Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation
A robust physics-based combined active-passive (C-AP), or active-passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function, which constrains similar resoluti...
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| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 2017-07, Vol.55 (7), p.4098-4110 |
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| container_title | IEEE transactions on geoscience and remote sensing |
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| creator | Akbar, Ruzbeh Cosh, Michael H. O'Neill, Peggy E. Entekhabi, Dara Moghaddam, Mahta |
| description | A robust physics-based combined active-passive (C-AP), or active-passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function, which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in C-AP estimation, surface roughness can be considered a free parameter. Extensive Monte Carlo numerical simulations and assessment using field data have been performed both to evaluate the algorithm's performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors range from 0.18 to 0.03 cm 3 /cm 3 for two different land-cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented. |
| doi_str_mv | 10.1109/TGRS.2017.2688403 |
| format | Article |
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Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function, which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in C-AP estimation, surface roughness can be considered a free parameter. Extensive Monte Carlo numerical simulations and assessment using field data have been performed both to evaluate the algorithm's performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors range from 0.18 to 0.03 cm 3 /cm 3 for two different land-cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.</description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2017.2688403</identifier><identifier>PMID: 29657350</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Computer simulation ; Corn ; Emission ; Estimation ; Land cover ; Mathematical models ; Measurement ; Moisture ; Monte Carlo simulation ; Noise ; Noise measurement ; Noise prediction ; Optimization ; Physics ; Radar ; radiometer ; Radiometers ; Radiometry ; Regularization ; Retrieval ; Robustness (mathematics) ; Rough surfaces ; Roughness ; Scattering ; Soil ; Soil measurements ; Soil moisture ; Soil Moisture Active–Passive (SMAP) ; Soils ; Statistical methods ; Surface roughness</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2017-07, Vol.55 (7), p.4098-4110</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c447t-f1c734fe2e540e5b9c1c233004c15023a2b249727d03d5829e7a96b9929329cc3</citedby><cites>FETCH-LOGICAL-c447t-f1c734fe2e540e5b9c1c233004c15023a2b249727d03d5829e7a96b9929329cc3</cites><orcidid>0000-0002-9963-0488</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7912225$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,780,784,796,885,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7912225$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29657350$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Akbar, Ruzbeh</creatorcontrib><creatorcontrib>Cosh, Michael H.</creatorcontrib><creatorcontrib>O'Neill, Peggy E.</creatorcontrib><creatorcontrib>Entekhabi, Dara</creatorcontrib><creatorcontrib>Moghaddam, Mahta</creatorcontrib><title>Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><addtitle>IEEE Trans Geosci Remote Sens</addtitle><description>A robust physics-based combined active-passive (C-AP), or active-passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function, which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in C-AP estimation, surface roughness can be considered a free parameter. Extensive Monte Carlo numerical simulations and assessment using field data have been performed both to evaluate the algorithm's performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors range from 0.18 to 0.03 cm 3 /cm 3 for two different land-cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.