Generating a monthly variability of sea surface salinity based on source tracing of salt concentration and the estimated SEBAL-evaporation
The variation and spatial distribution of sea surface salinity (SSS) depend on the geographic condition of the water surfaces and the temporal variation of atmospheric conditions. The SSS might differ in a local coastal area compared to similar situations in global and regional oceans. The SSS value...
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| creator | Ghazali, M F Saepuloh, A Wikantika, K |
| description | The variation and spatial distribution of sea surface salinity (SSS) depend on the geographic condition of the water surfaces and the temporal variation of atmospheric conditions. The SSS might differ in a local coastal area compared to similar situations in global and regional oceans. The SSS values have been estimated based on spatial regression of extracted water-salt concentration as a source tracing of salt against corrected Landsat 8 satellite data during the drought season of April 2023. Here, the electrical conductivity (EC) from the Cimanuk River can be used as primary data. This result, paired with the evaporation-derived surface energy balance algorithm for land (SEBAL) algorithm, explains a monthly SSS variability after the validation using pre-defined resampled regional SSS and evaporation data. The result shows variations in estimated SSS values along with fluctuated SEBAL evaporation ranging from 1.64 to 1.62 dS/m and 1.04 to 0.41 W/m
2
, respectively. It describes monthly variability and their relationship in a local coastal area limited to the condition of a drought season. However, the validation shows that the root means square error (RMSE) of 1.00 from the SSS map, produced by the regression model involving band 7 of Landsat 8 and 9, has satisfied the reasonable SSS value ranges besides the best accuracy. |
| doi_str_mv | 10.1088/1755-1315/1350/1/012039 |
| format | Article |
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2
, respectively. It describes monthly variability and their relationship in a local coastal area limited to the condition of a drought season. However, the validation shows that the root means square error (RMSE) of 1.00 from the SSS map, produced by the regression model involving band 7 of Landsat 8 and 9, has satisfied the reasonable SSS value ranges besides the best accuracy.</description><identifier>ISSN: 1755-1307</identifier><identifier>EISSN: 1755-1315</identifier><identifier>DOI: 10.1088/1755-1315/1350/1/012039</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Algorithms ; Atmospheric conditions ; Atmospheric correction ; Coastal waters ; Coastal zone ; Coasts ; Drought ; Electrical conductivity ; Electrical resistivity ; Energy balance ; Evaporation ; Geographical distribution ; Landsat ; Oceans ; Regression models ; Remote sensing ; Root-mean-square errors ; Salinity ; Salinity effects ; Salts ; Spatial data ; Spatial distribution ; Surface energy ; Surface properties ; Temporal variations ; Tracing ; Variability</subject><ispartof>IOP conference series. Earth and environmental science, 2024-06, Vol.1350 (1), p.12039</ispartof><rights>Published under licence by IOP Publishing Ltd</rights><rights>Published under licence by IOP Publishing Ltd. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2039-c5c609cb0adf4b07a5183ae27d842246f4780f601c66aed350db780159976efe3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://iopscience.iop.org/article/10.1088/1755-1315/1350/1/012039/pdf$$EPDF$$P50$$Giop$$Hfree_for_read</linktopdf><link.rule.ids>314,776,780,27903,27904,38847,38869,53819,53846</link.rule.ids></links><search><creatorcontrib>Ghazali, M F</creatorcontrib><creatorcontrib>Saepuloh, A</creatorcontrib><creatorcontrib>Wikantika, K</creatorcontrib><title>Generating a monthly variability of sea surface salinity based on source tracing of salt concentration and the estimated SEBAL-evaporation</title><title>IOP conference series. Earth and environmental science</title><addtitle>IOP Conf. Ser.: Earth Environ. Sci</addtitle><description>The variation and spatial distribution of sea surface salinity (SSS) depend on the geographic condition of the water surfaces and the temporal variation of atmospheric conditions. The SSS might differ in a local coastal area compared to similar situations in global and regional oceans. The SSS values have been estimated based on spatial regression of extracted water-salt concentration as a source tracing of salt against corrected Landsat 8 satellite data during the drought season of April 2023. Here, the electrical conductivity (EC) from the Cimanuk River can be used as primary data. This result, paired with the evaporation-derived surface energy balance algorithm for land (SEBAL) algorithm, explains a monthly SSS variability after the validation using pre-defined resampled regional SSS and evaporation data. The result shows variations in estimated SSS values along with fluctuated SEBAL evaporation ranging from 1.64 to 1.62 dS/m and 1.04 to 0.41 W/m
2
