{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T16:08:37Z","timestamp":1773590917917,"version":"3.50.1"},"reference-count":230,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T00:00:00Z","timestamp":1575849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Regardless of political boundaries, river basins are a functional unit of the Earth\u2019s land surface and provide an abundance of resources for the environment and humans. They supply livelihoods supported by the typical characteristics of large river basins, such as the provision of freshwater, irrigation water, and transport opportunities. At the same time, they are impacted i.e., by human-induced environmental changes, boundary conflicts, and upstream\u2013downstream inequalities. In the framework of water resource management, monitoring of river basins is therefore of high importance, in particular for researchers, stake-holders and decision-makers. However, land surface and surface water properties of many major river basins remain largely unmonitored at basin scale. Several inventories exist, yet consistent spatial databases describing the status of major river basins at global scale are lacking. Here, Earth observation (EO) is a potential source of spatial information providing large-scale data on the status of land surface properties. This review provides a comprehensive overview of existing research articles analyzing major river basins primarily using EO. Furthermore, this review proposes to exploit EO data together with relevant open global-scale geodata to establish a database and to enable consistent spatial analyses and evaluate past and current states of major river basins.<\/jats:p>","DOI":"10.3390\/rs11242951","type":"journal-article","created":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T11:22:51Z","timestamp":1575890571000},"page":"2951","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Review of Earth Observation-Based Analyses for Major River Basins"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3733-0049","authenticated-orcid":false,"given":"Soner","family":"Uereyen","sequence":"first","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Strasse 20, D-82234 Wessling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudia","family":"Kuenzer","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Strasse 20, D-82234 Wessling, Germany"},{"name":"Institute for Geography and Geology, University of Wuerzburg, Am Hubland, D-97074 Wuerzburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kummu, M., de Moel, H., Ward, P.J., and Varis, O. (2011). How close do we live to water? A global analysis of population distance to freshwater bodies. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0020578"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1038\/s41561-018-0262-x","article-title":"Anthropogenic stresses on the world\u2019s big rivers","volume":"12","author":"Best","year":"2018","journal-title":"Nat. Geosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1175\/WCAS-D-13-00059.1","article-title":"Water, Drought, Climate Change, and Conflict in Syria","volume":"6","author":"Gleick","year":"2014","journal-title":"Weather. Clim. Soc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.jhydrol.2018.10.045","article-title":"Hydropower dams of the Mekong River basin: A review of their hydrological impacts","volume":"568","author":"Hecht","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14745","DOI":"10.1038\/srep14745","article-title":"Linking rapid erosion of the Mekong River delta to human activities","volume":"5","author":"Anthony","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1038\/s41586-019-1111-9","article-title":"Mapping the world\u2019s free-flowing rivers","volume":"569","author":"Grill","year":"2019","journal-title":"Nature"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/nature09440","article-title":"Global threats to human water security and river biodiversity","volume":"467","author":"McIntyre","year":"2010","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3251","DOI":"10.1073\/pnas.1222475110","article-title":"Global water resources affected by human interventions and climate change","volume":"111","author":"Haddeland","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.scitotenv.2017.09.056","article-title":"Physical water scarcity metrics for monitoring progress towards SDG target 6.4: An evaluation of indicator 6.4.2 \u201cLevel of water stress\u201d","volume":"613\u2013614","author":"Vanham","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e1500323","DOI":"10.1126\/sciadv.1500323","article-title":"Four billion people facing severe water scarcity","volume":"2","author":"Mekonnen","year":"2016","journal-title":"Sci. Adv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9724","DOI":"10.1029\/2017WR022437","article-title":"Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data-Poor Regions","volume":"54","author":"Sheffield","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.gloenvcha.2017.04.008","article-title":"Assessment of transboundary river basins for potential hydro-political tensions","volume":"45","author":"Sproles","year":"2017","journal-title":"Glob. Environ. Chang."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1029\/2008EO100001","article-title":"New global hydrography derived from spaceborne elevation data","volume":"89","author":"Lehner","year":"2008","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Alsdorf, D.E., Rodr\u00edguez, E., and Lettenmaier, D.P. (2007). Measuring surface water from space. Rev. Geophys., 45.","DOI":"10.1029\/2006RG000197"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.rse.2018.04.032","article-title":"Quantifying Australia\u2019s dryland vegetation response to flooding and drought at sub-continental scale","volume":"212","author":"Broich","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wohlfart, C., Liu, G.H., Huang, C., and Kuenzer, C. (2016). A River Basin over the Course of Time: Multi-Temporal Analyses of Land Surface Dynamics in the Yellow River Basin (China) Based on Medium Resolution Remote Sensing Data. Remote Sens., 8.","DOI":"10.3390\/rs8030186"},{"key":"ref_17","unstructured":"GRDC (2019). Major River Basins of the World, The Global Runoff Data Centre."},{"key":"ref_18","unstructured":"(2019, October 01). Center for International Earth Science Information Network, CIESIN, Columbia University. Available online: https:\/\/sedac.ciesin.columbia.edu\/data\/collection\/gpw-v4."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.rse.2017.07.014","article-title":"The ESA Climate Change Initiative (CCI): A European contribution to the generation of the Global Climate Observing System","volume":"203","author":"Plummer","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nyland, K.E., Gunn, G.E., Shiklomanov, N.I., Engstrom, R.N., and Streletskiy, D.A. (2018). Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine. Remote Sens., 10.","DOI":"10.3390\/rs10081226"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.rse.2014.04.030","article-title":"Time-series analysis of Landsat-MSS\/TM\/OLI images over Amazonian waters impacted by gold mining activities","volume":"157","author":"Lobo","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2005.09.007","article-title":"Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers","volume":"100","author":"Sakamoto","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_23","first-page":"350","article-title":"Mapping spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery","volume":"26","author":"Huang","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1080\/014311600210155","article-title":"New perspectives on global ecosystems from wide-area radar mosaics: flooded forest mapping in the tropics","volume":"21","author":"Mayaux","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cao, N., Lee, H., Jung, H.C., and Yu, H.W. (2018). Estimation of Water Level Changes of Large-Scale Amazon Wetlands Using ALOS2 ScanSAR Differential Interferometry. Remote Sens., 10.","DOI":"10.3390\/rs10060966"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1002\/rra.3479","article-title":"Remote sensing of river corridors: A review of current trends and future directions","volume":"35","author":"Tomsett","year":"2019","journal-title":"River Res. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1007\/s10712-016-9378-y","article-title":"Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges","volume":"37","author":"Grimaldi","year":"2016","journal-title":"Surv. Geophys."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1177\/0309133314536583","article-title":"Progress in integrating remote sensing data and hydrologic modeling","volume":"38","author":"Xu","year":"2014","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1177\/0309133309346650","article-title":"Remote sensing: Hydrology","volume":"33","author":"Tang","year":"2009","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Nitze, I., Grosse, G., Jones, B.M., Arp, C.D., Ulrich, M., Fedorov, A., and Veremeeva, A. (2017). Landsat-Based Trend Analysis of Lake Dynamics across Northern Permafrost Regions. Remote Sens., 9.","DOI":"10.3390\/rs9070640"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.rse.2018.02.060","article-title":"Quantifying CDOM and DOC in major Arctic rivers during ice-free conditions using Landsat TM and ETM+ data","volume":"209","author":"Griffin","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.rse.2013.03.009","article-title":"Using Hyperion imagery to monitor the spatial and temporal distribution of colored dissolved organic matter in estuarine and coastal regions","volume":"134","author":"Zhu","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1109\/JSTARS.2013.2267092","article-title":"Adaptive Retracking of Jason-1, 2 Satellite Altimetry Data for the Volga River Reservoirs","volume":"7","author":"Troitskaya","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2018.03.015","article-title":"Monitoring thirty years of small water reservoirs proliferation in the southern Brazilian Amazon with Landsat time series","volume":"145","author":"Arvor","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1080\/01431160110092957","article-title":"Biophysical properties and mapping of aquatic vegetation during the hydrological cycle of the Amazon floodplain using JERS-1 and Radarsat","volume":"23","author":"Costa","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","first-page":"463","article-title":"Spatial and temporal analysis of a tidal floodplain landscape\u2014Arnapi, Brazil\u2014Using geographic information systems and remote sensing","volume":"68","author":"Pereira","year":"2002","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.rse.2015.12.013","article-title":"Dual-season and full-polarimetric C band SAR assessment for vegetation mapping in the Amazon varzea wetlands","volume":"174","author":"Furtado","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, F., Wang, Z.M., Yang, H.B., Zhao, Y., Li, Z.H., and Wu, J.P. (2018). Capability of Remotely Sensed Drought Indices for Representing the Spatio-Temporal Variations of the Meteorological Droughts in the Yellow River Basin. Remote Sens., 10.","DOI":"10.20944\/preprints201811.0476.v1"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.rse.2018.09.019","article-title":"Modeling alpine grassland cover based on MODIS data and support vector machine regression in the headwater region of the Huanghe River, China","volume":"218","author":"Ge","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/j.rse.2016.07.029","article-title":"Implementation of satellite based fractional water cover indices in the pan-Arctic region using AMSR-E and MODIS","volume":"184","author":"Du","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"8516","DOI":"10.3390\/rs70708516","article-title":"Remote Sensing of River Delta Inundation: Exploiting the Potential of Coarse Spatial Resolution, Temporally-Dense MODIS Time Series","volume":"7","author":"Kuenzer","year":"2015","journal-title":"Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.rse.2004.12.018","article-title":"Ganges and Indus river basin land use\/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data","volume":"95","author":"Thenkabail","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1175\/1520-0450(1983)022<0474:GVALUN>2.0.CO;2","article-title":"Global vegetation and land use: New high-resolution data bases for climate studies","volume":"22","author":"Matthews","year":"1983","journal-title":"J. Clim. Appl. Meteorol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1080\/014311600210191","article-title":"Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data","volume":"21","author":"Loveland","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wang, C., Jia, M.M., Chen, N.C., and Wang, W. (2018). Long-Term Surface Water Dynamics Analysis Based on Landsat Imagery and the Google Earth Engine Platform: A Case Study in the Middle Yangtze River Basin. Remote Sens., 10.","DOI":"10.3390\/rs10101635"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1016\/j.rse.2010.11.017","article-title":"Tropical forest backscatter anomaly evident in Sea Winds scatterometer morning overpass data during 2005 drought in Amazonia","volume":"115","author":"Frolking","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3153","DOI":"10.3390\/rs70303153","article-title":"Toward Estimating Wetland Water Level Changes Based on Hydrological Sensitivity Analysis of PALSAR Backscattering Coefficients over Different Vegetation Fields","volume":"7","author":"Yuan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1080\/01431160412331291297","article-title":"GLC2000: A new approach to global land cover mapping from Earth observation data","volume":"26","author":"Belward","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/S0034-4257(02)00078-0","article-title":"Global land cover mapping from MODIS: Algorithms and early results","volume":"83","author":"Friedl","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Arino, O., Gross, D., Ranera, F., Leroy, M., Bicheron, P., Brockman, C., Defourny, P., Vancutsem, C., Achard, F., and Durieux, L. (2007, January 23\u201328). GlobCover: ESA service for global land cover from MERIS. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4423328"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1038\/514434c","article-title":"Open access to Earth land-cover map","volume":"514","author":"Jun","year":"2014","journal-title":"Nature"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.3390\/rs2071844","article-title":"Estimating global cropland extent with multi-year MODIS data","volume":"2","author":"Pittman","year":"2010","journal-title":"Remote Sens."},{"key":"ref_53","first-page":"55","article-title":"A digital global map of irrigated areas","volume":"49","author":"Siebert","year":"2000","journal-title":"ICID J."