References
- Aubert, D., Loumagne, C., and Oudin, L., 2003, Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall-runoff model, Journal of Hydrology, 280(1-4), 145-161. https://doi.org/10.1016/S0022-1694(03)00229-4
- Coelho, L. S., Freire, R. Z., Santos, G. H., and Mendes, N., 2009, Identification of temperature and moisture content fields using a combined neural network and clustering method approach, International Communications in Heat and Mass Transfer, 36(4), 304-313. https://doi.org/10.1016/j.icheatmasstransfer.2009.01.012
- Frate, F. D., Ferrazzoli, P., and Schiavon, G., 2003, Retrieving soil moisture and agricultural variables by microwave radiometry using neural networks, Remote Sensing of Environment, 84(2), 174-183. https://doi.org/10.1016/S0034-4257(02)00105-0
- Gautam, M. R., Watanabe, K., and Ohno, H., 2004, Effect of bridge construction on floodplain hydrology-assessment by using monitored data and artificial neural network models, Journal of Hydrology, 292(1-4), 182-197.
- Hong, W. Y., Park, M. J., Park, J. Y., Park, G. A., and Kim S. J., 2009, The correlation analysis between SWAT predicted forest soil moisture and MODIS NDVI image, The Korean Society of Remote Sensing 2009 spring Conference, 111-115 (in Korean).
- Kim, G. S., 2007, The soil moisture analysis for watershed management(I): Research trends of soil moisture observation (김광섭, 2007, 유역관리를 위한 토양수분 분석(I): 토양수분 관측 연구동향), Magazine of Korea Water Resources Association, 40(1), 62-71 (in Korean).
- Kim, Y. S., Yang, J. L., Lee, H. S., and Koh, D. K., 2007, Water resources experimental watershed in Yongdam dam basin (김영성.양재린.이현석.고덕구, 2007, 용담댐 수자원 시험유역), Magazine of Korea Water Resources Association, 40(6), 48-53 (in Korean).
- Kyoung, M. S., Kim, B. S., and Kim, H. S., 2009, Assessment of climate change effect on drought in Korea, Korea Water Resources Association 2009 Conference, 1457-1461 (in Korean).
- Lee, W. H., Jun, K. W., Kim, J. G., and Yeon, I.-S., 2007, Construction of system for water quality forecasting at Dalchun using neural network model, Journal of the Korean Society of Water and Wastewater, 21(3), 305-314 (in Korean).
- Maier, H. R. and Dandy, G. C., 1996, The use of artificial neural networks for the prediction of water quality parameters, Water Resources Research, 32(4), 1013-1022. https://doi.org/10.1029/96WR03529
- Minns, A. W. and Hall, M. J., 1996, Artificial neural networks as rainfall-runoff models, Hydrological Sciences Journal, 41(3), 399-417. https://doi.org/10.1080/02626669609491511
- Ohkubo, A., Mohamed, M., and Niijima, K., 1998, A soil moisture map generated from satellite data by using domains of attraction in neural networks, International Conference on Neural Information Processing, 356-359.
- Park, D. K., Yi, S. K., and Cho, W.-C., 2002, Rainfall estimation at an ungaged point using artificial neural network theory, Korean Society of Civil Engineers Conference, 1242-1245 (in Korean).
- Park, E. J., Hwang, C. S., and Seong, J. C., 2002, The analysis of drought susceptibility using soil moisture information and spatial factors involved in satellite imagery, The Journal of GIS Association of Korea, 10(3), 481-492 (in Korean).
- Pierdicca, N., Pulvirenti, L., and Bignami, C., 2010, Soil moisture estimation over vegetated terrains using multitemporal remote sensing data, Remote Sensing of Environment, 114(2), 440-448. https://doi.org/10.1016/j.rse.2009.10.001
- Qiu, Y., Fu, B., Wang, J., and Chen, L., 2003, Spatiotemporal prediction of soil moisture content using multiple-linear regression in a small catchment of the Loess Plateau, China, CATENA, 54(1-2), 173-195. https://doi.org/10.1016/S0341-8162(03)00064-X
- Rouse, J. W., Haas, R. H., Schell, J. A., and Deering, D. W., 1974, Monitoring vegetation systems in the Great Plains with ERTS, Third Earth Resources Technology Satellite-1 Symposium, 1, 309-317.
- Rumelhart, D. E., Hinton, G. E., and Williams, R. J., 1986, Learning internal representations by Error Propagation. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press Cambridge, MA, USA, 1, 318-362.
- Santanello Jr., J. A., Peters-Lidard, C. D., Garcia, M. E., Mocko, D. M., Tischler, M. A., Moran, M. S., and Thoma, D. P., 2007, Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed, Remote Sensing of Environment, 110(1), 79-97. https://doi.org/10.1016/j.rse.2007.02.007
- Wang, H., Li, X., Long, H., Xu, X., and Bao, Y., 2010, Monitoring the effects of land use and cover type changes on soil moieture using remotesensing data: A case study in China's Yongding River basin, CATENA, 82(3), 135-145. https://doi.org/10.1016/j.catena.2010.05.008
- Wigneron, J. P., Calvet, J. C., Pellarin, T., Van de Griend, A. A., Berger, M., and Ferrazzoli, P., 2003, Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans, Remote Sensing of Environment, 85(4), 489-506. https://doi.org/10.1016/S0034-4257(03)00051-8
- Zealand, C. M., Burn, D. H., and Simonovic, S. P., 1999, Short term streamflow forecasting using artificial neural network, Journal of Hydrology, 214(1-4), 32-48. https://doi.org/10.1016/S0022-1694(98)00242-X
- Zribi, M., Baghdadi, N., Holah, N., and Fafin, O., 2005, New nethodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion, Remote Sensing of Environment, 96(3-4), 485-496. https://doi.org/10.1016/j.rse.2005.04.005