Soil Moisture Estimation and Drought Assessment at the Spatio-Temporal Scales using Remotely Sensed Data: (II) Drought

원격탐사자료를 이용한 시⋅공간적으로 분포되어 있는 토양수분산정 및 가뭄평가: (II) 가뭄

  • Shin, Yongchul ;
  • Choi, Kyung-Sook ;
  • Jung, Younghun ;
  • Yang, Jae E. ;
  • Lim, Kyoung-Jae
  • 신용철 ;
  • 최경숙 ;
  • 정영훈 ;
  • 양재의 ;
  • 임경재
  • Received : 2015.12.02
  • Accepted : 2015.12.29
  • Published : 2016.01.30


Based on the soil moisture data assimilation suggested in the first paper (I), we estimated root zone soil moisture and evaluated drought severity using remotely sensed (RS) data. We tested the impacts of various spatial resolutions on soil moisture variations, and the model outputs showed that resolutions of more than 2-3 km resulted in over-/under-estimation of soil moisture values. Thus, we derived the 2 km resolution-scaled soil moisture dynamics and assessed the drought severity at the study sites (Chungmi-cheon sites 1 and 2) based on the estimated soil/root parameters and weather forcings. The drought indices at the sites were affected mainly by precipitation during the spring season, while both the precipitation and land surface characteristics influence the spatial distribution of drought during the rainy season. Also, the drought severity showed a periodic cycle, but additional research on drought cycles should be conducted using long-term historical data. Our proposed approach enabled estimation of daily root zone soil moisture dynamics and evaluation of drought severity at various spatial scales using MODIS data. Thus, this approach will facilitate efficient management of water resources.


Drought;MODIS;Remotely sensed data;Root zone soil moisture;Soil and root parameters


  1. Entekhabi, D. G. R., Asrar, A. K., Betts, K. J., Beven, R., Bras, L., and Duffy, C. J. (1999). An Agenda for Land Surface Hydrology Research and a Call for the Second International Hydrological Decade, Bulletin of American Meteorological Society, 80(10), pp. 2043-2058.<2043:AAFLSH>2.0.CO;2
  2. Bartsch, A., Balzter, H., and George, C. (2009). The Influence of Regional Surface Soil Moisture Anomalies on Forest Fires in Siberia Observed from Satellites, Environmental Research Letters, 4, 045021 (9pp) doi:10.1088/1748-9326/4/4/045021.
  3. Crow, W. T., Wood, E. F., and Dubayah, R. (2000). Potential for Downscaling Soil Moisture Maps Derived from Space Borne Imaging Radar Data, Journal of Geophysical Research, 105, pp. 2203-2212.
  4. Engman, T. (1991). Application of Microwave Remote Sensing of Soil Moisture for Water Resources and Agriculture, Remote Sensing of Environment, 35, pp. 213-226.
  5. Narasimhan, B. and Srinivasan, R. (2005). Development and Evaluationof Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for Agricultural Drought Monitoring, Agricultural and Forest Meteorology, 133, pp. 69-88.
  6. Ines, A. V. M., Mohanty, B. P., and Shin, Y. (2013). An Un-Mixing Algorithm for Remotely Sensed Soil Moisture, Water Resources Research, 49, doi:10.1029/2012WR012379.
  7. Kerr, Y. H., Waldteufel, P., Wigneron, J. P., Martinuzzi, J. M., Font, J., and Berger, M. (2001). Soil Moisture Retrieval from Space: The Soil Moisture and Ocean Salinity (SMOS) Mission, Transactions on Geoscience and Remote Sensing, 39(8), pp. 1729-1735.
  8. McKee, T. B., Doesken, N. J., and Kleist, J. (1993). The Relationship of Drought Frequency and Duration to Time Scales, In: Proceedings of the 8th Conference on Applied Climatology, American Meteorological Society, Anaheim, CA, Boston, MA, 17-22, January, pp. 179-184.
  9. Njoku, E. G., Jackson, T. L., Lakshmi, V., Chan, T., and Nghiem, S. V. (2003). Soil Moisture Retrieval from AMSR-E, Transactions on Geoscience and Remote Sensing, 41(2), pp. 215-229.
  10. Palmer, W. C. (1965). Research Paper, 45, U.S. Dept. of Commerce, pp. 58.
  11. Palmer, W. C. (1968). Keeping Track of Crop Moisture Conditions, Nationwide: the New Crop Moisture Index, Weatherwise, 21(4), pp. 156-161.
  12. Scott, C. A., Bastiaanssen, W. G. M., and Ahmad, M. D. (2003). Mapping Root Zone Soil Moisture Using Remotely Sensed Optical Imagery, Journal of Irrigation and Drainage Engineering, 129(5), pp. 362-335.
  13. Shin, Y. and Mohanty, B. P. (2013). Development of a Deterministic Downscaling Algorithm for Remote Sensing Soil Moisture Footprint Using Soil and Vegetation Classifications, Water Resources Research, 49, pp. 1-21, doi:10.1002/wrce.20495.
  14. Shin, Y. and Mohanty, B. P. (2015). Soil Moisture Controls of Soil Carbon Sequestration in the Unsaturated Zone under Different Hydro-Climatic Conditions, in the revision to Water Resources Research.
  15. Shin, Y. and Jung, Y. (2014). Development of Irrigation Water Management Model for Reducing Drought Severity Using Remotely Sensed Soil Moisture Footprints, Journal of Irrigation and Drainage Engineering, 140(7), pp. 1-11 10.1061/(ASCE)IR.1943-4774.0000736, 04014021.