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-Resolution Global Maps of 21st-Century Forest Cover Change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0034-4257(02)00084-6","article-title":"An overview of MODIS Land data processing and product status","volume":"83","author":"Justice","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3473","DOI":"10.1109\/JSTARS.2014.2328632","article-title":"Near Real-Time Vegetation Monitoring at Global Scale","volume":"7","author":"Verger","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Tansey, K., Gr\u00e9goire, J.M., Defourny, P., Leigh, R., Pekel, J.F., van Bogaert, E., and Bartholom\u00e9, E. (2008). A new, global, multi-annual (2000\u20132007) burnt area product at 1 km resolution. Geophys. Res. Lett., 35.","DOI":"10.1029\/2007GL031567"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2102","DOI":"10.1109\/JSTARS.2013.2271445","article-title":"A global human settlement layer from optical HR\/VHR RS data: Concept and first results","volume":"6","author":"Pesaresi","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1109\/LGRS.2013.2272953","article-title":"Urban footprint processor\u2014Fully automated processing chain generating settlement masks from global data of the TanDEM-X mission","volume":"10","author":"Esch","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Marconcini, M., Metz-Marconcini, A., \u00dcreyen, S., Palacios-Lopez, D., Hanke, W., Bachofer, F., Zeidler, J., Esch, T., Gorelick, N., and Kakarla, A. (2019). Outlining where humans live\u2013The World Settlement Footprint 2015. arXiv.","DOI":"10.1038\/s41597-020-00580-5"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"180127","DOI":"10.1038\/sdata.2018.127","article-title":"A global dataset of river network geometry","volume":"5","author":"Giachetta","year":"2018","journal-title":"Sci. Data"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1016\/j.asr.2012.07.033","article-title":"Detection of Envisat RA2\/ICE-1 retracked radar altimetry bias over the Amazon basin rivers using GPS","volume":"51","author":"Calmant","year":"2013","journal-title":"Adv. Space Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1016\/j.asr.2011.01.004","article-title":"SOLS: A lake database to monitor in the Near Real Time water level and storage variations from remote sensing data","volume":"47","author":"Jelinski","year":"2011","journal-title":"Adv. Space Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.rse.2017.06.045","article-title":"Global WaterPack\u2014A 250 m resolution dataset revealing the daily dynamics of global inland water bodies","volume":"198","author":"Klein","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1080\/2150704X.2015.1084551","article-title":"Global SnowPack: A new set of snow cover parameters for studying status and dynamics of the planetary snow cover extent","volume":"6","author":"Dietz","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1080\/20964471.2018.1433790","article-title":"Exploiting big earth data from space\u2014First experiences with the timescan processing chain","volume":"2","author":"Esch","year":"2018","journal-title":"Big Earth Data"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1890\/100125","article-title":"High-resolution mapping of the world\u2019s reservoirs and dams for sustainable river-flow management","volume":"9","author":"Lehner","year":"2011","journal-title":"Front. Ecol. Environ."},{"key":"ref_69","unstructured":"(2019, April 12). OpenStreetMap Contributors. Available online: https:\/\/www.openstreetmap.org."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1080\/014311600210146","article-title":"Mapping land cover types in the Amazon Basin using 1 km JERS-1 mosaic","volume":"21","author":"Saatchi","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1080\/01431160600675911","article-title":"Land cover in East Asia classified using Terra MODIS and DMSP OLS products","volume":"28","author":"Matsuoka","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"5573","DOI":"10.1080\/01431160802687482","article-title":"Landscape evolution in the Yellow River Basin using satellite remote sensing and GIS during the past decade","volume":"30","author":"Wang","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.rse.2013.05.004","article-title":"Characterisation of land surface phenology and land cover based on moderate resolution satellite data in cloud prone areas\u2014A novel product for the Mekong Basin","volume":"136","author":"Leinenkugel","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.rse.2008.10.013","article-title":"Land cover classification of tundra environments in the Arctic Lena Delta based on Landsat 7 ETM+ data and its application for upscaling of methane emissions","volume":"113","author":"Schneider","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1016\/j.rse.2009.02.009","article-title":"Spectral characterization of periglacial surfaces and geomorphological units in the Arctic Lena Delta using field spectrometry and remote sensing","volume":"113","author":"Ulrich","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"8565","DOI":"10.3390\/rs6098565","article-title":"Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery\u2014Richards Island, Canada","volume":"6","author":"Ullmann","year":"2014","journal-title":"Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1109\/JSTARS.2010.2076398","article-title":"Large-Area Classification and Mapping of Forest and Land Cover in the Brazilian Amazon: A Comparative Analysis of ALOS\/PALSAR and Landsat Data Sources","volume":"3","author":"Walker","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1080\/01431161.2016.1165888","article-title":"Land-cover classification of the Yellow River Delta wetland based on multiple end-member spectral mixture analysis and a Random Forest classifier","volume":"37","author":"Liu","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"6989","DOI":"10.1109\/TGRS.2017.2737780","article-title":"An Object-Based Linear Weight Assignment Fusion Scheme to Improve Classification Accuracy Using Landsat and MODIS Data at the Decision Level","volume":"55","author":"Guan","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"6714","DOI":"10.1080\/01431161.2017.1363437","article-title":"Classification of the Yellow River delta area using fully polarimetric SAR measurements","volume":"38","author":"Buono","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_81","first-page":"403","article-title":"Changing landscape in the Three Gorges Reservoir Area of Yangtze River from 1977 to 2005: Land use\/land cover, vegetation cover changes estimated using multi-source satellite data","volume":"11","author":"Zhang","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1080\/01431160903475381","article-title":"Land use and land cover change detection using satellite remote sensing techniques in the mountainous Three Gorges Area, China","volume":"31","author":"Chen","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_83","first-page":"17","article-title":"Landscape change and hydrologic alteration associated with dam construction","volume":"16","author":"Zhao","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_84","first-page":"15","article-title":"Role of reservoir construction in regional land use change in Pengxi River basin upstream of the Three Gorges Reservoir in China","volume":"75","author":"Wang","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Feng, Y.Y., Lu, D.S., Moran, E.F., Dutra, L.V., Calvi, M.F., and de Oliveira, M.A.F. (2017). Examining Spatial Distribution and Dynamic Change of Urban Land Covers in the Brazilian Amazon Using Multitemporal Multisensor High Spatial Resolution Satellite Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9040381"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"4079","DOI":"10.1080\/01431160410001688312","article-title":"Detection of land use land cover changes for the northern part of the Nile delta (Burullus region), Egypt","volume":"25","author":"Dewidar","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2977","DOI":"10.1080\/01431160802558675","article-title":"Application of multitemporal Landsat data to monitor land cover changes in the Eastern Nile Delta region, Egypt","volume":"30","author":"Abdulaziz","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1016\/j.apgeog.2009.10.008","article-title":"Monitoring land cover changes in a newly reclaimed area of Egypt using multi-temporal Landsat data","volume":"30","author":"Bakr","year":"2010","journal-title":"Appl. Geogr."