Article highlights
-
Each component of the terrestrial water storage is a key hydrological variable to understand floods and drought events
-
Their monitoring at river basin scale and over long periods of time is facilitated by large scale sensors.
-
The combination of Earth observations with other datasets can be an asset for the prediction of hydrological events and for monitoring.
Abstract
Hydrological extremes, in particular floods and droughts, impact all regions across planet Earth. They are mainly controlled by the temporal evolution of key hydrological variables like precipitation, evaporation, soil moisture, groundwater storage, surface water storage and discharge. Precise knowledge of the spatial and temporal evolution of these variables at the scale of river basins is essential to better understand and forecast floods and droughts. In this article, we present recent advances on the capability of Earth observation (EO) satellites to provide global monitoring of floods and droughts. The local scale monitoring of these events which is traditionally done using high-resolution optical or SAR (synthetic aperture radar) EO and in situ data will not be addressed. We discuss the applications of moderate- to low-spatial-resolution space-based observations, e.g., satellite gravimetry (GRACE and GRACE-FO), passive microwaves (i.e. SMOS) and satellite altimetry (i.e. the JASON series and the Copernicus Sentinel missions), with supporting examples. We examine the benefits and drawbacks of integrating these EO datasets to better monitor and understand the processes at work and eventually to help in early warning and management of flood and drought events. Their main advantage is their large monitoring scale that provides a “big picture” or synoptic view of the event that cannot be achieved with often sparse in situ measurements. Finally, we present upcoming and future EO missions related to this topic including the SWOT mission.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abelen S, Seitz F, Abarca-del-Rio R, Güntner A (2015) Droughts and floods in the La Plata Basin in soil moisture data and GRACE. Remote Sensing 7:7324–7349. https://doi.org/10.3390/rs70607324
Aires F, Miolane L, Prigent C et al (2017) A global dynamic long-term inundation extent dataset at high spatial resolution derived through downscaling of satellite observations. J Hydrometeor 18:1305–1325. https://doi.org/10.1175/JHM-D-16-0155.1
Al Bitar A, Kerr Y, Merlin O et al (2013) Root zone soil moisture and drought index from SMOS. In: Satellite soil moisture validation and application workshop. 2013-07-012013-07-03, Frascati, ITA.
Al Bitar A, Mialon A, Kerr Y et al (2017) The Global SMOS Level 3 daily soil moisture and brightness temperature maps. Earth Syst Sci Data 9:293–315. https://doi.org/10.5194/essd-9-293-2017
Al Bitar A, Parrens M, Frappart F et al (2016) How are the wetlands over tropical basins impacted by the extreme hydrological events? AGU Fall Meeting Abstracts 51.
Albergel C, Rüdiger C, Pellarin T et al (2008) From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations. Hydrol Earth Syst Sci 12:1323–1337. https://doi.org/10.5194/hess-12-1323-2008
Anderson MC, Norman JM, Mecikalski JR et al (2007) A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. J Geophys Res Atmospheres 112. https://doi.org/10.1029/2006JD007507
Andreadis KM, Schumann GJ-P (2014) Estimating the impact of satellite observations on the predictability of large-scale hydraulic models. Adv Water Resources 73:44–54. https://doi.org/10.1016/j.advwatres.2014.06.006
Antoine R, Fauchard C, Gaillet L et al (2020) Geoscientists in the sky: unmanned aerial Vehicles for geohazards response. Surv Geophys. https://doi.org/10.1007/s10712-020-09611-7
Babaeian E, Sadeghi M, Jones SB et al (2019) Ground, proximal, and satellite remote sensing of soil moisture. Rev Geophys 57:530–616. https://doi.org/10.1029/2018RG000618
Bates PD, Neal JC, Alsdorf D, Schumann GJ-P (2014) Observing global surface water flood dynamics. Surv Geophys 35:839–852. https://doi.org/10.1007/s10712-013-9269-4
