Hydrologists rely heavily on satellite sensors because they provide useful information for tracking, evaluating, managing water resources, aiding provision of safe drinking water, help preventing waterborne diseases, and address the challenges posed by climate change. Water conservation and the collection of hydrologic data have made remote sensing (RS) an invaluable tool. As a result, there are fewer hydrologic stations globally in terms of space because of various topography landforms, human limitations, and financial limits. A thorough examination of the RS satellite products' hydrological applications is essential to finding a solution to this issue. By doing this, academicians, researchers, and conservationists in various professions can better understand the products and obtain the data needed for conservation. This paper primarily focuses on the following two objectives: 1). To synthesize the scientific information on satellite remote sensing application for hydrology, and 2). To explain the RS dataset sources for hydrologic parameters. Extensive literature search from reputable journal publishers. This review article synthesized vital sources of information for academicians, researchers, and government agencies involved in hydrology and water resources management. It is recommended that RS can be used as a data source for scarce, sparsely gauged, and inaccessible regions.

  • Satellite sensors for hydrology.

  • Satellite sensors for water resource management.

  • Familiarity with satellite products.

  • Ease of hydrological data collection.

  • Monitoring, assessment, and management of water resources.

Using satellite data to observe hydrological processes is crucial for the sustainable management of water resources across a wide area. The process of remotely sensing water resources entails producing data on a variety of topics, including the routine inventory of surface water bodies and the evaluation of precipitation, soil moisture, evapotranspiration (ET), ground water, and snowmelt runoff (Singh 2018). These days, hydrological cycle components such as precipitation, evaporation, lake and river levels, surface water, soil moisture, snow, and total water storage may all be measured directly or indirectly using satellite-based sensors (McCabe et al. 2017). For precise and dependable data on Earth observations, satellite remote sensing (RS) is a valuable resource in atmospheric and environmental science. Hydrological modeling finds it attractive due its seamless availability throughout ungauged regions, boosting spatial and temporal resolution. Earth observation data have already proven to be immensely beneficial to the field of hydrology sciences (Tang et al. 2016; McCabe et al. 2017; Alfieri et al. 2020).

Table 1

Satellite products for precipitation

Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelReferences
TMPA 0.25° 3 h 1998–2019 https://gpm.nasa.gov/missions/trmm SWAT, CREST Huffman et al. (2007)  
CHIRPS v2.0 0.05° Daily 1981 to present https://data.chc.ucsb.edu/products/CHIRPS-2.0/https://climateserv.servirglobal.net/
https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY 
 Funk et al. (2015)  
Climate Prediction Center morphing technique (CMORPH) –30 min 0.07° 30 min 1998 to present https://www.ncei.noaa.gov/products/climate-data-records/precipitation-cmorph
https://www.ncei.noaa.gov/data/cmorph-high-resolution-global-precipitation-estimates/access/30min/ 
SWAT Joyce et al. (2004)  
PERSIANN 0.25° 1 h 2000 to present https://chrsdata.eng.uci.edu/  Hsu et al. (1997), Sellars et al. (2013), Sorooshian et al. (2000)  
PERSIANN –CCS 0.04° 1 h 2003 to present  Hong et al. (2012)  
PERSIANN –CDR 0.25° Daily 1983 to present SWAT Ashouri et al. (2015)  
PERSIANN –CONNECT 0.25° 1 h 1983 to present  Sellars et al. (2013)  
GSMaP 0.1° 1 h 2003–2015 http://sharaku.eorc.jaxa.jp/GSMaP/index.htm
http://sharaku.eorc.jaxa.jp/GSMaP_crest/ 
SWAT Kubota et al. (2009); Chen et al. 2020  
GPM/IMERG 0.1° 30 min Mar 2014–Dec 2021 https://pmm.nasa.gov/data-access/downloads/gpm DRYP, SWAT Aonashi et al. (2009), Quichimbo (2021)  
MSWEP 0.1° 3 h 1979 to present http://www.gloh2o.org XAJ, SWAT, DRYP Beck et al. (2016), Beck et al. (2017)  
GPCC 1° global 3 months 1891 to present https://psl.noaa.gov/data/gridded/data.gpcc.html SWT, XAJ, VIC Rudolf & Schneider (2005), Ma & Sun (2018)  
GPCC-daily 1.0° × 1.0° Daily 1982 to present GPCC HBV, SWMM Schamm et al. (2014), Bergström (1992)  
CRU 0.5° × 0.5° Monthly 1901–2018 The CRU of the University of East Anglia  New et al. (2000), Harris et al. (2014)  
GHCN-M 5° × 5° Monthly 1998–2014 National Climatic Data Center TOPMODEL Peterson & Vose (1997), Naz et al. (2020)  
PREC/L 0.5° × 0.5°,
1.0° × 1.0°, 
Monthly 1948 to present NCEP/NOAA  Chen et al. (2011)  
UDEL 0.5° × 0.5°, Monthly  University of Delaware  Willmott & Matsuura (1995)  
CPC-Global 0.5° × 0.5°, Daily 1979 to present https://www.cpc.ncep.noaa.gov/  Xie et al. (2010)  
SM2RAIN 1° global Monthly 1998–2021 https://opendata.dwd.de/climate_environment/GPCC/html/download_gate.html Mosaffa et al. (2023)  
Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelReferences
TMPA 0.25° 3 h 1998–2019 https://gpm.nasa.gov/missions/trmm SWAT, CREST Huffman et al. (2007)  
CHIRPS v2.0 0.05° Daily 1981 to present https://data.chc.ucsb.edu/products/CHIRPS-2.0/https://climateserv.servirglobal.net/
https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY 
 Funk et al. (2015)  
Climate Prediction Center morphing technique (CMORPH) –30 min 0.07° 30 min 1998 to present https://www.ncei.noaa.gov/products/climate-data-records/precipitation-cmorph
https://www.ncei.noaa.gov/data/cmorph-high-resolution-global-precipitation-estimates/access/30min/ 
SWAT Joyce et al. (2004)  
PERSIANN 0.25° 1 h 2000 to present https://chrsdata.eng.uci.edu/  Hsu et al. (1997), Sellars et al. (2013), Sorooshian et al. (2000)  
PERSIANN –CCS 0.04° 1 h 2003 to present  Hong et al. (2012)  
PERSIANN –CDR 0.25° Daily 1983 to present SWAT Ashouri et al. (2015)  
PERSIANN –CONNECT 0.25° 1 h 1983 to present  Sellars et al. (2013)  
GSMaP 0.1° 1 h 2003–2015 http://sharaku.eorc.jaxa.jp/GSMaP/index.htm
http://sharaku.eorc.jaxa.jp/GSMaP_crest/ 
SWAT Kubota et al. (2009); Chen et al. 2020  
GPM/IMERG 0.1° 30 min Mar 2014–Dec 2021 https://pmm.nasa.gov/data-access/downloads/gpm DRYP, SWAT Aonashi et al. (2009), Quichimbo (2021)  
MSWEP 0.1° 3 h 1979 to present http://www.gloh2o.org XAJ, SWAT, DRYP Beck et al. (2016), Beck et al. (2017)  
GPCC 1° global 3 months 1891 to present https://psl.noaa.gov/data/gridded/data.gpcc.html SWT, XAJ, VIC Rudolf & Schneider (2005), Ma & Sun (2018)  
GPCC-daily 1.0° × 1.0° Daily 1982 to present GPCC HBV, SWMM Schamm et al. (2014), Bergström (1992)  
CRU 0.5° × 0.5° Monthly 1901–2018 The CRU of the University of East Anglia  New et al. (2000), Harris et al. (2014)  
GHCN-M 5° × 5° Monthly 1998–2014 National Climatic Data Center TOPMODEL Peterson & Vose (1997), Naz et al. (2020)  
PREC/L 0.5° × 0.5°,
1.0° × 1.0°, 
Monthly 1948 to present NCEP/NOAA  Chen et al. (2011)  
UDEL 0.5° × 0.5°, Monthly  University of Delaware  Willmott & Matsuura (1995)  
CPC-Global 0.5° × 0.5°, Daily 1979 to present https://www.cpc.ncep.noaa.gov/  Xie et al. (2010)  
SM2RAIN 1° global Monthly 1998–2021 https://opendata.dwd.de/climate_environment/GPCC/html/download_gate.html Mosaffa et al. (2023)  

TMPA, Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis; CFSR, Climate Forest System Reanalysis system; CHIRPS, Climate Hazards Group InfraRed Precipitation with Station data; CMORPH, Climate Prediction Center morphing technique; CRU, Climate Research Unit; GHCN-M, Global Historical Climatology Network monthly; GPCC, Global Precipitation Climatology Centre; GPCP 1dd, GPCP one-degree daily precipitation analysis; GPCP, Global Precipitation Climatology Project; GPM, Global Precipitation Measurement; GSMaP, Global Satellite Mapping of Precipitation; MSWEP, Multi-Source Weighted-Ensemble Precipitation; version PERSIANN, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks; PREC, Precipitation reconstruction; PRECL, Precipitation reconstruction over land; Tropical Rainfall Measuring Mission (TRMM); SWAT, Soil and Water Assessment Tools; TOPMODEL, TOPography-based hydrological MODEL; XAJ, Xinanjiang Model; DRYP, distributed, integrated, hydrological model; CREST, coupled routing and excess storage model; SWMM, Storm Water Management Model; HBV, Hydrologiska Byrans Vattenbalansavdelning; VIC, Variable Infiltration Capacity.

