Biancamaria, S., Hossain, F., & Lettenmaier, D. P. (2011). Forecasting transboundary river water
elevations from space. Geophysical Research Letters, 38, L11401. https://doi.org/10.1029/
2011GL047290.
Borga, M., Anagnostou, E. N., Blöschl, G., & Creutin, J.-D. (2011). Flash flood forecasting,
warning and risk management: The HYDRATE project. Environmental Science & Policy, 14,
834–844. https://doi.org/10.1016/j.envsci.2011.05.017.
Brivio, P. A., Colombo, R., Maggi, M., & Tomasoni, R. (2002). Integration of remote sensing data
and GIS for accurate mapping of flooded areas. International Journal of Remote Sensing, 23,
429–441. https://doi.org/10.1080/01431160010014729.
Campolo, M., Andreussi, P., & Soldati, A. (1999). River flood forecasting with a neural network
model. Water Resources Research, 35, 1191–1197.
Chen, J., Jönsson, P., Tamura, M., et al. (2004). A simple method for reconstructing a high-quality
NDVI time-series data set based on the Savitzky–Golay filter. Remote Sensing of Environment,
91, 332–344.
Chormanski, J., Okruszko, T., Ignar, S., et al. (2011). Flood mapping with remote sensing and
hydrochemistry: A new method to distinguish the origin of flood water during floods. Ecological Engineering, 37, 1334–1349. https://doi.org/10.1016/j.ecoleng.2011.03.016.
Cihlar, J., & Howarth, J. (1994). Detection and removal of cloud contamination from AVHRR
images. IEEE Transactions on Geoscience and Remote Sensing, 32, 583–589.
Di, L., Yu, G., & Kang, L. et al. (2013). A Remote-sensing-based flood crop loss assessment cyberservice system for supporting crop statistics and insurance decision making. In: Proceedings of
IEEE international conference on systems, man, and cybernetics (IEEE SMC 2013) special
session on environmental sensing, networking and decision making, October 13–16, 2013,
Manchester, UK.
FEMA. (2011). Mississippi flooding. https://www.fema.gov/disaster/1983. Accessed 31 May 2017.
Frappart, F., Seyler, F., Martinez, J.-M., et al. (2005). Floodplain water storage in the Negro River
basin estimated from microwave remote sensing of inundation area and water levels. Remote
Sensing of Environment, 99, 387–399. https://doi.org/10.1016/j.rse.2005.08.016.
Frazier, P. S., & Page, K. J. (2000). Water body detection and delineation with Landsat TM data.
Photogrammetric Engineering and Remote Sensing, 66, 1461–1468.
Gatebe, C. K., King, M. D., Tsay, S.-C., et al. (2001). Sensitivity of off-nadir zenith angles to
correlation between visible and near-infrared reflectance for use in remote sensing of aerosol
over land. IEEE Transactions on Geoscience and Remote Sensing, 39, 805–819.
Greenough, G., McGeehin, M., Bernard, S. M., et al. (2001). The potential impacts of climate
variability and change on health impacts of extreme weather events in the United States.
Environmental Health Perspectives, 109, 191.
Groten, S. M. E. (1993). NDVI—Crop monitoring and early yield assessment of Burkina Faso.
Remote Sensing, 14, 1495–1515.
Han, W., Yang, Z., Di, L., & Mueller, R. (2012). CropScape: A web service based application for
exploring and disseminating US conterminous geospatial cropland data products for decision
support. Computers and Electronics in Agriculture, 84, 111–123. https://doi.org/10.1016/j.
compag.2012.03.005.
Henry, J.-B., Chastanet, P., Fellah, K., & Desnos, Y.-L. (2006). Envisat multi-polarized ASAR data
for flood mapping. International Journal of Remote Sensing, 27, 1921–1929. https://doi.org/10.
1080/01431160500486724.
Heremans, R., Willekens, A., Borghys, D., et al. (2003). Automatic detection of flooded areas on
ENVISAT/ASAR images using an object-oriented classification technique and an active contour algorithm. In Recent advances in space technologies, 2003. RAST’03. international
conference on. Proceedings of. IEEE (pp. 311–316).
Hess, L. L., Melack, J. M., Filoso, S., & Wang, Y. (1995). Delineation of inundated area and
vegetation along the Amazon floodplain with the SIR-C synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing, 33, 896–904. https://doi.org/10.1109/36.406675.
Hirabayashi, Y., Mahendran, R., Koirala, S., et al. (2013). Global flood risk under climate change.
Nature Climate Change, 3, 816.
