others.
- optical remote sensing,
- thermal remote sensing,
- microwave remote sensing,
- synergy between in-situ and remote sensing
Citation: Khaled Hazaymeh, Quazi K. Hassan. Remote sensing of agricultural drought monitoring: A state of art review[J]. AIMS Environmental Science, 2016, 3(4): 604-630. doi: 10.3934/environsci.2016.4.604

Related Papers:

Abstract

Agricultural drought is a natural hazard that can be characterized by shortage of water supply. In the scope of this paper, we synthesized the importance of agricultural drought and methods commonly employed to monitor agricultural drought conditions. These include: (i) in-situ based methods, (ii) optical remote sensing methods, (iii) thermal remote sensing methods, (iv) microwave remote sensing methods, (v) combined remote sensing methods, and (vi) synergy between in-situ and remote sensing based methods. The in-situ indices can provide accurate results at the point of measurements; however, unable to provide spatial dynamics over large area. This can potentially be addressed by using remote sensing based methods because remote sensing platforms have the ability to view large area at a near continuous fashion. The remote sensing derived agricultural drought related indicators primarily depend on the characteristics of reflected/emitted energy from the earth surface, thus the results can be relatively less accurate in comparison to the in-situ derived outcomes. Despite a significant amount of research and development has been accomplished in particular to the area of remote sensing of agricultural drought, still there are several challenges. Those include: monitoring relatively small area, filling gaps in the data, developing consistent historical dataset, developing remote sensing-based agricultural drought forecasting system, integrating the recently launched and upcoming remote sensors, and developing standard validation schema, among others.

References

[1]	Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391: 202-216. doi: 10.1016/j.jhydrol.2010.07.012
[2]	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: 763-774.
[3]	FAO (2008) A Review of Drought Occurrence and Monitoring and Planning Activities in the Near East Region. Cairo, Egypt. Available from: http://www.ais.unwater.org/ais/pluginfile.php/516/course/section/175/Drought%20Occurrence%20and%20Activities%20in%20the%20Near%20East.pdf.
[4]	De Pauw E (2005) Monitoring agricultural drought in the Near East. Monitoring and Predicting Agricultural Drought: a global study, 208-226.
[5]	Hu G, Wang Y, Cui W (2008) Drought monitoring based on remotely sensed data in the key growing period of winter wheat: A case study in Hebei province, China. Int Arch Photogramm Remote Sens Spat Inf Sci Beijing, 403-408.
[6]	Zargar A, Sadiq R, Naser B, et al. (2011) A review of drought indices. Environ Rev 19: 333-349. doi: 10.1139/a11-013
[7]	Otun J, Adewumi J (2009) Drought quantifications in semi-arid regions using precipitation effectiveness variables. 18th World IMACS/MODSIM Congress, 13-17.
[8]	Guha-sapir D, Hoyois P, Below R (2013) Annual Disaster Statistical Review 2013: The numbers and trends. Brussels, Belgium. Available from: http://cred.be/sites/default/files/ADSR_2013.pdf.
[9]	Hayes MJ, Svoboda MD, Wardlow BD, et al. (2012) Drought Monitoring: Historical and Current Perspectives. Remote Sens Drought: 1-19.
[10]	Cammalleri C, Anderson MC, Gao F, et al. (2013) A data fusion approach for mapping daily evapotranspiration at field scale. Water Resour Res 49: 4672-4686. doi: 10.1002/wrcr.20349
[11]	Rembold F, Meroni M, Rojas O, et al. (2015) Agricultural drought monitoring using space-derived vegetation and biophysical products. Remote Sens Water Resour Disasters Urban Stud: 349.
[12]	Roy DP, Wulder MA, Loveland TR, et al. (2014) Landsat-8: Science and product vision for terrestrial global change research. Remote Sens Environ 145: 154-172. doi: 10.1016/j.rse.2014.02.001
[13]	Walker JJ, de Beurs KM, Wynne RH, Gao F (2012) Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology. Remote Sens Environ 117: 381-393. doi: 10.1016/j.rse.2011.10.014
[14]	Anderson MC, Kustas WP, Norman JM, et al. (2011) Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery. Hydrol Earth Syst Sci 15: 223-239. doi: 10.5194/hess-15-223-2011
[15]	Hazaymeh K, Hassan QK (2015a) Fusion of MODIS and Landsat-8 surface temperature images: A new approach. PLoS One 10: e0117755.