Table 2

Satellite products for the estimation of ET

Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelsReferences
RS-PM 0.5° Daily  Princeton University  Vinokullo et al. (2011)  
MOD16 ET 1 km 8 days 2001–2015 http://www.ntsg.umt.edu/project/modis/mod16.php  Mu et al. (2011)  
PT-JPL 1–0° Monthly  https://lpdaac.usgs.gov/products/eco3etptjplv001/  Fisher et al. (2008)  
GLEAM 0.25° Daily 1980–2022 https://www.gleam.eu  Martens et al. (2017), Senay et al. (2013)  
ALEXI-DisALEXI 30 m (Landsat),
1 km (MODIS) 
Hourly/daily  Atmosphere-Land Exchange Inverse Model (ALEXI)  Anderson et al. (2007)  
ESI 0.05° 4 and 12 weeks  https://servirglobal.net/Global/Evaporative-Stress-Index  Anderson et al. (2011)  
Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelsReferences
RS-PM 0.5° Daily  Princeton University  Vinokullo et al. (2011)  
MOD16 ET 1 km 8 days 2001–2015 http://www.ntsg.umt.edu/project/modis/mod16.php  Mu et al. (2011)  
PT-JPL 1–0° Monthly  https://lpdaac.usgs.gov/products/eco3etptjplv001/  Fisher et al. (2008)  
GLEAM 0.25° Daily 1980–2022 https://www.gleam.eu  Martens et al. (2017), Senay et al. (2013)  
ALEXI-DisALEXI 30 m (Landsat),
1 km (MODIS) 
Hourly/daily  Atmosphere-Land Exchange Inverse Model (ALEXI)  Anderson et al. (2007)  
ESI 0.05° 4 and 12 weeks  https://servirglobal.net/Global/Evaporative-Stress-Index  Anderson et al. (2011)  

RS-PM, Remote Sensing Penman Montheith; MOD16 ET, MODIS Global Evapotranspiration Project; PT-JPL, Priestly-Taylor Jet Propulsion Laboratory; GLEAM, Global Land Evaporation Amsterdam Model; FEWS, Famine Early Warning system; ALEXI, Atmosphere-Land Exchange Inverse Model; DisALEXI, Disaggregated ALEXI algorithm; ESI, Evaporative Stress Index.

Table 3

Satellite products for vegetation

Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelReferences
Landsat 15 m 16 days 1985 to date https://landsat.usgs.gov  NASA (2001), Chaves et al. (2020)  
AVHRR/GIMMS 1 km 7-/14-day composites 1980–2015 https://catalog.data.gov/dataset/ W3RA Khaki et al. (2020), Pinzon & Tucker (2014)  
MODIS 250 m, 1 km, 0.05° 16-day, monthly 2017–2019 https://modis.gsfc.nasa.gov/  Huete et al. (2002)  
VIIRS 375 m (swath), 500 m Daily and 8-day composite 2012–2021 https://modis.gsfc.nasa.gov/data/dataprod/mod13.php  Vargas et al. (2013)  
Sentinel-2 10–60 m 5/10 days 2015–2020 https://sentinel.esa.int/web/sentinel/sentinel-data-access  ESA (2013)  
Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelReferences
Landsat 15 m 16 days 1985 to date https://landsat.usgs.gov  NASA (2001), Chaves et al. (2020)  
AVHRR/GIMMS 1 km 7-/14-day composites 1980–2015 https://catalog.data.gov/dataset/ W3RA Khaki et al. (2020), Pinzon & Tucker (2014)  
MODIS 250 m, 1 km, 0.05° 16-day, monthly 2017–2019 https://modis.gsfc.nasa.gov/  Huete et al. (2002)  
VIIRS 375 m (swath), 500 m Daily and 8-day composite 2012–2021 https://modis.gsfc.nasa.gov/data/dataprod/mod13.php  Vargas et al. (2013)  
Sentinel-2 10–60 m 5/10 days 2015–2020 https://sentinel.esa.int/web/sentinel/sentinel-data-access  ESA (2013)  

VIIRS, Visible Infrared Imaging Radiometer Suite; AVHRR, Advanced Very High-Resolution Radiometer; SWE, snow water equivalence; MODIS, Moderate Resolution Imaging Spectroradiometer; ECOSTRESS, ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station; W3RA, World-Wide Water Resources Assessment model.

Table 4

Satellite products for soil moisture

Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelReferences
AMSR-E 25 km 1-day revisit 2002–2011 https://nsidc.org/data/ae_land3 W3RA Khaki et al. (2020), Njoku (2004)  
AMSR2 25 km 1-day revisit 2012–2021 http://nsidc.org/data/au_land SHEELS Owe et al. (2008), Koike (2013)  
SMOS 15, 25, and 50 km 1- to 3-day revisit;
daily, 9-day and
monthly products 
2018–2021 https://earth.esa.int/eogateway/catalog  Kerr et al. (2012)  
SMAP
SMAP L4
Root zone
Soil moisture 
36 km, 9 km 1- to 3-day revisit 3 h 2015 to present
2015–2021 
https://smap.jpl.nasa.gov/data/  Entekhabi et al. (2010), Reichle et al. (2016)  
Sentinel-1 < 1 km2 8 days 2016 to date https://sentinel.esa.int/web/sentinel/sentinel-data-access  Paloscia et al. (2013)  
ASCAT 0.1° 1–3 days revisit time 2007–2018 https://land.copernicus.eu/global/products/swi  Wagner et al. (1999)  
FY3-B 25 km  2011–2018 http://satellite.nsmc.org.cn/PortalSite/Default.aspx  Shi et al. (2006)  
Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelReferences
AMSR-E 25 km 1-day revisit 2002–2011 https://nsidc.org/data/ae_land3 W3RA Khaki et al. (2020), Njoku (2004)  
AMSR2 25 km 1-day revisit 2012–2021 http://nsidc.org/data/au_land SHEELS Owe et al. (2008), Koike (2013)  
SMOS 15, 25, and 50 km 1- to 3-day revisit;
daily, 9-day and
monthly products 
2018–2021 https://earth.esa.int/eogateway/catalog  Kerr et al. (2012)  
SMAP
SMAP L4
Root zone
Soil moisture 
36 km, 9 km 1- to 3-day revisit 3 h 2015 to present
2015–2021 
https://smap.jpl.nasa.gov/data/  Entekhabi et al. (2010), Reichle et al. (2016)  
Sentinel-1 < 1 km2 8 days 2016 to date https://sentinel.esa.int/web/sentinel/sentinel-data-access  Paloscia et al. (2013)  
ASCAT 0.1° 1–3 days revisit time 2007–2018 https://land.copernicus.eu/global/products/swi  Wagner et al. (1999)  
FY3-B 25 km  2011–2018 http://satellite.nsmc.org.cn/PortalSite/Default.aspx  Shi et al. (2006)  

AMSR2, Advanced Microwave Scanning Radiometer 2; AMSR-E, Advanced Microwave Scanning Radiometer for the Earth Observing System; SMAP, Soil Moisture Active Passive; SMOS, Soil Moisture and Ocean Salinity; ASCAT, Advanced Scatterometer; FY3-B, Fengyan; SHEELS, Simulator for hydrology and energy exchange at the land surface.

Table 5

Satellite products for surface temperature

Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelsReferences
CHIRTS; CHIRTS-daily 60°S–70°N Daily, monthly 1983–2016 https://cds.climate.copernicus.eu/cdsapp#!/home  Verdin et al. (2020)  
Landsat 30 multispectral (Landsat-8)
100 m thermal (Landsat-9) 
16 days 1972 to date https://landsat.usgs.gov  NASA (2001)  
AVHRR 1 km 1 day  https://lta.cr.usgs.gov/AVHRR  Gao et al. (2012), Gao (2019)  
ASTER 90 m 16 days 1999 to date https://asterweb.jpl.nasa.gov/data.asp  NASA (2001)  
MODIS 1 km/6 km 1 day, 8 days 2000–2017 https://modis.gsfc.nasa.gov/data/dataprod/mod11.php CLM Gao et al. (2012), Naz et al. (2020)  
VIIRS 375–750 m 1 day 2012–2021 https://www.nesdis.noaa.gov/our-satellites/currently-flying/joint-polar-satellite-system/visible-infrared-imaging-radiometer-suite-viirs  Su (2002), Kustas et al. (1995)  
Sentinel-3 1 km < 2 days 2017 to date https://sentinel.esa.int/web/sentinel/sentinel-data-access  ESA (2018)  
ECOSTRESS 38 × 69 m 4 days    Hulley et al. (2017)  
Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelsReferences
CHIRTS; CHIRTS-daily 60°S–70°N Daily, monthly 1983–2016 https://cds.climate.copernicus.eu/cdsapp#!/home  Verdin et al. (2020)  
Landsat 30 multispectral (Landsat-8)
100 m thermal (Landsat-9) 
16 days 1972 to date https://landsat.usgs.gov  NASA (2001)  
AVHRR 1 km 1 day  https://lta.cr.usgs.gov/AVHRR  Gao et al. (2012), Gao (2019)  
ASTER 90 m 16 days 1999 to date https://asterweb.jpl.nasa.gov/data.asp  NASA (2001)  
MODIS 1 km/6 km 1 day, 8 days 2000–2017 https://modis.gsfc.nasa.gov/data/dataprod/mod11.php CLM Gao et al. (2012), Naz et al. (2020)  
VIIRS 375–750 m 1 day 2012–2021 https://www.nesdis.noaa.gov/our-satellites/currently-flying/joint-polar-satellite-system/visible-infrared-imaging-radiometer-suite-viirs  Su (2002), Kustas et al. (1995)  
Sentinel-3 1 km < 2 days 2017 to date https://sentinel.esa.int/web/sentinel/sentinel-data-access  ESA (2018)  
ECOSTRESS 38 × 69 m 4 days    Hulley et al. (2017)  

AVHRR, Advanced Very High-Resolution Radiometer; VIIRS, Visible Infrared Imaging Radiometer Suite; ASTER, Advanced Spaceborne Thermal Emission and Reflection Radiometer; MODIS, Moderate Resolution Imaging Spectroradiometer; ECOSTRESS, ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station; CLM, Community Land Model.