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.apgeog.2010.10.012","article-title":"Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data","volume":"31","author":"Ismail","year":"2011","journal-title":"Appl. Geogr."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"7024","DOI":"10.1080\/01431161.2012.697207","article-title":"Monitoring land-use change-associated land development using multitemporal Landsat data and geoinformatics in Kom Ombo area, South Egypt","volume":"33","author":"Faid","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"3533","DOI":"10.1080\/01431160701758699","article-title":"Long-term monitoring of land cover changes based on Landsat imagery to improve hydrological modelling in West Africa","volume":"29","author":"Ruelland","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1080\/01431161003623433","article-title":"Comparison of methods for LUCC monitoring over 50 years from aerial photographs and satellite images in a Sahelian catchment","volume":"32","author":"Ruelland","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.apgeog.2014.07.002","article-title":"Land surface dynamics and environmental challenges of the Niger Delta, Africa: Remote sensing-based analyses spanning three decades (1986-2013)","volume":"53","author":"Kuenzer","year":"2014","journal-title":"Appl. Geogr."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Verhegghen, A., Eva, H., Ceccherini, G., Achard, F., Gond, V., Gourlet-Fleury, S., and Cerutti, P.O. (2016). The Potential of Sentinel Satellites for Burnt Area Mapping and Monitoring in the Congo Basin Forests. Remote Sens., 8.","DOI":"10.3390\/rs8120986"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.apgeog.2008.09.007","article-title":"Tourism, forest conversion, and land transformations in the Angkor basin, Cambodia","volume":"29","author":"Gaughan","year":"2009","journal-title":"Appl. Geogr."},{"key":"ref_96","first-page":"42","article-title":"Urban growth and environmental impacts in Jing-Jin-Ji, the Yangtze, River Delta and the Pearl River Delta","volume":"30","author":"Haas","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"3245","DOI":"10.1007\/s12665-013-2389-9","article-title":"Driving forces of aeolian desertification in the source region of the Yellow River: 1975-2005","volume":"70","author":"Hu","year":"2013","journal-title":"Environ. Earth Sci."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Ren, X.B., Dong, Z.B., Hu, G.Y., Zhang, D.H., and Li, Q. (2016). A GIS-Based Assessment of Vulnerability to Aeolian Desertification in the Source Areas of the Yangtze and Yellow Rivers. Remote Sens., 8.","DOI":"10.3390\/rs8080626"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.apgeog.2017.06.004","article-title":"Multi-faceted land cover and land use change analyses in the Yellow River Basin based on dense Landsat time series: Exemplary analysis in mining, agriculture, forest, and urban areas","volume":"85","author":"Wohlfart","year":"2017","journal-title":"Appl. Geogr."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.rse.2015.10.015","article-title":"Seasonality and drought effects of Amazonian forests observed from multi-angle satellite data","volume":"171","author":"Hilker","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.rse.2015.05.020","article-title":"On the measurability of change in Amazon vegetation from MODIS","volume":"166","author":"Hilker","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1016\/j.rse.2007.07.026","article-title":"Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts","volume":"112","author":"Duveiller","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"4867","DOI":"10.1109\/TGRS.2016.2552462","article-title":"A Comparison of Tropical Rainforest Phenology Retrieved From Geostationary (SEVIRI) and Polar-Orbiting (MODIS) Sensors Across the Congo Basin","volume":"54","author":"Yan","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Yan, D., Zhang, X.Y., Yu, Y.Y., and Guo, W. (2017). Characterizing Land Cover Impacts on the Responses of Land Surface Phenology to the Rainy Season in the Congo Basin. Remote Sens., 9.","DOI":"10.3390\/rs9050461"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1080\/01431160110093000","article-title":"Application of multiscale texture in classifying JERS-1 radar data over tropical vegetation","volume":"23","author":"Podest","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.rse.2002.06.001","article-title":"Linear mixture model applied to Amazonian vegetation classification","volume":"87","author":"Lu","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1080\/01431160701311333","article-title":"Refining forest classifications in the western Amazon using an intra-annual multitemporal approach","volume":"29","author":"McCleary","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.1016\/j.rse.2010.04.007","article-title":"Spatial and temporal variability of macrophyte cover and productivity in the eastern Amazon floodplain: A remote sensing approach","volume":"114","author":"Silva","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"4717","DOI":"10.1109\/TGRS.2011.2157972","article-title":"Mapping Macrophyte Species in the Amazon Floodplain Wetlands Using Fully Polarimetric ALOS\/PALSAR Data","volume":"49","author":"Sartori","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"4795","DOI":"10.1080\/01431160412331270858","article-title":"Assessment from space of mangroves evolution in the Mekong Delta, in relation to extensive shrimp farming","volume":"25","author":"Tong","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"183","DOI":"10.3390\/rs5010183","article-title":"Remote Sensing in Mapping Mangrove Ecosystems\u2014An Object-Based Approach","volume":"5","author":"Vo","year":"2013","journal-title":"Remote Sens."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Lagomasino, D., Fatoyinbo, T., Lee, S., Feliciano, E., Trettin, C., and Simard, M. (2016). A Comparison of Mangrove Canopy Height Using Multiple Independent Measurements from Land, Air, and Space. Remote Sens., 8.","DOI":"10.3390\/rs8040327"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"3446","DOI":"10.1016\/j.rse.2011.08.008","article-title":"Assessment of deforestation in the Lower Amazon floodplain using historical Landsat MSS\/TM imagery","volume":"115","author":"Reno","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"5493","DOI":"10.3390\/rs5115493","article-title":"Ten-Year Landsat Classification of Deforestation and Forest Degradation in the Brazilian Amazon","volume":"5","author":"Souza","year":"2013","journal-title":"Remote Sens."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.apgeog.2015.06.001","article-title":"Spatiotemporal patterns of tropical deforestation and forest degradation in response to the operation of the Tucuru\u00ed hydroelectricdam in the Amazon basin","volume":"63","author":"Chen","year":"2015","journal-title":"Appl. Geogr."