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Estimation of Snow Water Equivalence Using the Polarimetric Scanning Radiometer From the Cold Land Processes Experiments (CLPX03)
In this letter, we investigated an inversion technique to estimate snow water equivalence (SWE) under Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sensor configurations. Through our numerical simulations by the advanced integral equation model (AIEM), we found that the...
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Published in: | IEEE geoscience and remote sensing letters 2011-03, Vol.8 (2), p.359-363 |
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description | In this letter, we investigated an inversion technique to estimate snow water equivalence (SWE) under Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sensor configurations. Through our numerical simulations by the advanced integral equation model (AIEM), we found that the ground surface emission signals at 18.7 and 36.5 GHz were highly correlated regardless of the ground surface properties (dielectric and roughness properties) and can be well described by a linear function. It leads to a new development for describing the relationship between snow emission signals observed at 18.7 and 36.5 GHz as a linear function. The intercept (A) and slope (B) of this linear equation depend only on snow properties and can be estimated from the observations directly. This development provides a new technique that separates the snowpack and ground surface emission signals. With the parameterized snow emission model from a simulated database that was derived using a multiscattering microwave emission model (dense medium radiative transfer model-AIEM-matrix doubling) over dry snow covers, we developed an algorithm to estimate the SWE using the microwave radiometer measurements. Evaluations on this technique using both the model simulated data and the field experimental data with the airborne Polarimetric Scanning Radiometer data from National Aeronautics and Space Administration Cold Land Processes Experiment 2003 showed promising results, with root-mean-square errors of 32.8 and 31.85 mm, respectively. This newly developed inversion method has the advantages over the AMSR-E SWE baseline algorithm when applied to high-resolution airborne observations. |
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Through our numerical simulations by the advanced integral equation model (AIEM), we found that the ground surface emission signals at 18.7 and 36.5 GHz were highly correlated regardless of the ground surface properties (dielectric and roughness properties) and can be well described by a linear function. It leads to a new development for describing the relationship between snow emission signals observed at 18.7 and 36.5 GHz as a linear function. The intercept (A) and slope (B) of this linear equation depend only on snow properties and can be estimated from the observations directly. This development provides a new technique that separates the snowpack and ground surface emission signals. With the parameterized snow emission model from a simulated database that was derived using a multiscattering microwave emission model (dense medium radiative transfer model-AIEM-matrix doubling) over dry snow covers, we developed an algorithm to estimate the SWE using the microwave radiometer measurements. Evaluations on this technique using both the model simulated data and the field experimental data with the airborne Polarimetric Scanning Radiometer data from National Aeronautics and Space Administration Cold Land Processes Experiment 2003 showed promising results, with root-mean-square errors of 32.8 and 31.85 mm, respectively. This newly developed inversion method has the advantages over the AMSR-E SWE baseline algorithm when applied to high-resolution airborne observations.