Table 6

Satellite products for water storage

Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelReferences
GRACE 500,000 km2 30 days 2006 to present https://grace.jpl.nasa.gov/data/get-data/ W3RA Han et al. (2009), Muskett & Romanovsky (2009), Rodell et al. (2009), Khaki et al. (2020)  
SWOT 10 km (larger waterbodies)
250 m2 (lakes, reservoirs, wetlands) 
11 days 2000–2019 https://directory.eoportal.org/web/eoportal/satellite-missions/s/swot
https://dahiti.dgfi.tum.de/en/ 
 Fjørtoft et al. (2014), Schwatke et al. (2019)  
Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelReferences
GRACE 500,000 km2 30 days 2006 to present https://grace.jpl.nasa.gov/data/get-data/ W3RA Han et al. (2009), Muskett & Romanovsky (2009), Rodell et al. (2009), Khaki et al. (2020)  
SWOT 10 km (larger waterbodies)
250 m2 (lakes, reservoirs, wetlands) 
11 days 2000–2019 https://directory.eoportal.org/web/eoportal/satellite-missions/s/swot
https://dahiti.dgfi.tum.de/en/ 
 Fjørtoft et al. (2014), Schwatke et al. (2019)  
Table 7

Satellite products for the surface water level

Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelsReferences
Jason-2/3 Lakes > 100 km2 10 days 2003–2016 https://ipad.fas.usda.gov/cropexplorer/global_reservoir/
https://www.jpl.nasa.gov/missions/jason-3 
 Lambin et al. (2010)  
Sentinel-3 350 m along the track 27 days 2017 to date https://sentinel.esa.int/web/sentinel/sentinel-data-access  ESA (2018)  
ENVISAT  10 days  https://earth.esa.int/eogateway/missions/envisat  Resti et al. (1999)  
Topex/Poseidon  10 days 1992–2001 https://sealevel.jpl.nasa.gov/missions/topex-poseidon/summary/  Resti et al. (1999)  
SWOT 10 km (larger waterbodies)
250 m2 (lakes, reservoirs, wetlands) 
11 days  https://directory.eoportal.org/web/eoportal/satellite-missions/s/swot
https://dahiti.dgfi.tum.de/en/ 
  Fjørtoft et al. (2014), Schwatke et al. (2019)  
Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelsReferences
Jason-2/3 Lakes > 100 km2 10 days 2003–2016 https://ipad.fas.usda.gov/cropexplorer/global_reservoir/
https://www.jpl.nasa.gov/missions/jason-3 
 Lambin et al. (2010)  
Sentinel-3 350 m along the track 27 days 2017 to date https://sentinel.esa.int/web/sentinel/sentinel-data-access  ESA (2018)  
ENVISAT  10 days  https://earth.esa.int/eogateway/missions/envisat  Resti et al. (1999)  
Topex/Poseidon  10 days 1992–2001 https://sealevel.jpl.nasa.gov/missions/topex-poseidon/summary/  Resti et al. (1999)  
SWOT 10 km (larger waterbodies)
250 m2 (lakes, reservoirs, wetlands) 
11 days  https://directory.eoportal.org/web/eoportal/satellite-missions/s/swot
https://dahiti.dgfi.tum.de/en/ 
  Fjørtoft et al. (2014), Schwatke et al. (2019)  
Table 8

Satellite products for surface water extent

Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelsReferences
Landsat 30 multispectral (Landsat-8)
100 m thermal (Landsat-9) 
16 days 1985–2019 https://landsat.usgs.gov   Landsat 8 Data Users Handbook (2020)  
MODIS 500 m Daily  https://floodmap.gsfc.nasa.gov   Radočaj et al. (2020)  
Sentinel-2 10–60 m 5/10 days 2016 to date https://sentinel.esa.int/web/sentinel/sentinel-data-access   Sentinel-2 User Handbook (2020)  
Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelsReferences
Landsat 30 multispectral (Landsat-8)
100 m thermal (Landsat-9) 
16 days 1985–2019 https://landsat.usgs.gov   Landsat 8 Data Users Handbook (2020)  
MODIS 500 m Daily  https://floodmap.gsfc.nasa.gov   Radočaj et al. (2020)  
Sentinel-2 10–60 m 5/10 days 2016 to date https://sentinel.esa.int/web/sentinel/sentinel-data-access   Sentinel-2 User Handbook (2020)  
Table 9

Satellite products for stream flow

Satellite sensorSpatialresolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelsReference
SWOT 10 km 11 days  https://swot.jpl.nasa.gov/  Alsdorf & Lettenmaier (2003)  
Satellite sensorSpatialresolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelsReference
SWOT 10 km 11 days  https://swot.jpl.nasa.gov/  Alsdorf & Lettenmaier (2003)  

SWOT, Surface Water Ocean Topography.

Table 10

Satellite products for discharge

Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelsReference
GRDC Global Daily  https://www.bafg.de/GRDC/EN/01_GRDC/13_dtbse/database_node.html  Dai et al. (2012)  
Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrological modelsReference
GRDC Global Daily  https://www.bafg.de/GRDC/EN/01_GRDC/13_dtbse/database_node.html  Dai et al. (2012)  

GRDC, Global Runoff Data Center.

Table 11

Satellite products for water budget

Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceReferences
GRACE 500,000 km2 30 days January 2004–December 2015 https://grace.jpl.nasa.gov/data/get-data/ Swenson & Milly (2006), Syed et al. (2007), Awange et al. (2008), Jiang et al. (2014)  
SWOT 10 km 11 days  https://swot.jpl.nasa.gov/ Crowley et al. (2008)  
Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceReferences
GRACE 500,000 km2 30 days January 2004–December 2015 https://grace.jpl.nasa.gov/data/get-data/ Swenson & Milly (2006), Syed et al. (2007), Awange et al. (2008), Jiang et al. (2014)  
SWOT 10 km 11 days  https://swot.jpl.nasa.gov/ Crowley et al. (2008)  

GRACE, Gravity Recovery, and Climate Experiment; SWOT, Surface Water Ocean Topography.

Table 12

Satellite products for groundwater

Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrologic modelsReferences
GRACE 500,000 km2 30 days 2002–2017 https://grace.jpl.nasa.gov/data/get-data/ MODFLOW Rodell et al. (2009), Durand et al. (2016), Tapley et al. (2004), Frappart et al. (2015), Azarderakhsh et al. (2011), Yeh et al. (2006)  
GRACE FO 500,000 km2 30 days 2018 to date https://gracefo.jpl.nasa.gov/  Niu & Yang (2006)  
Satellite sensorSpatial resolutionTemporal frequencyTemporal intervalAccess/sourceHydrologic modelsReferences
GRACE 500,000 km2 30 days 2002–2017 https://grace.jpl.nasa.gov/data/get-data/ MODFLOW Rodell et al. (2009), Durand et al. (2016), Tapley et al. (2004), Frappart et al. (2015), Azarderakhsh et al. (2011), Yeh et al. (2006)  
GRACE FO 500,000 km2 30 days 2018 to date https://gracefo.jpl.nasa.gov/  Niu & Yang (2006)  

GRACE, Gravity Recovery, and Climate Experiment; GRACE FO, GRACE Follow-On.

Quantifying the hydrological budget over large spatial domains and lengthy time periods by direct observation is relatively difficult because in situ observations are expensive and labor-intensive. This is particularly true in ungauged basins, where stream flow measurements are either nonexistent or insufficient, and little to no observations of spatially variable hydrological parameters are made. Hydrological modeling for water resource estimates is, therefore, quite difficult. Satellite RS provides a method to address these issues with broad spatial coverage and reproducible temporal coverage. Hydrological studies are hampered in ungauged basins because they do not have the calibration and validation data needed to employ land surface models. Therefore, it is necessary to make use of satellite data (Lakshmi 2018).

Currently, little is known about these satellite sensors in developing countries such as East Africa, particularly Ethiopia. Therefore, it has been an urgent issue to conduct a review of these satellite sensors and thereby work on the adoption of such technologies in universities and research institutions. Many research works have been done extensively on validating these satellite sensors. However, it is not designed to be easily understood by users. In addition, they focused on only a few satellite sensors. On the contrary, this review paper covers various satellites and sensors with their spatial and temporal resolutions, which are suitable for hydrological modeling, and gives valuable information on satellites and sensors for the modeling to academicians and researchers who are not familiar with satellite-derived data. It is much preferable to review all hydrologic response units and store them in a simple form on a single paper that paves the way for easier access to input data on water conservation, which is the ultimate goal of this article. On the other hand, many developing countries in the world do not have hydrology and water resource data centers at an acceptable distance. Meanwhile, these hydrological and water resource data are in high demand in relation to climate change. Obtaining data on hydrology and water resource parameters from existing gage stations in the required quantity and timeliness has been a constraint. So, where do we find these clues? From which website can we get it in the shortest time without spending money? Is it a question that awaits an answer? In this regard, this article is intended to play an important role in answering these questions in a concise manner.

In this research, the function of satellite RS in hydrology and water resource management is reviewed, along with its possible applications in the future. This review paper primarily focuses on the following two objectives: (1) to synthesize the scientific information on satellite RS applications for hydrology and water resources and (2) to explain the RS dataset sources for hydrological parameters.

Description of satellite sensors

Remote sensors are devices that receive and respond to a signal or stimulus and convert any type of energy into electrical energy (Al-Aubidy 2007). RS satellites are equipped with various sensors designed to capture different types of data about the Earth's surface, atmosphere, and other phenomena. These sensors gather information across the electromagnetic spectrum, ranging from visible light to microwaves and beyond.

Passive microwave sensors: These sensors measure microwave radiation emitted or scattered by the Earth's surface and atmosphere. They are particularly useful for estimating soil moisture and detecting precipitation. Examples include the Advanced Microwave Scanning Radiometer and the Soil Moisture and Ocean Salinity satellite.

Active microwave sensors: These sensors emit microwave pulses toward the Earth's surface and measure the reflected signal. They are used for measuring surface water extent, soil moisture, and snow cover. Synthetic-aperture radar sensors, such as those on the European Space Agency's (ESA) Sentinel-1 satellites, are examples of active microwave sensors.

Optical sensors: Optical sensors capture images of the Earth's surface using visible and infrared light. They are used for monitoring vegetation health, land cover changes, and snow cover extent. Examples include the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite.

LiDAR (Light Detection and Ranging): LiDAR sensors emit laser pulses and measure the time it takes for the pulses to return after reflecting off the Earth's surface. LiDAR data can be used to measure river and lake levels, as well as terrain elevation. The Ice, Cloud, and Land Elevation Satellite mission and the Global Ecosystem Dynamics Investigation aboard the International Space Station are examples of LiDAR missions.

Gravimetric sensors: Gravimetric sensors measure variations in the Earth's gravitational field, which can provide information on changes in water storage, such as groundwater depletion and changes in surface water storage. The Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE Follow-On, are examples of gravimetric missions.

Multispectral sensors: Multispectral sensors capture data in several discrete spectral bands within the visible and near-infrared (NIR) portions of the electromagnetic spectrum (Light 1990).

Hyperspectral sensors: These sensors measure a wide range of wavelengths across the electromagnetic spectrum, providing detailed information about the composition and properties of the Earth's surface. They are used for tasks such as identifying different types of vegetation and detecting water quality parameters. Examples include the Hyperspectral Infrared Imager and the Environmental Mapping and Analysis Program.

Aspects of satellite sensors

Positive aspects of satellite sensors

Satellite sensors used in hydrology are designed to capture various aspects of the Earth's surface and atmosphere relevant to the water cycle (Azarderakhsh et al. 2011). Several key aspects are essential for these sensors to effectively monitor and study hydrological processes:

Global coverage: Satellite sensors can observe remote and inaccessible regions by providing comprehensive coverage. This allows for monitoring water resources in diverse environments, including remote areas, mountainous regions, and polar regions, where ground-based observations may be limited or unavailable.