[16]	Hazaymeh K, Hassan QK (2015) Spatiotemporal image-fusion model for enhancing the temporal resolution of Landsat-8 surface reflectance images using MODIS images. J Appl Remote Sens 9: 096095. doi: 10.1117/1.JRS.9.096095
[17]	Alberta Agriculture and Forestry, 2013. Available from: http://www1.agric.gov.ab.ca/$Department/deptdocs.nsf/all/ppe9019.
[18]	Ross T, Lott N, A climatology of 1980–2003 extreme weather and climate events. National Climatic Data Center Technical Report No. 2003-01. NOAA/ NESDIS. National Climatic Data Center, Asheville, NC, 2003. Available from: https://www.ncdc.noaa.gov/billions/docs/lott-and-ross-2003.pdf.
[19]	Wheaton EE, Wittrock V, Kulshreshtha S, et al., Lessons learned from the Canadian drought years of 2001 and 2002: synthesis report. Saskatchewan Research Council, Publication No. 11602-46E03, 2005. Available from: http://www.agr.gc.ca/eng/programs-and-services/list-of-programs-and-services/drought-watch/managing-agroclimate-risk/lessons-learned-from-the-canadian-drought-years-2001-and-2002/?id=1463593613430.
[20]	Wong G, Lambert MF, Leonard M, et al. (2010) Drought analysis using trivariate copulas conditional on climatic states. J Hydrol Eng 15: 129-141. doi: 10.1061/(ASCE)HE.1943-5584.0000169
[21]	European Communities (2007) Addressing the challenge of water scarcity and droughts in the European Union. Commission of the European communities 2007, 414 Final, Brussels. Available from: http://www.eea.europa.eu/policy-documents/addressing-the-challenge-of-water.
[22]	Bates BC, Kundzewicz ZW, Wu S, et al. (2009) Technical Paper, International Panel on Climate Change (IPCC) Secretariat, Geneva. Available from: https://www.ipcc.ch/pdf/technical-papers/climate-change-water-en.pdf.
[23]	World Bank, Report on financing rapid onset natural disaster losses in India: A risk management approach. Report No. 26844-IN, Washington, DC. 2003. Available from: http://www.gfdrr.org/sites/gfdrr/files/publication/India%20Financing%20Rapid%20Onset%20Natural%20Disaster%20Losses%20in%20India-A%20Risk%20Management%20Approach_0.pdf
[24]	World Resources Institute, 2015, available from: http://www.wri.org/applications/maps/aqueduct-atlas/#x=39.92&y=18.14&s=ws!20!28!c&t=waterrisk&w=def&g=0&i=BWS-16!WSV-16!SV-2!HFO-4!DRO-4!STOR-8!GW-8!WRI-4!ECOS-2!MC-4!WCG-8!ECOV-2&tr=ind-1!prj-1&l=3&b=terr
[25]	Sheffield J, Wood EF (2007) Characteristics of global and regional drought, 1950–2000: Analysis of soil moisture data from off-line simulation of the terrestrial hydrologic cycle. J Geophys Res 112: D17115. doi: 10.1029/2006JD008288
[26]	Maes WH (2012) Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: A review. J Exp Bot 63: 695-709. doi: 10.1093/jxb/err313
[27]	Kanellou E, Domenikiotis C, Tsiros E, et al. (2008) Satellite-based drought estimation in Thessaly. Eur Water 23: 111-122.
[28]	Palmer WC, Meteorological drought. Research Paper No. 45. U.S. Weather Bureau 1965. Available from: https://www.ncdc.noaa.gov/temp-and-precip/drought/docs/palmer.pdf.
[29]	Palmer WC (1968) Keeping track of crop moisture conditions, nationwide: The new crop moisture index. Weatherwise 21: 156-161. doi: 10.1080/00431672.1968.9932814
[30]	Jackson RD, Idso SB, Reginato RJ (1981) Canopy temperature as a crop water stress indicator. Water Resour Res 17: 1133-1138. doi: 10.1029/WR017i004p01133
[31]	Meyer SJ, Hubbard KG, Wilhite DA (1993) A crop-specific drought index for corn: I. model development and validation. Agron J 85: 388.
