Regional drought monitoring based on multisensor remote sensing

Remote Sensing of Water Resources, Disasters, and Urban Studies

Volumn 3, Issue , 2015, Pages 401-415

  Rhee, Jinyoung ^a   Im, Jungho ^b   Park, Seonyoung ^b  

^a APEC Climate Center   (South Korea)

^b Ulsan National Institute of Science and Technology   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

EARTH SCIENCES;

DROUGHT MONITORING; MULTISENSOR REMOTE SENSING;

REMOTE SENSING;

EID: 84979541367     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b19321     Document Type: Chapter

References (64)

1. Anderson, L., Y. Malhi, L. Aragão, R. Ladle, E. Arai, N. Barbier, and O. Phillips. 2010. Remote sensing detection of droughts in Amazonian forest canopies. New Phytol 187:733–750.
2. Anderson, M., R. Allen, A. Morse, and W. Kustas. 2012. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sensing Environ 122:50–65.
3. Anderson, M., C. Hain, J. Otkin, X. Zhan, K. Mo, M. Svoboda et al. 2013. An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with US drought monitor classifcations. J Hydrometeorol 14:1035–1056.
4. Anderson, M., C. Hain, B. Wardlow, A. Pimstein, J. Mecikalski, and W. Kustas. 2011. Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States. J Climate 24:2024–2044.
5. Bolten, J., W. Crow, X. Zhan, T. Jackson, and C. Reynolds. 2010. Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring. IEEE J Sel Top Appl Earth Observ Remote Sensing 3:57–66.
6. Brown, J. F., B. D. Wardlow, T. Tadesse, M. J. Hayes, and B. C. Reed. 2008. The vegetation drought response index (VegDRI): A new integrated approach for monitoring drought stress in vegetation. GISci Remote Sensing 45:16–46.
7. Caccamo, G., L. Chisholm, R. Bradstock, and M. Puotinen. 2011. Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems. Remote Sensing Environ 115:2626–2639.
8. Carbone, G., J. Rhee, H. Mizzell, and R. Boyles. 2007. A regional-scale drought monitoring tool for the Carolinas. Bull Am Meteorol Soc 89:20–28.
9. Choi, M., J. Jacobs, M. Anderson, and D. Bosch. 2013. Evaluation of drought indices via remotely sensed data with hydrologi-cal variables. J Hydrol 476:265–273.
10. Choudhary, S. S., P. K. Garg, and S. K. Ghosh. 2012. Mapping of agriculture drought using remote sensing and GIS. Int. J. Sci. Eng. Technol., 1(4):149–157.
11. Cook, E., M. Kablack, and G. Jacoby. 1988. The 1986 drought in the southeastern United States: How rare an event was it? J Geophys Res 93:14257–14260.
12. Gao, B.-C. 1996. NDWI—A normalized diference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3):257–266.
13. Gao, Z., Q. Wang, X. Cao, and W. Gao. 2014. The responses of vegetation water content (EWT) and assessment of drought monitoring along a coastal region using remote sensing. GISci Remote Sensing 51:1–16.
14. Garcia, M., N. Fernández, L. Villagarcía, F. Domingo, J. Puigdefábregas, and I. Sandholt. 2014. Accuracy of the Temperature–Vegetation Dryness Index using MODIS under water-limited vs. energy-limited evapotranspiration conditions. Remote Sensing Environ 149:100–117.
15. Gu, Y., J. F. Brown, J. P. Verdin, and B. Wardlow. 2007. A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysical Research Letters, 34(6). doi: 10.1029/2006GL029127.
16. Han, J. and M. Kamber. 2011. Data Mining: Concepts and Techniques. Burlington, MA: Elsevier.
17. Han, P., P. Wang, S. Zhang, and D. Zhu. 2010. Drought forecasting based on the remote sensing data using ARIMA models. Math Comput Model 51:1398–1403.
18. Huang, J., H. Van den Dool, and K. P. Georgakakos. 1996. Analysis of model-calibrated soil moisture over the United States (1931–93) and application to long-range temperature forecasts. J Climate 9:1350–1362.
19. Huang, S., D. Dahal, C. Young, G. Chander, and S. Liu. 2011. Integration of Palmer Drought Severity Index and remote sensing data to simulate wetland water surface from 1910 to 2009 in Cottonwood Lake area, North Dakota. Remote Sensing Environ 115:3377–3389.