Bayissa Y, Tadesse T, Demisse G, Shiferaw A (2017) Evaluation of satellite-based rainfall estimates and application to monitor meteorological drought for the Upper Blue Nile Basin. Ethiopia Remote Sensing 9:669. https://doi.org/10.3390/rs9070669
Bezděk A, Sebera J, Teixeira da Encarnação J, Klokočník J (2016) Time-variable gravity fields derived from GPS tracking of Swarm. Geophys J Int 205:1665–1669. https://doi.org/10.1093/gji/ggw094
Biancamaria S, Hossain F, Lettenmaier DP (2011) Forecasting transboundary river water elevations from space. Geophys Res Lett. https://doi.org/10.1029/2011GL047290
Biancamaria S, Lettenmaier DP, Pavelsky TM (2016) The SWOT mission and its capabilities for land hydrology. Surv Geophys 37:307–337. https://doi.org/10.1007/s10712-015-9346-y
Blumstein D, Guérin A, Lamy A, et al (2019) SMASH: a constellation of small altimetry satellites dedicated to hydrology. In: 6th Workshop on advanced RF sensors and remote sensing instruments (ARSI’19) and 4th Ka-band Earth Observation Radar Missions Workshop (KEO’19). ESA-ESTEC, Noordwijk, The Netherlands
Boening C, Willis JK, Landerer FW et al (2012) The 2011 La Niña: So strong, the oceans fell. Geophysical Res Lett. https://doi.org/10.1029/2012GL053055
Boergens E, Dettmering D, Seitz F (2019) Observing water level extremes in the Mekong River Basin: the benefit of long-repeat orbit missions in a multi-mission satellite altimetry approach. J Hydrol 570:463–472. https://doi.org/10.1016/j.jhydrol.2018.12.041
Bojinski S, Verstraete M, Peterson TC et al (2014) The concept of essential climate variables in support of climate research, applications, and policy. Bull Am Meteor Soc 95:1431–1443. https://doi.org/10.1175/BAMS-D-13-00047.1
Bonn F, Dixon R (2005) Monitoring flood extent and forecasting excess runoff risk with RADARSAT-1 data. Nat Hazards 35:377–393. https://doi.org/10.1007/s11069-004-1798-1
Bonsor H, Shamsudduha M, Marchant B et al (2018) Seasonal and decadal groundwater changes in African sedimentary aquifers estimated using GRACE products and LSMs. Remote Sensing 10:904. https://doi.org/10.3390/rs10060904
Brisset P, Monnier J, Garambois P-A, Roux H (2018) On the assimilation of altimetric data in 1D Saint-Venant river flow models. Adv Water Resour 119:41–59. https://doi.org/10.1016/j.advwatres.2018.06.004
Cao Y, Nan Z, Cheng G (2015) GRACE gravity satellite observations of terrestrial water storage changes for drought characterization in the arid land of Northwestern China. Remote Sensing 7:1021–1047. https://doi.org/10.3390/rs70101021
Chao N, Wang Z (2017) Characterized flood potential in the Yangtze River Basin from GRACE gravity observation, hydrological model, and in-situ hydrological station. J Hydrol Eng 22:05017016. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001547
Chen JL, Wilson CR, Tapley BD et al (2016) Long-term groundwater storage change in Victoria, Australia from satellite gravity and in situ observations. Global Planet Change 139:56–65. https://doi.org/10.1016/j.gloplacha.2016.01.002
Chen JL, Wilson CR, Tapley BD (2010) The 2009 exceptional Amazon flood and interannual terrestrial water storage change observed by GRACE. Water Resour Res. https://doi.org/10.1029/2010WR009383
Chen JL, Wilson CR, Tapley BD et al (2009) 2005 drought event in the Amazon River basin as measured by GRACE and estimated by climate models. J Geophys Res Solid Earth 114. https://doi.org/10.1029/2008JB006056
Cheymol C, Coutin-Faye S, Le Traon P-Y, et al (2019) WiSA: a wide Swath Altimetry Mission for Operational Oceanography and Hydrology A good candidate for Copernicus-NG Sentinel 3-Topo Program. Ocean Surface Topography Science Team Meeting (OSTST)
Chinnasamy P (2017) Inference of basin flood potential using nonlinear hysteresis effect of basin water storage: case study of the Koshi basin. Hydrol Res 48:1554–1565. https://doi.org/10.2166/nh.2016.268
Coe MT, Birkett CM (2004) Calculation of river discharge and prediction of lake height from satellite radar altimetry: Example for the Lake Chad basin. Water Resour Res. https://doi.org/10.1029/2003WR002543
Costa MPF, Silva TS, Evans TL (2013) Wetland classification. Remote sensing of natural resources. CRC Press, Boca Raton.