Cost and time-saving: Satellite sensors enable non-invasive RS of the Earth's surface and atmosphere, allowing for continuous monitoring of hydrological parameters over large spatial scales. This capability reduces the need for costly and time-consuming fieldwork, making it possible to assess water resources in areas with challenging terrain or limited accessibility.

Multi-sensor integration: Satellite missions often include multiple sensors with complementary capabilities, such as optical, microwave, and infrared sensors. Integrating data from different sensors allows for a more comprehensive understanding of hydrological processes, including the estimation of soil moisture, precipitation, ET, and surface water extent.

Large-scale monitoring: Satellite sensors can monitor large-scale hydrological phenomena, such as river basins, watersheds, and continental-scale water cycles. This ability to observe broad spatial extents facilitates the assessment of water availability, distribution, and movement across different regions, supporting regional water resource management and transboundary water management efforts.

Real-time and near real-time data: Many satellite missions provide real-time or near real-time data, allowing for timely monitoring and response to hydrological events such as floods, landslides, and water quality changes. Rapid access to satellite data supports early warning systems, emergency response planning, and disaster management activities, helping to mitigate the impacts of water-related hazards.

Long-term monitoring: Satellite sensors have enabled long-term monitoring of hydrological parameters, facilitating the analysis of historical trends and the detection of long-term changes in water resources. Long-term satellite datasets contribute to climate change studies, water resource planning, and ecosystem management by providing insights into the impacts of climate variability and human activities on water availability and quality.

Spatio-temporal resolution: Many satellite sensors offer high temporal resolution, capturing frequent observations of hydrological processes over time. This temporal continuity allows for monitoring changes in precipitation patterns, snowmelt dynamics, soil moisture levels, and water body dynamics, facilitating the detection of trends, seasonal variations, and extreme events, such as floods and droughts. Spatial resolution refers to the level of detail in an image captured by a satellite sensor. Higher spatial resolution sensors can distinguish smaller features on the Earth's surface, which are important for tasks such as monitoring small water bodies, river networks, and urban areas. Different hydrological applications may require sensors with varying spatial resolutions to meet specific monitoring needs. Temporal resolution refers to how frequently a satellite revisits the same location on Earth. Hydrological processes, such as precipitation, snowmelt, and vegetation growth, can exhibit rapid changes over time. Sensors with high temporal resolution can capture these dynamic processes more frequently, allowing for better monitoring and forecasting of hydrological events like floods and droughts.

Spectral bands: Satellite sensors are equipped with different spectral bands to capture electromagnetic radiation across the spectrum. Each spectral band is sensitive to specific features of the Earth's surface and atmosphere. For hydrological applications, sensors with bands sensitive to water-related parameters, such as NIR and microwave bands, are crucial for tasks like estimating soil moisture, detecting water bodies, and monitoring vegetation health.

Radiometric accuracy: Radiometric accuracy refers to the precision with which a satellite sensor measures the intensity of electromagnetic radiation. Accurate radiometric measurements are essential for quantifying surface properties related to hydrology, such as land surface temperature, vegetation indices (VIs), and water reflectance. Calibration and validation procedures are employed to ensure the radiometric accuracy of satellite data, enabling reliable hydrological analysis and modeling.

Data accessibility and availability: The accessibility and availability of satellite data are critical for operational hydrological monitoring and research. Open-access satellite missions, such as those provided by space agencies, such as NASA, ESA, and National Oceanic and Atmospheric Administration (NOAA), enable researchers and water resource managers to access a wealth of satellite data for various hydrological applications. In addition, data archives and distribution platforms facilitate easy access to historical and real-time satellite data for ongoing hydrological studies.

Integration with ground-based observations: Satellite sensors are often used in conjunction with ground-based observations, such as weather stations, stream gauges, and groundwater monitoring networks, to validate and supplement satellite-derived data. Integrating satellite observations with ground-based measurements improves the accuracy and reliability of hydrological models and enhances our understanding of local-scale processes.

By considering these important aspects, satellite sensors can effectively contribute to hydrological research, water resource management, and environmental monitoring efforts worldwide.

Negative aspects of satellite sensors

The use of satellite precipitation products (SPPs) in environmental applications has been limited by their accuracy and spatial resolution (Beyene 2023). While satellite sensors offer numerous advantages for hydrological monitoring, they also have some limitations and disadvantages.

Spatial resolution: Many satellite sensors have limited spatial resolution, particularly for freely available data. This can be a drawback when monitoring small-scale features such as small rivers, wetlands, or urban water bodies. Higher spatial resolution data may be available from commercial satellites, but often at a higher cost.

Temporal resolution: Sensors have varied revisit times to a specific location depending on their satellite's orbit and mission design (Barsi et al. 2019). For instance, Sentinel 2 has slow revisit times to the same location (in the order of 1 or 2 weeks). Images are often obstructed by clouds, which, together with slow revisit times, cause extended periods without any data (Guillermo et al. 2022).

Cloud cover and weather conditions: Satellite sensors reliant on optical imagery are hindered by cloud cover and adverse weather conditions, which can obscure the Earth's surface and limit data availability. Persistent cloud cover in tropical regions or during the rainy season can pose challenges for consistent monitoring of hydrological processes.

Atmospheric interference: Atmospheric conditions, such as aerosols, water vapor, and atmospheric scattering, can introduce errors in satellite-derived measurements, particularly in the case of optical sensors. Corrections for atmospheric interference are necessary to ensure the accuracy of hydrological data derived from satellite observations.

Data processing and interpretation: Processing satellite data requires specialized expertise and computational resources, including algorithms for image correction, calibration, and interpretation. In addition, integrating satellite data with ground-based observations and hydrological models can be complex and may introduce uncertainties in the analysis.

Cost: While some satellite data sources offer free or low-cost access to data, high-resolution or specialized satellite imagery may come at a significant cost, particularly for commercial data providers. This can be a barrier for researchers and organizations with limited budgets, especially in developing regions where funding for RS applications may be scarce.

Data validation and accuracy: Satellite-derived hydrological data require validation against ground-based observations to ensure accuracy and reliability. In situ measurements are essential for calibrating and validating satellite observations, but establishing and maintaining ground-based monitoring networks can be resource-intensive and challenging in remote or inaccessible areas.

Limited sensitivity to subsurface processes: Satellite sensors primarily observe surface water and moisture dynamics, but they have limited sensitivity to subsurface processes, such as groundwater recharge, aquifer depletion, and soil moisture at deeper depths. Integrating satellite observations with ground-based measurements and hydrological models is necessary for a comprehensive understanding of the water cycle.

Review method and data collection

The library research method, which includes (i) analyzing historical records (recording of notes and analysis) and (ii) analyzing documents (statistical compilations and manipulations, references, and abstract guides), was done. An extensive literature search was conducted. Secondary data that are relevant to the preset objectives were collected and compiled from recent literature.

Satellite sensors for hydrological parameters

Precipitation

Earth's precipitation is measured using multiple satellite sensors. These sensors use a variety of methods to monitor and quantify precipitation in various forms (rain, snow, sleet, etc.) in various parts of the world (Table 1).

Evapotranspiration

In order to estimate the combined processes of water evaporation from the Earth's surface (such as water bodies, soil, and vegetation) and transpiration from plants, ET from space is measured using a variety of satellite sensors and techniques (Table 2).

Vegetation

Satellite sensors for monitoring vegetation health and dynamics gather information about plant health, growth, and environmental conditions using various wavelengths of light. Different sensors are used to measure certain elements of vegetation (Table 3). Satellite products of VIs have been widely used for various purposes, including vegetation change monitoring (Zhang et al. 2017; Zeng et al. 2020), vegetation phenology extraction (Buyantuyev & Wu 2012; Pastor-Guzman et al. 2018), terrestrial carbon circulation modeling (Tucker & Sellers 1986; Guan et al. 2019), dynamic environmental simulations (Tong et al. 2017; Zhao et al. 2020), and land coverage and change detection (Jia et al. 2014; Hu 2021). Among them, the normalized difference vegetation index (NDVI) calculated from the NIR band and the visible red band (RED) obtained by optical satellites is one of the most popular indices (Holben et al. 1980). Similar to the NDVI, the enhanced vegetation index (EVI) minimizes the canopy background variations and maintains its sensitivity under dense vegetation conditions. The EVI also uses the blue band (BLUE) to remove residual atmospheric contamination caused by smoke and thin sub-pixel clouds (Huete et al. 2002).

The ratio vegetation index is calculated by simply dividing the reflectance values of the NIR band by those of the red band. The result clearly captures the contrast between the red and infrared bands for vegetated pixels, with high index values being produced by combinations of low red (because of absorption by chlorophyll) and high infrared (as a result of leaf structure) reflectance. The perpendicular vegetation index (PVI) is the parent index from which the entire group of distance-based VIs is derived. The PVI uses the perpendicular distance from each pixel coordinate to the soil line. The main objective of the PVI is to cancel the effect of soil brightness in cases where vegetation is sparse and pixels contain a mixture of green vegetation and soil background.

Soil moisture

Satellite sensors for measuring soil moisture (Table 4) are critical tools in environmental monitoring, agriculture, hydrology, and climate studies. These sensors work by sensing microwave radiation emitted or reflected by the Earth's surface.

Surface temperature

Satellite sensors developed for surface temperature measurements (Table 5) work by absorbing thermal radiation released by the Earth's surface using various technologies and spectral bands. These sensors aid in the monitoring and analysis of temperature differences in landscapes, oceans, and cities.

Satellite sensors for water resource management

Water storage

Satellite sensors are critical for monitoring and quantifying water storage in a variety of environments, including oceans, lakes, rivers, and subsurface reservoirs. These sensors estimate water storage levels (Table 6) using various methodologies and wavelengths.

Surface water level

Several technologies and satellite-based sensors are used to measure and monitor surface water levels across oceans, lakes, rivers, and reservoirs (Table 7).

Surface water extent

Various types of satellite sensors (Table 8) are used to detect and track surface water extent in oceans, lakes, rivers, and reservoirs.

Stream flow

Stream flow is monitored using a variety of satellite sensors and technology (Table 9). Satellite sensors play an important role in stream flow monitoring by providing vital data on water levels, surface water extent, and other characteristics.

Discharge

Monitoring river discharge with satellite sensors entails determining the amount of water flowing through a river or stream at a specific spot (Table 10). While the direct measurement of discharge from space is difficult, data from multiple satellite sensors can be utilized to estimate discharge indirectly.