[32]	Mckee TB, Doesken NJ, Kleist J, The relationship of drought frequency and duration to time scales. In Preprints, 8th Conference on Applied Climatology; Anaheim, California, 1993, 179-183.
[33]	Quiring SM, Papakryiakou TN (2003) An evaluation of agricultural drought indices for the Canadian prairies. Agric For Meteorol 118: 49-62. doi: 10.1016/S0168-1923(03)00072-8
[34]	Paulo A, Pereira LS (2006) Drought concepts and characterization. Water Int 31: 37-49. doi: 10.1080/02508060608691913
[35]	Pashiardis S, Michaelides S (2008) Implementation of the Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI) for regional drought assessment: A case study for Cyprus. Eur Water 23: 57-65.
[36]	WMO (World Meterological Organization). Standardized precipitation index user guide. 2012. WMO-No. 1090, ISBN 978-92-63-11091-6. Available from: http://www.wamis.org/agm/pubs/SPI/WMO_1090_EN.pdf.
[37]	Idso SB, Jackson RD, Reginato RJ (1977) Remote-sensing of crop yields. Science 196: 19-25. doi: 10.1126/science.196.4285.19
[38]	Narasimhan B, Srinivasan R (2005) Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring. Agric For Meteorol 133: 69-88. doi: 10.1016/j.agrformet.2005.07.012
[39]	Li J, Heap AD (2014) Spatial interpolation methods applied in the environmental sciences: A review. Environ Model Softw 53: 173-189. doi: 10.1016/j.envsoft.2013.12.008
[40]	Anjum SA, Xie X, Wang L, et al. (2011) Morphological, physiological and biochemical responses of plants to drought stress. Afr J Agric Res 6: 2026-2032.
[41]	Dalezios NR, Blanta A, Spyropoulos NV (2012) Assessment of remotely sensed drought features in vulnerable agriculture. Nat Hazards Earth Syst Sci 12: 3139-3150. doi: 10.5194/nhess-12-3139-2012
[42]	Farooq M, Wahid A, Kobayashi N, et al. (2009) Plant drought stress: Effects, mechanisms and management. Agron Sustain Dev 29: 185-212. doi: 10.1051/agro:2008021
[43]	Ghulam A, Li ZL, Qin Q, et al. (2008) Estimating crop water stress with ETM+ NIR and SWIR data. Agric For Meteorol 148: 1679-1695. doi: 10.1016/j.agrformet.2008.05.020
[44]	Yang N, Qin Q, Jin C, et al. (2008) The comparison and application of the methods for monitoring farmland drought based on NIR-Red spectral space. IGARSS 2008–2008 IEEE Int Geosci Remote Sens Symp IEEE, 871-874.
[45]	Tucker CJ, Choudhury BJ (1987) Satellite remote sensing of drought conditions. Remote Sens Environ 23: 243-251.
[46]	Hunt ER, Rock BN (1989) Detection of changes in leaf water content using near-and middle-infrared reflectances. Remote Sens Environ 30: 43-54. doi: 10.1016/0034-4257(89)90046-1
[47]	Gao B (1996) NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58: 257-266. doi: 10.1016/S0034-4257(96)00067-3
[48]	Anyamba A, Tucker C, Eastman J (2001) NDVI anomaly patterns over Africa during the 1997/98 ENSO warm event. Int J Remote Sens 22: 1847-1859. doi: 10.1080/01431160010029156
[49]	Kogan F (2002) World droughts in the new millennium from AVHRR-based vegetation health indices. Eos Trans Am Geophys Union 83: 557.
[50]	Peters A, Walter-Shea E, Ji L (2002) Drought monitoring with NDVI-based standardized vegetation index. Photogarmm Eng Remote Sens 68: 71-75.
[51]	Hillerislambers R, Rietkerk M, Van Den Bosch F, et al. (2001) Vegetation pattern formation in semi-arid grazing systems. Ecology 82: 50-61.
[52]	Wang H, Li X, Long H, et al. (2010) Monitoring the effects of land use and cover type changes on soil moisture using remote-sensing data: A case study in China’s Yongding River basin. Catena 82: 135-145. doi: 10.1016/j.catena.2010.05.008
[53]	Fensholt R, Sandholt I (2003) Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment. Remote Sens Environ 87: 111-121. doi: 10.1016/j.rse.2003.07.002
[54]	Wang L, Qu JJ (2007) NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophys Res Lett 34: 1-5.
