Mayes, W. M., Walsh, C. L., Bathurst, J. C., et al. (2006). Monitoring a flood event in a densely
instrumented catchment, the Upper Eden, Cumbria, UK. Water and Environment Journal, 20,
217–226.
McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the
delineation of open water features. International Journal of Remote Sensing, 17, 1425–1432.
Meroni, M., Marinho, E., Sghaier, N., et al. (2013). Remote sensing based yield estimation in a
stochastic framework—Case study of durum wheat in Tunisia. Remote Sensing, 5, 539–557.
Michener, W. K., & Houhoulis, P. F. (1997). Detection of vegetation changes associated with
extensive flooding in a forested ecosystem. Photogrammetric Engineering and Remote Sensing,
63, 1363–1374.
Mkhabela, M. S., Mkhabela, M. S., & Mashinini, N. N. (2005). Early maize yield forecasting in the
four agro-ecological regions of Swaziland using NDVI data derived from NOAA’s-AVHRR.
Agricultural and Forest Meteorology, 129, 1–9. https://doi.org/10.1016/j.agrformet.2004.12.
006.
Mkhabela, M. S., Bullock, P., Raj, S., et al. (2011). Crop yield forecasting on the Canadian Prairies
using MODIS NDVI data. Agricultural and Forest Meteorology, 151, 385–393. https://doi.org/
10.1016/j.agrformet.2010.11.012.
Moriondo, M., Maselli, F., & Bindi, M. (2007). A simple model of regional wheat yield based on
NDVI data. European Journal of Agronomy, 26, 266–274. https://doi.org/10.1016/j.eja.2006.
10.007.
Prasad, A. K., Chai, L., Singh, R. P., & Kafatos, M. (2006). Crop yield estimation model for Iowa
using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation, 8, 26–33. https://doi.org/10.1016/j.jag.2005.06.002.
Quarmby, N. A., Milnes, M., Hindle, T. L., & Silleos, N. (1993). The use of multi-temporal NDVI
measurements from AVHRR data for crop yield estimation and prediction. International
Journal of Remote Sensing, 14, 199–210. https://doi.org/10.1080/01431169308904332.
Rahman, M. S., & Di, L. (2017). The state of the art of spaceborne remote sensing in flood
management. Natural Hazards, 85, 1223–1248.
Rahman, M. S., Di, L., Shrestha, R., et al. (2016). Comparison of selected noise reduction
techniques for MODIS daily NDVI: An empirical analysis on corn and soybean. In Agrogeoinformatics (Agro-geoinformatics), 2016 fifth international conference on (pp. 1–5). IEEE.
Rasmussen, M. S. (1992). Assessment of millet yields and production in northern Burkina Faso
using integrated NDVI from the AVHRR. International Journal of Remote Sensing, 13,
3431–3442. https://doi.org/10.1080/01431169208904132.
Rossman, L. A. (2010). Storm water management model user’s manual, version 5.0. National Risk
Management Research Laboratory, Office of Research and Development, US Environmental
Protection Agency Cincinnati, OH.
Sanyal, J., & Lu, X. X. (2004). Application of remote sensing in flood management with special
reference to monsoon Asia: A review. Natural Hazards, 33, 283–301. https://doi.org/10.1023/
B:NHAZ.0000037035.65105.95.
Schumann, G. J.-P., & Moller, D. K. (2015). Microwave remote sensing of flood inundation.
Physics and Chemistry of the Earth, Parts A/B/C, 83–84, 84–95. https://doi.org/10.1016/j.pce.
2015.05.002.
Schumann, G., Bates, P. D., Horritt, M. S., et al. (2009). Progress in integration of remote sensing–
derived flood extent and stage data and hydraulic models. Reviews of Geophysics, 47, RG4001.
https://doi.org/10.1029/2008RG000274.
Schut, A. G. T., Stephens, D. J., Stovold, R. G. H., et al. (2009). Improved wheat yield and
production forecasting with a moisture stress index, AVHRR and MODIS data. Crop and
Pasture Science, 60, 60–70.
Sheng, Y., Gong, P., & Xiao, Q. (2001). Quantitative dynamic flood monitoring with NOAA
AVHRR. International Journal of Remote Sensing, 22, 1709–1724. https://doi.org/10.1080/
01431160118481.
Shrestha, R., & Di, L. (2013). Land/water detection and delineation with Landsat data using Matlab/
ENVI. In Agro-geoinformatics (Agro-geoinformatics), 2013 second international conference on
(pp. 211–214). https://doi.org/10.1109/Argo-Geoinformatics.2013.6621909.