Desai S (2018) Surface Water and Ocean Topography Mission (SWOT) Project, Science Requirements Document. NASA/JPL technical document D–61923 Revision B:29
Dionisio S, Anselmi A, Bonino L et al (2018) The “Next Generation Gravity Mission”: challenges and consolidation of the system concepts and technological innovations. In: 15th International conference on space operations. https://doi.org/10.2514/6.2018-2495
Döll P, Kaspar F, Lehner B (2003) A global hydrological model for deriving water availability indicators: model tuning and validation. J Hydrol 270:105–134. https://doi.org/10.1016/S0022-1694(02)00283-4
Domeneghetti A, Schumann GJ-P, Tarpanelli A (2019) Preface: remote sensing for flood mapping and monitoring of flood dynamics. Remote Sensing 11:943. https://doi.org/10.3390/rs11080943
Drinkwater M, Haagmans R, Muzi D et al (2006) The GOCE gravity mission: ESA’sfirst core explorer. In: Proceedings 3rd GOCE User Workshop. Frascati, Italy, pp 1–7
Du J, Kimball JS, Velicogna I et al (2019) Multicomponent satellite assessment of drought severity in the contiguous United States From 2002 to 2017 using AMSR-E and AMSR2. Water Resour Res 55:5394–5412. https://doi.org/10.1029/2018WR024633
Durand M, Gleason CJ, Garambois PA et al (2016) An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope. Water Resour Res 52:4527–4549. https://doi.org/10.1002/2015WR018434
Emery CM, Paris A, Biancamaria S et al (2018) Large-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product. Hydrol Earth Syst Sci 22:2135–2162. https://doi.org/10.5194/hess-22-2135-2018
Entekhabi D, Njoku EG, O’Neill PE et al (2010) The soil moisture active passive (SMAP) mission. Proc IEEE 98:704–716. https://doi.org/10.1109/JPROC.2010.2043918
Er-Raki S, Chehbouni A, Guemouria N et al (2009) Citrus orchard evapotranspiration: comparison between eddy covariance measurements and the FAO-56 approach estimates. Plant Biosyst Int J Deal Aspects Plant Biol 143:201–208. https://doi.org/10.1080/11263500802709897
Escorihuela MJ, Kerr Y (2018) Low frequency passive microwave user requirement consolidation study: D-02 white paper on L-band radiometry for earth observation: status and achievements
Famiglietti JS (2014) The global groundwater crisis. Nat Climate Change 4:945–948. https://doi.org/10.1038/nclimate2425
Famiglietti JS, Lo M, Ho SL et al (2011) Satellites measure recent rates of groundwater depletion in California’s Central Valley. Geophys Res Lett 38:L03403. https://doi.org/10.1029/2010GL046442
Fasullo JT, Boening C, Landerer FW, Nerem RS (2013) Australia’s unique influence on global sea level in 2010–2011. Geophys Res Lett 40:4368–4373. https://doi.org/10.1002/grl.50834
Finsen F, Milzow C, Smith R et al (2014) Using radar altimetry to update a large-scale hydrological model of the Brahmaputra river basin. Hydrol Res 45:148–164. https://doi.org/10.2166/nh.2013.191
Flechtner F, Morton P, Watkins M, Webb F (2014) Status of the GRACE Follow-On mission. Gravity, geoid and height systems,pp 117–121. https://doi.org/10.1007/978-3-319-10837-7_15
Fok H, He Q, Chun K et al (2018) Application of ENSO and drought indices for water level reconstruction and prediction: a case study in the Lower Mekong River Estuary. Water 10:58. https://doi.org/10.3390/w10010058
Foster S, Loucks DP (2006) Non-renewable groundwater resources: a guidebook on socially-sustainable management for water-policy makers, United Nations Educational. Scientific and Cultural Organization, UNESCO, Paris, France
de Frasson RP, Schumann GJ -P, Kettner AJ et al (2019) Will the surface water and ocean topography (SWOT) satellite mission observe floods? Geophys Res Lett 46:10435–10445. https://doi.org/10.1029/2019GL084686
Frappart F, Ramillien G (2018) Monitoring groundwater storage changes using the gravity recovery and climate experiment (GRACE) satellite mission: a review. Remote Sensing 10:829. https://doi.org/10.3390/rs10060829
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 Sens Environ 99:387–399. https://doi.org/10.1016/j.rse.2005.08.016
Garrison JL, Kurum M, Nold B et al (2018) Remote sensing of root-zone soil moisture using I- and P-band signals of opportunity: Instrument Validation Studies. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, pp 8305–8308. https://doi.org/10.1109/IGARSS.2018.8518772