[55]	Zhang N, Hong Y, Qin Q, et al. (2013) VSDI: a visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing. Int J Remote Sens 34: 4585-4609. doi: 10.1080/01431161.2013.779046
[56]	Zarco-Tejada PJ, Rueda CA, Ustin SL (2003) Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sens Environ 85: 109-124. doi: 10.1016/S0034-4257(02)00197-9
[57]	Hardisky KV, Smart RM (1983) The influence of soft salinity, growth form, mad leaf moisture on the spectral reflectance of Spartina Alterniflora canopies. Photogramm Eng Remote Sensing 49: 77-83.
[58]	Xiao X, Hollinger D, Aber J, et al. (2004) Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens Environ 89: 519-534. doi: 10.1016/j.rse.2003.11.008
[59]	Tang H, Li Z (2014) Application of thermal remote sensing in agriculture drought monitoring and thermal anomaly detection. In: Quant. Remote Sens. Therm. Infrared. Springer Remote Sensing/Photogrammetry, 203-256.
[60]	Claps P, Laguardia G (2004) Assessing spatial variability of soil water content through thermal inertia and NDVI. Remote Sensing, International Society for Optics and Photonics, 378-387.
[61]	Verstraeten WW, Veroustraete F, van der Sande CJ, et al. (2006) Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests. Remote Sens Environ 101: 299-314.
[62]	Van doninck J, Peters J, de Baets B, et al. (2011) The potential of multitemporal Aqua and Terra MODIS apparent thermal inertia as a soil moisture indicator. Int J Appl Earth Obs Geoinf 13: 934-941.
[63]	Moran M (2004) Thermal infrared measurement as an indicator of planet ecosystem health. Therm Remote Sens L Surf Process: 257-282.
[64]	Rahimzadeh-Bajgiran P, Omasa K, Shimizu Y (2012) Comparative evaluation of the Vegetation Dryness Index (VDI), the Temperature Vegetation Dryness Index (TVDI) and the improved TVDI (iTVDI) for water stress detection in semi-arid regions of Iran. ISPRS J Photogramm Remote Sens 68: 1-12.
[65]	Kogan FN (1997) Global drought watch from space. Bull Am Meteorol Soc 78: 621-636.
[66]	Kogan FN (1995) Application of vegetation index and brightness temperature for drought detection. Adv Sp Res 15: 91-100.
[67]	McVicar TR, Jupp DL (1998) The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: A review. Agric Syst 57: 399-468.
[68]	Wang L, Qu JJ (2009) Satellite remote sensing applications for surface soil moisture monitoring: A review. Front Earth Sci China 3: 237-247. doi: 10.1007/s11707-009-0023-7
[69]	Njoku EG, Jackson TJ, Lakshmi V, et al. (2003) Soil moisture retrieval from AMSR-E. IEEE Trans Geosci Remote Sens 41: 215-228. doi: 10.1109/TGRS.2002.808243
[70]	Mo T, Schmugge T (1987) A Parameterization of the Effect of Surface Roughness on Microwave Emission. IEEE Trans Geosci Remote Sens GE-25: 481-486. doi: 10.1109/TGRS.1987.289860
[71]	Shi J, Chen KS, Li Q, et al. (2002) A parameterized surface reflectivity model and estimation of bare-surface soil moisture with L-band radiometer. IEEE Trans Geosci Remote Sens 40: 2674-2686. doi: 10.1109/TGRS.2002.807003
[72]	Shi J, Jiang L, Zhang L, et al. (2005) A parameterized multifrequency-polarization surface emission model. IEEE Trans Geosci Remote Sens 43: 2831-2841.
[73]	Wigneron JP, Calvet JC, Pellarin T, et al. (2003) Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans. Remote Sens Environ 85: 489-506. doi: 10.1016/S0034-4257(03)00051-8
[74]	Owe M, De Jeu R, Walker J (2001) A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Trans Geosci Remote Sens 39: 1643-1654. doi: 10.1109/36.942542
[75]	Meesters AGCA, De Jeu RAM, Owe M (2005) Analytical derivation of the vegetation optical depth from the microwave polarization difference index. IEEE Geosci Remote Sens Lett 2: 121-123.