Water budget

Satellite RS can be used to examine the important terms in the water balance equation. Retrievals of all components of the terrestrial water cycle have evolved in recent years, and there is now the possibility of performing continuous worldwide observations of the terrestrial water cycle in real time (Alsdorf & Lettenmaier 2003). The terrestrial water budget can be defined as the balance between the change in water storage (ΔS) and the difference between the incoming water fluxes of precipitation (P) and outgoing fluxes of ET and discharge (Q) at the Earth's surface in the following equation:
formula
(1)

Each water budget component in Equation (1) has different temporal dynamics. For example, precipitation has faster dynamics than storage change. Irrespective of the different temporal dynamics of each flux, Equation (1) holds at any time interval. There are several products from previous and continuing satellite missions that measure these components at various time and space scales, either separately or as an aggregate. By combining microwave and infrared satellite observations, global precipitation is retrieved with very high spatial and temporal resolution (Sorooshian et al. 2000; Kummerow et al. 2001; Joyce et al. 2004; Huffman et al. 2007). Satellite sensors play an important role in monitoring various components of the water budget, which includes tracking the transport and distribution of water throughout the Earth's hydrological cycle (Table 11). They give statistics on various crucial characteristics that help to understand the water budget.

Groundwater

Groundwater monitoring is more difficult than monitoring surface water bodies such as rivers or lakes. Satellite sensors, on the other hand, play a role in indirectly assessing and analyzing groundwater by observing numerous surface indicators related to groundwater levels, land surface changes, and hydrological processes (Table 12).

Bias correction methods

Accuracy assessments determine the quality of the information derived from remotely sensed data (Congalton & Green 2009; Beyene 2023). Some correction methods (atmospheric correction, topographic correction, geometric correction, and radiometric correction) need to be applied for obtaining high-quality data. Atmospheric corrections are methods used to convert the radiance measured at the satellite to the outgoing radiance measured at the ground (Lu et al. 2002). It considers that selective scattering and absorption of light alter reflectance (Silleos et al. 2006). Radiometric correction takes into account sensor calibration, illumination, and view angle (Carlson & Ripley 1997). Geometric correction uses careful Ground Control Point selection, which is required for satellite images. Accurate and consistent georeferencing is especially important for automatic land cover change algorithms because change detection is performed by overlaying images from different sources (Russ 1995). Radiometric corrections include correcting the data for sensor irregularities and unwanted sensor or atmospheric noise and converting the data, so they accurately represent the reflected or emitted radiation measured by the sensor. Geometric corrections include correcting for geometric distortions due to sensor-Earth geometry variations and converting the data to real-world coordinates (e.g., latitude and longitude) on the Earth's surface (Jain 1989; Lillesand & Kiefer 1994).

In order to reduce the position errors, Hoffman & Grassotti (1996) proposed a feature correction and alignment technique (FCA), which used a variational approach to solve a nonlinear least squares estimation problem with side constraints to vary the displacement and amplification errors of the prior background field until usable observations were available (Hoffman & Grassotti 1996). Based on the FCA method, Grassotti fused radar and satellite precipitation estimates and performed the ideal experiment for Typhoon Andrew, using the precipitation observed by radar to adjust the precipitation estimates from Special Sensor Microwave/Imager (SSM/I). The adjusted SSM/I precipitation estimates can better fit radar observations and satisfy other constraints (Grassotti et al. 1999).

Systematic errors and random errors are common in satellite rainfall products (Goshime 2020). Error sources are mostly related to the imperfection of the retrieval algorithm, data source, and postprocessing procedures (Dubovik et al. 2021; Zhang et al. 2021). Compared to some meteorological variables like temperature, which has a steadier geographical and temporal pattern, bias correction of satellite rainfall data is thought to be the most difficult (Soo et al. 2020). From the available methods, distribution mapping tends to address bias by correlating patterns of different rainfall magnitudes (Valdés-Pineda et al. 2016; Katiraie-Boroujerdy et al. 2020).

Many previous studies have shown that the assimilation of hydrological parameter data from satellite sensor products and gauges could complement each other and achieve good performance with sufficient ground observations (Tan & Yang 2020; Zhou et al. 2021, 2022). Tian & Peters-Lidard (2010) corrected the CMORPH and TRMM data by reducing the error by 47–63% based on rain gauges in the USA. Stisen & Sandholt ( 2010) improved hydrological simulation efficiency by correcting the SPPs in the Senegal River Basin in West Africa. Zhou et al. (2021, 2022) employed statistical and dynamic bias correction to correct Global Satellite Mapping of Precipitation (GSMaP) and Global Precipitation Measurement (GPM) series data in the Fuji River Basin, Japan. The results showed that the corrected SPPs significantly improved and benefited the efficiency of runoff simulation. Recently, Liu et al. (2023) addressed a three-step bias correction method incorporating the statistic and dynamic bias correction method, the cumulative distribution function matching method and the inverse error variance weighting method for SPPs by correcting the bias of SPPs in regions with limited gauge data, which poses a significant challenge, especially when aiming for reliable precipitation data for hydrological simulations, particularly in near-real-time scenarios.

The use of satellite sensors and RS data in hydrology is critically important to the water and health sectors. It enhances the ability to monitor, manage, and protect water resources, thereby ensuring the provision of safe drinking water and aiding in the prevention of waterborne diseases and the challenges posed by climate change. The continuous advancement and utilization of these technologies are essential for promoting public health and achieving sustainable water resource management.

Hydrological observations and modeling utilizing satellite data are critical for the long-term management of water resources across wide areas. Water resource RS entails gathering data ranging from a regular inventory of surface water bodies to an assessment of rainfall, soil moisture, ET, groundwater, and snow melt runoff (Singh 2018). Satellite RS is increasingly being utilized as a supplement to in-person monitoring networks, and in many situations, it is the only viable source. Satellite-based sensors can now measure nearly all components of the hydrological cycle, both directly and indirectly (Flechtner et al. 2015; Lettenmaier et al. 2015). These include precipitation, evaporation, lake and river levels, surface water, soil moisture, snow, and total water storage (surface and subsurface water). As a result, these sensors are capable of providing crucial information in support of water management and monitoring the evolution of hazards and their repercussions (Van Dijk & Renzullo 2011).

The authors recommend satellite sensors for several advantages in monitoring hydrologic parameters, providing a wealth of information that can be invaluable for understanding and managing water resources as follows:

  • Wide coverage and frequent monitoring: Satellites can cover large and remote areas, including regions that are otherwise inaccessible or difficult to monitor regularly. This capability allows for the comprehensive and consistent monitoring of hydrological parameters over vast areas, such as entire watersheds or river basins. Satellites can monitor hydrological parameters globally, making them valuable for studying water resources on a worldwide scale and facilitating international collaboration and understanding of water-related issues. Satellites provide frequent and repetitive observations over time. This allows for the assessment of temporal changes in hydrological conditions, enabling the detection of trends, seasonal variations, and sudden changes such as floods or droughts.

  • Multispectral and multi-temporal data: Satellite sensors can capture data in various spectral bands, including visible, infrared, and microwave wavelengths. This multispectral data allow for the analysis of different hydrological parameters, such as soil moisture, ET, snow cover, and surface water bodies.

  • Consistency and standardization: Satellite data are consistent and standardized, providing a continuous record that facilitates comparison and analysis over different spatial and temporal scales. This consistency is crucial for assessing long-term trends and changes in hydrological parameters.

  • Integration with geographic information systems (GISs): Satellite data can be easily integrated with GIS technology, allowing for the creation of spatially explicit maps and models. This integration enables better decision-making in water resource management, land use planning, and disaster risk reduction.

  • Timely and rapid response: Satellites can provide near-real-time data, enabling rapid response to hydrological events such as floods or droughts. This information is valuable for early warning systems and emergency management.

  • Cost-effectiveness: While there are initial costs associated with satellite systems, they can be cost-effective in the long run compared to traditional ground-based monitoring, especially in remote or inaccessible regions where establishing and maintaining ground stations might be challenging.

  • Monitoring water quality and quantity: Satellite sensors play a crucial role in monitoring various water quality parameters, such as turbidity, chlorophyll concentration, and harmful algal blooms. By providing continuous and large-scale data, RS helps detect and track pollution sources, monitor the extent of contamination, and assess the overall health of water bodies. This information is vital for ensuring safe drinking water and managing water resources effectively.

  • Early warning systems for water-related health hazards: RS datasets are essential for developing early warning systems for water-related health hazards such as floods and likely associated waterborne disease outbreaks. Satellite data can predict extreme weather events and their impact on water resources, allowing for timely interventions to mitigate health risks. For instance, flood prediction can lead to the evacuation of at-risk populations and exposures to contaminated water.

  • Addressing climate change impacts on water resources: Climate change poses significant challenges to water availability and quality, affecting public health. Satellite sensors offer insights into changing precipitation patterns, melting glaciers, and shifting river flows. Understanding these changes helps in developing adaptive strategies to ensure reliable water supplies and mitigate health impacts related to water scarcity and quality degradation.

  • Supporting research and policy development: The integration of satellite sensor data into hydrology and water resource management research supports evidence-based policy-making. It enables researchers and policymakers to analyze trends, evaluate the effectiveness of interventions, and make informed decisions to protect public health. This data-driven approach fosters the development of robust water management policies that safeguard communities against water-related health issues.

