L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records
The CCI Soil Moisture dataset (CCI SM) is the most extensive climate data record of satellite soil moisture to date. To maximize its function as a climate benchmark, both long-term consistency and (model-) independence are high priorities. Two unique L-band missions integrated into the CCI SM are SM...
Gespeichert in:
| Veröffentlicht in: | Remote sensing (Basel, Switzerland) Switzerland), 2021-07, Vol.13 (13), p.2480 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 13 |
| container_start_page | 2480 |
| container_title | Remote sensing (Basel, Switzerland) |
| container_volume | 13 |
| creator | van der Schalie, Robin van der Vliet, Mendy Rodríguez-Fernández, Nemesio Dorigo, Wouter Scanlon, Tracy Preimesberger, Wolfgang Madelon, Rémi de Jeu, Richard |
| description | The CCI Soil Moisture dataset (CCI SM) is the most extensive climate data record of satellite soil moisture to date. To maximize its function as a climate benchmark, both long-term consistency and (model-) independence are high priorities. Two unique L-band missions integrated into the CCI SM are SMOS and SMAP. However, they lack the high-frequency microwave sensors needed to determine the effective temperature and snow/frozen flagging, and therefore use input from (varying) land surface models. In this study, the impact of replacing this model input by temperature and filtering based on passive microwave observations is evaluated. This is derived from an inter-calibrated dataset (ICTB) based on six passive microwave sensors. Generally, this leads to an expected increase in revisit time, which goes up by about 0.5 days (~15% loss). Only the boreal regions have an increased coverage due to more accurate freeze/thaw detection. The boreal regions become wetter with an increased dynamic range, while the tropics are dryer with decreased dynamics. Other regions show only small differences. The skill was evaluated against ERA5-Land and in situ observations. The average correlation against ERA5-Land increased by 0.05 for SMAP ascending/descending and SMOS ascending, whereas SMOS descending decreased by 0.01. For in situ sensors, the difference is less pronounced, with only a significant change in correlation of 0.04 for SM SMOS ascending. The results indicate that the use of microwave-based input for temperature and filtering is a viable and preferred alternative to the use of land surface models in soil moisture climate data records from passive microwave sensors. |
| doi_str_mv | 10.3390/rs13132480 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_hal_04790215v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_bf79b46179b44a90b2260ce2ce19ea5a</doaj_id><sourcerecordid>2549628183</sourcerecordid><originalsourceid>FETCH-LOGICAL-c395t-e594f753a83ba361ddb6c9a3db33e463705ab2e8fd177586f8f4e27fb5c1a6e3</originalsourceid><addsrcrecordid>eNpVkU1v3CAQQK2qkRqlufQXIPXUSk6BAdsck23TrLRRpHZ7RmMzTlk5ZgvsVjnlr5fNVv3gMIxGjzfAVNUbwS8ADP8QkwABUnX8RXUqeStrJY18-U_-qjpPacPLAhCGq9PqaVVf4ezY1-Andht8yrtI7Avl6GmPU2Lfkp_v2a0fYviJe2JXmMixNT1sKeIzfDh-7adMsZAXbF246FKROZrq5exoSyXMmS0m_4CZ2EfMWFoMoWCvq5OxtKHz3_tZtb7-tF7c1Ku7z8vF5aoewOhckzZqbDVgBz1CI5zrm8EguB6AVAMt19hL6kYn2lZ3zdiNimQ79noQ2BCcVcuj1gXc2G0sF4mPNqC3z4UQ7y3G7IeJbD-2pleNOESFhvdSNnwgOZAwhBqL693R9R2n_1Q3lyt7qHHVGi6F3ovCvj2y2xh-7Chluwm7OJeXWqmVaWQnOijU-yNVPjmlSOMfreD2MFr7d7TwC9K0lb0</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2549628183</pqid></control><display><type>article</type><title>L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records</title><source>DOAJ Directory of Open Access Journals</source><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>van der Schalie, Robin ; van der Vliet, Mendy ; Rodríguez-Fernández, Nemesio ; Dorigo, Wouter ; Scanlon, Tracy ; Preimesberger, Wolfgang ; Madelon, Rémi ; de Jeu, Richard</creator><creatorcontrib>van der Schalie, Robin ; van der Vliet, Mendy ; Rodríguez-Fernández, Nemesio ; Dorigo, Wouter ; Scanlon, Tracy ; Preimesberger, Wolfgang ; Madelon, Rémi ; de Jeu, Richard</creatorcontrib><description>The CCI Soil Moisture dataset (CCI SM) is the most extensive climate data record of satellite soil moisture to date. To maximize its function as a climate benchmark, both long-term consistency and (model-) independence are high priorities. Two unique L-band missions integrated into the CCI SM are SMOS and SMAP. However, they lack the high-frequency microwave sensors needed to determine the effective temperature and snow/frozen flagging, and therefore use input from (varying) land surface models. In this study, the impact of replacing this model input by temperature and filtering based on passive microwave observations is evaluated. This is derived from an inter-calibrated dataset (ICTB) based on six passive microwave sensors. Generally, this leads to an expected increase in revisit time, which goes up by about 0.5 days (~15% loss). Only the boreal regions have an increased coverage due to more accurate freeze/thaw