[76]	Jackson TJ, Schmugge TJ, Wang JR (1982) Passive microwave sensing of soil moisture under vegetation canopies. Water Resour Res 18: 1137-1142. doi: 10.1029/WR018i004p01137
[77]	Theis SW, Blanchard BJ, Newton RW (1984) Utilization of vegetation indices to improve microwave soil moisture estimates over agricultural lands. IEEE Trans Geosci Remote Sens GE-22: 490-496. doi: 10.1109/TGRS.1984.6499159
[78]	Dubois PC, van Zyl J, Engman T (1995) Measuring Soil Moisture with Imaging Radars. IEEE Trans Geosci Remote Sens 33: 915-926. doi: 10.1109/36.406677
[79]	Fung AK, Li Z, Chen KS (1992) Backscattering from a randomly rough dielectric surface. IEEE Trans Geosci Remote Sens 30: 356-369. doi: 10.1109/36.134085
[80]	Shoshany M, Svoray T, Curran PJ, et al. (2000) The relationship between ERS-2 SAR backscatter and soil moisture: generalization from a humid to semi-arid transect. Int J Remote Sens 21: 2337-2343.
[81]	Hassan QK, Bourque CPA (2015) Development of a new wetness index based on RADARSAT-1 ScanSAR data. Monitoring and Modeling of Global Changes: A Geomatics Perspective, Switzerland: Springer International Publishing, 301-314.
[82]	Hao Z, AghaKouchak A (2013) Multivariate Standardized Drought Index: A parametric multi-index model. Adv Water Resour 57: 12-18. doi: 10.1016/j.advwatres.2013.03.009
[83]	Wang W, Liang S, Meyers T (2008) Validating MODIS land surface temperature products using long-term nighttime ground measurements. Remote Sens Environ 112: 623-635. doi: 10.1016/j.rse.2007.05.024
[84]	Gu Y, Brown JF, Verdin JP, et al. (2007) A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophys Res Lett 34: L06407.
[85]	Jang J, Viau A, Anctil F (2006) Thermal-water stress index from satellite images. Int J Remote Sens 27: 1619-1639. doi: 10.1080/01431160500509194
[86]	Zhang A, Jia G (2013) Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sens Environ 134: 12-23. doi: 10.1016/j.rse.2013.02.023
[87]	Lambin EF, Ehrlich D (1996) The surface temperature-vegetation index space for land cover and land-cover change analysis. Int J Remote Sens 17: 463-487.
[88]	Cai G, Du M, Liu Y (2011) Regional drought monitoring and analyzing using MODIS data—A case study in Yunnan Province. International conference on Computer and Computing Technologies in Agriculture. Springer Berlin Heidelberg, 243-251.
[89]	Abbas S, Nichol J, Qamer F, et al. (2014) Characterization of drought development through remote sensing: A case study in central Yunnan, China. Remote Sens 6: 4998-5018. doi: 10.3390/rs6064998
[90]	Hassan QK, Bourque CP, Meng FR, et al. (2007) A wetness index using terrain-corrected surface temperature and normalized difference vegetation index derived from standard MODIS products: An evaluation of its use in a humid forest-dominated region of eastern Canada. Sensor 7: 2028-2048. doi: 10.3390/s7102028
[91]	Petropoulos G, Carlson TN, Wooster MJ, et al. (2009) A review of Ts/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture. Prog Phys Geogr 33: 224-250. doi: 10.1177/0309133309338997
[92]	Karnieli A, Agam N, Pinker RT, et al. (2010) Use of NDVI and land surface temperature for drought assessment: Merits and limitations. J Clim 23: 618-633. doi: 10.1175/2009JCLI2900.1
[93]	Rojas O, Vrieling, Rembold F (2011) Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sens Environ 115: 343-352. doi: 10.1016/j.rse.2010.09.006
[94]	Smith RCG, Choudhury BJ (1991) Analysis of normalized difference and surface temperature observations over southeastern Australia. Remote Sens 12: 2021-2044. doi: 10.1080/01431169108955234
[95]	Nemani R, Pierce L, Running S (1993) Developing satellite-derived estimates of surface moisture status. J Appl Meteorolgy 32: 548-557.
[96]	Carlson TN, Gillies RR, Schmugge TJ (1995) An interpretation of methodologies for indirect measurement of soil water content. Agric For Meteorol 77: 191-205. doi: 10.1016/0168-1923(95)02261-U
[97]	Dupigny-Giroux L, Lewis J (1999) Index for surface characterization over Area a Semiarid. Photogramm Eng Remote Sens 65: 937-945.
