Recent advances in data-driven modeling of remote sensing applications in hydrology

Journal of Hydroinformatics

Volumn 11, Issue 3-4, 2009, Pages 194-201

  Evora, Noël D ^a   Coulibaly, Paulin ^b  

^a MANITOBA HYDRO   (Canada)

^b MCMASTER UNIVERSITY   (Canada)

Author keywords

Artificial neural network; Hydrology; Inverse model; Rainfall; Remote sensing; Snow water equivalent

Indexed keywords

EID: 68949148992     PISSN: 14647141     EISSN: None     Source Type: Journal    
DOI: 10.2166/hydro.2009.036     Document Type: Article

References (40)

1. Arkin, P. A. 1979 The relationship between the fractional coverage of high cloud and rainfall accumulations during GATE over the B-scale array. Mon. Weather Rev. 107, 1382-1387.
2. Arkin, P. A., Joyce, R. & Janowiak, J. E. 1994 The estimation of global monthly mean rainfall using infrared satellite data: the GOES precipitation index. Remote Sens. Rev. 11, 107-124.
3. Aschbacher, J. 1989 Land Surface Studies and Atmospheric Effects by Satellite Microwave Radiometry. PhD Dissertation, University of Innsbruck, Innsbruck, Austria.
4. Bellerby, T., Todd, M., Kniveton, D. & Kidd, C. 2000 Rainfall estimation from a combination of TRMM precipitation radar and GOES multispectral satellite imagery through the use of an artificial neural network. J. Appl. Meteorol. 39, 2115-2128.
5. Bundas, D. 2006 Global precipitation measurement mission-architecture and mission concept. In: Proceedings of the 2006 IEEE Aerospace Conference, Big Sky, MT. IEEE, New York.
6. Carroll, S. S., Carroll, T. R. & Poston, R. W. 1999 Spatial modeling and prediction of snow-water equivalent using ground-based, airborne, and satellite snow data. J. Geophys. Res. 104 (D16), 19623-19629.
7. Chang, A. T. C, Foster, J. L. & Hall, D. K. 1987 Nimbus-7 SMMR derived global snow cover parameters. Ann. Glaciol. 9, 39-44.
8. Chang, A. T. C, Foster, J. L. & Hall, D. K. 1996 Effects of forest on the snow parameters derived from microwave measurements during the BOREAS winter field campaign. Hydrol. Process. 10, 1565-1574.
9. Chen, C.-T., Nijssen, B., Guo, J., Tsang, L., Wood, A. W., Hwang, J.-N. & Lettenmaier, D. P. 2001 Passive microwave remote sensing of snow constrained by hydrological simulations. IEEE Trans. Geosci. Remote Sens. 39, 1744-1756.
10. Chen, F. W. & Staelin, D. H. 2003 AIRS/AMSU/HSB precipitation estimates. IEEE Trans. Geosci. Remote Sens. 41, 410-417.
11. Chiang, Y. M., Chang, F. - J., Jou, B. J. -D. & Lin, P. -F. 2007 Dynamic ANN for precipitation estimation and forecasting from radar observations. J. Hydrol. 334, 250-261.
12. Coppola, E., Grimes, D. I. F., Verdecchia, M. & Visconti, G. 2006 Validation of improved TAMANN neural network for operational satellite-derived rainfall estimation in Africa. J. Appl. Meteorol. Climatol. 45, 1557-1572.
13. De Sève, D., Bernier, M., Fortin, J. -P. & Walker, A. 1997 Preliminary analysis of snow microwave radiometry using the SSM/I passive-microwave data: the case of La Grande River watershed (Quebec). Ann. Glaciol. 25, 353-361.
14. Evora, N. D., Tapsoba, D. & De Sève, D. 2008 Combining artificial neural network models, geostatistics and passive microwave data for snow water equivalent retrieval and mapping. IEEE Trans. Geosci. Remote Sens. 46, 1925-1939.
15. Foster, J. L., Chang, A. T. C. & Hall, D. K. 1997 Comparison of snow mass estimates from a prototype passive microwave snow algorithm, a revised algorithm and snow depth climatology. Remote Sens. Environ. 62, 132-142.
16. Gan, T. Y. 1996 Passive microwave snow research at the Canadian High Arctic. Can. J. Remote Sens. 22, 36-44.
17. Ghedira, H., Arevalo, J. C, Lakhankar, T. & Khanbilvardi, R. 2005 An adaptive neural network system for improving the filtration of non-snow pixels from SSM/I images. In: Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium, Seoul, Korea. IEEE, New York, Vol.8, pp. 5459-5462.
18. Goodison, B. E., Rubinstein, I., Thirkettle, F. W. & Langham, E. J. 1986 Determination of snow water equivalent on the Canadian prairies using microwave radiometry. IAHS Publ. 155, pp. 163-173.
