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http://dx.doi.org/10.15681/KSWE.2016.32.1.60

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

Shin, Yongchul (Department of Agricultural Civil Eng. Kyungpook National University)
Choi, Kyung-Sook (Department of Agricultural Civil Eng. Kyungpook National University)
Jung, Younghun (Water Resources Research Center, K-water)
Yang, Jae E. (Department of Biological Environment, Kangwon National University)
Lim, Kyoung-Jae (Department of Regional Infrastructure Engineering, Kangwon National University)
Publication Information
Journal of Korean Society on Water Environment / v.32, no.1, 2016 , pp. 60-69 More about this Journal
Abstract
In this study, we estimated root zone soil moisture dynamics using remotely sensed (RS) data. A soil moisture data assimilation scheme was used to derive the soil and root parameters from MODerate resolution Imaging Spectroradiometer (MODIS) data. Based on the estimated soil/root parameters and weather forcings, soil moisture dynamics were simulated at spatio-temporal scales based on a hydrological model. For calibration/validation, the Little Washita (LW13) in Oklahoma and Chungmi-cheon/Seolma-cheon sites were selected. The derived water retention curves matched the observations at LW 13. Also, the simulated soil moisture dynamics at these sites was in agreement with the Time Domain Reflectrometry (TDR)-based measurements. To test the applicability of this approach at ungauged regions, the soil/root parameters at the pixel where the Seolma-cheon site is located were derived from the calibrated MODIS-based (Chungmi-cheon) soil moisture data. Then, the simulated soil moisture was validated using the measurements at the Seolma-cheon site. The results were slightly overestimated compared to the measurements, but these findings support the applicability of this proposed approach in ungauged regions with predictable uncertainties. These findings showed the potential of this approach in Korea. Thus, this proposed approach can be used to assess root zone soil moisture dynamics at spatio-temporal scales across Korea, which comprises mountainous regions with dense forest.
Keywords
Data assimilation; MODIS; Remotely sensed data; Root zone soil moisture; Soil and root parameters;
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  • Reference
1 Carroll, K. L. (2015). Genetic Algorithm, http://www.cuaerospace.com/carroll/ga.html. (Accessed 9 Nov. 2015).
2 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.   DOI
3 Feddes, R. A., Kowalik, P. J., and Zarandy, H. (1978). Simulation of Field Water Use and Crop Yield, Simulation Monograph, PUDOC, Wageningen, Netherlands.
4 Ek, M., K. Mitchell, E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D. (2003). Implementation of Noah Land Surface Model Advances in the National Centers for Environmental Prediction Operational Mesoscale Eta Model, Journal of Geophysical Research, 108(D22), 8851, doi:10.1029/2002JD003296.   DOI
5 Engman, T. (1991). Application of Microwave Remote Sensing of Soil Moisture for Water Resources and Agriculture, Remote Sensing of Environment, 35, pp. 213-226.   DOI
6 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.   DOI
7 Ines, A. V. M. and Droogers, P. (2002). Inverse Modeling in Estimating Soil Hydraulic Functions; A Genetic Algorithm Approach, Hydrology and Earth System Sciences, 6(1), pp. 49-65.   DOI
8 Ines, A. V. M. and Honada, K. (2005). On Quantifying Agricultural and Water Management Practices from Low Spatial Resolution RS Data using Genetic Algorithm: A Numerical Study for Mixed Pixel Environment, Advances in Water Resources, 28, pp. 856-790.   DOI
9 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.   DOI
10 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.   DOI
11 Ministry of Land, Infrastructure and Transport (MOLIT). (2012). Hydrologic investigation report II-2. Soil moisture investigation in 2012, 11-1611000-002213-10, Ministry of Land, Infrastructure and Transport, pp. 21-268. [korean Literature]
12 Liang, X., Lettenmaier, D. P., Wood, E. F., and Burgs, S. J. (1994). A Simple Hydrologically Based Model of Land Surface Water, Energy Fluxes for General Circulation Models, Journal of Geophysical Research, 99(D7), pp. 14415-14428, doi:10.1029/94JD00483.   DOI
13 Merlin, O., Chehbouni, G., Kerr, Y., Njoku, E. G., and Entekhabi, D. (2005). A Combined Modeling and Multi-Spectral/Multi-Resolution Remote Sensing Approach for Disaggregation of Surface Soil Moisture: Application to SMOS Configuration, Transactions on Geoscience and Remote Sensing, 43(9), pp. 2036-2050, doi:10.1109/tgrs.2005.853192.   DOI
14 Merlin, O., Rudiger, C., Al Bitar, A., Walker, J. P., and Kerr, Y. H. (2012). Disaggregation of SMOS Soil Moisture in Southeastern Australia, Transactions on Geoscience and Remote Sensing, 50(5), pp. 1556-1571.   DOI
15 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.   DOI
16 Mualem, Y. (1976). A New Model for Predicting the Hydraulic Conductivity of Unsaturated Posours Media, Water Resources Research, 12, pp. 513-522.   DOI
17 Oleson, K. W., Lawrence, D. M., Bonan, G. B., Flanner, M. G., Kluzek, E.,. Lawrence, P. J., Levis, S., Swenson, S. C., and Thornton, P. E. (2010). Technical Description of Version 4.0 of the Community Land Model (CLM), NCAR TECHNICAL NOTES (NCAR/TN-478+STR).
18 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.   DOI
19 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.
20 Šimůnek, J (1991). Numerical Simulation of the Transport Processes in Soil, Vodohosp. Cas., 39, pp. 20-34 (in Czech).
21 Šim˚unek, J., Šejna, M., Saito, H., Sakai, M., and van Genuchten, M. T. (2013). The Hydrus-1D Software Package for Simulating the Movement of Water, Heat, and Multiple Solutes in Variably Saturated Media, Version 4.16, HYDRUS Software Series 3, Department of Environmental Sciences, University of California Riverside, Riverside, California, USA, pp. 340.
22 van Genuchten, M. T. (1980). A Closed-Form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils, Soil Science Society of America Journal, 44, pp. 892-898.   DOI