20. Im, J., J. R. Jensen, M. Coleman, and E. Nelson. 2009. Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatments. Geocarto Int 24:293–312.
21. Im, J., Z. Lu, J. Rhee, and L. J. Quackenbush. 2012. Impervious surface quantifcation using a synthesis of artifcial immune networks and decision/regression trees from multi-sensor data. Remote Sensing Environ 117:102–113.
22. IPCC. 2012. Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of Working Groups I and II of the Intergovernmental Panel on Climate Change [Field, C. B., V. Balrros, T. F. Stocker, D. Qin, D. J. Dokken, K. L. Ebi, M. D. Mastrandrea, K. J. Mach, G.-K. Plattner, S. K. Allen, M. Tignor, and P. M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK/New York, 582pp.
23. Ivits, E., S. Horion, R. Fensholt, and M. Cherlet. 2014. Drought footprint on European ecosystems between 1999 and 2010 assessed by remotely sensed vegetation phenology and productivity. Glob Chang Biol 20:581–593.
24. Jensen, J. R. 2000. Remote Sensing of the Environment: An Earth Resource Perspective. Upper Saddle River, NJ: Prentice Hall, Chapter 10.
25. Ji, L. and A. Peters. 2003. Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sensing Environ 87:85–98.
26. Karl, T. and P. Young. 1987. Te 1986 Southeast drought in historical perspective. Bull Am Meteorol Soc 68:773–778.
27. Keetch, J. and G. Byram. 1968. A drought index for forest fre control. Forest Service Research Paper SE-38. U.S. Department of Agriculture, Washington, DC, 32pp.
28. Keshavarz, M., M. Vazifedoust, and A. Alizadeh. 2014. Drought monitoring using a Soil Wetness Defcit Index (SWDI) derived from MODIS satellite data. Agric Water Manage 132:37–45.
29. Khaleghi, B., A. Khamis, F. O. Karray, and S. N. Razavi. 2013. Multisensor data fusion: A review of the state-of-the-art. Inform Fusion 14(1): 28–44.
30. Kogan, F. N. 1995a. Drought of the late 1980s in the United States as derived from NOAA polar–orbiting satellite data. Bull Am Meteorol Soc 76:655–668.
31. Kogan, F. N. 1995b. Application of vegetation index and brightness temperature for drought detection. Adv Space Res 15:91–100.
32. Knutson, C. and M. Hayes. 2002. South Carolina drought mitigation and response assessment: 1998–2000 drought. Quick Response Research Report #136. National Hazards Research and Applications Information Center, University of Colorado, Boulder, CO.
33. Li, M., J. Im, L. Quackenbush, and L. Tao. 2014. Forest biomass and carbon stock quantifcation using airborne LiDAR data: A case study over Huntington Wildlife Forest in the Adirondack Park. IEEE J Sel Top Appl Earth Observ Remote Sensing 7(7):3143–3156.
34. McKee, T. B., N. J. Doesken, and J. Kleist. 1993. The relationship of drought frequency and duration to time scales. Preprints, Eighth Conference on Applied Climatology, Dallas, TX. Am Meteorol Soc 179–184.
35. Mishra, A. K. and V. R. Desai. 2006. Drought forecasting using feed-forward recursive neural network. Ecol Model 198:127–138.
36. Mishra, A. K. and V. Singh. 2011. Drought modeling—A review. J Hydrol 403:157–175.
37. Mu, Q., M. Zhao, J. Kimball, N. McDowell, and S. Running. 2013. A remotely sensed global terrestrial drought severity index. Bull Am Meteorol Soc 94:83–98.
38. Palmer, W. C. 1965. Meteorological drought. Research Paper No. 45. U.S. Department of Commerce, Weather Bureau, Washington, DC, p. 58.
39. Palmer, W. C. 1968. Keeping track of crop moisture conditions, nationwide: Te new crop moisture index. Weatherwise 21:156–161.
40. Park, S., J. Im, E. Jang, H. Yoon, and J. Rhee. 2015. Multi-sensor blending for regional drought monitoring using machine learning approaches. Agric Forest Meteorol, in review.
41. Quinlan, J. 1993. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufman.
42. R Core Development Team. 2008. R: A language and environment for statistical computing. http://www.R-project.org/. (Accessed May 17, 2014.)