</description><subject>Computer simulation</subject><subject>Corn</subject><subject>Emission</subject><subject>Estimation</subject><subject>Land cover</subject><subject>Mathematical models</subject><subject>Measurement</subject><subject>Moisture</subject><subject>Monte Carlo simulation</subject><subject>Noise</subject><subject>Noise measurement</subject><subject>Noise prediction</subject><subject>Optimization</subject><subject>Physics</subject><subject>Radar</subject><subject>radiometer</subject><subject>Radiometers</subject><subject>Radiometry</subject><subject>Regularization</subject><subject>Retrieval</subject><subject>Robustness (mathematics)</subject><subject>Rough surfaces</subject><subject>Roughness</subject><subject>Scattering</subject><subject>Soil</subject><subject>Soil measurements</subject><subject>Soil moisture</subject><subject>Soil Moisture Active–Passive (SMAP)</subject><subject>Soils</subject><subject>Statistical methods</subject><subject>Surface roughness</subject><issn>0196-2892</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkVFrFDEQx4NY7Fn9ACLIQl982TOZJJvkRZCjtkKL0KvPIZudbVN2NzXZFfrtzXHnob7MPMxv_szwI-Qdo2vGqPl0d3m7XQNlag2N1oLyF2TFpNQ1bYR4SVaUmaYGbeCUvM75kVImJFOvyCmYRiou6YpcbeLYhgm76tZ1LtWlhjjijKnaLql3HqttDEN1E0Oel4SVmwoal_uHCXOuLvIcRjeHOL0hJ70bMr499DPy4-vF3eaqvv5--W3z5br2Qqi57plXXPQIKAVF2RrPPHBOqfBMUuAOWhBGgeoo76QGg8qZpjUGDAfjPT8jn_e5T0s7YudxmpMb7FMqd6RnG12w_06m8GDv4y8rteGsESXg4yEgxZ8L5tmOIXscBjdhXLIFClIZzYUu6Pl_6GNc0lTes8wwLoHqRhWK7SmfYs4J--MxjNqdJ7vzZHee7MFT2fnw9xfHjT9iCvB-DwREPI6VYQAg-W8kIpZF</recordid><startdate>20170701</startdate><enddate>20170701</enddate><creator>Akbar, Ruzbeh</creator><creator>Cosh, Michael H.</creator><creator>O'Neill, Peggy E.</creator><creator>Entekhabi, Dara</creator><creator>Moghaddam, Mahta</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>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-9963-0488</orcidid></search><sort><creationdate>20170701</creationdate><title>Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation</title><author>Akbar, Ruzbeh ; Cosh, Michael H. ; O'Neill, Peggy E. ; Entekhabi, Dara ; Moghaddam, Mahta</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c447t-f1c734fe2e540e5b9c1c233004c15023a2b249727d03d5829e7a96b9929329cc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Computer simulation</topic><topic>Corn</topic><topic>Emission</topic><topic>Estimation</topic><topic>Land cover</topic><topic>Mathematical models</topic><topic>Measurement</topic><topic>Moisture</topic><topic>Monte Carlo simulation</topic><topic>Noise</topic><topic>Noise measurement</topic><topic>Noise prediction</topic><topic>Optimization</topic><topic>Physics</topic><topic>Radar</topic><topic>radiometer</topic><topic>Radiometers</topic><topic>Radiometry</topic><topic>Regularization</topic><topic>Retrieval</topic><topic>Robustness (mathematics)</topic><topic>Rough surfaces</topic><topic>Roughness</topic><topic>Scattering</topic><topic>Soil</topic><topic>Soil measurements</topic><topic>Soil moisture</topic><topic>Soil Moisture Active–Passive (SMAP)</topic><topic>Soils</topic><topic>Statistical methods</topic><topic>Surface roughness</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Akbar, Ruzbeh</creatorcontrib><creatorcontrib>Cosh, Michael H.</creatorcontrib><creatorcontrib>O'Neill, Peggy E.</creatorcontrib><creatorcontrib>Entekhabi, Dara</creatorcontrib><creatorcontrib>Moghaddam, Mahta</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>PubMed</collection><collection>CrossRef</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>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>IEEE transactions on geoscience and remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Akbar, Ruzbeh</au><au>Cosh, Michael H.</au><au>O'Neill, Peggy E.</au><au>Entekhabi, Dara</au><au>Moghaddam, Mahta</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation</atitle><jtitle>IEEE transactions on geoscience and remote sensing</jtitle><stitle>TGRS</stitle><addtitle>IEEE Trans Geosci Remote Sens</addtitle><date>2017-07-01</date><risdate>2017</risdate><volume>55</volume><issue>7</issue><spage>4098</spage><epage>4110</epage><pages>4098-4110</pages><issn>0196-2892</issn><eissn>1558-0644</eissn><coden>IGRSD2</coden><abstract>A robust physics-based combined active-passive (C-AP), or active-passive, surface soil moisture and roughness estimation methodology is presented. 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| ispartof | IEEE transactions on geoscience and remote sensing, 2017-07, Vol.55 (7), p.4098-4110 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Computer simulation Corn Emission Estimation Land cover Mathematical models Measurement Moisture Monte Carlo simulation Noise Noise measurement Noise prediction Optimization Physics Radar radiometer Radiometers Radiometry Regularization Retrieval Robustness (mathematics) Rough surfaces Roughness Scattering Soil Soil measurements Soil moisture Soil Moisture Active–Passive (SMAP) Soils Statistical methods Surface roughness |
| title | Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation |
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