Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures
Inland surface waters in tropical environments play a major role in the water and carbon cycle. Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. However, som...
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| Veröffentlicht in: | Water (Basel) 2017-05, Vol.9 (5), p.350-26 |
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| description | Inland surface waters in tropical environments play a major role in the water and carbon cycle. Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. However, some of these estimates are unable to detect water under dense vegetation and/or in the presence of cloud coverage. To overcome these limitations, the brightness temperature data at L-band frequency from the Soil Moisture and Ocean Salinity (SMOS) mission are used here to estimate flood extent in a contextual radiative transfer model over the Amazon Basin. At this frequency, the signal is highly sensitive to the standing water above the ground, and the signal provides information from deeper vegetation density than higher-frequencies. Three-day and (25 km × 25 km) resolution maps of water fraction extent are produced from 2010 to 2015. The dynamic water surface extent estimates are compared to altimeter data (Jason-2), land cover classification maps (IGBP, GlobeCover and ESA CCI) and the dynamic water surface product (GIEMS). The relationships between the water surfaces, precipitation and in situ discharge data are examined. The results show a high correlation between water fraction estimated by SMOS and water levels from Jason-2 (R > 0.98). Good spatial agreements for the land cover classifications and the water cycle are obtained. |
| doi_str_mv | 10.3390/w9050350 |
| format | Article |
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Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. However, some of these estimates are unable to detect water under dense vegetation and/or in the presence of cloud coverage. To overcome these limitations, the brightness temperature data at L-band frequency from the Soil Moisture and Ocean Salinity (SMOS) mission are used here to estimate flood extent in a contextual radiative transfer model over the Amazon Basin. At this frequency, the signal is highly sensitive to the standing water above the ground, and the signal provides information from deeper vegetation density than higher-frequencies. Three-day and (25 km × 25 km) resolution maps of water fraction extent are produced from 2010 to 2015. The dynamic water surface extent estimates are compared to altimeter data (Jason-2), land cover classification maps (IGBP, GlobeCover and ESA CCI) and the dynamic water surface product (GIEMS). The relationships between the water surfaces, precipitation and in situ discharge data are examined. The results show a high correlation between water fraction estimated by SMOS and water levels from Jason-2 (R > 0.98). 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Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. However, some of these estimates are unable to detect water under dense vegetation and/or in the presence of cloud coverage. To overcome these limitations, the brightness temperature data at L-band frequency from the Soil Moisture and Ocean Salinity (SMOS) mission are used here to estimate flood extent in a contextual radiative transfer model over the Amazon Basin. At this frequency, the signal is highly sensitive to the standing water above the ground, and the signal provides information from deeper vegetation density than higher-frequencies. Three-day and (25 km × 25 km) resolution maps of water fraction extent are produced from 2010 to 2015. The dynamic water surface extent estimates are compared to altimeter data (Jason-2), land cover classification maps (IGBP, GlobeCover and ESA CCI) and the dynamic water surface product (GIEMS). The relationships between the water surfaces, precipitation and in situ discharge data are examined. The results show a high correlation between water fraction estimated by SMOS and water levels from Jason-2 (R > 0.98). 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Al Bitar, Ahmad ; Frappart, Frédéric ; Papa, Fabrice ; Calmant, Stephane ; Crétaux, Jean-François ; Wigneron, Jean-Pierre ; Kerr, Yann</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c362t-c7dda2c8124ce0d5bcfe1cde76e4628c00fed31326d8eaa017d19a0737b192543</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Altimeters</topic><topic>Aquatic resources</topic><topic>Brightness</topic><topic>Brightness temperature</topic><topic>Carbon cycle (Biogeochemistry)</topic><topic>Classification</topic><topic>Correlation</topic><topic>Estimates</topic><topic>Forests</topic><topic>France</topic><topic>Hydrologic cycle</topic><topic>Hydrologic data</topic><topic>Information dissemination</topic><topic>Land use</topic><topic>Life Sciences</topic><topic>Mapping</topic><topic>Moisture</topic><topic>Precipitation</topic><topic>Radiative transfer</topic><topic>Rain</topic><topic>Rain forests</topic><topic>Rainfall</topic><topic>Rainforests</topic><topic>Remote sensing</topic><topic>River basins</topic><topic>Salinity</topic><topic>Salinity effects</topic><topic>Soil moisture</topic><topic>Soil temperature</topic><topic>Surface water</topic><topic>Temperature effects</topic><topic>Tropical environments</topic><topic>Vegetation</topic><topic>Water levels</topic><topic>Water scarcity</topic><topic>Wavelengths</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Parrens, Marie</creatorcontrib><creatorcontrib>Al Bitar, Ahmad</creatorcontrib><creatorcontrib>Frappart, Frédéric</creatorcontrib><creatorcontrib>Papa, Fabrice</creatorcontrib><creatorcontrib>Calmant, Stephane</creatorcontrib><creatorcontrib>Crétaux, Jean-François</creatorcontrib><creatorcontrib>Wigneron, Jean-Pierre</creatorcontrib><creatorcontrib>Kerr, Yann</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Water (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Parrens, Marie</au><au>Al Bitar, Ahmad</au><au>Frappart, Frédéric</au><au>Papa, Fabrice</au><au>Calmant, Stephane</au><au>Crétaux, Jean-François</au><au>Wigneron, Jean-Pierre</au><au>Kerr, Yann</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures</atitle><jtitle>Water (Basel)</jtitle><date>2017-05-17</date><risdate>2017</risdate><volume>9</volume><issue>5</issue><spage>350</spage><epage>26</epage><pages>350-26</pages><issn>2073-4441</issn><eissn>2073-4441</eissn><abstract>Inland surface waters in tropical environments play a major role in the water and carbon cycle. 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The dynamic water surface extent estimates are compared to altimeter data (Jason-2), land cover classification maps (IGBP, GlobeCover and ESA CCI) and the dynamic water surface product (GIEMS). The relationships between the water surfaces, precipitation and in situ discharge data are examined. The results show a high correlation between water fraction estimated by SMOS and water levels from Jason-2 (R > 0.98). Good spatial agreements for the land cover classifications and the water cycle are obtained.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/w9050350</doi><tpages>26</tpages><orcidid>https://orcid.org/0000-0002-1756-1096</orcidid><orcidid>https://orcid.org/0000-0001-6305-6253</orcidid><orcidid>https://orcid.org/0000-0001-6352-1717</orcidid><orcidid>https://orcid.org/0000-0002-4661-8274</orcidid><orcidid>https://orcid.org/0000-0001-6439-4174</orcidid><orcidid>https://orcid.org/0000-0001-5345-3618</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Altimeters Aquatic resources Brightness Brightness temperature Carbon cycle (Biogeochemistry) Classification Correlation Estimates Forests France Hydrologic cycle Hydrologic data Information dissemination Land use Life Sciences Mapping Moisture Precipitation Radiative transfer Rain Rain forests Rainfall Rainforests Remote sensing River basins Salinity Salinity effects Soil moisture Soil temperature Surface water Temperature effects Tropical environments Vegetation Water levels Water scarcity Wavelengths |
| title | Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures |
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