Ghobadi-Far K, Han S-C, Weller S et al (2018) A transfer function between line-of-sight gravity difference and GRACE intersatellite ranging data and an application to hydrological surface mass variation. J Geophys Res Solid Earth 123:9186–9201. https://doi.org/10.1029/2018JB016088
Gomez C, Dharumarajan S, Féret J-B et al (2019) Use of Sentinel-2 time-series images for classification and uncertainty analysis of inherent biophysical property: case of soil texture mapping. Remote Sensing 11:565. https://doi.org/10.3390/rs11050565
Gouweleeuw BT, Kvas A, Gruber C et al (2018) Daily GRACE gravity field solutions track major flood events in the Ganges-Brahmaputra Delta. Hydrol Earth Syst Sci 22:2867–2880. https://doi.org/10.5194/hess-22-2867-2018
Grillakis MG, Koutroulis AG, Komma J et al (2016) Initial soil moisture effects on flash flood generation—a comparison between basins of contrasting hydro-climatic conditions. J Hydrol 541:206–217. https://doi.org/10.1016/j.jhydrol.2016.03.007
Gruber C, Gouweleeuw B (2019) Short-latency monitoring of continental, ocean- and atmospheric mass variations using GRACE intersatellite accelerations. Geophys J Int 217:714–728. https://doi.org/10.1093/gji/ggz042
Han S-C, Kim H, Yeo I-Y et al (2009) Dynamics of surface water storage in the Amazon inferred from measurements of inter-satellite distance change. Geophys Res Lett 36. https://doi.org/10.1029/2009GL037910
Hébert H, Gailler A, Gupta H, et al (2020) Contribution of space missions to a better tsunami science: observations, models and warning. Surv Geophys 41 (in press)
Hillel D (1998) Environmental soil physics: fundamentals, applications, and environmental considerations. Elsevier, Amsterdam
Hirpa FA, Hopson TM, De Groeve T et al (2013) Upstream satellite remote sensing for river discharge forecasting: application to major rivers in South Asia. Remote Sens Environ 131:140–151. https://doi.org/10.1016/j.rse.2012.11.013
Hoekstra AY, Chapagain AK, Mekonnen MM, Aldaya MM (2011) The water footprint assessment manual: Setting the global standard. Routledge
Hossain F, Siddique-E-Akbor AH, Mazumder LC et al (2014) Proof of concept of an Altimeter-based river forecasting system for transboundary flow inside Bangladesh. IEEE J Selected Topics Appl Earth Observ Remote Sensing 7:587–601. https://doi.org/10.1109/JSTARS.2013.2283402
Houborg R, Rodell M, Li B et al (2012) Drought indicators based on model-assimilated Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage observations. Water Resour Res 48. https://doi.org/10.1029/2011WR011291
Huntingford C, Marsh T, Scaife AA et al (2014) Potential influences on the United Kingdom’s floods of winter 2013/14. Nature Clim Change 4:769–777. https://doi.org/10.1038/nclimate2314
Idowu Z (2019) Performance evaluation of a potential component of an early flood warning system—a case study of the 2012 flood, Lower Niger River Basin. Nigeria Remote Sensing 11:1970. https://doi.org/10.3390/rs11171970
Jäggi A, Weigelt M, Flechtner F et al (2019) European Gravity Service for Improved Emergency Management (EGSIEM)—from concept to implementation. Geophys J Int 218:1572–1590. https://doi.org/10.1093/gji/ggz238
Jean Y, Meyer U, Jäggi A (2018) Combination of GRACE monthly gravity field solutions from different processing strategies. J Geodesy 92:1313–1328. https://doi.org/10.1007/s00190-018-1123-5
Jiang L, Madsen H, Bauer-Gottwein P (2019) Simultaneous calibration of multiple hydrodynamic model parameters using satellite altimetry observations of water surface elevation in the Songhua River. Remote Sens Environ 225:229–247. https://doi.org/10.1016/j.rse.2019.03.014
Kerr YH, Waldteufel P, Richaume P et al (2012) The SMOS soil moisture retrieval algorithm. IEEE Trans Geosci Remote Sens 50:1384–1403. https://doi.org/10.1109/TGRS.2012.2184548
Kerr YH, Wigneron J-P, Al Bitar A et al (2016) Soil moisture from space: techniques and limitations. Satellite Soil Moisture Retrieval, pp 3–27. https://doi.org/10.1016/B978-0-12-803388-3.00001-2
Kolusu SR, Shamsudduha M, Todd MC et al (2019) The El Niño event of 2015–2016: climate anomalies and their impact on groundwater resources in East and Southern Africa. Hydrol Earth Syst Sci 23:1751–1762. https://doi.org/10.5194/hess-23-1751-2019