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1080\/01431160903475225","article-title":"Deforestation dynamics in Mato Grosso in the southern Brazilian Amazon using GIS and NOAA\/AVHRR data","volume":"32","author":"Yoshikawa","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.rse.2014.10.021","article-title":"Tree cover and forest cover dynamics in the Mekong Basin from 2001 to 2011","volume":"158","author":"Leinenkugel","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2013.02.010","article-title":"Reconstructing satellite images to quantify spatially explicit land surface change caused by fires and succession: A demonstration in the Yukon River Basin of interior Alaska","volume":"79","author":"Huang","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1080\/01431161.2012.742215","article-title":"MODIS-informed greenness responses to daytime land surface temperature fluctuations and wildfire disturbances in the Alaskan Yukon River Basin","volume":"34","author":"Tan","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1080\/01431161.2011.582187","article-title":"Characteristics and causes of vegetation variation in the source regions of the Yellow River, China","volume":"33","author":"Liang","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"9130","DOI":"10.3390\/rs6099130","article-title":"Changes in Spring Phenology in the Three-Rivers Headwater Region from 1999 to 2013","volume":"6","author":"Liu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"6513","DOI":"10.3390\/rs5126513","article-title":"Combined Spatial and Temporal Effects of Environmental Controls on Long-Term Monthly NDVI in the Southern Africa Savanna","volume":"5","author":"Southworth","year":"2013","journal-title":"Remote Sens."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"16504","DOI":"10.3390\/rs71215838","article-title":"The Mangroves of the Zambezi Delta: Increase in Extent Observed via Satellite from 1994 to 2013","volume":"7","author":"Shapiro","year":"2015","journal-title":"Remote Sens."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Bunting, E.L., Southworth, J., Herrero, H., Ryan, S.J., and Waylen, P. (2018). Understanding Long-Term Savanna Vegetation Persistence across Three Drainage Basins in Southern Africa. Remote Sens., 10.","DOI":"10.3390\/rs10071013"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"135","DOI":"10.3390\/rs6010135","article-title":"A Phenology-Based Classification of Time-Series MODIS Data for Rice Crop Monitoring in Mekong Delta, Vietnam","volume":"6","author":"Son","year":"2014","journal-title":"Remote Sens."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2015.08.004","article-title":"Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data","volume":"169","author":"Kontgis","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"15868","DOI":"10.3390\/rs71215808","article-title":"Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data","volume":"7","author":"Nguyen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1080\/01431161.2017.1404162","article-title":"Mapping rice areas with Sentinel-1 time series and superpixel segmentation","volume":"39","author":"Clauss","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_129","first-page":"574","article-title":"Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data","volume":"73","author":"Clauss","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.rse.2002.09.001","article-title":"Agricultural land-use change in Brazilian Amazonia between 1980 and 1995: Evidence from integrated satellite and census data","volume":"87","author":"Cardille","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"4459","DOI":"10.1080\/01431161.2017.1323285","article-title":"Monitoring cropland changes along the Nile River in Egypt over past three decades (1984\u20132015) using remote sensing","volume":"38","author":"Xu","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"8883","DOI":"10.3390\/rs70708883","article-title":"Monitoring Spatio-Temporal Distribution of Rice Planting Area in the Yangtze River Delta Region Using MODIS Images","volume":"7","author":"Shi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.scitotenv.2016.07.012","article-title":"Influences of land cover types, meteorological conditions, anthropogenic heat and urban area on surface urban heat island in the Yangtze River Delta Urban Agglomeration","volume":"571","author":"Du","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.scitotenv.2018.02.074","article-title":"Remote sensing of the urban heat island effect in a highly populated urban agglomeration area in East China","volume":"628\u2013629","author":"Zhou","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Yao, R., Wang, L.C., Gui, X., Zheng, Y.K., Zhang, H.M., and Huang, X. (2017). Urbanization Effects on Vegetation and Surface Urban Heat Islands in China\u2019s Yangtze River Basin. Remote Sens., 9.","DOI":"10.3390\/rs9060540"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"4095","DOI":"10.1109\/JSTARS.2014.2302855","article-title":"Detecting China\u2019s Urban Expansion Over the Past Three Decades Using Nighttime Light Data","volume":"7","author":"Xiao","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1117\/1.JRS.11.046029","article-title":"Monitoring evolving urban cluster systems using DMSP\/OLS nighttime light data: A case study of the Yangtze River Delta region, China","volume":"11","author":"Wang","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Zou, Y.H., Peng, H.Q., Liu, G., Yang, K.D., Xie, Y.H., and Weng, Q.H. (2017). Monitoring Urban Clusters Expansion in the Middle Reaches of the Yangtze River, China, Using Time-Series Nighttime Light Images. Remote Sens., 9.","DOI":"10.3390\/rs9101007"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.apgeog.2018.03.016","article-title":"Identifying the relationship between urban land expansion and human activities in the Yangtze River Economic Belt, China","volume":"94","author":"Liu","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"9359","DOI":"10.3390\/rs6109359","article-title":"The Integrated Use of DMSP-OLS Nighttime Light and MODIS Data for Monitoring Large-Scale Impervious Surface Dynamics: A Case Study in the Yangtze River Delta","volume":"6","author":"Shao","year":"2014","journal-title":"Remote Sens."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.isprsjprs.2018.05.016","article-title":"An improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data","volume":"142","author":"Zhuo","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Liu, D.D., and Chen, N.C. (2017). Satellite Monitoring of Urban Land Change in the Middle Yangtze River Basin Urban Agglomeration, China between 2000 and 2016. Remote Sens., 9.","DOI":"10.3390\/rs9111086"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"3007","DOI":"10.1016\/j.rse.2011.06.004","article-title":"Settlement detection and impervious surface estimation in the Mekong Delta using optical and SAR remote sensing data","volume":"115","author":"Leinenkugel","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.rse.2017.06.030","article-title":"Propensity for erosion and deposition in a deltaic wetland complex: Implications for river management and coastal restoration","volume":"199","author":"Amer","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1007\/s12665-010-0564-9","article-title":"Change detection of the coastal zone east of the Nile Delta using remote sensing","volume":"62","author":"Hereher","year":"2011","journal-title":"Environ. Earth Sci."