19. Grimes, D. I. F., Coppola, E., Verdecchia, M. & Visconti, G. 2003 A neural network approach to real-time rainfall estimation for Africa using satellite data. J. Hydrometeorol. 4, 1119-1133.
20. Grody, N. & Basist, A. 1996 Global identification of snow cover using SSM/I measurements. IEEE Trans. Geosci. Remote Sens. 34, 237-249.
21. Guo, J., Tsang, L., Josberger, E. G., Wood, A. W., Hwang, J. -N. & Lettenmaier, D. P. 2003 Mapping the spatial distribution and time evolution of snow water equivalent with passive microwave measurements. IEEE Trans. Geosci. Remote Sens. 41, 612-621.
22. Hallikainen, M. T. 1989 Microwave radiometry of snow. Adv. Space Res. 9, 267-275.
23. Hecht-Nielsen, R. 1987 Counter-propagation networks. Appl. Opt. 26, 4979-4984.
24. Hong, Y., Hsu, K. -L., Sorooshian, S. & Gao, X. 2004 Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteorol. 43, 1834-1852.
25. Hong, Y., Hsu, K.-L., Sorooshian, S. & Gao, X. 2005 Improved representation of diurnal variability of rainfall retrieved from the Tropical Rainfall Measurement Mission Microwave Imager adjusted precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN) system. J. Geophys. Res. 110, D06102 (doi:10.1029/2004JD005301). (Pubitemid 40771607)
26. Hossain, F. & Katiyar, N. 2006 Improving flood forecasting in international river basins. EOS Trans. Am. Geophys. Union 87, 49 (see also 54).
27. Hsu, K. -L., Gao, X., Sorooshian, S. & Gupta, H. V. 1997 Precipitation estimation from remotely sensed informationusing artificial neural networks. J. Appl. Meteorol. 36, 1176-1190.
28. Krasnopolsky, V. M. & Schiller, H. 2003 Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements. Neural Netw. 16, 321-334.
29. Liu, H., Chandrasekar, V. & Xu, G. 2001 An adaptive neural network scheme for radar rainfall estimation from WSR-88D observations. J. Appl. Meteorol. 40, 2038-2050. (Pubitemid 33127833)
30. Loyola, R. D. G. 2006 Applications of neural network methods to the processing of earth observation satellite data. Neural Netw. 19, 168-177.
31. Moradkhani, H., Hsu, K., Hong, Y. & Sorooshian, S. 2006 Investigating the impact of remotely sensed precipitation and hydrologic model uncertainties on the ensemble streamflow forecasting. Geophys. Res. Lett. 33, L12 401.
32. Simpson, J. J. & Mclntire, T. J. 2001A recurrent neural network classifier for improved retrievals of areal extent of snow cover. IEEE Trans. Geosci. Remote Sens. 39, 2135-2147.
33. Singh, P. R. & Gan, T. Y. 2000 Retrieval of snow water equivalent using passive microwave brightness temperature data. Remote Sens. Environ. 74, 275-286.
34. Staelin, D. H. & Chen, F. W. 2000 Precipitation observations near 54 and 183 GHz using the NOAA-15 satellite. IEEE Trans. Geosci. Remote Sens. 38, 2322-2332.
35. Tait, A. B. 1998 Estimation of snow water equivalent using passive microwave radiation data. Remote Sens. Environ. 64, 286-291.
36. Tapiador, F. J., Kidd, C, Levizzani, V. & Marzano, F. S. 2004 A neural networks-based fusion technique to estimate half-hourly rainfall estimates at 0.1° resolution from satellite passive microwave and infrared data. J. Appl. Meteorol. 43, 576-594.
37. Tedesco, M., Pampaloni, P., Pulliainen, J. & Hallikainen, M. 2003 Classification and retrieval of dry snow parameters by means of SSM/I data and artificial neural networks. In: Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France. IEEE, New York. Vol.1, pp. 330-332.
38. Tedesco, M., Pulliainen, J., Takala, M., Hallikainen, M. & Pampaloni, P. 2004 Artificial neural network-based techniques for the retrieval of SWE and snow depth from SSM/I data. Remote Sens. Environ. 90, 76-85.
39. Trafalis, T. B., Richman, M. B., White, A. & Santosa, B. 2002 Data mining techniques for improved WSR-88D rainfall estimation. Comput. Ind. Eng. 43, 775-786.
40. Vann, L. & Hu, Y. 2002 A neural network inversion system for atmospheric remote-sensing measurements. In: IEEE Instrumentation and Measurement Technology Conference, Anchorage, AK. IEEE, New York. pp. 1613-1615.