DOI QR코드

DOI QR Code

Application of Evaporative Stress Index (ESI) for Satellite-based Agricultural Drought Monitoring in South Korea

위성영상기반 농업가뭄 모니터링을 위한 Evaporative Stress Index (ESI)의 적용성 평가

  • Yoon, Dong-Hyun (Department of Bioresources and Rural Systems Engineering, Hankyong National University, Anseong, Republic of Korea) ;
  • Nam, Won-Ho (Department of Bioresources and Rural Systems Engineering, Institute of Agricultural Environmental Science, Hankyong National University, Anseong, Republic of Korea) ;
  • Lee, Hee-Jin (Department of Bioresources and Rural Systems Engineering, Hankyong National University, Anseong, Republic of Korea) ;
  • Hong, Eun-Mi (School of Natural Resources and Environmental Science, Kangwon National University, Chuncheon, Republic of Korea) ;
  • Kim, Taegon (Institute on the Environment, University of Minnesota) ;
  • Kim, Dae-Eui (Rural Research Institute, Korea Rural Community Corporation, Ansan, Republic of Korea) ;
  • Shin, An-Kook (Rural Research Institute, Korea Rural Community Corporation, Ansan, Republic of Korea) ;
  • Svoboda, Mark D. (National Drought Mitigation Center, School of Natural Resources, University of Nebraska-Lincoln)
  • Received : 2018.10.11
  • Accepted : 2018.10.15
  • Published : 2018.11.30

Abstract

Climate change has caused changes in environmental factors that have a direct impact on agriculture such as temperature and precipitation. The meteorological disaster that has the greatest impact on agriculture is drought, and its forecasts are closely related to agricultural production and water supply. In the case of terrestrial data, the accuracy of the spatial map obtained by interpolating the each point data is lowered because it is based on the point observation. Therefore, acquisition of various meteorological data through satellite imagery can complement this terrestrial based drought monitoring. In this study, Evaporative Stress Index (ESI) was used as satellite data for drought determination. The ESI was developed by NASA and USDA, and is calculated through thermal observations of GOES satellites, MODIS, Landsat 5, 7 and 8. We will identify the difference between ESI and other satellite-based drought assessment indices (Vegetation Health Index, VHI, Leaf Area Index, LAI, Enhanced Vegetation Index, EVI), and use it to analyze the drought in South Korea, and examines the applicability of ESI as a new indicator of agricultural drought monitoring.