348
R. M. Shrestha and M. S. Rahman
instrumented catchment, the Upper Eden, Cumbria, UK. Water and Environment Journal, 20,
217–226.
McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the
delineation of open water features. International Journal of Remote Sensing, 17, 1425–1432.
Meroni, M., Marinho, E., Sghaier, N., et al. (2013). Remote sensing based yield estimation in a
stochastic framework—Case study of durum wheat in Tunisia. Remote Sensing, 5, 539–557.
Michener, W. K., & Houhoulis, P. F. (1997). Detection of vegetation changes associated with
extensive flooding in a forested ecosystem. Photogrammetric Engineering and Remote Sensing,
63, 1363–1374.
Mkhabela, M. S., Mkhabela, M. S., & Mashinini, N. N. (2005). Early maize yield forecasting in the
four agro-ecological regions of Swaziland using NDVI data derived from NOAA’s-AVHRR.
Agricultural and Forest Meteorology, 129, 1–9. https://doi.org/10.1016/j.agrformet.2004.12.
006.
Mkhabela, M. S., Bullock, P., Raj, S., et al. (2011). Crop yield forecasting on the Canadian Prairies
using MODIS NDVI data. Agricultural and Forest Meteorology, 151, 385–393. https://doi.org/
10.1016/j.agrformet.2010.11.012.
Moriondo, M., Maselli, F., & Bindi, M. (2007). A simple model of regional wheat yield based on
NDVI data. European Journal of Agronomy, 26, 266–274. https://doi.org/10.1016/j.eja.2006.
10.007.
Prasad, A. K., Chai, L., Singh, R. P., & Kafatos, M. (2006). Crop yield estimation model for Iowa
using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation, 8, 26–33. https://doi.org/10.1016/j.jag.2005.06.002.
Quarmby, N. A., Milnes, M., Hindle, T. L., & Silleos, N. (1993). The use of multi-temporal NDVI
measurements from AVHRR data for crop yield estimation and prediction. International
Journal of Remote Sensing, 14, 199–210. https://doi.org/10.1080/01431169308904332.
Rahman, M. S., & Di, L. (2017). The state of the art of spaceborne remote sensing in flood
management. Natural Hazards, 85, 1223–1248.
Rahman, M. S., Di, L., Shrestha, R., et al. (2016). Comparison of selected noise reduction
techniques for MODIS daily NDVI: An empirical analysis on corn and soybean. In Agrogeoinformatics (Agro-geoinformatics), 2016 fifth international conference on (pp. 1–5). IEEE.
Rasmussen, M. S. (1992). Assessment of millet yields and production in northern Burkina Faso
using integrated NDVI from the AVHRR. International Journal of Remote Sensing, 13,
3431–3442. https://doi.org/10.1080/01431169208904132.
Rossman, L. A. (2010). Storm water management model user’s manual, version 5.0. National Risk
Management Research Laboratory, Office of Research and Development, US Environmental
Protection Agency Cincinnati, OH.
Sanyal, J., & Lu, X. X. (2004). Application of remote sensing in flood management with special
reference to monsoon Asia: A review. Natural Hazards, 33, 283–301. https://doi.org/10.1023/
B:NHAZ.0000037035.65105.95.
Schumann, G. J.-P., & Moller, D. K. (2015). Microwave remote sensing of flood inundation.
Physics and Chemistry of the Earth, Parts A/B/C, 83–84, 84–95. https://doi.org/10.1016/j.pce.
2015.05.002.
Schumann, G., Bates, P. D., Horritt, M. S., et al. (2009). Progress in integration of remote sensing–
derived flood extent and stage data and hydraulic models. Reviews of Geophysics, 47, RG4001.
https://doi.org/10.1029/2008RG000274.
Schut, A. G. T., Stephens, D. J., Stovold, R. G. H., et al. (2009). Improved wheat yield and
production forecasting with a moisture stress index, AVHRR and MODIS data. Crop and
Pasture Science, 60, 60–70.
Sheng, Y., Gong, P., & Xiao, Q. (2001). Quantitative dynamic flood monitoring with NOAA
AVHRR. International Journal of Remote Sensing, 22, 1709–1724. https://doi.org/10.1080/
01431160118481.
Shrestha, R., & Di, L. (2013). Land/water detection and delineation with Landsat data using Matlab/
ENVI. In Agro-geoinformatics (Agro-geoinformatics), 2013 second international conference on
(pp. 211–214). https://doi.org/10.1109/Argo-Geoinformatics.2013.6621909.
348
R. M. Shrestha and M. S. Rahman