A Global Dynamic Long-Term Inundation Extent Dataset at High Spatial Resolution Derived through Downscaling of Satellite Observations
Tài liệu tham khảo
Adam, L., P.Döll, C.Prigent, and F.Papa, 2010: Global-scale analysis of satellite-derived time series of naturally inundated areas as a basis for floodplain modeling. Adv. Geosci., 27, 45–50, doi:10.5194/adgeo-27-45-2010.10.5194/adgeo-27-45-2010
Aires, F., F.Papa, and C.Prigent, 2013: A long-term, high-resolution wetland dataset over the Amazon basin, downscaled from a multiwavelength retrieval using SAR data. J. Hydrometeor., 14, 594–607, doi:10.1175/JHM-D-12-093.1.10.1175/JHM-D-12-093.1
Aires, F., F.Papa, C.Prigent, J.-F.Crétaux, and M.Bergé-Nguyen, 2014: Characterization and space–time downscaling of the inundation extent over the Inner Niger Delta using GIEMS and MODIS data. J. Hydrometeor., 15, 171–192, doi:10.1175/JHM-D-13-032.1.10.1175/JHM-D-13-032.1
Bergé-Nguyen, M., and J.-F.Crétaux, 2015: Inundations in the Inner Niger Delta: Monitoring and analysis using MODIS and global precipitation datasets. Remote Sensing, 7, 2127–2151, doi:10.3390/rs70202127.10.3390/rs70202127
Bishop, C. M., 1996: Neural Networks for Pattern Recognition. Clarendon Press, 504 pp.10.1201/9781420050646.ptb6
Bousquet, P., and Coauthors, 2006: Contribution of anthropogenic and natural sources to atmospheric methane variability. Nature, 443, 439–443, doi:10.1038/nature05132.10.1038/nature05132
Bwangoy, J.-R. B., M. C.Hansen, D. P.Roy, G.De Grandi, and C. O.Justice, 2010: Wetland mapping in the Congo basin using optical and radar remotely sensed data and derived topographical indices. Remote Sens. Environ., 114, 73–86, doi:10.1016/j.rse.2009.08.004.10.1016/j.rse.2009.08.004
Ciais, P., and Coauthors, 2013: Carbon and other biogeochemical cycles. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 465–570.
Cohen, J., 1960: A coefficient of agreement for nominal scales. Educ. Psychol. Meas., 20, 37–46, doi:10.1177/001316446002000104.10.1177/001316446002000104
Decharme, B., R.Alkama, F.Papa, S.Faroux, H.Douville, and C.Prigent, 2012: Global off-line evaluation of the ISBA-TRIP flood model. Climate Dyn., 38, 1389–1412, doi:10.1007/s00382-011-1054-9.10.1007/s00382-011-1054-9
Fluet-Chouinard, E., B.Lehner, L.-M.Rebelo, F.Papa, and S. K.Hamilton, 2015: Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sens. Environ., 158, 348–361, doi:10.1016/j.rse.2014.10.015.10.1016/j.rse.2014.10.015
Frappart, F., F.Papa, J. S.da Silva, G.Ramillien, C.Prigent, F.Seyler, and S.Calmant, 2012: Surface freshwater storage and dynamics in the Amazon basin during the 2005 exceptional drought. Environ. Res. Lett., 7, 044010, doi:10.1088/1748-9326/7/4/044010.10.1088/1748-9326/7/4/044010
Guyon, I., and A.Elisseeff, 2003: An introduction to variable and feature selection. J. Mach. Learn. Res., 3, 1157–1182 [Available online at http://dl.acm.org/citation.cfm?id=944919.944968.]
Hess, L. L., J. M.Melack, E.Novo, C.Barbosa, and M.Gastil, 2003: Dual-season mapping of wetland inundation and vegetation for the central Amazon basin. Remote Sens. Environ., 87, 404–428, doi:10.1016/j.rse.2003.04.001.10.1016/j.rse.2003.04.001
Hornik, K., M.Stinchcombe, and H.White, 1989: Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359–366, doi:10.1016/0893-6080(89)90020-8.10.1016/0893-6080(89)90020-8
Landis, J. R., and G. G.Koch, 1977: The measurement of observer agreement for categorical data. Biometrics, 33, 159–174. [Available online at http://www.jstor.org/stable/2529310.]10.2307/2529310
Lehner, B., and P.Döll, 2004: Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol., 296, 1–22, doi:10.1016/j.jhydrol.2004.03.028.10.1016/j.jhydrol.2004.03.028
Lehner, B., K.Verdin, and A.Jarvis, 2008: New global hydrography derived from spaceborne elevation data. Eos, Trans. Amer. Geophys. Union, 89, 93–94, doi:10.1029/2008EO100001.10.1029/2008EO100001
Nakaegawa, T., 2012: Comparison of water-related land cover types in six 1-km global land cover datasets. J. Hydrometeor., 13, 649–664, doi:10.1175/JHM-D-10-05036.1.10.1175/JHM-D-10-05036.1
Papa, F., F.Frappart, A.Güntner, C.Prigent, F.Aires, A. C. V.Getirana, and R.Maurer, 2013: Surface freshwater storage and variability in the Amazon basin from multi-satellite observations, 1993–2007. J. Geophys. Res. Atmos., 118, 11 951–11 965, doi:10.1002/2013JD020500.10.1002/2013JD020500
Papa, F., and Coauthors, 2015: Satellite-derived surface and sub-surface water storage in the Ganges–Brahmaputra River basin. J. Hydrol.: Reg. Stud., 4A, 15–35, doi:10.1016/j.ejrh.2015.03.004.