, respectively. It describes monthly variability and their relationship in a local coastal area limited to the condition of a drought season. However, the validation shows that the root means square error (RMSE) of 1.00 from the SSS map, produced by the regression model involving band 7 of Landsat 8 and 9, has satisfied the reasonable SSS value ranges besides the best accuracy.</description><subject>Algorithms</subject><subject>Atmospheric conditions</subject><subject>Atmospheric correction</subject><subject>Coastal waters</subject><subject>Coastal zone</subject><subject>Coasts</subject><subject>Drought</subject><subject>Electrical conductivity</subject><subject>Electrical resistivity</subject><subject>Energy balance</subject><subject>Evaporation</subject><subject>Geographical distribution</subject><subject>Landsat</subject><subject>Oceans</subject><subject>Regression models</subject><subject>Remote sensing</subject><subject>Root-mean-square errors</subject><subject>Salinity</subject><subject>Salinity effects</subject><subject>Salts</subject><subject>Spatial data</subject><subject>Spatial distribution</subject><subject>Surface energy</subject><subject>Surface properties</subject><subject>Temporal variations</subject><subject>Tracing</subject><subject>Variability</subject><issn>1755-1307</issn><issn>1755-1315</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFkNFKwzAUhosoOKfPYMArL-aSpm3aSx1zCgMvptfhNE1cR5fUJBvsFXxqEyoTQfAq4Zzv_885f5JcE3xHcFlOCcvzCaEknxKa4ymZYpJiWp0ko2Pn9PjH7Dy5cG6DccEyWo2Sz4XU0oJv9TsCtDXar7sD2oNtoW671h-QUchJQG5nFQiJHHStjvUanGyQ0ciZnQ0Nb0FEl8hD55EwWkjto3eAQDfIryWSzrdb8EG5mj_cLydyD70ZmMvkTEHn5NX3O07eHuevs6fJ8mXxPAusiHdNRC4KXIkaQ6OyGjPISUlBpqwpszTNCpWxEqsCE1EUIJuQSVOHCsmrihVSSTpObgbf3pqPXViIb8IFOozkFJeEYVakZaDYQAlrnLNS8d6Gze2BE8xj8DxGymO8PAbPCR-CD0o6KFvT_1j_r7r9QzWfr35zvG8U_QIvPJPm</recordid><startdate>20240601</startdate><enddate>20240601</enddate><creator>Ghazali, M F</creator><creator>Saepuloh, A</creator><creator>Wikantika, K</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PYCSY</scope></search><sort><creationdate>20240601</creationdate><title>Generating a monthly variability of sea surface salinity based on source tracing of salt concentration and the estimated SEBAL-evaporation</title><author>Ghazali, M F ; Saepuloh, A ; Wikantika, K</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2039-c5c609cb0adf4b07a5183ae27d842246f4780f601c66aed350db780159976efe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Atmospheric conditions</topic><topic>Atmospheric correction</topic><topic>Coastal waters</topic><topic>Coastal zone</topic><topic>Coasts</topic><topic>Drought</topic><topic>Electrical conductivity</topic><topic>Electrical resistivity</topic><topic>Energy balance</topic><topic>Evaporation</topic><topic>Geographical distribution</topic><topic>Landsat</topic><topic>Oceans</topic><topic>Regression models</topic><topic>Remote sensing</topic><topic>Root-mean-square errors</topic><topic>Salinity</topic><topic>Salinity effects</topic><topic>Salts</topic><topic>Spatial data</topic><topic>Spatial distribution</topic><topic>Surface energy</topic><topic>Surface properties</topic><topic>Temporal variations</topic><topic>Tracing</topic><topic>Variability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ghazali, M F</creatorcontrib><creatorcontrib>Saepuloh, A</creatorcontrib><creatorcontrib>Wikantika, K</creatorcontrib><collection>IOP Publishing Free Content</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Environmental Science Database</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>Environmental Science Collection</collection><jtitle>IOP conference series. Earth and environmental science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ghazali, M F</au><au>Saepuloh, A</au><au>Wikantika, K</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Generating a monthly variability of sea surface salinity based on source tracing of salt concentration and the estimated SEBAL-evaporation</atitle><jtitle>IOP conference series. Earth and environmental science</jtitle><addtitle>IOP Conf. Ser.: Earth Environ. Sci</addtitle><date>2024-06-01</date><risdate>2024</risdate><volume>1350</volume><issue>1</issue><spage>12039</spage><pages>12039-</pages><issn>1755-1307</issn><eissn>1755-1315</eissn><abstract>The variation and spatial distribution of sea surface salinity (SSS) depend on the geographic condition of the water surfaces and the temporal variation of atmospheric conditions. The SSS might differ in a local coastal area compared to similar situations in global and regional oceans. The SSS values have been estimated based on spatial regression of extracted water-salt concentration as a source tracing of salt against corrected Landsat 8 satellite data during the drought season of April 2023. Here, the electrical conductivity (EC) from the Cimanuk River can be used as primary data. This result, paired with the evaporation-derived surface energy balance algorithm for land (SEBAL) algorithm, explains a monthly SSS variability after the validation using pre-defined resampled regional SSS and evaporation data. The result shows variations in estimated SSS values along with fluctuated SEBAL evaporation ranging from 1.64 to 1.62 dS/m and 1.04 to 0.41 W/m
2
, respectively. It describes monthly variability and their relationship in a local coastal area limited to the condition of a drought season. However, the validation shows that the root means square error (RMSE) of 1.00 from the SSS map, produced by the regression model involving band 7 of Landsat 8 and 9, has satisfied the reasonable SSS value ranges besides the best accuracy.</abstract><cop>Bristol</cop><pub>IOP Publishing</pub><doi>10.1088/1755-1315/1350/1/012039</doi><tpages>16</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Atmospheric conditions Atmospheric correction Coastal waters Coastal zone Coasts Drought Electrical conductivity Electrical resistivity Energy balance Evaporation Geographical distribution Landsat Oceans Regression models Remote sensing Root-mean-square errors Salinity Salinity effects Salts Spatial data Spatial distribution Surface energy Surface properties Temporal variations Tracing Variability |
| title | Generating a monthly variability of sea surface salinity based on source tracing of salt concentration and the estimated SEBAL-evaporation |
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