43. Rhee, J., J. Im, and G. J. Carbone. 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing Environ 114:2875–2887.
44. Rojas, O., A. Vrieling, and F. Rembold. 2011. Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sensing Environ 115:343–352.
45. Rulequest Research. 2012. Data mining with Cubist. http://www. rulequest.com/cubist-info.html. (Accessed May 17, 2014.)
46. Sakamoto, T., A. Gitelson, and T. Arkebauer. 2014. Near real-time prediction of US corn yields based on time-series MODIS data. Remote Sensing Environ 147:219–231.
47. Savitsky, B. G. 1986. Agricultural remote sensing in South Carolina: A study of crop identifcation capabilities utilizing Landsat multispectral scanner data. Unpublished master thesis. University of South Carolina Geography Department, Columbia, SC, 78pp.
48. Scaini, A., N. Sánchez, S. Vicente-Serrano, and J. Martínez-Fernández. 2014. SMOS‐derived soil moisture anomalies and drought indices: A comparative analysis using in situ measurements. Hydrol Processes 29(3):373–383.
49. Seitz, F., K. Hedman, F. Meyer, and H. Lee. 2014. Multi-sensor space observation of heavy food and drought conditions in the Amazon Region. In Earth on the Edge: Science for a Sustainable Planet. Berlin, Germany: Springer, pp. 311–317.
50. Seneviratne, S. I., T. Corti, E. L. Davin, M. Hirschi, E. B. Jaeger, I. Lehner, B. Orlowskly, and A. J. Teuling. 2010. Investigating soil moisture-climate interactions in a changing climate: A review. Earth Sci Rev 99:125–161.
51. Shaheen, A. and M. Baig. 2011. Drought severity assessment in arid area of Tal Doab using remote sensing and GIS. Int J Water Resour Arid Environ 1:92–101.
52. Svoboda, M., D. LeComte, M. Hayes, R. Heim, K. Gleason, J. Angel, B. Rippey et al. 2002. Te drought monitor. Bull Am Meteorol Soc 83:1181–1190.
53. Tadesse, T., J. F. Brown, and M. J. Hayes. 2005. A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the US central plains. ISPRS Journal of Photogrammetry and Remote Sensing 59(4):244–253.
54. Tadesse, T., B. D. Wardlow, M. J. Hayes, M. D. Svoboda, and J. F. Brown. 2010. Te vegetation outlook (VegOut): A new method for predicting vegetation seasonal greenness. GIScience & Remote Sensing 47(1):25–52.
55. Wan, Z., P. Wang, and X. Li. 2004. Using MODIS Land Surface Temperature and Normalized Diference Vegetation Index products for monitoring drought in the southern Great Plains, USA. Int J Remote Sensing 25:61–72.
56. Wang, L. and J. J. Qu. 2007. NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophysical Research Letters, 34(20). L20405.
57. Wang, R., J. Xu, D. Wang, X. Xie, and P. Wang. 2013. Construction of Drought Indices from passive microwave remote sensing AMSR-E data in Huaihe River Basin. Appl Mech Mater 397:2503–2506.
58. Wardlow, B. D., M. C. Anderson, and J. P. Verdin (Eds.). 2012. Remote Sensing of Drought: Innovative Monitoring Approaches. Boca Raton, FL: CRC Press, 422pp.
59. Weaver, J. C. 2005. The drought of 1998–2002 in North Carolina— Precipitation and hydrologic conditions. Scientifc Investigations Report, 2005-5053. U.S. Geological Survey, U.S. Department of the Interior, Reston, VA, 88pp.
60. Wells, N., S. Goddard, and M. J. Hayes. 2004. A self-calibrating Palmer Drought Severity Index. J Climate 17:2235–2351.
61. Willeke, G., J. R. M. Hosking, J. R. Wallis, and N. B. Guttman. 1994. The National Drought Atlas. Institute for Water Resources Report 94-NDS-4. U.S. Army Corps of Engineers, Washington, DC, CD-ROM.
62. Zhang, A. and G. Jia. 2013. Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sensing Environ 134:12–23.
63. Zhang, N., Y. Hong, Q. Qin, and L. Liu. 2013. VSDI: A visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing. Int J Remote Sensing 34:4585–4609.
64. Zhong, L., Y. Ma, Y. Fu, X. Pan, W. Hu, Z. Su et al. 2014. Assessment of soil water defcit for the middle reaches of Yarlung-Zangbo River from optical and passive microwave images. Remote Sensing Environ 142:1–8.