detection. The boreal regions become wetter with an increased dynamic range, while the tropics are dryer with decreased dynamics. Other regions show only small differences. The skill was evaluated against ERA5-Land and in situ observations. The average correlation against ERA5-Land increased by 0.05 for SMAP ascending/descending and SMOS ascending, whereas SMOS descending decreased by 0.01. For in situ sensors, the difference is less pronounced, with only a significant change in correlation of 0.04 for SM SMOS ascending. The results indicate that the use of microwave-based input for temperature and filtering is a viable and preferred alternative to the use of land surface models in soil moisture climate data records from passive microwave sensors.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs13132480</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Calibration ; Climate change ; Climate models ; Climatic data ; Continental interfaces, environment ; Datasets ; effective temperature ; Filtration ; Freeze-thawing ; LPRM ; Microwave sensors ; passive microwave radiometry ; Radiometers ; Remote sensing ; Satellites ; Sciences of the Universe ; Sensors ; SMAP ; SMOS ; Soil moisture ; Soil temperature ; Temperature ; Tropical environments ; Vegetation</subject><ispartof>Remote sensing (Basel, Switzerland), 2021-07, Vol.13 (13), p.2480</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Attribution</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c395t-e594f753a83ba361ddb6c9a3db33e463705ab2e8fd177586f8f4e27fb5c1a6e3</citedby><cites>FETCH-LOGICAL-c395t-e594f753a83ba361ddb6c9a3db33e463705ab2e8fd177586f8f4e27fb5c1a6e3</cites><orcidid>0000-0003-1697-8321 ; 0000-0002-6655-0588 ; 0000-0003-3796-149X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,864,885,2102,27924,27925</link.rule.ids><backlink>$$Uhttps://hal.science/hal-04790215$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>van der Schalie, Robin</creatorcontrib><creatorcontrib>van der Vliet, Mendy</creatorcontrib><creatorcontrib>Rodríguez-Fernández, Nemesio</creatorcontrib><creatorcontrib>Dorigo, Wouter</creatorcontrib><creatorcontrib>Scanlon, Tracy</creatorcontrib><creatorcontrib>Preimesberger, Wolfgang</creatorcontrib><creatorcontrib>Madelon, Rémi</creatorcontrib><creatorcontrib>de Jeu, Richard</creatorcontrib><title>L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records</title><title>Remote sensing (Basel, Switzerland)</title><description>The CCI Soil Moisture dataset (CCI SM) is the most extensive climate data record of satellite soil moisture to date. To maximize its function as a climate benchmark, both long-term consistency and (model-) independence are high priorities. Two unique L-band missions integrated into the CCI SM are SMOS and SMAP. However, they lack the high-frequency microwave sensors needed to determine the effective temperature and snow/frozen flagging, and therefore use input from (varying) land surface models. In this study, the impact of replacing this model input by temperature and filtering based on passive microwave observations is evaluated. This is derived from an inter-calibrated dataset (ICTB) based on six passive microwave sensors. Generally, this leads to an expected increase in revisit time, which goes up by about 0.5 days (~15% loss). Only the boreal regions have an increased coverage due to more accurate freeze/thaw detection. The boreal regions become wetter with an increased dynamic range, while the tropics are dryer with decreased dynamics. Other regions show only small differences. The skill was evaluated against ERA5-Land and in situ observations. The average correlation against ERA5-Land increased by 0.05 for SMAP ascending/descending and SMOS ascending, whereas SMOS descending decreased by 0.01. For in situ sensors, the difference is less pronounced, with only a significant change in correlation of 0.04 for SM SMOS ascending. The results indicate that the use of microwave-based input for temperature and filtering is a viable and preferred alternative to the use of land surface models in soil moisture climate data records from passive microwave sensors.</description><subject>Algorithms</subject><subject>Calibration</subject><subject>Climate change</subject><subject>Climate models</subject><subject>Climatic data</subject><subject>Continental interfaces, environment</subject><subject>Datasets</subject><subject>effective temperature</subject><subject>Filtration</subject><subject>Freeze-thawing</subject><subject>LPRM</subject><subject>Microwave sensors</subject><subject>passive microwave radiometry</subject><subject>Radiometers</subject><subject>Remote sensing</subject><subject>Satellites</subject><subject>Sciences of the Universe</subject><subject>Sensors</subject><subject>SMAP</subject><subject>SMOS</subject><subject>Soil moisture</subject><subject>Soil temperature</subject><subject>Temperature</subject><subject>Tropical