In summary, satellite sensors offer a powerful and versatile tool for monitoring hydrological parameters, providing valuable data that support water resource management, environmental conservation, disaster mitigation, mitigating health impacts related to water scarcity, and scientific research. Therefore, it is advisable to use RS data for sparsely gauged stations for water resource assessment and management at all levels.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Al-Aubidy
K.
2007
Sensor Characteristics: Advanced Measurement Systems & Sensors (0640732), Lecture 2, p. 19, Philadelphia University-Jordan
.
Alfieri
L.
,
Lorini
V.
,
Hirpa
F. A.
,
Harrigan
S.
,
Zsoter
E.
,
Prudhomme
C.
&
Salamon
P.
2020
A global streamflow reanalysis for 1980–2018
.
Journal of Hydrology X
6
,
100049
.
https://doi.org/10.1016/j.hydroa.2019.100049
.
Alsdorf
D. A.
&
Lettenmaier
D. P.
2003
Tracking fresh water from space
.
Science
301
,
1491
1494
.
Anderson
M. C.
,
Norman
J. M.
,
Mecikalski
J. R.
,
Otkin
J. A.
&
Kustas
W. P.
2007
A climatological study of evapotranspiration and moisture stress across the continental U.S. based on thermal remote sensing: 1. Model formulation
.
Journal of Geophysical Research
112
,
D10117
.
https://doi.org/10110.11029/12006JD007506
.
Anderson
M. C.
,
Hain
C. R.
,
Wardlow
B.
,
Mecikalski
J. R.
&
Kustas
W. P.
2011
Evaluation of a drought index based on thermal remote sensing of evapotranspiration over the continental U.S
.
Journal of Climate
24
(
8
),
2025
2044
.
https://doi.org/10.1175/ 2010JCLI3812.1
.
Aonashi
K.
,
Awaka
J.
,
Hirose
M.
,
Kozu
T.
,
Kubota
T.
,
Liu
G.
,
Shige
S.
,
Kida
S.
,
Seto
S.
,
Takahashi
N.
&
Takayabu
Y. N.
2009
GSMap passive microwave precipitation retrievals: Algorithm description and validation
.
Journal of Applied Meteorology
87A
,
119
136
.
Ashouri
H.
,
Hsu
K. L.
,
Sorooshian
S.
,
Braithwaite
D. K.
,
Knapp
K. R.
,
Cecil
L. D.
,
Nelson
B. R.
&
Prat
O. P.
2015
PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies
.
Bulletin of the American Meteorological Society
96
(
1
),
69
83
.
https://doi.org/10.1175/BAMS-D-13-00068.1
.
Awange
J. L.
,
Sharifi
M. A.
,
Ogonda
G.
,
Wickert
J.
,
Grafarend
E. W.
&
Omulo
M. A.
2008
The falling Lake Victoria water level: GRACE, TRIMM and CHAMP satellite analysis of the lake basin
.
Water Resources Management
22
,
775
796
.
Azarderakhsh
M.
,
Rossow
W. B.
,
Papa
F.
,
Norouzi
H.
&
Khanbilvardi
R.
2011
Diagnosing water variations within the Amazon Basin using satellite data
.
Journal of Geophysical Research
116
,
D24107
.
https://doi.org/10.1029/2011JD015997
.
Barsi
Á.
,
Kugler
Z.
,
Juhász
A.
,
Szabó
G.
,
Batini
C.
,
Abdulmuttalib
H.
,
Huang
G.
&
Shen
H.
2019
Remote sensing data quality model: From data sources to lifecycle phases
.
International Journal of Image and Data Fusion
.
https://doi.org/10.1080/19479832.2019.1625977
.
Beck
H. E.
,
van Dijk
A. I. J. M.
,
Levizzani
V.
,
Schellekens
J.
,
Miralles
D. G.
,
Martens
B.
&
de Roo
A.
2016
MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data
.
Hydrology and Earth System Sciences
21
,
589
615
.
Beck
H. E.
,
Vergopolan
N.
,
Pan
M.
,
Levizzani
V.
,
Van Dijk
A. I. J. M.
,
Weedon
G. P.
,
Brocca
L.
,
Pappenberger
F.
,
Huffman
G. J.
&
Wood
E. J.
2017
Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling
.
Hydrology and Earth System Sciences
21
(
12
),
6201
6217
.
doi: 10.5194/hess-21-6201-2017
.
Bergström
S.
1992
The HBV Model – Its Structure and Applications
.
Swedish Meteorological and Hydrological Institute
,
Norrköping, Sweden
.
Beyene
T. D.
2023
Evaluation of a multi-staged bias correction approach on CHIRP and CHIRPS rainfall product: A case study of the Lake Hawassa watershed
.
Journal of Water and Climate Change
14
(
6
),
1847
1867
.
https://doi.org/10.2166/wcc.2023.457
.
Carlson
N. T.
&
Ripley
D. A.
1997
On the relation between NDVI, fractional vegetation cover, and leaf area index
.
Remote Sensing of Environment
62
,
241
252
.
Chaves
M. E. D.
,
Picoli
M. C. A.
&
Sanches
I. D.
2020
Recent applications of Landsat 8/OLI and sentinel-2/MSI for land use and land cover mapping: A systematic review
.
Remote Sensing
12
(
18
),
3062
.
https://doi.org/10.3390/rs12183062
.
Chen
F.
,
Crow
W. T.
,
Starks
P. J.
&
Moriasi
D. N.
2011
Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture
.
Advances in Water Resources
34
,
526
536
.
https://doi.org/10.1016/j.advwatres.2011.01.011
.
Chen
J.
,
Li
Z.
,
Li
L.
,
Wang
J.
,
Qi
W.
,
Xu
C.
&
Kim
J.
2020
Evaluation of multi-satellite precipitation datasets and their error propagation in hydrological modeling in a Monsoon-Prone Region. Remote Sensing 12 (21), 3550
.
Congalton
R. G.
&
Green
K.
2009
Assessing the Accuracy of Remotely Sensed Data –Principles and Practices
2nd edn..
CRC Press, Taylor & Francis Group
,
Boca Raton, FL
.
Crowley
J. W.
,
Mitrovica
J. X.
,
Bailey
R. C.
,
Tamisiea
M. E.
&
Davis
J. L.
2008
Annual variations in water storage and precipitation in the Amazon Basin
.
Journal of Geodesy
82
,
9
13
.
Dai
L.
,
Che
T.
,
Wang
J.
&
Zhang
P.
2012
Snow depth and snow water equivalent estimation from AMSR-E data based on a priori snow characteristics in Xinjiang, China
.
Remote Sensing of Environment
127
(
December
),
14
29
.
Dubovik
O.
,
Schuster
G. L.
,
Xu
F.
,
Hu
Y.
,
Bösch
H.
,
Landgraf
J.
&
Li
Z.
2021
Grand challenges in satellite remote sensing
.
Frontiers in Remote Sensing
2
,
619818
.
Durand
M.
,
Gleason
C. J.
,
Garambois
P. A.
,
Bjerklie
D.
,
Smith
L. C.
&
Roux
H.
2016
An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope
.
Water Resources Research
52
,
4527
4549
.
https://doi. org/10.1002/2015WR018434 Earth Observation for Water Resources Management. (n.d.). Earth Observation for Water Resources Management. (n.d.)
.
Entekhabi
D.
,
Njoku
E. G.
,
Njoku
E. G.
,
Kellogg
K. H.
,
Kellogg
K. H.
&
Edelstein
W. N.
2010
The Soil Moisture Active Passive (SMAP) mission
.
Proceedings of the IEEE
98
(
5
),
704
716
.
https://doi.org/10.1109/JPROC.2010.2043918
.
ESA
2013
The Operational Copernicus Optical High-Resolution Land Mission
.
Available from: www.esa.int/copernicus.
Fjørtoft
R.
,
Gaudin
J. M.
,
Pourthié
N.
,
Lalaurie
J.
,
Mallet
A.
,
Nouvel
J.
,
Martinot-Lagarde
J.
,
Oriot
H.
,
Borderies
P.
,
Ruiz
C.
&
Daniel
S.
2014
KaRIn on SWOT: Characteristics of Near-Nadir Ka-band interferometric SAR imagery
.
IEEE Transactions on Geoscience and Remote Sensing
52
,
2172
2185
.
doi:10.1109/TGRS.2013.2258402
.
Flechtner
F.
,
Christoph
K. N.
&
Gu
A.
2015
What Can be Expected from the GRACE-FO Laser Ranging Interferometer for Earth Science Applications? 88090. https://doi.org/10.1007/978-3-319-32449-4
.
Frappart
F.
,
Papa
F.
,
Malbeteau
Y.
,
León
J. G.
,
Ramillien
G.
,
Prigent
C.
,
Seoane
P.
,
Seyler
F.
&
Calmant
S.
2015
Surface freshwater storage variations in the Orinoco floodplains using multi-satellite observations
.
Remote Sensing
7
,
89
110
.
https://doi.org/10.3390/rs70100089
.
Funk
C.
,
Peterson
P.
,
Landsfeld
M.
,
Pedreros
D.
,
Verdin
J.
,
Shukla
S.
,
Husak
G.
,
Rowland
J.
,
Harrison
L.
,
Hoell
A.
&
Michaelson
J.
2015
The climate hazards infrared precipitation with stations – A new environmental record for monitoring extremes
.
Scientific Data
2
,
150066
.
https://doi.org/10.1038/sdata.2015.66
.
Gao
Q.
2019
Estimation of Water Resources on Continental Surfaces by Multi-Sensor Microwave Remote Sensing
, pp.
1
3
.
Gao
H.
,
Birkett
C.
&
Lettenmaier
D. P.
2012
Global monitoring of large reservoir storage from satellite remote sensing
.
Water Resources Research
48
,
W09504
.
https://doi.org/10.1029/2012WR012063
.
Goshime
D.
2020
Integration of Satellite and Ground-Based Rainfall Data for Water Resources Assessment in Central Rift Valley Lakes Basin, Ethiopia
.
CY Cergy Paris Université
,
Paris
.
Grassotti
C.
,
Iskenderian
H.
&
Hoffman
R. N.
1999
Calibration and alignment
.
Journal of Applied Meteorology
38
,
677
695
.
Guan
X. B.
,
Shen
H. F.
,
Li
X. H.
,
Gan
W. X.
&
Zhang
L. P.
2019
A long-term and comprehensive assessment of the urbanization-induced impacts on vegetation net primary productivity
.
Science of the Total Environment
669
,
342
352
.
Guillermo
J.
,
Pedreros
G.
&
Ospina-noreña
J. E.
2022
Remote sensing applications: Society and environment spatial distribution model of terrestrial radiation imbalance measured by means of orbiting radiometers
.
Remote Sensing Applications: Society and Environment