[98]	Carlson TN, Gillies RR, Perry EM (1994) A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sens Rev 9: 161-173. doi: 10.1080/02757259409532220
[99]	Wang P, Li X, Gong J, et al., Vegetation temperature condition index and its application for drought monitoring. Geosci Remote Sens Symp, 2001. IGARSS’01. IEEE 2001 Int 1: 141-143.
[100]	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.
[101]	Anderson MC, Hain C, Wardlow B, et al. (2011) Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States. J Clim 24: 2025-2044. doi: 10.1175/2010JCLI3812.1
[102]	Wang W, Huang D, Wang XG, et al. (2010) Estimate soil moisture using trapezoidal relationship between remotely sensed land surface temperature and vegetation index. Hydrol Earth Syst Sci Discuss 7: 8703-8740. doi: 10.5194/hessd-7-8703-2010
[103]	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.
[104]	Rhee J, Im J, Carbone GJ (2010) Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sens Environ 114: 2875-2887. doi: 10.1016/j.rse.2010.07.005
[105]	Zhang A, Jia G (2013) Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sens Environ 134: 12-23. doi: 10.1016/j.rse.2013.02.023
[106]	AghaKouchak A, Farahmand A, Melton FS, et al. (2015) Remote sensing of drought: progress, challenges and opportunities. Rev Geophys 53: 452-480. doi: 10.1002/2014RG000456
[107]	Tsakiris G, Vangelis H (2004) Towards a drought watch system based on spatial SPI. Water Resour Manag 18: 1-12. doi: 10.1023/B:WARM.0000015410.47014.a4
[108]	Cancelliere A, Di Mauro G, Bonaccorso B, et al. (2006) Drought forecasting using the Standardized Precipitation Index. Water Resour Manag 21: 801-819.
[109]	Mavromatis T (2007) Drought index evaluation for assessing future wheat production in Greece. Int J Climatol 27: 911-924. doi: 10.1002/joc.1444
[110]	Quiring SM, Papakryiakou TN (2003) An evaluation of agricultural drought indices for the Canadian prairies. Agric For Meteorol 118: 49-62. doi: 10.1016/S0168-1923(03)00072-8
[111]	Morid S, Smakhtin V, Moghaddasi M (2006) Comparison of seven meteorological indices for drought monitoring in Iran. Int J Climatol 26: 971-985. doi: 10.1002/joc.1264
[112]	Bayarjargal Y, Karnieli A, Bayasgalan M, et al. (2006) A comparative study of NOAA–AVHRR derived drought indices using change vector analysis. Remote Sens Environ 105: 9-22. doi: 10.1016/j.rse.2006.06.003
[113]	Quiring SM (2009) Monitoring drought: An evaluation of meteorological drought indices. Geogr Compass 3: 64-88. doi: 10.1111/j.1749-8198.2008.00207.x
[114]	Svoboda M, LeComte D, Hayes M, et al. (2002) The drought monitor. Bull Am Meteorol Soc 83: 1181-1190.
[115]	Sun L, Mitchell SW, Davidson A (2012) Multiple drought indices for agricultural drought risk assessment on the Canadian prairies. Int J Climatol 32: 1628-1639. doi: 10.1002/joc.2385
[116]	Steinemann A, Cavalcanti L (2006) Developing multiple indicators and triggers for drought plans. J Water Resour Plan Manag 132: 164-174. doi: 10.1061/(ASCE)0733-9496(2006)132:3(164)
[117]	Sun L, Sun R, Li X, et al. (2012) Monitoring surface soil moisture status based on remotely sensed surface temperature and vegetation index information. Agric For Meteorol 166-167: 175-187. doi: 10.1016/j.agrformet.2012.07.015
[118]	Brown JF, Wardlow BD, Tadesse T, et al. (2008) The Vegetation Drought Response Index (VegDRI): A New integrated approach for monitoring drought stress in vegetation. GIScience Remote Sens 45: 16-46. doi: 10.2747/1548-1603.45.1.16
[119]	Karamouz M, Rasouli K, Nazif S (2009) Development of a Hybrid Index for Drought Prediction: Case study. J Hydrol Eng 14: 617-627. doi: 10.1061/(ASCE)HE.1943-5584.0000022
[120]	Tadesse T, Wardlow BD, Hayes MJ, et al. (2010) The Vegetation Outlook (VegOut): A new method for predicting vegetation seasonal greenness. GIScience Remote Sens 47: 25-52. doi: 10.2747/1548-1603.47.1.25
[121]	Wu 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. Int J Appl Earth Obs Geoinf 23: 397-410.