Keywords

References

  1. Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998. Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, Food and Agriculture Organization, Rome.
  2. Anderson, M. C., J . M. Norman, G. R. Diak, W. P. Ku stas, and J. R. Mecikalski, 1997. A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sensing of Environment 60(2): 195-216. doi:10.1016/S0034-4257(96)00215-5.
  3. Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. A. Otkin, and W. P. Kustas, 2007. A climatological study of evapotranspiration and moisture stress across the continental U.S. based on thermal remote sensing: I. model formulation. Journal of Geophysical Research 112: D10117. doi: 10.1029/2006JD007506.
  4. Anderson, M. C., C. Hain, J. Otkin, X. Zhan, K. Mo, M. Svoboda, B. Wardlow, and A. Pimstein, 2013. An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with U.S. drought monitor classifications. Journal of Hydrometeorology 14: 1035-1056. doi:10.1175/JHM-D-12-0140.1.
  5. Anderson, M. C., C. A. Zolin, C. R. Hain, K. Semmens, M. T. Yilmaz, and F. Gao, 2015. Comparison of satellitederived LAI and precipitation anomalies over Brazil with a thermal infrared-based Evaporative Stress Index for 2003-2013. Journal of Hydrology 526: 287-302. doi:10.1016/j.jhydrol.2015.01.005.
  6. Anderson, M. C., C. A. Zolin, P. C. Sentelhas, C. R. Hain, K. Semmens, M. T. Yilmaz, F. Gao, J. A. Otkin, and R. Tetrault, 2016. The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts. Remote Sensing of Environment 174(1): 82-99. doi:10.1016/j.rse.2015.11.034.
  7. Bento, V. A., I. F. Trigo, C. M. Gouveia, and C. C. DaCamara, 2018. Contribution of land surface temperature (TCI) to Vegetation Health Index: A comparative study using clear sky and all-weather climate data records. Remote Sensing 10(9): 1324. doi:10.3390/rs10091324.
  8. Bhuiyan, C., R. P. Singh, and F. N. Kogan, 2006. Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. International Journal of Applied Earth Observation and Geoinformation 8(4): 289-302. doi:10.1016/j.jag.2006.03.002.
  9. Bolstad, P. V. and S. T. Gower, 1990. Estimation of leaf area index in fourteen southern Wisconsin forest stands using a portable radiometer. Tree Physiology 7: 115-124. doi:10.1093/treephys/7.1-2-3-4.115.
  10. Chang, E. M. and E. J. Park, 2004. Mapping of drought index using satellite imagery. Journal of Korean Society for Geospatial Information System 12(4): 3-12 (in Korean).
  11. Chen, J. M. and T. A. Black, 1992. Defining leaf area index for non-flat leaves. Plant, Cell & Environment 15(4): 421-429. doi:10.1111/j.1365-3040.1992.tb00992.x.
  12. Fassnacht, K. S., S. T. Gower, J. M. Norman, and R. E. McMurtrie, 1994. A comparison of optical and direct methods for estimating foliage surface area index in forests. Agricultural and Forest Meteorology 71(1-2): 183-207. doi:10.1016/0168-1923(94)90107-4.
  13. Hong, E. M., W. H. Nam, and J. Y. Choi, 2015. Climate change impacts on agricultural drought for major upland crops using soil moisture model -Focused on the Jeollanam-do-. Journal of the Korean Society of Agricultural Engineers 57(3): 65-76 (in Korean). doi: 10.5389/KSAE.2015.57.3.065.
  14. Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira, 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83(1-2): 195-213. doi:10.1016/S0034-4257(02)00096-2.
  15. James, P., R. F. Banay, J. E. Hart, and F. Laden, 2015. A review of the health benefits of greenness. Current Epidemiology Reports 2(2): 131-142. doi:10.1007/s40471-015-0043-7.
  16. Jang, M. W., S. H. Yoo, and J. Y. Choi, 2007. Analysis of spring drought using NOAA/AVHRR NDVI for North Korea. Journal of the Korean Society of Agricultural Engineers 49(6): 21-33 (in Korean). doi:10.5389/KSAE.2007.49.6.021.
  17. Jeoung, J., D. Kim, and M. Choi, 2017. A study on the utilization of geostationary ocean color imager on communication, ocean and meteorological satellite for drought monitoring. Journal of the Korean Society of Hazard Mitigation 17(3): 69-77 (in Korean). doi:10.9798/KOSHAM.2017.17.3.69.
  18. Ji, L. and A. J. Peters, 2003. Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sensing of Environment 87(1): 85-98. doi:10.1016/S0034-4257(03)00174-3.
  19. Jonckheere, I., S. Fleck, K. Nackaerts, B. Muys, P. Coppin, M. Weiss, and F. Baret, 2004. Review of methods for in situ leaf area index determination: Part I. theories, sensors and hemispherical photography. Agricultural and Forest Meteorology 121(1-2): 19-35. doi:10.1016/j.agrformet.2003.08.027.
  20. Kim, G. S. and J. P. Kim, 2010. Analysis of spatialtemporal variability of NOAA/AVHRR NDVI in Korea. Journal of the Korean Society of Civil Engineers 30(3B): 295-303 (in Korean).
  21. Kogan, F. N., 1997. Global drought watch from space. Bulletin of the American Meteorological Society 78(4): 621-636. doi:10.1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2.
  22. Kogan, F. N., 2001. Operational space technology for global vegetation assessment. Bulletin of the American Meteorological Society 82(9): 1949-1964. doi:10.1175/1520-0477(2001)082<1949:OSTFGV>2.3.CO;2.
  23. Kogan, F., B. Yang, G. Wei, Z. Pei, and X. Jiao, 2005. Modelling corn production in China using AVHRR-based vegetation health indices. International Journal of Remote Sensing 26(11): 2325-2336. doi:10.1080/01431160500034235.
  24. Kogan, F., L. Salazar, and L. Roytman, 2012. Forecasting crop production using satellite-based vegetation health indices in Kansas, USA. International Journal of Remote Sensing 33(9): 2798-2814. doi:10.1080/01431161.2011.621464.