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1007\/s12665-011-0928-9","article-title":"Mapping coastal erosion at the Nile Delta western promontory using Landsat imagery","volume":"64","author":"Hereher","year":"2011","journal-title":"Environ. Earth Sci."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.jhydrol.2014.02.013","article-title":"Shoreline change of Chongming Dongtan and response to river sediment load: A remote sensing assessment","volume":"511","author":"Li","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1007\/s12665-014-3565-2","article-title":"Impacts of human activities on the evolution of estuarine wetland in the Yangtze Delta from 2000 to 2010","volume":"73","author":"Zhang","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_149","first-page":"238","article-title":"55-year (1960\u20132015) spatiotemporal shoreline change analysis using historical DISP and Landsat time series data in Shanghai","volume":"68","author":"Qiao","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_150","first-page":"165","article-title":"Detecting coastline change from satellite images based on beach slope estimation in a tidal flat","volume":"23","author":"Liu","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.apgeog.2014.08.015","article-title":"Earth observation-based coastal zone monitoring of the Yellow River Delta: Dynamics in China\u2019s second largest oil producing region over four decades","volume":"55","author":"Kuenzer","year":"2014","journal-title":"Appl. Geogr."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.jhydrol.2014.09.038","article-title":"Evolution of the Yellow River Delta and its relationship with runoff and sediment load from 1983 to 2011","volume":"520","author":"Kong","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.rse.2012.10.035","article-title":"Monitoring flood extent in the lower Amazon River floodplain using ALOS\/PALSAR ScanSAR images","volume":"130","author":"Arnesen","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.1002\/hyp.5559","article-title":"Seasonal inundation patterns in two large savanna floodplains of South America: The Llanos de Moxos (Bolivia) and the Llanos del Orinoco (Venezuela and Colombia)","volume":"18","author":"Hamilton","year":"2004","journal-title":"Hydrol. Process."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"5440","DOI":"10.3390\/rs70505440","article-title":"Mapping Regional Inundation with Spaceborne L-Band SAR","volume":"7","author":"Chapman","year":"2015","journal-title":"Remote Sens."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1080\/01431160110092902","article-title":"Evaluation of JERS-1 SAR mosaics for hydrological applications in the Congo river basin","volume":"23","author":"Rosenqvist","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"2393","DOI":"10.3390\/rs6032393","article-title":"Multi-Sensor Imaging and Space-Ground Cross-Validation for 2010 Flood along Indus River, Pakistan","volume":"6","author":"Khan","year":"2014","journal-title":"Remote Sens."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1109\/JSTARS.2013.2284607","article-title":"Near Real-Time Flood Volume Estimation From MODIS Time-Series Imagery in the Indus River Basin","volume":"7","author":"Kwak","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Donchyts, G., Schellekens, J., Winsemius, H., Eisemann, E., and van de Giesen, N. (2016). A 30 m Resolution Surface Water Mask Including Estimation of Positional and Thematic Differences Using Landsat 8, SRTM and OpenStreetMap: A Case Study in the Murray-Darling Basin, Australia. Remote Sens., 8.","DOI":"10.3390\/rs8050386"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.5194\/hess-20-2227-2016","article-title":"Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of Earth observation data","volume":"20","author":"Heimhuber","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.034","article-title":"Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region","volume":"178","author":"Tulbure","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1002\/2016WR019858","article-title":"Modeling multidecadal surface water inundation dynamics and key drivers on large river basin scale using multiple time series of Earth-observation and river flow data","volume":"53","author":"Heimhuber","year":"2017","journal-title":"Water Resour. Res."},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Rao, P.Z., Jiang, W.G., Hou, Y.K., Chen, Z., and Jia, K. (2018). Dynamic Change Analysis of Surface Water in the Yangtze River Basin Based on MODIS Products. Remote Sens., 10.","DOI":"10.3390\/rs10071025"},{"key":"ref_164","unstructured":"Guerschman, J.P., Warren, G., Byrne, G., Lymburner, L., Mueller, N., and Van-Dijk, A. (2011). MODIS-Based Standing Water Detection for Flood and Large Reservoir Mapping: Algorithm Development and Applications for the Australian Continent, CSIRO."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.rse.2017.03.005","article-title":"Monthly flooded area classification using low resolution SAR imagery in the Sudd wetland from 2007 to 2011","volume":"194","author":"Wilusz","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1080\/01431160110092911","article-title":"The use of spaceborne radar data to model inundation patterns and trace gas emissions in the central Amazon floodplain","volume":"23","author":"Rosenqvist","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.rse.2003.04.001","article-title":"Dual-season mapping of wetland inundation and vegetation for the central Amazon basin","volume":"87","author":"Hess","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.rse.2007.01.011","article-title":"Detecting temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta from MODIS time-series imagery","volume":"109","author":"Sakamoto","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"7291","DOI":"10.1080\/01431161.2012.700421","article-title":"Multi-sensoral and automated derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data","volume":"33","author":"Gstaiger","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"687","DOI":"10.3390\/rs5020687","article-title":"Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses","volume":"5","author":"Kuenzer","year":"2013","journal-title":"Remote Sens."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1117\/1.JRS.6.063609","article-title":"Estimating surface water area changes using time-series Landsat data in the Qingjiang River Basin, China","volume":"6","author":"Du","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"5530","DOI":"10.3390\/rs5115530","article-title":"A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM plus and ALI","volume":"5","author":"Li","year":"2013","journal-title":"Remote Sens."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"3161","DOI":"10.1002\/hyp.7384","article-title":"The dynamics of floods in the Bolivian Amazon Basin","volume":"23","author":"Bourrel","year":"2009","journal-title":"Hydrol. Process."},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Cooley, S.W., Smith, L.C., Stepan, L., and Mascaro, J. (2017). Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9121306"},{"key":"ref_175","first-page":"67","article-title":"Empirical models for estimating the suspended sediment concentration in Amazonian white water rivers using Landsat 5\/TM","volume":"29","author":"Montanher","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.rse.2013.05.028","article-title":"Spatial and temporal evolution of the St. Lawrence River spectral profile: A 25-year case study using Landsat 5 and 7 imagery","volume":"136","author":"Massicotte","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"2910","DOI":"10.1080\/01431161.2014.890300","article-title":"Operational multi-sensor monitoring of turbidity for the entire Mekong Delta","volume":"35","author":"Heege","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1016\/j.scitotenv.2018.01.036","article-title":"Remote observation of water clarity patterns in Three Gorges Reservoir and Dongting Lake of China and their probable linkage to the Three Gorges Dam based on Landsat 8 imagery","volume":"625","author":"Ren","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.rse.2019.01.023","article-title":"Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity","volume":"224","author":"Kuhn","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_180","doi-asserted-by":"crossref","unstructured":"Peterson, K.T., Sagan, V., Sidike, P., Cox, A.L., and Martinez, M. (2018). Suspended Sediment Concentration Estimation from Landsat Imagery along the Lower Missouri and Middle Mississippi Rivers Using an Extreme Learning Machine. Remote Sens., 10.","DOI":"10.3390\/rs10101503"},{"key":"ref_181","doi-asserted-by":"crossref","unstructured":"Markert, K.N., Schmidt, C.M., Griffin, R.E., Flores, A.I., Poortinga, A., Saah, D.S., Muench, R.E., Clinton, N.E., Chishtie, F., and Kityuttachai, K. (2018). Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing. Remote Sens., 10.","DOI":"10.3390\/rs10060909"},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.jhydrol.2017.11.026","article-title":"Use of multispectral satellite remote sensing to assess mixing of suspended sediment downstream of large river confluences","volume":"556","author":"Umar","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.rse.2018.10.038","article-title":"Mapping spatial-temporal sediment dynamics of river-floodplains in the Amazon","volume":"221","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"6197","DOI":"10.1002\/2014WR016757","article-title":"Surface water types and sediment distribution patterns at the confluence of mega rivers: The Solimoes-Amazon and Negro Rivers junction","volume":"51","author":"Park","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_185","doi-asserted-by":"crossref","unstructured":"Lobo, F.D., Costa, M., Novo, E., and Telmer, K. (2016). Distribution of Artisanal and Small-Scale Gold Mining in the Tapajos River Basin (Brazilian Amazon) over the Past 40 Years and Relationship with Water Siltation. Remote Sens., 8.","DOI":"10.3390\/rs8070579"},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1016\/j.jhydrol.2018.12.033","article-title":"Spatio-temporal analysis of urban changes and surface water quality","volume":"569","author":"Carstens","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"4345","DOI":"10.5194\/hess-19-4345-2015","article-title":"DAHITI\u2014An innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry","volume":"19","author":"Schwatke","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"LBA 26-1","DOI":"10.1029\/2001JD000609","article-title":"Surface water dynamics in the Amazon Basin: Application of satellite radar altimetry","volume":"107","author":"Birkett","year":"2002","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_189","doi-asserted-by":"crossref","unstructured":"Berry, P.A.M., Garlick, J.D., Freeman, J.A., and Mathers, E.L. (2005). Global inland water monitoring from multi-mission altimetry. Geophys. Res. Lett., 32.","DOI":"10.1029\/2005GL022814"},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.rse.2005.10.027","article-title":"Preliminary results of ENVISAT RA-2-derived water levels validation over the Amazon basin","volume":"100","author":"Frappart","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"2160","DOI":"10.1016\/j.rse.2010.04.020","article-title":"Water levels in the Amazon basin derived from the ERS 2 and ENVISAT radar altimetry missions","volume":"114","author":"Calmant","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"3323","DOI":"10.1080\/01431161.2010.531914","article-title":"Water level dynamics of Amazon wetlands at the watershed scale by satellite altimetry","volume":"33","author":"Seyler","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"651","DOI":"10.5194\/hess-21-751-2017","article-title":"Application of CryoSat-2 altimetry data for river analysis and modelling","volume":"21","author":"Schneider","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.rse.2018.04.018","article-title":"An improved approach to monitoring Brahmaputra River water levels using retracked altimetry data","volume":"211","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"9340","DOI":"10.3390\/rs6109340","article-title":"Water Level Fluctuations in the Congo Basin Derived from ENVISAT Satellite Altimetry","volume":"6","author":"Becker","year":"2014","journal-title":"Remote Sens."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"7021","DOI":"10.1080\/01431161.2017.1371867","article-title":"Mapping spatio-temporal water level variations over the central Congo River using PALSAR ScanSAR and Envisat altimetry data","volume":"38","author":"Kim","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.1016\/j.rse.2011.02.011","article-title":"Inter-comparison study of water level estimates derived from hydrodynamic-hydrologic model and satellite altimetry for a complex deltaic environment","volume":"115","author":"Hossain","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1080\/01431161.2013.870678","article-title":"Water level estimation by remote sensing for the 2008 flooding of the Kosi River","volume":"35","author":"Pandey","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_199","doi-asserted-by":"crossref","unstructured":"Boergens, E., Dettmering, D., Schwatke, C., and Seitz, F. (2016). Treating the Hooking Effect in Satellite Altimetry Data: A Case Study along the Mekong River and Its Tributaries. Remote Sens., 8.","DOI":"10.3390\/rs8020091"},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.rse.2018.04.034","article-title":"Deriving daily water levels from satellite altimetry and land surface temperature for sparsely gauged catchments: A case study for the Mekong River","volume":"212","author":"Pham","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.rse.2015.05.025","article-title":"CryoSat-2 altimetry for river level monitoring - Evaluation in the Ganges-Brahmaputra River basin","volume":"168","author":"Villadsen","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_202","doi-asserted-by":"crossref","unstructured":"Boergens, E., Nielsen, K., Andersen, O.B., Dettmering, D., and Seitz, F. (2017). River Levels Derived with CryoSat-2 SAR Data ClassificationA Case Study in the Mekong River Basin. Remote Sens., 9.","DOI":"10.3390\/rs9121238"},{"key":"ref_203","doi-asserted-by":"crossref","unstructured":"Liu, K.T., Tseng, K.H., Shum, C.K., Liu, C.Y., Kuo, C.Y., Liu, G.M., Jia, Y.Y., and Shang, K. (2016). Assessment of the Impact of Reservoirs in the Upper Mekong River Using Satellite Radar Altimetry and Remote Sensing Imageries. Remote Sens., 8.","DOI":"10.3390\/rs8050367"},{"key":"ref_204","unstructured":"Tarpanelli, A., Camici, S., Nielsen, K., Brocca, L., Moramarco, T., and Benveniste, J. (2019). Potentials and limitations of Sentinel-3 for river discharge assessment. Adv. Sp. Res."