</description><identifier>ISSN: 1545-598X</identifier><identifier>EISSN: 1558-0571</identifier><identifier>DOI: 10.1109/LGRS.2010.2076345</identifier><identifier>CODEN: IGRSBY</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Cold Land Processes Experiment 2003 (CLPX03) ; Computer simulation ; Emission ; Grounds ; inversion technique ; Mathematical model ; Mathematical models ; Microwave measurements ; Microwave radiometry ; Microwave theory and techniques ; parameterized model ; passive microwave ; Radiometers ; Remote sensing ; Scanning ; Snow ; snow water equivalence (SWE)</subject><ispartof>IEEE geoscience and remote sensing letters, 2011-03, Vol.8 (2), p.359-363</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Mar 2011</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c405t-357bb3d307b81e69076ad092748d1dafc005a615a33be2f1d04a4dc39a5979063</citedby><cites>FETCH-LOGICAL-c405t-357bb3d307b81e69076ad092748d1dafc005a615a33be2f1d04a4dc39a5979063</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5604644$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,54796</link.rule.ids></links><search><creatorcontrib>Lingmei Jiang</creatorcontrib><creatorcontrib>Jiancheng Shi</creatorcontrib><creatorcontrib>Tjuatja, S</creatorcontrib><creatorcontrib>Kun Shan Chen</creatorcontrib><creatorcontrib>Jinyang Du</creatorcontrib><creatorcontrib>Lixin Zhang</creatorcontrib><title>Estimation of Snow Water Equivalence Using the Polarimetric Scanning Radiometer From the Cold Land Processes Experiments (CLPX03)</title><title>IEEE geoscience and remote sensing letters</title><addtitle>LGRS</addtitle><description>In this letter, we investigated an inversion technique to estimate snow water equivalence (SWE) under Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sensor configurations. Through our numerical simulations by the advanced integral equation model (AIEM), we found that the ground surface emission signals at 18.7 and 36.5 GHz were highly correlated regardless of the ground surface properties (dielectric and roughness properties) and can be well described by a linear function. It leads to a new development for describing the relationship between snow emission signals observed at 18.7 and 36.5 GHz as a linear function. The intercept (A) and slope (B) of this linear equation depend only on snow properties and can be estimated from the observations directly. This development provides a new technique that separates the snowpack and ground surface emission signals. With the parameterized snow emission model from a simulated database that was derived using a multiscattering microwave emission model (dense medium radiative transfer model-AIEM-matrix doubling) over dry snow covers, we developed an algorithm to estimate the SWE using the microwave radiometer measurements. Evaluations on this technique using both the model simulated data and the field experimental data with the airborne Polarimetric Scanning Radiometer data from National Aeronautics and Space Administration Cold Land Processes Experiment 2003 showed promising results, with root-mean-square errors of 32.8 and 31.85 mm, respectively. This newly developed inversion method has the advantages over the AMSR-E SWE baseline algorithm when applied to high-resolution airborne observations.</description><subject>Algorithms</subject><subject>Cold Land Processes Experiment 2003 (CLPX03)</subject><subject>Computer simulation</subject><subject>Emission</subject><subject>Grounds</subject><subject>inversion technique</subject><subject>Mathematical model</subject><subject>Mathematical models</subject><subject>Microwave measurements</subject><subject>Microwave radiometry</subject><subject>Microwave theory and techniques</subject><subject>parameterized model</subject><subject>passive microwave</subject><subject>Radiometers</subject><subject>Remote sensing</subject><subject>Scanning</subject><subject>Snow</subject><subject>snow water equivalence (SWE)</subject><issn>1545-598X</issn><issn>1558-0571</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNp9kU1v1DAQhiMEEqXwAxAXiwvlkHYcf-aIVtuCFIlVtxW9WV57Aqmy9tbOFjjyz-uwFQcOPc3X844081bVWwqnlEJ71l1crk8bKGUDSjIunlVHVAhdg1D0-ZxzUYtW37ysXuV8C9BwrdVR9WeZp2FrpyEGEnuyDvEn-WYnTGR5tx_u7YjBIbnOQ_hOph9IVnG0adjilAZH1s6GME8urR9iaRbZeYrbv-Qijp50NniyStFhzpjJ8tcOZ3WYMjlZdKsbYB9fVy96O2Z88xiPq-vz5dXic919vfiy-NTVjoOYaibUZsM8A7XRFGVbrrQe2kZx7am3vQMQVlJhGdtg01MP3HLvWGtFq1qQ7Lj6cNi7S_Fuj3ky2yE7HEcbMO6z0RKYEqrRhTx5kqRSUUlly0RB3_-H3sZ9CuUOo4XknHKuCkQPkEsx54S92ZUn2PTbUDCze2Z2z8zumUf3iubdQTMg4j9eSOBlK3sASPiVJQ</recordid><startdate>201103</startdate><enddate>201103</enddate><creator>Lingmei Jiang</creator><creator>Jiancheng Shi</creator><creator>Tjuatja, S</creator><creator>Kun Shan Chen</creator><creator>Jinyang Du</creator><creator>Lixin Zhang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope></search><sort><creationdate>201103</creationdate><title>Estimation