Kummu M, De Moel H, Ward PJ, Varis O (2011) How close do we live to water? A global analysis of population distance to freshwater bodies. PLoS ONE 6:e20578. https://doi.org/10.1371/journal.pone.0020578
Kusche J, Eicker A, Forootan E et al (2016) Mapping probabilities of extreme continental water storage changes from space gravimetry. Geophys Res Lett 43:8026–8034. https://doi.org/10.1002/2016GL069538
Landerer FW, Flechtner FM, Save H et al (2020) Extending the Global Mass Change Data Record: GRACE Follow-On Instrument and Science Data Performance. Geophys Res Lett 47:e2020GL088306. https://doi.org/10.1029/2020GL088306
Lewis SL, Brando PM, Phillips OL et al (2011) The 2010 Amazon drought. Science 331:554–554. https://doi.org/10.1126/science.1200807
Li B, Rodell M, Zaitchik BF et al (2012) Assimilation of GRACE terrestrial water storage into a land surface model: evaluation and potential value for drought monitoring in western and central Europe. J Hydrol 446–447:103–115. https://doi.org/10.1016/j.jhydrol.2012.04.035
Lissak C, De Michele M, Bartsch A et al (2020) Remote sensing for mass movement assessment. Surv Geophys. https://doi.org/10.1007/s10712-020-09609-1
Long D, Scanlon BR, Longuevergne L et al (2013) GRACE satellite monitoring of large depletion in water storage in response to the 2011 drought in Texas. Geophys Res Lett 40:3395–3401. https://doi.org/10.1002/grl.50655
Long D, Shen Y, Sun A et al (2014) Drought and flood monitoring for a large karst plateau in Southwest China using extended GRACE data. Remote Sens Environ 155:145–160. https://doi.org/10.1016/j.rse.2014.08.006
Margat J, Van der Gun J (2013) Groundwater around the world: a geographic synopsis. CRC Press, Taylor & Francis Group
Matgen P, Hostache R, Schumann G et al (2011) Towards an automated SAR-based flood monitoring system: lessons learned from two case studies. Phys Chem Earth A/B/C 36:241–252. https://doi.org/10.1016/j.pce.2010.12.009
Melet A, Teatini P, Le Cozannet G et al (2020) Earth observations for monitoring Marine Coastal Hazards and their drivers. Surv Geophys. https://doi.org/10.1007/s10712-020-09594-5
Merlin O, Al Bitar A, Walker JP, Kerr Y (2010) An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data. Remote Sens Environ 114:2305–2316. https://doi.org/10.1016/j.rse.2010.05.007
Michailovsky CI, Milzow C, Bauer-Gottwein P (2013) Assimilation of radar altimetry to a routing model of the Brahmaputra River: Radar Altimetry to a Routing Model. Water Resour Res 49:4807–4816. https://doi.org/10.1002/wrcr.20345
Molodtsova T, Molodtsov S, Kirilenko A et al (2016) Evaluating flood potential with GRACE in the United States. Natural Hazards Earth Syst Sci 16:1011–1018. https://doi.org/10.5194/nhess-16-1011-2016
Mueller N, Lewis A, Roberts D et al (2016) Water observations from space: mapping surface water from 25 years of Landsat imagery across Australia. Remote Sens Environ 174:341–352. https://doi.org/10.1016/j.rse.2015.11.003
Munier S, Polebistki A, Brown C et al (2015) SWOT data assimilation for operational reservoir management on the upper Niger River Basin. Water Resour Res 51:554–575. https://doi.org/10.1002/2014WR016157
National Academies of Sciences, Engineering, and Medecine (2018) Thriving on our changing planet: a decadal strategy for earth observation from space. https://doi.org/10.17226/24938
Nie N, Zhang W, Chen H, Guo H (2018) A global hydrological drought index dataset based on Gravity Recovery and Climate Experiment (GRACE) Data. Water Resour Manage 32:1275–1290. https://doi.org/10.1007/s11269-017-1869-1
Niu J, Chen J, Sivakumar B (2014) Teleconnection analysis of runoff and soil moisture over the Pearl River basin in southern China. Hydrol Earth Syst Sci 18:1475–1492. https://doi.org/10.5194/hess-18-1475-2014
Njoku EG, Jackson TJ, Lakshmi V et al (2003) Soil moisture retrieval from AMSR-E. IEEE Trans Geosci Remote Sens 41:215–229. https://doi.org/10.1109/TGRS.2002.808243
Oubanas H, Gejadze I, Malaterre P-O et al (2018) Discharge estimation in ungauged basins through variational data assimilation: the potential of the SWOT mission. Water Resour Res 54:2405–2423. https://doi.org/10.1002/2017WR021735