28
(
October
),
100857
.
https://doi.org/10.1016/j.rsase.2022.100857
.
Han
S.
,
Kim
H.
,
Yeo
I.
,
Yeh
P.
,
Oki
T.
,
Seo
K.
,
Alsdorf
D.
&
Luthcke
S.
2009
Dynamics of surface water storage in the Amazon inferred from measurements of inter-satellite distance change
.
Geophysical Research Letter
26
,
L09403
.
doi:10.1029/2009GL037910
.
Harris
I.
,
Jones
P. D.
,
Osborn
T. J.
&
Lister
D. H.
2014
Updated high-resolution grids of monthly climatic observations – The CRU TS3.10 dataset
.
International Journal of Climatology
34
(
3
),
623
642
.
Hoffman
R. N.
&
Grassotti
C.
1996
A technique for assimilating SSM/I observations of marine atmospheric storms: Tests with ECMWF analyses
.
Journal of Applied Meteorology and Climatology
35
,
1177
1188
.
Holben
B. N.
,
Tucker
C. J.
&
Fan
C. J.
1980
Spectral assessment of soybean leaf-area and leaf biomass
.
Photogrammetric Engineering and Remote Sensing
46
,
651
656
.
Hong
Y.
,
Chen
S.
,
Xue
X.
,
Hodges
G.
,
2012
Global precipitation estimation and applications
. In:
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
(
Chang
N.
&
Hong
Y.
, eds).
CRC Press
,
Boca Raton, FL
, pp.
371
386
.
Hsu
K.-l.
,
Gao
X.
,
Sorooshian
S.
&
Gupta
H.
1997
Precipitation estimation from remotely sensed information using artificial neural networks
.
Journal of Applied Meteorology
36
(
9
),
1176
1190
.
Huete
A. R.
,
Didan
K.
,
Miura
T.
,
Rodriguez
E. P.
,
Gao
X.
&
Ferreira
L. G.
2002
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
.
Remote Sensing of Environment
83
,
195
213
.
Huffman
G. J.
,
Bolvin
D. T.
,
Nelkin
E. J.
,
Wolff
D. B.
,
Adler
R. F.
,
Gu
G.
,
Hong
Y.
,
Bowman
K. P.
&
Stocker
E. F.
2007
The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales
.
Journal of Hydrometeorology
8
,
38
55
.
Hulley
G.
,
Hook
S.
,
Fisher
J.
&
Lee
C.
2017
ECOSTRESS, a NASA earth-ventures instrument for studying links between the water cycle and plant health over the diurnal cycle
. In:
International Geoscience and Remote Sensing Symposium (IGARSS) 8128248
, pp.
5494
5496
.
Jain
A. K.
1989
Fundamentals of Digital Image Processing
.
Prentice-Hall
,
New Jersey
.
Jia
J.
,
Deng
P.
,
Zhang
M.
,
Guo
H.
,
Xu
C.
&
Bing
J.
2014
Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data
.
Isprs Journal of Photogrammetry and Remote Sensing
93
,
49
55
.
Jiang
D.
,
Wang
J.
,
Huang
Y.
,
Zhou
K.
,
Ding
X.
&
Fu
J.
2014
The review of GRACE data applications in terrestrial hydrology monitoring
.
Advances in Meteorology
2014
.
https://doi.org/10.1155/2014/725131
.
Joyce
R. J.
,
Janowiak
J. E.
,
Arkin
P. A.
&
Xie
P.
2004
CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution
.
Journal of Hydrometeorology
5
(
3
),
487
503
.
https://doi.org/10.1175/ 1525-7541(2004)005 < 0487: CAMTPG > 2.0.CO;2
.
Katiraie-Boroujerdy
P.-S.
,
Rahnamay Naeini
M.
,
Akbari Asanjan
A.
,
Chavoshian
A.
,
Hsu
K.-L.
&
Sorooshian
S.
2020
Bias correction of satellite-based precipitation estimations using quantile mapping approach in different climate regions of Iran
.
Remote Sensing
12
,
2102
.
Kerr
Y. H.
,
Waldteufel
P.
,
Richaume
P.
,
Wigneron
J. P.
,
Ferrazzoli
P.
,
Mahmoodi
A.
,
Bitar
A.
,
Cabot
F.
,
Gruhier
C.
,
Juglea
S. E.
,
Leroux
D.
,
Mialon
A.
&
Delwart
S.
2012
The SMOS soil moisture retrieval algorithm
.
Geoscience and Remote Sensing
50
,
1384
1403
.
Khaki
M.
,
Franssen
H. J. H.
&
Han
S. C.
2020
Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation
.
Scientific Reports
0123456789
,
1
23
.
https://doi.org/10.1038/s41598-020-75710-5
.
Koike
T.
2013
Description of the GCOM-W1 AMSR2 Soil Moisture Algorithm. Technical Report NDX-120015A: Chapter 8. Japan Aerospace Exploration Agency Earth Observation Research Center Bulletin
.
Kubota
T.
,
Ushio
T.
,
Shige
S.
,
Kida
S.
,
Kachi
M.
&
Okamoto
K.
2009
Verification of high-resolution satellite-based rainfall estimates around Japan using a gauge-calibrated ground-radar dataset
.
Journal of the Meteorological Society of Japan
87A
,
203
222
.
Kummerow
C.
,
Hong
Y.
,
Oleson
W. S.
,
Yang
S.
,
Adler
R. F.
,
Mccollum
J.
,
Ferraro
R.
,
Petty
G.
&
Shin
D.
2001
The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors
.
Journal of Applied Meteorology
40
,
1801
1820
.
Kustas
W. P.
,
Humes
K. S.
,
Norman
J. M.
&
Moran
M. S.
1995
Single-and dual-source modeling of surface energy fluxes with radiometric surface temperature
.
Journal of Applied Meteorology
35
,
110
121
.
Lakshmi
V.
2018
Use of satellite remote sensing in hydrological predictions in ungaged basins. In: Department of Geological Sciences, University of South Carolina, Columbia SC 29208
.
Lambin
J.
,
Morrow
R.
,
Fu
L.-L.
,
Willis
J. K.
,
Bonekamp
H.
&
Lillibridge
J.
2010
The OSTM/Jason-2 mission
.
Marine Geodesy
,
33
(supp1)
,
4
25
.
https://doi.org/10.1080/01490419.2010.491030
.
Landsat 8 Data Users Handbook
2020
.
Lettenmaier
D. P.
,
Alsdorf
D.
,
Dozier
J.
,
Huffman
G. J.
,
Pan
M.
&
Wood
E. F.
2015
Inroads of remote sensing into hydrologic science during the WRR era
.
Water Resources Research
51
,
7309
7342
.
https://doi.org/10.1002/2015WR017616
.
Light
D. L.
1990
Characteristics of remote sensors for mapping and earth science applications
.
United States Geological Survey
56
(
12
),
1613
1623
.
Lillesand
T. M.
&
Kiefer
R. W.
1994
Remote Sensing and Image Interpretation
.
John Wiley and Sons Inc.
,
New York
.
Liu
X.
,
Yong
Z.
,
Liu
L.
,
Chen
T.
,
Li
J.
&
Zhou
L.
2023
Improving hydrological simulation accuracy through a three-step bias correction method for satellite precipitation
.
MDPI: Water
15
,
3615
.
Lu
D.
,
Mausel
P.
,
Brondízio
E.
&
Moran
E. F.
2002
Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon Basin LBA research
.
International Journal of Remote Sensing
3
,
487
503
.
Ma
J. U. N.
&
Sun
W.
2018
Hydrological analysis using satellite remote sensing big data and CREST model
.
IEEE Access
6
,
9006
9016
.
https://doi.org/10.1109/ACCESS.2018.2810252
.
Martens
B.
,
Miralles
D. G.
,
Lievens
H.
,
van der Schalie
R.
,
de Jeu
R. A. M.
,
Fernández-Prieto
D.
,
Beck
H. E.
,
Dorigo
W. A.
&
Verhoest
N. E.
2017
GLEAM v3: Satellite based land evaporation and root-zone soil moisture
.
Geoscientific Model Development
10
,
1903
1925
.
https://doi.org/10.5194/gmd-
.
McCabe
M. F.
,
Rodell
M.
,
Alsdorf
D. E.
,
Miralles
D. G.
,
Uijlenhoet
R.
,
Wagner
W.
,
Lucieer
A.
,
Houborg
R.
,
Verhoest
N. E.
C.,
Franz
T. E.
,
Shi
J.
,
Gao
H.
&
Wood
E. F.
2017
The future of earth observation in hydrology
.
Hydrology and Earth System Sciences Discussions
21
,
3879
3914
.
Mosaffa
H.
,
Filippucci
P.
,
Massari
C.
,
Ciabatta
L.
&
Brocca
L.
2023
SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies
.
Nature Scientific Data
1
14
.
https://doi.org/10.1038/s41597-023-02654-6
.
Mu
Q.
,
Zhao
M.
&
Running
S. W.
2011
Improvements to a MODIS global terrestrial evapotranspiration algorithm
.
Remote Sensing of Environment
115
,
1781
1800
.
https://doi.org/10.1016/j.rse.2011.02.019
.
Muskett
R. R.
&
Romanovsky
V. E.
2009
Groundwater storage change in Arctic permafrost watersheds from GRACE and in situ measurements
.
Environmental Research Letter
4
.
doi:10.1088/1748-9326/4/4/045009
.
NASA
2001
National Snow and Ice Data Center Distributed Active Archive Center
.
https://doi.org/10.5067/AMSR-E/AE_LAND3.002
.
Naz
B. S.
,
Kollet
S.
,
Franssen
H.-J. H.
,
Montzka
C.
&
Kurtz
W.
2020
A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015
.
Nture: Scientific Data
1
14
.
https://doi.org/10.1038/s41597-020-0450-6
.
Niu
G. Y.
&
Yang
Z. L.
2006
Assessing a land surface model's improvement with GRACE estimates
.
Geophysical Research Letters
33
,
L07401
.
doi:10.1029/2005GL025555
.
Njoku
E. G.
2004
AMSR-E/Aqua Daily L3 surface soil moisture, interpretive parameters, & QC EASE-Grids, Version 2. Boulder, Colorado, USA
.
Owe
M.
,
de Jeu
R.
&
Holmes
T.
2008
Multisensor historical climatology of satellite-derived global land surface moisture
.
Journal of Geophysical Research
113
,
F01002
.
https://doi.org/10.1029/2007JF000769
.
Paloscia
S.
,
Pettinato
S.
,
Santi
E.
,
Notarnicola
C.
,
Pasolli
L.
&
Reppucci
A.
2013
Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation
.
Remote Sensing of Environment
134
,
234
248
.
Pastor-Guzman
J.
,
Dash
J.
&
Atkinson
P. M.
2018
Remote sensing of mangrove forest phenology and its environmental drivers
.
Remote Sensing of Environment
205
,
71
84
.
Peterson
T. C.
&
Vose
R. S.
1997