Pekel, J.-F., A.Cottam, N.Gorelick, and A. S.Belward, 2016: High-resolution mapping of global surface water and its long-term changes. Nature, 540, 418–422, doi:10.1038/nature20584.10.1038/nature20584
Powers, D. M., 2011: Evaluation: From Precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Technol., 2 (1), 37–63.
Prigent, C., E.Matthews, F.Aires, and W. B.Rossow, 2001: Remote sensing of global wetland dynamics with multiple satellite data sets. Geophys. Res. Lett., 28, 4631–4634, doi:10.1029/2001GL013263.10.1029/2001GL013263
Prigent, C., F.Papa, F.Aires, W. B.Rossow, and E.Matthews, 2007: Global inundation dynamics inferred from multiple satellite observations, 1993–2000. J. Geophys. Res., 112, D12107, doi:10.1029/2006JD007847.10.1029/2006JD007847
Prigent, C., F.Papa, F.Aires, C.Jimenez, W. B.Rossow, and E.Matthews, 2012: Changes in land surface water dynamics since the 1990s and relation to population pressure. Geophys. Res. Lett., 39, L08403, doi:10.1029/2012GL051276.10.1029/2012GL051276
Prigent, C., D. P.Lettenmaier, F.Aires, and F.Papa, 2016: Toward a high resolution monitoring of continental surface water extent and dynamics, at global scale: From GIEMS (Global Inundation Extent from Multi-Satellites) to SWOT (Surface Water Ocean Topography). Surv. Geophys., 37, 399–355, doi:10.1007/s10712-015-9339-x.10.1007/s10712-015-9339-x
Richard, M. D., and R. P.Lippmann, 1991: Neural network classifiers estimate Bayesian a posteriori probabilities. Neural Comput., 3, 461–483, doi:10.1162/neco.1991.3.4.461.10.1162/neco.1991.3.4.461
Ringeval, B., N.de Noblet-Ducoudré, P.Ciais, P.Bousquet, C.Prigent, F.Papa, and W. B.Rossow, 2010: An attempt to quantify the impact of changes in wetland extent on methane emissions on the seasonal and interannual time scales. Global Biogeochem. Cycles, 24, GB2003, doi:10.1029/2008GB003354.10.1029/2008GB003354
Rodriguez, E., 2012: Surface Water and Ocean Topography mission (SWOT): Science requirements document. JPL Tech. Rep., 22 pp. [Available online at https://swot.jpl.nasa.gov/files/SWOT_science_reqs_release2_v1.14.pdf.]
Sakamoto, T., N.Van Nguyen, A.Kotera, H.Ohno, N.Ishitsuka, and M.Yokozawa, 2007: Detecting temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta from MODIS time-series imagery. Remote Sens. Environ., 109, 295–313, doi:10.1016/j.rse.2007.01.011.10.1016/j.rse.2007.01.011
Santoro, M., U.Wegmüller, and J. I. H.Askne, 2010: Signatures of ERS–Envisat interferometric SAR coherence and phase of short vegetation: An analysis in the case of maize fields. IEEE Trans. Geosci. Remote Sens., 48, 1702–1713, doi:10.1109/TGRS.2009.2034257.10.1109/TGRS.2009.2034257
Schumann, G. J. P., K. M.Andreadis, and P. D.Bates, 2014: Downscaling coarse grid hydrodynamic model simulations over large domains. J. Hydrol., 508, 289–298, doi:10.1016/j.jhydrol.2013.08.051.10.1016/j.jhydrol.2013.08.051
Simard, M., N.Pinto, J. B.Fisher, and A.Baccini, 2011: Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res., 116, G04021, doi:10.1029/2011JG001708.
USGS, 2016: HYDRO1k Elevation Derivative Database. LP DAAC, accessed 2 August 2016. [Available online at https://lta.cr.usgs.gov/HYDRO1K.]
Winsemius, H. C., B.Jongman, T. I. E.Veldkamp, S.Hallegatte, M.Bangalore, and P. J.Ward, 2015: Disaster risk, climate change, and poverty: Assessing the global exposure of poor people to floods and droughts. Policy Research Working Paper 7480, World Bank, 35 pp. [Available online at http://documents.worldbank.org/curated/en/965831468189531165/pdf/WPS7480.pdf.]10.1596/1813-9450-7480
Wood, E. F., and Coauthors, 2011: Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth’s terrestrial water. Water Resour. Res., 47, W05301, doi:10.1029/2010WR010090.10.1029/2010WR010090
Yamazaki, D., F.O’Loughlin, M. A.Trigg, Z. F.Miller, T. M.Pavelsky, and P. D.Bates, 2014: Development of the Global Width Database for Large Rivers. Water Resour. Res., 50, 3467–3480, doi:10.1002/2013WR014664.10.1002/2013WR014664
Yamazaki, D., M. A.Trigg, and D.Ikeshima, 2015: Development of a global ~90 m water body map using multi-temporal Landsat images. Remote Sens. Environ., 171, 337–351, doi:10.1016/j.rse.2015.10.014.10.1016/j.rse.2015.10.014
Zhuang, Q., X.Zhu, Y.He, C.Prigent, J. M.Melillo, A. D.McGuire, R. G.Prinn, and D. W.Kicklighter, 2015: Influence of changes in wetland inundation extent on net fluxes of carbon dioxide and methane in northern high latitudes from 1993 to 2004. Environ. Res. Lett., 10, 095009, doi:10.1088/1748-9326/10/9/095009.10.1088/1748-9326/10/9/095009