346
R. M. Shrestha and M. S. Rahman
elevations from space. Geophysical Research Letters, 38, L11401. https://doi.org/10.1029/
2011GL047290.
Borga, M., Anagnostou, E. N., Blöschl, G., & Creutin, J.-D. (2011). Flash flood forecasting,
warning and risk management: The HYDRATE project. Environmental Science & Policy, 14,
834–844. https://doi.org/10.1016/j.envsci.2011.05.017.
Brivio, P. A., Colombo, R., Maggi, M., & Tomasoni, R. (2002). Integration of remote sensing data
and GIS for accurate mapping of flooded areas. International Journal of Remote Sensing, 23,
429–441. https://doi.org/10.1080/01431160010014729.
Campolo, M., Andreussi, P., & Soldati, A. (1999). River flood forecasting with a neural network
model. Water Resources Research, 35, 1191–1197.
Chen, J., Jönsson, P., Tamura, M., et al. (2004). A simple method for reconstructing a high-quality
NDVI time-series data set based on the Savitzky–Golay filter. Remote Sensing of Environment,
91, 332–344.
Chormanski, J., Okruszko, T., Ignar, S., et al. (2011). Flood mapping with remote sensing and
hydrochemistry: A new method to distinguish the origin of flood water during floods. Ecological Engineering, 37, 1334–1349. https://doi.org/10.1016/j.ecoleng.2011.03.016.
Cihlar, J., & Howarth, J. (1994). Detection and removal of cloud contamination from AVHRR
images. IEEE Transactions on Geoscience and Remote Sensing, 32, 583–589.
Di, L., Yu, G., & Kang, L. et al. (2013). A Remote-sensing-based flood crop loss assessment cyberservice system for supporting crop statistics and insurance decision making. In: Proceedings of
IEEE international conference on systems, man, and cybernetics (IEEE SMC 2013) special
session on environmental sensing, networking and decision making, October 13–16, 2013,
Manchester, UK.
FEMA. (2011). Mississippi flooding. https://www.fema.gov/disaster/1983. Accessed 31 May 2017.
Frappart, F., Seyler, F., Martinez, J.-M., et al. (2005). Floodplain water storage in the Negro River
basin estimated from microwave remote sensing of inundation area and water levels. Remote
Sensing of Environment, 99, 387–399. https://doi.org/10.1016/j.rse.2005.08.016.
Frazier, P. S., & Page, K. J. (2000). Water body detection and delineation with Landsat TM data.
Photogrammetric Engineering and Remote Sensing, 66, 1461–1468.
Gatebe, C. K., King, M. D., Tsay, S.-C., et al. (2001). Sensitivity of off-nadir zenith angles to
correlation between visible and near-infrared reflectance for use in remote sensing of aerosol
over land. IEEE Transactions on Geoscience and Remote Sensing, 39, 805–819.
Greenough, G., McGeehin, M., Bernard, S. M., et al. (2001). The potential impacts of climate
variability and change on health impacts of extreme weather events in the United States.
Environmental Health Perspectives, 109, 191.
Groten, S. M. E. (1993). NDVI—Crop monitoring and early yield assessment of Burkina Faso.
Remote Sensing, 14, 1495–1515.
Han, W., Yang, Z., Di, L., & Mueller, R. (2012). CropScape: A web service based application for
exploring and disseminating US conterminous geospatial cropland data products for decision
support. Computers and Electronics in Agriculture, 84, 111–123. https://doi.org/10.1016/j.
compag.2012.03.005.
Henry, J.-B., Chastanet, P., Fellah, K., & Desnos, Y.-L. (2006). Envisat multi-polarized ASAR data
for flood mapping. International Journal of Remote Sensing, 27, 1921–1929. https://doi.org/10.
1080/01431160500486724.
Heremans, R., Willekens, A., Borghys, D., et al. (2003). Automatic detection of flooded areas on
ENVISAT/ASAR images using an object-oriented classification technique and an active contour algorithm. In Recent advances in space technologies, 2003. RAST’03. international
conference on. Proceedings of. IEEE (pp. 311–316).
Hess, L. L., Melack, J. M., Filoso, S., & Wang, Y. (1995). Delineation of inundated area and
vegetation along the Amazon floodplain with the SIR-C synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing, 33, 896–904. https://doi.org/10.1109/36.406675.
Hirabayashi, Y., Mahendran, R., Koirala, S., et al. (2013). Global flood risk under climate change.
Nature Climate Change, 3, 816.
346
R. M. Shrestha and M. S. Rahman