Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
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Development of Agricultural Drought Assessment Approach Using SMAP Soil Moisture Footprints

SMAP 토양수분 이미지를 이용한 농업가뭄 평가 기법 개발

  • Shin, Yongchul (School of Agricultural Civil & Bio-Industrial Engineering, Kyungpook National University) ;
  • Lee, Taehwa (School of Agricultural Civil & Bio-Industrial Engineering, Kyungpook National University) ;
  • Kim, Sangwoo (School of Agricultural Civil & Bio-Industrial Engineering, Kyungpook National University) ;
  • Lee, Hyun-Woo (School of Agricultural Civil & Bio-Industrial Engineering, Kyungpook National University) ;
  • Choi, Kyung-Sook (School of Agricultural Civil & Bio-Industrial Engineering, Kyungpook National University) ;
  • Kim, Jonggun (Institute of Agriculture and Life Science, Kangwon National University) ;
  • Lee, Giha (Construction & Disaster Prevention Engineering, Kyungpook National University)
  • Received : 2016.11.03
  • Accepted : 2016.12.01
  • Published : 2017.01.31
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Abstract

In this study, we evaluated daily root zone soil moisture dynamics and agricultural drought using a near-surface soil moisture data assimilation scheme with Soil Moisture Active & Passive (SMAP, $3km{\times}3km$) soil moisture footprints under different hydro-climate conditions. Satellite-based LANDSAT and MODIS image footprints were converted to spatially-distributed soil moisture estimates based on the regression model, and the converted soil moisture distributions were used for assessing uncertainties and applicability of SMAP data at fields. In order to overcome drawbacks of the discontinuity of SMAP data at the spatio-temporal scales, the data assimilation was applied to SMAP for estimating daily soil moisture dynamics at the spatial domain. Then, daily soil moisture values were used to estimate weekly agricultural drought based on the Soil Moisture Deficit Index (SMDI). The Yongdam-dam and Soyan river-dam watersheds were selected for validating our proposed approach. As a results, the MODIS/SMAP soil moisture values were relatively overestimated compared to those of the TDR-based measurements and LANDSAT data. When we applied the data assimilation scheme to SMAP, uncertainties were highly reduced compared to the TDR measurements. The estimated daily root zone soil moisture dynamics and agricultural drought from SMAP showed the variability at the sptio-temporal scales indicating that soil moisture values are influenced by not only the precipitation, but also the land surface characteristics. These findings can be useful for establishing efficient water management plans in hydrology and agricultural drought.

