Dao, P.D., Mong, N.T., & Chan, H.-P. (2019). Landsat-MODIS image fusion and object-based image analysis for observing flood inundation in a heterogeneous vegetated scene. GIScience & Remote Sensing, 56, 1148-1169
Du, Y. Zhang, Y. Ling, F. Wang, Q. Li, W. & Li, X. (2016). Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sensing, 8, 354
Gebrehiwot, A. Hashemi-Beni, L. Thompson, G. Kordjamshidi, P. & Langan, T.E. (2019). Deep convolutional neural network for flood extent mapping using unmanned aerial vehicles data. Sensors, 19, 1486
Hashemi-Beni, L. & Gebrehiwot, A.A. (2021). Flood extent mapping: an integrated method using deep learning and region growing using UAV optical data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 2127-2135
Isikdogan, F. Bovik, A.C. & Passalacqua, P. (2017). Surface water mapping by deep learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 4909-4918
Jain, P. Schoen-Phelan, B. & Ross, R. (2020). Automatic flood detection in SentineI-2 images using deep convolutional neural networks. In, Proceedings of the 35th Annual ACM Symposium on Applied Computing (pp. 617-623)
Jiménez-Jiménez, S.I. Ojeda-Bustamante, W. Ontiveros-Capurata, R.E. & Marcial-Pablo, M.d.J. (2020). Rapid urban flood damage assessment using high resolution remote sensing data and an object-based approach. Geomatics, Natural Hazards and Risk, 11, 906-927
Merz, B. Kreibich, H. Schwarze, R. & Thieken, A. (2010). Review article" Assessment of economic flood damage". Natural Hazards and Earth System Sciences, 10, 1697-1724
Mohammadizadeh, P. Hamzeh, S. Kiavarz, M. & Darvishi Blorani, A. (2018). Derivation daily and high spatial resolution Land Surface Temperature using Fusion of Landsat and Modis Satellite Imagery. Journal of Geospatial Information Technology, 6, 77-99
Mousavi, S.M. Ebadi, H. & Kiani, A. (2019). Provide an Optimal Deep-network Method for Spectral-spatial Classifying of High Resolution Images. Journal of Geomatics Science and Technology, 9, 151-170
Sharma, A. Liu, X. Yang, X. & Shi, D. (2017). A patch-based convolutional neural network for remote sensing image classification. Neural Networks, 95, 19-28
Singh, A. (1989). Review article digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10, 989-1003
Song, H. Kim, Y. & Kim, Y. (2019). A patch-based light convolutional neural network for land-cover mapping using Landsat-8 images. Remote Sensing, 11, 114
Tamimi, E. Ebadi, H. & Kiani, A. (2017). Evaluation of different metaheuristic optimization algorithms in feature selection and parameter determination in SVM classification. Arabian Journal of Geosciences, 10, 478
Wang, B. Jia, K. Wei, X. Xia, M. Yao, Y. Zhang, X. Liu, D. & Tao, G. (2020). Generating spatiotemporally consistent fractional vegetation cover at different scales using spatiotemporal fusion and multiresolution tree methods. ISPRS journal of photogrammetry and remote sensing, 167, 214-229
Zaffaroni, M. & Rossi, C. (2020). Water Segmentation with Deep Learning Models for Flood Detection and Monitoring. In, Proceedings of the 17th ISCRAM Conference, Blacksburg, VA, USA (pp. 24-27)
Zhang, F. Zhu, X. & Liu, D. (2014). Blending MODIS and Landsat images for urban flood mapping. International Journal of Remote Sensing, 35, 3237-3253
Zhou, X. Wang, P. Tansey, K. Zhang, S. Li, H. & Tian, H. (2020). Reconstruction of time series leaf area index for improving wheat yield estimates at field scales by fusion of Sentinel-2,3 and MODIS imagery. Computers and Electronics in Agriculture, 177, 105692
)