Ovando A, Martinez JM, Tomasella J et al (2018) Multi-temporal flood mapping and satellite altimetry used to evaluate the flood dynamics of the Bolivian Amazon wetlands. Int J Appl Earth Obs Geoinf 69:27–40. https://doi.org/10.1016/j.jag.2018.02.013
Pail R, Bamber J, Biancale R et al (2019) Mass variation observing system by high low inter-satellite links (MOBILE)—a new concept for sustained observation of mass transport from space. J Geodetic Sci 9:48–58. https://doi.org/10.1515/jogs-2019-0006
Pail R, Braitenberg C, Dobslaw H et al (2015) Science and user needs for observing global mass transport to understand global change and to benefit society. Surv Geophys 36:743–772. https://doi.org/10.1007/s10712-015-9348-9
Paloscia S, Pettinato S, Santi E et al (2013) Soil moisture mapping using Sentinel-1 images: algorithm and preliminary validation. Remote Sens Environ 134:234–248. https://doi.org/10.1016/j.rse.2013.02.027
Parrens M, Al Bitar A, Frappart F et al (2017) Mapping dynamic water fraction under the tropical rain forests of the Amazonian Basin from SMOS brightness temperatures. Water 9:350. https://doi.org/10.3390/w9050350
Parrens M, Bitar AA, Frappart F et al (2019) High resolution mapping of inundation area in the Amazon basin from a combination of L-band passive microwave, optical and radar datasets. Int J Appl Earth Obs Geoinf 81:58–71. https://doi.org/10.1016/j.jag.2019.04.011
Pedinotti V, Boone A, Ricci S et al (2014) Assimilation of satellite data to optimize large-scale hydrological model parameters: a case study for the SWOT mission. Hydrol Earth Syst Sci 18:4485–4507. https://doi.org/10.5194/hess-18-4485-2014
Pekel J-F, Cottam A, Gorelick N, Belward AS (2016) High-resolution mapping of global surface water and its long-term changes. Nature 540:418–422. https://doi.org/10.1038/nature20584
Petiteville I, Ishida C, Danzeglocke J et al (2015) WCDRR and the CEOS activites on disasters. Int Arch Photogrammetry Remote Sensing Spatial Information Sci 76: https://doi.org/10.5194/isprsarchives-XL-7-W3-845-2015
Pettinari ML, Chuvieco E (2020) Fire danger observed from space. Surv Geophys. https://doi.org/10.1007/s10712-020-09610-8
Pham HT, Marshall L, Johnson F, Sharma A (2018) Deriving daily water levels from satellite altimetry and land surface temperature for sparsely gauged catchments: a case study for the Mekong River. Remote Sens Environ 212:31–46. https://doi.org/10.1016/j.rse.2018.04.034
Prigent C, Papa F, Aires F et al (2007) Global inundation dynamics inferred from multiple satellite observations, 1993–2000. J Geophys Res Atmospheres 112. https://doi.org/10.1029/2006JD007847
Reager J, Thomas A, Sproles E et al (2015) Assimilation of GRACE terrestrial water storage observations into a land surface model for the assessment of regional flood potential. Remote Sensing 7:14663–14679. https://doi.org/10.3390/rs71114663
Reager JT, Famiglietti JS (2009) Global terrestrial water storage capacity and flood potential using GRACE. Geophys Res Lett 36. https://doi.org/10.1029/2009GL040826
Reager JT, Thomas BF, Famiglietti JS (2014) River basin flood potential inferred using GRACE gravity observations at several months lead time. Nat Geosci 7:588–592. https://doi.org/10.1038/ngeo2203
Reichle RHA (2018) Soil Moisture Active Passive (SMAP) Mission Level 4 Surface and Root Zone Soil Moisture (L4_SM) Product Specification Document
Richey AS, Thomas BF, Lo M-H et al (2015) Uncertainty in global groundwater storage estimates in a total groundwater sress framework. Water Resour Res 51:5198–5216. https://doi.org/10.1002/2015WR017351
Robinson DA, Campbell CS, Hopmans JW et al (2008) Soil moisture measurement for ecological and hydrological watershed-scale observatories: a review. Vadose Zone J 7:358–389. https://doi.org/10.2136/vzj2007.0143
Rodell M (2012) Satellite Gravimetry applied to drought monitoring. Remote Sensing of Drought Innovative Monitoring Approaches 261. https://doi.org/10.1201/b11863-18
Rodríguez-Fernández NJ, Anterrieu E, Rougé B, et al (2019) SMOS-HR: a high resolution L-Band passive Radiometer for Earth Science and Applications. IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/IGARSS.2019.8897815
Santos da Silva J, Calmant S, Seyler F et al (2010) Water levels in the Amazon basin derived from the ERS 2 and ENVISAT radar altimetry missions. Remote Sens Environ 114:2160–2181. https://doi.org/10.1016/j.rse.2010.04.020