  25. Lang, A. R. G., R. E. McMurtrie, and M. L. Benson, 1991. Validity of surface area indices of Pinus radiata estimated from transmittance of the sun's beam. Agricultural and Forest Meteorology 57(1-3): 157-170. doi:10.1016/0168-1923(91)90084-4.
  26. Lee, T., S. Kim, Y. Jung, and Y. Shin, 2018. Assessment of agricultural drought using satellite-based TRMM/GPM precipitation images: At the province of Chungcheongbuk-do. Journal of the Korean Society of Agricultural Engineers 60(4): 73-82 (in Korean). doi:10.5389/KSAE.2018.60.4.073.
  27. Liu, H. Q. and A. Huete, 1995. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing 33(2): 457-465. doi:10.1109/36.377946.
  28. Mecikalski, J. R., G. R. Diak, M. C. Anderson, and J. M. Norman, 1999. Estimating fluxes on continental scales using remotely sensed data in an atmosphere-land exchange model. Journal of Applied Meteorology 38: 1352-1369. doi:10.1175/1520-0450(1999)038<1352:EFOCSU>2.0.CO;2.
  29. Nam, W. H., J. Y. Choi, S. H. Yoo, and B. A. Engel, 2012. A real-time online drought broadcast system for monitoring soil moisture index. KSCE Journal of Civil Engineering 16(3): 357-365. doi:10.1007/s12205-012-1357-3.
  30. Nam, W. H., T. Tadesse, B. D. Wardlow, M. W. Jang, and S. Y. Hong, 2015a. Satellite-based hybrid drought assessment using Vegetation Drought Response Index in South Korea (VegDRI-SKorea). Journal of the Korean Society of Agricultural Engineers 57(4): 1-9 (in Korean). doi:10.5389/KSAE.2015.57.4.001.
  31. Nam, W. H., M. J. Hayes, M. D. Svoboda, T. Tadesse, and D. A. Wilhite, 2015b. Drought hazard assessment in the context of climate change for South Korea. Agricultural Water Management 160: 106-117. doi:10.1016/j.agwat.2015.06.029.
  32. Nam W. H., T. Tadesse, B. D. Wardlow, M. J. Hayes, M. D. Svoboda, E. M. Hong, Y. A. Pachepsky, and M. W. Jang, 2018. Developing the vegetation drought response index for South Korea (VegDRI-SKorea) to assess the vegetation condition during drought events. International Journal of Remote Sensing 39(5): 1548-1574. doi:10.1080/01431161.2017.1407047.
  33. Norman, J. M., W. P. Kustas, and K. S. Humes, 1995. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agricultural and Forest Meteorology 77(3-4): 263-292. doi:10.1016/0168-1923(95)02265-Y.
  34. Otkin, J. A., M. C. Anderson, C. Hain, and M. Svoboda, 2014. Examining the relationship between drought development and rapid changes in the Evaporative Stress Index. Journal of Hydrometeorology 15: 938-956. doi: 10.1175/JHM-D-13-0110.1.
  35. Otkin, J. A., M. Svoboda, E. D. Hunt, T. W. Ford, M. C. Anderson, C. Hain, and J. B. Basara, 2018. Flash droughts: A review and assessment of the challenges imposed by rapid-onset droughts in the United States. Bulletin of the American Meteorological Society 99(5): 911-919. doi:10.1175/BAMS-D-17-0149.1.
  36. Reed, B. C., M. D. Schwartz, and X. Xiao, 2009. Remote sensing phenology: Status and the way forward. Phenology of Ecosystem Processes 231-246. doi:10.1007/978-1-4419-0026-5_10.
  37. Rowhani, P., M. Linderman, and E. F. Lambin, 2011. Global interannual variability in terrestrial ecosystems: Sources and spatial distribution using MODIS-derived vegetation indices, social and biophysical factors. International Journal of Remote Sensing 32(19): 5393-5411. doi:10.1080/01431161.2010.501042.
  38. Shin, S. C. and M. S. Eoh, 2004. Analysis of drought detection and propagation using satellite data. Journal of The Korean Society of Hazard Mitigation 4(2): 61-69 (in Korean).
  39. Singh, R. P., S. Roy, and F. Kogan, 2003. Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. International Journal of Remote Sensing 24(22): 4393-4402. doi:10.1080/0143116031000084323.
  40. Smith, N. J., 1991. Predicting radiation attenuation in stands of Douglas-fir. Forest Science 37(5): 1213-1223. doi:10.1093/forestscience/37.5.1213.
  41. Stenberg, P., S. Linder, H. Smolander, and J. Flower-Ellis, 1994. Performance of the LAI-2000 plant canopy analyzer in estimating leaf area index of some Scots pine stands. Tree Physiology 14(7-8-9): 981-995. doi:10.1093/treephys/14.7-8-9.981.
  42. Sur, C., K. Kim, W. Choi, J. Shim, and M. Choi, 2014. Drought assessments using satellite-based drought index in Korea; southern region case in 2013. Journal of the Korean Society of Hazard Mitigation 14(3): 127-131 (in Korean). doi:10.9798/KOSHAM.2014.14.3.127.
  43. 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 U.S. central plains. ISPRS Journal of Photogrammetry & Remote Sensing 59(4): 244-253. doi:10.1016/j.isprsjprs.2005.02.003.
  44. Watson, D. J., 1947. Comparative physiological studies in the growth of field crops: I. variation in net assimilation rate and leaf area between species and varieties, and within and between years. Annals of Botany 11(1): 41-76. doi:10.1093/oxfordjournals.aob.a083148.
  45. Wilhite, D. A. and M. H. Glantz, 1985. Understanding the drought phenomenon: The role of definitions. Water International 10(3): 111-120. doi:10.1080/02508068508686328.
  46. Wilhite, D. A., M. J. Hayes, C. Knutson, and K. H. Smith, 2000. Planning for drought: Moving from crisis to risk management. Journal of the American Water Resources Association 36(4): 697-710. doi:10.1111/j.1752-1688.2000.tb04299.x.
  47. Yang, Y., M. C. Anderson, F. Gao, B. Wardlow, C. R. Hain, J. A. Otkin, J. Alfieri, Y. Yang, L. Sun, and W. Dulaney, 2018. Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA. Remote Sensing of Environment 210(1): 387-402. doi:10.1016/j.rse.2018.02.020.