1. [1] F. N. Kogan, "Contribution of remote sensing to drought early warning," Early warning systems for drought preparedness and drought management, pp. 75-87, 2000. 2. [2] A. Zhang and G. Jia, "Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data," Remote Sensing of Environment, vol. 134, pp. 12-23, 2013. 3. [3] B. W. Heumann, "Satellite remote sensing of mangrove forests: Recent advances and future opportunities," Progress in Physical Geography, vol. 35, no. 1, pp. 87-108, 2011. 4. [4] D. A. Wilhite and M. Buchanan-Smith, "Drought as hazard: understanding the natural and social context," Drought and water crises: science, technology, and management issues, pp. 3-29, 2005. 5. [5] S. Szalai and C. Szinell, "Comparison of two drought indices for drought monitoring in Hungary-a case study," in Drought and drought mitigation in Europe: Springer, 2000, pp. 161-166. 6. [6] F. N. Kogan, "Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data," Bulletin of the American Meteorological Society, vol. 76, no. 5, pp. 655-668, 1995. https://doi.org/10.1175/1520-0477(1995)076<0655:DOTLIT>2.0.CO;2 [ DOI:10.1175/1520-0477(1995)0762.0.CO;2] 7. [7] J. Bai et al., "Assessment of the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) as an Agricultural Drought Index in China," Remote Sensing, vol. 10, no. 8, p. 1302, 2018. 8. [8] S. Barua, A. Ng, and B. Perera, "Artificial neural network-based drought forecasting using a nonlinear aggregated drought index," Journal of Hydrologic Engineering, vol. 17, no. 12, pp. 1408-1413, 2012. 9. [9] A. Belayneh and J. Adamowski, "Drought forecasting using new machine learning methods/Prognozowanie suszy z wykorzystaniem automatycznych samouczących się metod," Journal of Water and Land Development, vol. 18, no. 9, pp. 3-12, 2013. 10. [10] A. Belayneh, J. Adamowski, B. Khalil, and B. Ozga-Zielinski, "Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models," Journal of Hydrology, vol. 508, pp. 418-429, 2014. 11. [11] S. Park, J. Im, S. Park, and J. Rhee, "Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula," Agricultural and Forest Meteorology, vol. 237, pp. 257-269, 2017. 12. [12]M. R. Alizadeh and M. R. Nikoo, "A fusion-based methodology for meteorological drought estimation using remote sensing data," Remote sensing of environment, vol. 211, pp. 229-247, 2018. 13. [13] R. Tan and M. Perkowski, "Wavelet-Coupled machine learning methods for drought forecast utilizing hybrid meteorological and remotely-sensed data," in Proceedings of the International Conference on Data Mining (DMIN'15), 2015. 14. [14] M. Jalili, J. Gharibshah, S. M. Ghavami, M. Beheshtifar, and R. Farshi, "Nationwide prediction of drought conditions in Iran based on remote sensing data," IEEE Transactions on Computers, vol. 63, no. 1, pp. 90-101, 2013. 15. [15]A. AghaKouchak et al., "Remote sensing of drought: Progress, challenges and opportunities," Reviews of Geophysics, vol. 53, no. 2, pp. 452-480, 2015. 16. [16]J. F. Mas and J. J. Flores, "The application of artificial neural networks to the analysis of remotely sensed data," International Journal of Remote Sensing, vol. 29, no. 3, pp. 617-663, 2008. 17. [17]C. Dawson and R. Wilby, "Hydrological modelling using artificial neural networks," Progress in physical Geography, vol. 25, no. 1, pp. 80-108, 2001. 18. [18]I. Ali, F. Cawkwell, E. Dwyer, and S. Green, "Modeling managed grassland biomass estimation by using multitemporal remote sensing data-A machine learning approach," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 7, pp. 3254-3264, 2017. 19. [19]S. Mohammady and M. Delavar, "Urban Expansion Modeling with Logistic Regression," Journal of Geomatics Science and Technology, vol. 4, no. 2, pp. 77-86, 2014. 20. [20]O. Rioul and M. Vetterli, "Wavelets and signal processing," IEEE signal processing magazine, vol. 8, no. ARTICLE, pp. 14-38, 1991. 21. [21]R. Merry, "Wavelet theory and applications: a literature study," DCT rapporten, vol. 2005, 2005. 22. [22]B.-L. Zhang and Z.-Y. Dong, "An adaptive neural-wavelet model for short term load forecasting," Electric power systems research, vol. 59, no. 2, pp. 121-129, 2001. 23. [23]S. G. Mallat, "A theory for multiresolution signal decomposition: the wavelet representation," IEEE Transactions on Pattern Analysis & Machine Intelligence, no. 7, pp. 674-693, 1989. 24. [24]F. Zambrano, M. Lillo-Saavedra, K. Verbist, and O. Lagos, "Sixteen years of agricultural drought assessment of the BioBío region in Chile using a 250 m resolution Vegetation Condition Index (VCI)," Remote Sensing, vol. 8, no. 6, p. 530, 2016. 25. [25]D. B. Wolff et al., "Ground validation for the tropical rainfall measuring mission (TRMM)," Journal of Atmospheric and Oceanic Technology, vol. 22, no. 4, pp. 365-380, 2005. 26. [26]X. Zhang, "Time series analysis and prediction by neural networks," Optimization Methods and Software, vol. 4, no. 2, pp. 151-170, 1994. 27. [27]M. R. Peyghami and R. Khanduzi, "Predictability and forecasting automotive price based on a hybrid train algorithm of MLP neural network," Neural Computing and Applications, vol. 21, no. 1, pp. 125-132, 2012. 28. [28]V. Nourani, M. Komasi, and A. Mano, "A multivariate ANN-wavelet approach for rainfall-runoff modeling," Water resources management, vol. 23, no. 14, p. 2877, 2009. 29. [29]P. S. Thenkabail and M. Gamage, The use of remote sensing data for drought assessment and monitoring in Southwest Asia. Iwmi, 2004.
|