[122]	Becker-Reshef I, Justice C, Sullivan M, et al. (2010) Monitoring global croplands with coarse resolution earth observations: The Global Agriculture Monitoring (GLAM) project. Remote Sens 2: 1589-1609. doi: 10.3390/rs2061589
[123]	Rocha J, Perdigão A, Melo R, et al. (2012) Remote sensing based crop coefficients for water management in agriculture. In: Curkovic, S. Sustainable Development—Authoritative and Leading Edge Content for Environmental Management, 167-192.
[124]	Atzberger C (2013) Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sens 5: 949-981. doi: 10.3390/rs5020949
[125]	Zhang X, Friedl MA, Schaaf CB, et al. (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84: 471-475. doi: 10.1016/S0034-4257(02)00135-9
[126]	Kovalskyy V, Roy DP, Zhang XY, et al. (2012) The suitability of multi-temporal web-enabled Landsat data NDVI for phenological monitoring—a comparison with flux tower and MODIS NDVI. Remote Sens Lett 3: 325-334. doi: 10.1080/01431161.2011.593581
[127]	Al-wassai FA, Kalyankar NV, Major limitations of satellite images, in: Proceedings of computing research repository, 2013. Available from: http://arxiv.org/ftp/arxiv/papers/1307/1307.2434.pdf.
[128]	Yang B, Jing Z, Zhao H (2010) Review of pixel-level image fusion. J Shanghai Jiaotong Univ 15: 6-12.
[129]	Dong J, Dafang Z, Yaohuan H, et al., Survey of multispectral image fusion techniques in remote sensing applications, In: Zheng, Y. Image Fusion and Its Applications, InTech (2011).
[130]	Khaleghi B, Khamis A, Karray FO, et al. (2013) Multisensor data fusion: A review of the state-of-the-art. Inf Fusion 14: 28-44. doi: 10.1016/j.inffus.2011.08.001
[131]	Chowdhury EH, Hassan QK (2013) Use of remote sensing-derived variables in developing a forest fire danger forecasting system. Nat Hazards 67: 321-334. doi: 10.1007/s11069-013-0564-7
[132]	Akther MS, Hassan QK (2013) Remote sensing-based assessment of fire danger conditions over boreal forest. IEEE J Sel Top Appl Earth Obs Remote Sens 4: 992-999.
[133]	Chowdhury EH, Hassan QK (2015) Operational perspective of remote sensing-based forest fire danger forecasting systems. ISPRS J Photogramm Remote Sens 104: 224-236. doi: 10.1016/j.isprsjprs.2014.03.011
[134]	Chowdhury EH, Hassan QK (2015) Development of a new daily-scale forest fire danger forecasting system using remote sensing data. Remote Sens 7: 2431-2448. doi: 10.3390/rs70302431
[135]	Mosleh MK, Hassan QK, Chowdhury EH (2016) Development of a remote sensing-based rice yield forecasting model. Spanish J Agric Res 14: 0907.
[136]	Rembold F, Atzberger C, Savin I, et al. (2013) Using low resolution satellite imagery for yield prediction and yield anomaly detection. Remote Sens 5: 1704-1733.
[137]	Song W, Dong Q, Xue C (2016) A classified El Niño index using AVHRR remote-sensing SST data. Int J Remote Sens 37: 403-417. doi: 10.1080/01431161.2015.1125553
[138]	Kousari MR, Hosseini ME, Ahani H, et al. (2015) Introducing an operational method to forecast long-term regional drought based on the application of artificial intelligence capabilities. Theor Appl Climatol: 1-20.
[139]	Mishra AK, Ines AV, Das NN, et al. (2015) Anatomy of a local-scale drought: Application of assimilated remote sensing products, crop model, and statistical methods to an agricultural drought study. J Hydrol 526: 15-29.
[140]	Mishra AK, Singh VP (2011) Drought modeling—A review. J Hydrol 403: 157-175. doi: 10.1016/j.jhydrol.2011.03.049

Reader Comments

Your name:*

Email:*
© 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)