An overview of the global historical climatology network temperature database
.
Bulletin of the American Meteorological Society
78
(
12
),
2837
2849
.
Pinzon
J. E.
&
Tucker
C. J.
2014
A non-stationary 1981-2012 AVHRR NDVI3g time series
.
Remote Sensing
6
,
6929
6960
.
https://doi.org/ 10.3390/rs6086929
.
Quichimbo
E. A.
2021
Modelling Water Partitioning in Dryland Regions: A Multiscale Analysis
.
Cardiff University, Cardiff
.
Radočaj
D.
,
Obhodaš
J.
,
Jurišic
M.
&
Gašparovic
M.
2020
Global open data remote sensing satellite missions for land monitoring and conservation: A review
.
Land
9
,
402
.
doi:10.3390/land9110402
.
Reichle
R.
,
De Lannoy
G.
,
Koster
R.
,
Crow
W.
&
Kimball
J.
2016
SMAP L4 9km EASE grid surface and root zone soil moisture geophysical data. Version 2. [Indicate subset used]. Boulder, Colorado, USA: NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/YK70EPDHNF0L
.
Resti
A.
,
Benveniste
J.
,
Roca
M.
,
Levrini
G.
&
Johannessen
J.
1999
The Envisat radar altimeter system (RA-2)
.
ESA Bulletin
98
,
94
101
.
Rodell
M.
,
Velicogna
I.
&
Famiglietti
J. S.
2009
Satellite-based estimates of groundwater depletion in India
.
Nature
460
,
999
1002
.
https:// doi.org/10.1038/nature08238
.
Rudolf
B.
&
Schneider
U.
2005
Calculation of gridded precipitation data for the global land–surface using in-situ gauge observations
. In:
Proceedings of the 2nd Workshop of the International Precipitation Working Group IPWG, Monterey October 2004
, pp.
231
247
.
EUMETSAT92-9110-070-6 ISSN 1727-432X
.
Russ
J. C.
1995
The Image Processing Handbook
, 2nd edn.
CRC Press
,
Baca Raton, FL
.
Schamm
K.
,
Ziese
M.
,
Becker
A.
,
Finger
P.
,
Meyer-Christoffer
A.
,
Schneider
U.
&
Stender
P.
2014
Global gridded precipitation over land: A description of the new GPCC first guess daily product
.
Earth System Science Data
6
(
1
),
49
60
.
Schwatke
C.
,
Scherer
D.
&
Dettmering
D.
2019
Automated extraction of consistent time-variable water surfaces of lakes and reservoirs based on Landsat and Sentinel-2
.
Remote Sensing
11
(
9
),
1010
.
doi:10.3390/rs11091010, 2019
.
Sellars
S.
,
Nguyen
P.
,
Chu
W.
,
Gao
X.
,
Hsu
K.
&
Sorooshian
S.
2013
Computational earth science: Big data transformed into insight
.
EOS Transactions American Geophysical
94
(
32
),
277
278
.
Senay
G. B.
,
Bohms
S.
,
Singh
R. K.
,
Gowda
P. H.
,
Velpuri
N. M.
,
Alemu
H.
&
Verdin
J. P.
2013
Operational evapotranspiration mapping using remote sensing and weather datasets: A new parameterization for the SSEB approach
.
Journal of the American Water Resources Association
49
,
577
591
.
https://doi.org/10.1111/jawr.12057
.
Sentinel-2 User Handbook
2020
.
Shi
J.
,
Jian
L.
,
Zhang
L.
,
Chen
K. S.
,
Wigneron
J. P.
,
Chanzy
A.
&
Jackson
T. J.
2006
Physically based estimation of bare-surface soil moisture with the passive radiometers
.
IEEE Transactions on Geoscience and Remote Sensing
44
,
3145
3152
.
https://doi.org/10.1109/TGRS.2006.876706 (2006)
.
Silleos
N. G.
,
Alexandridis
T. K.
,
Gitas
I. Z.
&
Perakis
K.
2006
Advances Made in Biomass Estimation and Vegetation Monitoring in the Last 30 Years
.
Singh
R. P.
2018
Satellite Based Hydrology and Modeling. Land Hydrology Division Geosciences, Hydrology, Cryosphere Sciences and Applications Group (EPSA) Space Applications Centre, ISRO Ahmedabad
.
Soo
E. Z. X.
,
Jaafar
W. Z. W.
,
Lai
S. H.
,
Othman
F.
,
Elshafie
A.
,
Islam
T.
,
Srivastava
P.
&
Hadi
H. S. O.
2020
Evaluation of bias-adjusted satellite precipitation estimations for extreme flood events in Langat River basin, Malaysia
.
Hydrology Research
51
,
105
126
.
Sorooshian
S.
,
Hsu
K. L.
,
Gao
X.
,
Gupta
H. V.
,
Imam
B.
&
Braithwaite
D.
2000
Evaluation of PERSIANN System Satellite-Based Estimates of Tropical Rainfall
.
The University of Arizona, Tucson
.
Stisen
S.
&
Sandholt
I.
2010
Evaluation of remote-sensing-based rainfall products through predictive capability in hydrological runoff modelling
.
Hydrology Research
24
,
879
891
.
Swenson
S. C.
&
Milly
P. C. D.
2006
Climate model biases in seasonality of continental water storage revealed by satellite gravimetry
.
Water Resources Research
42
,
W03201
.
doi:10.1029/2005WR004628
.
Syed
T. H.
,
Famiglietti
J. S.
,
Zlotnicki
V.
&
Rodell
M.
2007
Contemporary estimates of Pan-Arctic freshwater discharge from GRACE and reanalysis
.
Geophysical Research Letters
34
(
19
),
1
6
.
https://doi.org/10.1029/2007GL031254
.
Tang
Q.
,
Zhang
X.
,
Duan
Q.
,
Huang
S.
,
Yuan
X.
,
Cui
H.
,
Li
Z.
&
Liu
X.
2016
Hydrological monitoring and seasonal forecasting: Progress and perspectives
.
Journal of Geographical Sciences
26
(
7
),
904
920
.
Tapley
B. D.
,
Bettadpur
S.
,
Watkins
M.
&
Reigber
C.
2004
The gravity recovery and climate experiment: Mission overview and early results
.
Geophysical Research Letters
31
,
L09607
.
https://doi.org/10.1029/2004GL019920
.
Tian
Y.
&
Peters-Lidard
C. D.
2010
A global map of uncertainties in satellite-based precipitation measurements
.
Geophysical Research Letters
37
,
L24407
.
Tong
X. W.
,
Wang
K.
,
Yue
Y.
,
Brandt
M.
,
Liu
B.
,
Zhang
C.
,
Liao
C.
&
Fensholt
R.
2017
Quantifying the effectiveness of ecological restoration projects on long-term vegetation dynamics in the karst regions of Southwest China
.
International Journal of Applied Earth Observation and Geoinformation
54
,
105
113
.
Tucker
C. J.
&
Sellers
P. J.
1986
Satellite remote-sensing of primary production
.
International Journal of Remote Sensing
7
,
1395
1416
.
Valdés-Pineda
R.
,
Demaría
E.
,
Valdés
J. B.
,
Wi
S.
&
Serrat-Capdevilla
A.
2016
Bias correction of daily satellite-based rainfall estimates for hydrologic forecasting in the Upper Zambezi, Africa
.
Hydrology and Earth System Sciences Discussions
10
,
1
28
.
Van Dijk
A. I. J. M.
&
Renzullo
L. J.
2011
Water resource monitoring systems and the role of satellite observations
.
Hydrology and Earth System Sciences
15
(
1
),
39
55
.
https://doi.org/10.5194/hess-15-39-2011
.
Vargas
M.
,
Miura
T.
,
Shabanov
N.
&
Kato
A.
2013
An initial assessment of Suomi NPP VIIRS vegetation index EDR
.
Journal of Geophysical Research: Atmospheres
118
,
1
16
.
https://doi.org/10.1002/2013JD020439
.
Verdin
A.
,
Funk
C.
,
Peterson
P.
,
Landsfeld
M.
,
Tuholske
C.
&
Grace
K.
2020
Development and validation of the CHIRTS-daily quasi-global high-resolution daily temperature data set
.
Scientific Data
7
(
1
),
1
14
.
https://doi.org/10.1038/s41597-020-00643-7
.
Vinokullo
R. V.
,
Meynadier
R.
,
Sheffield
J.
&
Wood
E. F.
2011
Multi-model, multi-sensor estimates of global evapotranspiration: Climatology, uncertainties and trends
.
Hydrological Procedure
.
https://doi.org/10.1002/hyp.8393
.
Wagner
W.
,
Lemoine
G.
&
Rott
H.
1999
A method for estimating soil moisture from ERS scatterometer and soil data
.
Remote Sensing of Environment
70
,
191
207
.
https://doi.org/10.1016/S0034-4257(99)00036-X
.
Willmott
C. J.
&
Matsuura
K.
1995
Smart interpolation of annually averaged air temperature in the United States
.
Journal of Applied Meteorology
34
,
2577
2586
.
Xie
P.
,
Chen
M.
&
Shi
W.
2010
CPC global unified gauge-based analysis of daily precipitation, Preprints
. In:
24th Conferences on Hydrology
,
Atlanta, GA
.
American Meteorological Society
, p.
2
.
Yeh
P. J. F.
,
Swenson
S. C.
,
Famiglietti
J. S.
&
Rodell
M.
2006
Remote sensing of groundwater storage changes in Illinois using the Gravity Recovery and Climate Experiment (GRACE)
.
Water Resources Research
42
,
W12203
.
doi:10.1029/2006WR005374
.
Zeng
L. L.
,
Wardlow
B. D.
,
Xiang
D. X.
,
Hu
S.
&
Li
D. R.
2020
A review of vegetation phenological metrics extraction using time-series, multispectral satellite data
.
Remote Sensing of Environment
237
,
20
.
Zhang
Y. L.
,
Song
C. H.
,
Band
L. E.
,
Sun
G.
&
Li
J. X.
2017
Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening?
Remote Sensing of Environment
191
,
145
155
.
Zhao
J.
,
Huang
S.
,
Huang
Q.
,
Wang
H.
,
Leng
G.
&
Fang
W.
2020
Time-lagged response of vegetation dynamics to climatic and teleconnection factors
.
Catena
189
(
12
),
6911
.
Zhou
L.
,
Koike
T.
,
Takeuchi
K.
,
Rasmy
M.
,
Onuma
K.
,
Ito
H.
,
Selvarajah
H.
,
Liu
L.
,
Li
X.
&
Ao
T.
2022
A study on availability of ground observations and its impacts on bias correction of satellite precipitation products and hydrologic simulation efficiency
.
Journal of Hydrology
610
,
127595
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).