Schneider R, Godiksen PN, Villadsen H et al (2017) Application of CryoSat-2 altimetry data for river analysis and modelling. Hydrol Earth Syst Sci 21:751–764. https://doi.org/10.5194/hess-21-751-2017
Schroeder R, McDonald KC, Chapman BD et al (2015) Development and evaluation of a multi-year fractional surface water data set derived from active/passive microwave remote sensing data. Remote Sensing 7:16688–16732. https://doi.org/10.3390/rs71215843
Schumann G, Di Baldassarre G, Alsdorf D, Bates PD (2010) Near real-time flood wave approximation on large rivers from space: application to the River Po. Italy Water Resources Res 46. https://doi.org/10.1029/2008WR007672
Seitz F, Schmidt M, Shum CK (2008) Signals of extreme weather conditions in Central Europe in GRACE 4-D hydrological mass variations. Earth Planet Sci Lett 268:165–170. https://doi.org/10.1016/j.epsl.2008.01.001
Siderius C, Gannon KE, Ndiyoi M et al (2018) Hydrological response and complex impact pathways of the 2015/2016 El Niño in Eastern and Southern Africa. Earth’s Future 6:2–22. https://doi.org/10.1002/2017EF000680
Soldo Y, Khazaal A, Cabot F, Kerr YH (2016) An RFI index to quantify the contamination of SMOS data by radio-frequency interference. IEEE J Selected Topics Appl Earth Observ Remote Sensing 9:1577–1589. https://doi.org/10.1109/JSTARS.2015.2425542
Stocker TF, Qin D, Plattner G-K, et al (2013) Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change 1535.
Stöckli R, Vermote E, Saleous N, et al (2005) Blue Marble Next Generation—a true color earth dataset including seasonal dynamics from MODIS. In: NASA Earth Observatory. https://earthobservatory.nasa.gov/features/BlueMarble. Accessed 29 Jan 2020
Sun Z, Zhu X, Pan Y, Zhang J (2017) Assessing terrestrial water storage and flood potential using GRACE data in the Yangtze River Basin. China Remote Sensing 9:1011. https://doi.org/10.3390/rs9101011
Svoboda M, Hayes M, Wood D (2012) Standardized precipitation index user guide. World Meteorological Organization Geneva, Switzerland
Swain S, Wardlow BD, Narumalani S et al (2013) Relationships between vegetation indices and root zone soil moisture under maize and soybean canopies in the US Corn Belt: a comparative study using a close-range sensing approach. Int J Remote Sens 34:2814–2828. https://doi.org/10.1080/01431161.2012.750020
Tapley BD, Bettadpur S, Ries JC et al (2004) GRACE measurements of mass variability in the earth system. Science 305:503–505. https://doi.org/10.1126/science.1099192
Tarpanelli A, Amarnath G, Brocca L et al (2017) Discharge estimation and forecasting by MODIS and altimetry data in Niger-Benue River. Remote Sens Environ 195:96–106. https://doi.org/10.1016/j.rse.2017.04.015
Tarpanelli A, Brocca L, Barbetta S et al (2015) Coupling MODIS and Radar altimetry data for discharge estimation in poorly gauged river basins. IEEE J Selected Topics Appl Earth Observ Remote Sensing 8:141–148. https://doi.org/10.1109/JSTARS.2014.2320582
Tarpanelli A, Santi E, Tourian MJ et al (2019) Daily river discharge estimates by merging satellite optical sensors and radar altimetry through artificial neural network. IEEE Trans Geosci Remote Sens 57:329–341. https://doi.org/10.1109/TGRS.2018.2854625
Thomas AC, Reager JT, Famiglietti JS, Rodell M (2014) A GRACE-based water storage deficit approach for hydrological drought characterization. Geophys Res Lett 41:1537–1545. https://doi.org/10.1002/2014GL059323
Thomas BF, Famiglietti JS, Landerer FW et al (2017) GRACE groundwater drought index: evaluation of California Central Valley groundwater drought. Remote Sens Environ 198:384–392. https://doi.org/10.1016/j.rse.2017.06.026
Tomer SK, Al Bitar A, Sekhar M et al (2015) Retrieval and multi-scale validation of soil moisture from multi-temporal SAR data in a semi-arid tropical region. Remote Sensing 7:8128–8153. https://doi.org/10.3390/rs70608128
Tomer SK, Al Bitar A, Sekhar M et al (2016) MAPSM: a spatio-temporal algorithm for merging soil moisture from active and passive microwave remote sensing. Remote Sensing 8:990. https://doi.org/10.3390/rs8120990