},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"3787","DOI":"10.1002\/2014WR016618","article-title":"Stage-discharge rating curves based on satellite altimetry and modeled discharge in the Amazon basin","volume":"52","author":"Paris","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"6435","DOI":"10.5194\/hess-22-6435-2018","article-title":"Using modelled discharge to develop satellite-based river gauging: A case study for the Amazon Basin","volume":"22","author":"Hou","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"7815","DOI":"10.1080\/01431161.2014.978033","article-title":"Evaluation of satellite-altimetry-derived river stage variation for the braided Brahmaputra River","volume":"35","author":"Dubey","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2018.10.008","article-title":"Discharge estimation in high-mountain regions with improved methods using multisource remote sensing: A case study of the Upper Brahmaputra River","volume":"219","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1016\/j.rse.2018.12.010","article-title":"Ensemble learning regression for estimating river discharges using satellite altimetry data: Central Congo River as a Test-bed","volume":"221","author":"Kim","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"3811","DOI":"10.1002\/hyp.7811","article-title":"Using satellite altimetry data to augment flow estimation techniques on the Mekong River","volume":"24","author":"Birkinshaw","year":"2010","journal-title":"Hydrol. Process."},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.5194\/hess-14-2011-2010","article-title":"Towards improving river discharge estimation in ungauged basins: calibration of rainfall-runoff models based on satellite observations of river flow width at basin outlet","volume":"14","author":"Sun","year":"2010","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_212","doi-asserted-by":"crossref","unstructured":"Sichangi, A.W., Wang, L., and Hu, Z.D. (2018). Estimation of River Discharge Solely from Remote-Sensing Derived Data: An Initial Study Over the Yangtze River. Remote Sens., 10.","DOI":"10.3390\/rs10091385"},{"key":"ref_213","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1117\/1.JRS.6.063564","article-title":"Monitoring river discharge with remotely sensed imagery using river island area as an indicator","volume":"6","author":"Ling","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_214","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1016\/j.jhydrol.2018.04.005","article-title":"Satellite remote sensing estimation of river discharge: Application to the Yukon River Alaska","volume":"561","author":"Bjerklie","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.rse.2004.07.007","article-title":"Ob\u2019 river discharge from TOPEX\/Poseidon satellite altimetry (1992\u20132002)","volume":"93","author":"Kouraev","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_216","doi-asserted-by":"crossref","unstructured":"Chao, N.F., Wang, Z.T., Hwang, C., Jin, T.Y., and Cheng, Y.S. (2017). Decline of Geladandong Glacier Elevation in Yangtze River\u2019s Source Region: Detection by ICESat and Assessment by Hydroclimatic Data. Remote Sens., 9.","DOI":"10.3390\/rs9010075"},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"3672","DOI":"10.1002\/hyp.11287","article-title":"Glacier retreat and its impact on summertime run-off in a high-altitude ungauged catchment","volume":"31","author":"Wang","year":"2017","journal-title":"Hydrol. Process."},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"3665","DOI":"10.1002\/hyp.10472","article-title":"Snow cover variability and snowmelt in a high-altitude ungauged catchment","volume":"29","author":"Wang","year":"2015","journal-title":"Hydrol. Process."},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.rse.2005.03.013","article-title":"Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey","volume":"97","author":"Tekeli","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"3637","DOI":"10.1002\/hyp.8090","article-title":"Investigation of the snow-cover dynamics in the Upper Euphrates Basin of Turkey using remotely sensed snow-cover products and hydrometeorological data","volume":"25","author":"Akyurek","year":"2011","journal-title":"Hydrol. Process."},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.rse.2004.07.018","article-title":"Spatial and temporal patterns in Arctic river ice breakup observed with MODIS and AVHRR time series","volume":"93","author":"Pavelsky","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_222","doi-asserted-by":"crossref","first-page":"3052","DOI":"10.1109\/TGRS.2013.2269014","article-title":"Retrieval of River Ice Thickness From C-Band PolSAR Data","volume":"52","author":"Mermoz","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_223","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1080\/01431161.2014.995271","article-title":"Use of Landsat TM\/ETM plus to monitor the spatial and temporal extent of spring breakup floods in the Lena River, Siberia","volume":"36","author":"Sakai","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_224","doi-asserted-by":"crossref","unstructured":"Antonova, S., Duguay, C.R., Kaab, A., Heim, B., Langer, M., Westermann, S., and Boike, J. (2016). Monitoring Bedfast Ice and Ice Phenology in Lakes of the Lena River Delta Using TerraSAR-X Backscatter and Coherence Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8110903"},{"key":"ref_225","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.rse.2016.01.004","article-title":"Spatial and temporal patterns in Arctic river ice breakup revealed by automated ice detection from MODIS imagery","volume":"175","author":"Cooley","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_226","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.rse.2016.05.003","article-title":"Spatio-temporal variability of X-band radar backscatter and coherence over the Lena River Delta, Siberia","volume":"182","author":"Antonova","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_227","doi-asserted-by":"crossref","unstructured":"Stettner, S., Beamish, A.L., Bartsch, A., Heim, B., Grosse, G., Roth, A., and Lantuit, H. (2018). Monitoring Inter- and Intra-Seasonal Dynamics of Rapidly Degrading Ice-Rich Permafrost Riverbanks in the Lena Delta with TerraSAR-X Time Series. Remote Sens., 10.","DOI":"10.3390\/rs10010051"},{"key":"ref_228","doi-asserted-by":"crossref","unstructured":"Strozzi, T., Antonova, S., Gunther, F., Matzler, E., Vieira, G., Wegmuller, U., Westermann, S., and Bartsch, A. (2018). Sentinel-1 SAR Interferometry for Surface Deformation Monitoring in Low-Land Permafrost Areas. Remote Sens., 10.","DOI":"10.3390\/rs10091360"},{"key":"ref_229","doi-asserted-by":"crossref","unstructured":"Whitley, M.A., Frost, G.V., Jorgenson, M.T., Macander, M.J., Maio, C.V., and Winder, S.G. (2018). Assessment of LiDAR and Spectral Techniques for High-Resolution Mapping of Sporadic Permafrost on the Yukon-Kuskokwim Delta, Alaska. Remote Sens., 10.","DOI":"10.3390\/rs10020258"},{"key":"ref_230","doi-asserted-by":"crossref","first-page":"2752","DOI":"10.1080\/01431161.2014.890305","article-title":"Comparing global land-cover products\u2014Implications for geoscience applications: An investigation for the trans-boundary Mekong Basin","volume":"35","author":"Kuenzer","year":"2014","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/24\/2951\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:40:50Z","timestamp":1760190050000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/24\/2951"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,9]]},"references-count":230,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["rs11242951"],"URL":"https:\/\/doi.org\/10.3390\/rs11242951","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,9]]}}}