of Snow Water Equivalence Using the Polarimetric Scanning Radiometer From the Cold Land Processes Experiments (CLPX03)</title><author>Lingmei Jiang ; Jiancheng Shi ; Tjuatja, S ; Kun Shan Chen ; Jinyang Du ; Lixin Zhang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c405t-357bb3d307b81e69076ad092748d1dafc005a615a33be2f1d04a4dc39a5979063</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Algorithms</topic><topic>Cold Land Processes Experiment 2003 (CLPX03)</topic><topic>Computer simulation</topic><topic>Emission</topic><topic>Grounds</topic><topic>inversion technique</topic><topic>Mathematical model</topic><topic>Mathematical models</topic><topic>Microwave measurements</topic><topic>Microwave radiometry</topic><topic>Microwave theory and techniques</topic><topic>parameterized model</topic><topic>passive microwave</topic><topic>Radiometers</topic><topic>Remote sensing</topic><topic>Scanning</topic><topic>Snow</topic><topic>snow water equivalence (SWE)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lingmei Jiang</creatorcontrib><creatorcontrib>Jiancheng Shi</creatorcontrib><creatorcontrib>Tjuatja, S</creatorcontrib><creatorcontrib>Kun Shan Chen</creatorcontrib><creatorcontrib>Jinyang Du</creatorcontrib><creatorcontrib>Lixin Zhang</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE geoscience and remote sensing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lingmei Jiang</au><au>Jiancheng Shi</au><au>Tjuatja, S</au><au>Kun Shan Chen</au><au>Jinyang Du</au><au>Lixin Zhang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimation of Snow Water Equivalence Using the Polarimetric Scanning Radiometer From the Cold Land Processes Experiments (CLPX03)</atitle><jtitle>IEEE geoscience and remote sensing letters</jtitle><stitle>LGRS</stitle><date>2011-03</date><risdate>2011</risdate><volume>8</volume><issue>2</issue><spage>359</spage><epage>363</epage><pages>359-363</pages><issn>1545-598X</issn><eissn>1558-0571</eissn><coden>IGRSBY</coden><abstract>In this letter, we investigated an inversion technique to estimate snow water equivalence (SWE) under Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sensor configurations. Through our numerical simulations by the advanced integral equation model (AIEM), we found that the ground surface emission signals at 18.7 and 36.5 GHz were highly correlated regardless of the ground surface properties (dielectric and roughness properties) and can be well described by a linear function. It leads to a new development for describing the relationship between snow emission signals observed at 18.7 and 36.5 GHz as a linear function. The intercept (A) and slope (B) of this linear equation depend only on snow properties and can be estimated from the observations directly. This development provides a new technique that separates the snowpack and ground surface emission signals. With the parameterized snow emission model from a simulated database that was derived using a multiscattering microwave emission model (dense medium radiative transfer model-AIEM-matrix doubling) over dry snow covers, we developed an algorithm to estimate the SWE using the microwave radiometer measurements. Evaluations on this technique using both the model simulated data and the field experimental data with the airborne Polarimetric Scanning Radiometer data from National Aeronautics and Space Administration Cold Land Processes Experiment 2003 showed promising results, with root-mean-square errors of 32.8 and 31.85 mm, respectively. This newly developed inversion method has the advantages over the AMSR-E SWE baseline algorithm when applied to high-resolution airborne observations.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/LGRS.2010.2076345</doi><tpages>5</tpages></addata></record> |
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subjects | Algorithms Cold Land Processes Experiment 2003 (CLPX03) Computer simulation Emission Grounds inversion technique Mathematical model Mathematical models Microwave measurements Microwave radiometry Microwave theory and techniques parameterized model passive microwave Radiometers Remote sensing Scanning Snow snow water equivalence (SWE) |
title | Estimation of Snow Water Equivalence Using the Polarimetric Scanning Radiometer From the Cold Land Processes Experiments (CLPX03) |
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