Keywords

References

  1. Belmans, C., J. G. Wesseling, and R. A. Feddes, 1983. Simulation model of the water balance of a cropped soil: SWATRE. J. Hydrol. 63(3-4): 271-296. https://doi.org/10.1016/0022-1694(83)90045-8
  2. Crow, W. T., E. F. Wood, and R. Dubayah, 2000. Potential for downscaling soil moisture maps derived from space borne imaging radar data. J. Geophys. Res. 105: 2203-2212. https://doi.org/10.1029/1999JD901010
  3. Engman, T., 1991. Application of microwave remote sensing of soil moisture for water resources and agriculture, Remote Sens. Environ. 35: 213-226. https://doi.org/10.1016/0034-4257(91)90013-V
  4. Entekhabi, D., G. R. Asrar, A. K. Betts, K. J. Beven, R. L. Bras, and C. J. Duffy, 1999. An agenda for land surface hydrology research and a call for the second international hydrological decade, Bulletin of American Meteorological Society 80(10): 2043-2058. https://doi.org/10.1175/1520-0477(1999)080<2043:AAFLSH>2.0.CO;2
  5. Goldberg, D. E., 1989. Genetic algorithms in search and optimization and machine learning, Addison-Wesley Publishing, New York.
  6. Feddes, R. A., P. J. Kowalik, and H. Zarandy, 1978. Simulation of field water use and crop yield, John Wiley & Sons, New York.
  7. Holland, J. H., 1975. Adaptation in natural and artificial system, University of Michigan press, Ann Arbor, MI.
  8. Ines, A. V. M. and P. Droogers, 2002. Inverse modeling in estimating soil hydraulic functions: a genetic algorithm approach. Hydrol. Earth Sys. Sci. 6(1): 49-65. https://doi.org/10.5194/hess-6-49-2002
  9. Ines, A. V. M. and B. P. Mohanty, 2008. Near-surface soil moisture assimilation to quantify effective soil hydraulic properties using genetic algorithm: I. Conceptual modeling. Water Resour. Res. 44, doi:10.1029/2007WR005990.
  10. Ines, A. V. M., B. P. Mohanty, and Y. Shin, 2013. An un-mixing algorithm for remotely sensed soil moisture. Water Resour. Res. 49, doi:10.1029/2012WR012379.
  11. Leij, F. J., W. J. Alves, M. Th. Van Genuchten, and J. R. Williams, 1999. The UNSODA unsaturated soil hydraulic database. In: M.Th. Van Genuchten F. J. Leij and L. Wu. (eds.). Characterization and measurement of the hydraulic properties of unsaturated porous media, University of California, Riverside, CA. 1269-1281.
  12. Merlin, O., G. Chehbouni, Y. Kerr, E. G. Njoku, and D. Entekhabi, 2005. A combined modeling and multi-spectral/multi-resolution remote sensing approach for disaggregation of surface soil moisture: Application to SMOS configuration. IEEE Trans. Geosci. Remote Sens. 43(9): 2036-2050, doi:10.1109/tgrs.2005.853192.
  13. Merlin, O., C. Rudiger, A. Al Bitar, J. P. Walker, and Y. H. Kerr, 2012. Disaggregation of SMOS soil moisture in southeastern Australia. IEEE Trans. Geosci. Remote Sens. 50(5): 1556-1571. https://doi.org/10.1109/TGRS.2011.2175000
  14. Mualem, Y., 1976. A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res. 12(3): 513-522. https://doi.org/10.1029/WR012i003p00513
  15. Narahimhan, B. and R. Srinivasan, 2005. Development and evaluation of soil moisture deficit index (SMDI) and evapotranspiration deficit index (ETDI) for agricultural drought monitoring. Agric. Forest Meteorol. 133(1-4): 69-88. https://doi.org/10.1016/j.agrformet.2005.07.012
  16. Njoku, E. and D. Entekkabi, 1996. Passive microwave remote sensing of soil moisture. J. Hydrol. 184: 101-129. https://doi.org/10.1016/0022-1694(95)02970-2
  17. Ottle, C. and D. Vidal-Madjar, 1994. Assimilation of soil moisture inferred from infrared remote sensing in a hydrological model over the HAPEX-MOBILHY region. J. Hydrol. 158: 241-264. https://doi.org/10.1016/0022-1694(94)90056-6
  18. Scott, C. A., W. G. M. Bastiaanssen, and M. D. Ahmad, 2003. Mapping root zone soil moisture using remotely sensed optical imagery. J. Irrig. Drain Eng. 129(5): 362-335. https://doi.org/10.1061/(ASCE)0733-9437(2003)129:5(326)
  19. Shin, Y., K. J. Lim, K. Park, and Y. Jung, 2016. Development of dynamic ground water data assimilation for quantifying soil hydraulic properties from remotely sensed soil moisture. Water 8(7), doi:10.3390/W8070311.
  20. Shin, Y. and B. P. Mohanty, 2013. Development of a deterministic downscaling algorithm for remote sensing soil moisture footprint using soil and vegetation classifications. Water Resources Research 49: 1-21, doi:10.1002/wrce.20495.
  21. Shin, Y., K. S. Choi, Y, Jung, J. E. Yang, and K. J. Lim, 2016a, Soil moisture estimation and drought assessment at the spatiotemporal scales using remotely sensed data: (I) Soil moisture, Journal of Korean Society on Water Environment, 32(1): 60-69. https://doi.org/10.15681/KSWE.2016.32.1.60
  22. Shin, Y., K. S. Choi, Y, Jung, J. E. Yang, and K. J. Lim, 2016a, Soil moisture estimation and drought assessment at the spatio-temporal scales using remotely sensed data: (II) Drought, Journal of Korean Society on Water Environment, 32(1): 70-79. https://doi.org/10.15681/KSWE.2016.32.1.70
  23. Ulaby, F., P. Dubois, and J. von Zyl, 1996. Radar mapping of surface soil moisture. J. Hydrol. 184: 57-84. https://doi.org/10.1016/0022-1694(95)02968-0
  24. van Dam, J. C., J. Huygen, J. G. Wesseling, R. A. Feddes, P. Kabat, P. E. V. van Waslum, P. Groenendjik, and C. A. van Diepen, 1997. Theory of SWAP version 2.0: Simulation of water flow and plant growth in the soil-water-atmosphere-plant environment, Technical Document 45, DLO Winand Staring Centre, Wageningen, Netherlands.
  25. van Dam, J. C., 2000. Field-scale water flow and solute transport: SWAP model concepts, parameter estimation, and case studies, Ph.D. dissertation, Department of Soil Physics, Agricultural Hydrology and Groundwater, Wageningen University, Wageningen, Netherlands.
  26. van Genuchten, M. T., 1980. A closed-form equation foe predicting the hydraulic conductivity of unsaturated soils. Soi. Sci. Soc. Am. 44(5): 892-898. https://doi.org/10.2136/sssaj1980.03615995004400050002x