Tourian MJ, Schwatke C, Sneeuw N (2017) River discharge estimation at daily resolution from satellite altimetry over an entire river basin. J Hydrol 546:230–247. https://doi.org/10.1016/j.jhydrol.2017.01.009
Tourian MJ, Tarpanelli A, Elmi O et al (2016) Spatiotemporal densification of river water level time series by multimission satellite altimetry. Water Resour Res 52:1140–1159. https://doi.org/10.1002/2015WR017654
Tralli DM, Blom RG, Zlotnicki V et al (2005) Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards. ISPRS J Photogrammetry Remote Sensing 59:185–198. https://doi.org/10.1016/j.isprsjprs.2005.02.002
Twele A, Cao W, Plank S, Martinis S (2016) Sentinel-1-based flood mapping: a fully automated processing chain. Int J Remote Sens 37:2990–3004. https://doi.org/10.1080/01431161.2016.1192304
Ulaby FTM (1981) Microwave remote sensing: active and passive. Volume 1—Microwave remote sensing fundamentals and radiometry
Wagner W, Hahn S, Kidd R et al (2013) The ASCAT soil moisture product: a review of its specifications, validation results, and emerging applications. Meteorol Z 22:5–33. https://doi.org/10.1127/0941-2948/2013/0399
Ward PJ, Jongman B, Kummu M et al (2014) Strong influence of El Nino Southern Oscillation on flood risk around the world. Proc Natl Acad Sci 111:15659–15664. https://doi.org/10.1073/pnas.1409822111
Wilhite DA (2000) Drought as a natural hazard: Concepts and Definitions, 22
WMO (2012) International glossary of Hydrology, UNESCO
World Resources Institute (2019) Aqueduct Floods. In: Aqueduct Floods. https://www.wri.org/applications/aqueduct/floods/. Accessed 28 Jul 2020
Yamazaki D, Watanabe S, Hirabayashi Y (2018) Global flood risk modeling and projections of climate change impacts. Global Flood Hazard Appl Model Mapping Forecasting 233:254. https://doi.org/10.1002/9781119217886.ch11
Yang Y, Lin P, Fisher CK et al (2019) Enhancing SWOT discharge assimilation through spatiotemporal correlations. Remote Sens Environ 234:111450. https://doi.org/10.1016/j.rse.2019.111450
Yoon Y, Beighley E, Lee H et al (2016) Estimating flood discharges in reservoir-regulated river basins by integrating synthetic SWOT satellite observations and hydrologic modeling. J Hydrol Eng 21:05015030. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001320
Yoon Y, Durand M, Merry CJ et al (2012) Estimating river bathymetry from data assimilation of synthetic SWOT measurements. J Hydrol 464–465:363–375. https://doi.org/10.1016/j.jhydrol.2012.07.028
Yu X, Moraetis D, Nikolaidis NP et al (2019) A coupled surface-subsurface hydrologic model to assess groundwater flood risk spatially and temporally. Environ Modell Softw 114:129–139. https://doi.org/10.1016/j.envsoft.2019.01.008
Zhang A, Jia G (2013) Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sens Environ 134:12–23. https://doi.org/10.1016/j.rse.2013.02.023
Zhang D, Zhang Q, Werner AD, Liu X (2016) GRACE-based hydrological drought evaluation of the Yangtze River Basin, China. J Hydrometeor 17:811–828. https://doi.org/10.1175/JHM-D-15-0084.1
Zhao L, Dong H, Edelmann RE et al (2017) Coupling of Fe(II) oxidation in illite with nitrate reduction and its role in clay mineral transformation. Geochim Cosmochim Acta 200:353–366. https://doi.org/10.1016/j.gca.2017.01.004
Zhou H, Luo Z, Tangdamrongsub N et al (2017) Characterizing drought and flood events over the Yangtze River Basin using the HUST-Grace2016 solution and Ancillary data. Remote Sensing 9:1100. https://doi.org/10.3390/rs9111100
Acknowledgements
This paper arose from the international workshop on “Natural and man-made hazards monitoring by the Earth Observation missions: current status and scientific gaps” held at the International Space Science Institute (ISSI), Bern, Switzerland, on April 15–18, 2019. The authors wish to thank the two anonymous reviewers for their constructive suggestions which significantly improved this article; Dr. Anny Cazenave, for her invitation to prepare this paper and Anne-Marie Cousin for her help in producing Fig. 1.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Lopez, T., Al Bitar, A., Biancamaria, S. et al. On the Use of Satellite Remote Sensing to Detect Floods and Droughts at Large Scales. Surv Geophys 41, 1461–1487 (2020). https://doi.org/10.1007/s10712-020-09618-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10712-020-09618-0