environments</subject><subject>Vegetation</subject><issn>2072-4292</issn><issn>2072-4292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>DOA</sourceid><recordid>eNpVkU1v3CAQQK2qkRqlufQXIPXUSk6BAdsck23TrLRRpHZ7RmMzTlk5ZgvsVjnlr5fNVv3gMIxGjzfAVNUbwS8ADP8QkwABUnX8RXUqeStrJY18-U_-qjpPacPLAhCGq9PqaVVf4ezY1-Andht8yrtI7Avl6GmPU2Lfkp_v2a0fYviJe2JXmMixNT1sKeIzfDh-7adMsZAXbF246FKROZrq5exoSyXMmS0m_4CZ2EfMWFoMoWCvq5OxtKHz3_tZtb7-tF7c1Ku7z8vF5aoewOhckzZqbDVgBz1CI5zrm8EguB6AVAMt19hL6kYn2lZ3zdiNimQ79noQ2BCcVcuj1gXc2G0sF4mPNqC3z4UQ7y3G7IeJbD-2pleNOESFhvdSNnwgOZAwhBqL693R9R2n_1Q3lyt7qHHVGi6F3ovCvj2y2xh-7Chluwm7OJeXWqmVaWQnOijU-yNVPjmlSOMfreD2MFr7d7TwC9K0lb0</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>van der Schalie, Robin</creator><creator>van der Vliet, Mendy</creator><creator>Rodríguez-Fernández, Nemesio</creator><creator>Dorigo, Wouter</creator><creator>Scanlon, Tracy</creator><creator>Preimesberger, Wolfgang</creator><creator>Madelon, Rémi</creator><creator>de Jeu, Richard</creator><general>MDPI AG</general><general>MDPI</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>1XC</scope><scope>VOOES</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-1697-8321</orcidid><orcidid>https://orcid.org/0000-0002-6655-0588</orcidid><orcidid>https://orcid.org/0000-0003-3796-149X</orcidid></search><sort><creationdate>20210701</creationdate><title>L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records</title><author>van der Schalie, Robin ; van der Vliet, Mendy ; Rodríguez-Fernández, Nemesio ; Dorigo, Wouter ; Scanlon, Tracy ; Preimesberger, Wolfgang ; Madelon, Rémi ; de Jeu, Richard</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c395t-e594f753a83ba361ddb6c9a3db33e463705ab2e8fd177586f8f4e27fb5c1a6e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Calibration</topic><topic>Climate change</topic><topic>Climate models</topic><topic>Climatic data</topic><topic>Continental interfaces, environment</topic><topic>Datasets</topic><topic>effective temperature</topic><topic>Filtration</topic><topic>Freeze-thawing</topic><topic>LPRM</topic><topic>Microwave sensors</topic><topic>passive microwave radiometry</topic><topic>Radiometers</topic><topic>Remote sensing</topic><topic>Satellites</topic><topic>Sciences of the Universe</topic><topic>Sensors</topic><topic>SMAP</topic><topic>SMOS</topic><topic>Soil moisture</topic><topic>Soil temperature</topic><topic>Temperature</topic><topic>Tropical environments</topic><topic>Vegetation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>van der Schalie, Robin</creatorcontrib><creatorcontrib>van der Vliet, Mendy</creatorcontrib><creatorcontrib>Rodríguez-Fernández, Nemesio</creatorcontrib><creatorcontrib>Dorigo, Wouter</creatorcontrib><creatorcontrib>Scanlon, Tracy</creatorcontrib><creatorcontrib>Preimesberger, Wolfgang</creatorcontrib><creatorcontrib>Madelon, Rémi</creatorcontrib><creatorcontrib>de Jeu, Richard</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</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>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Earth, Atmospheric & Aquatic 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>Engineering Collection</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Remote sensing (Basel, Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>van der Schalie, Robin</au><au>van der Vliet, Mendy</au><au>Rodríguez-Fernández, Nemesio</au><au>Dorigo, Wouter</au><au>Scanlon, Tracy</au><au>Preimesberger, Wolfgang</au><au>Madelon, Rémi</au><au>de Jeu, Richard</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records</atitle><jtitle>Remote sensing (Basel, Switzerland)</jtitle><date>2021-07-01</date><risdate>2021</risdate><volume>13</volume><issue>13</issue><spage>2480</spage><pages>2480-</pages><issn>2072-4292</issn><eissn>2072-4292</eissn><abstract>The CCI Soil Moisture dataset (CCI SM) is the most extensive climate data record of satellite soil moisture to date. To maximize its function as a climate benchmark, both long-term consistency and (model-) independence are high priorities. Two unique L-band missions integrated into the CCI SM are SMOS and SMAP. However, they lack the high-frequency microwave sensors needed to determine the effective temperature and snow/frozen flagging, and therefore use input from (varying) land surface models. In this study, the impact of replacing this model input by temperature and filtering based on passive microwave observations is evaluated. This is derived from an inter-calibrated dataset (ICTB) based on six passive microwave sensors. Generally, this leads to an expected increase in revisit time, which goes up by about 0.5 days (~15% loss). Only the boreal regions have an increased coverage due to more accurate freeze/thaw detection. The boreal regions become wetter with an increased dynamic range, while the tropics are dryer with decreased dynamics. Other regions show only small differences. The skill was evaluated against ERA5-Land and in situ observations. The average correlation against ERA5-Land increased by 0.05 for SMAP ascending/descending and SMOS ascending, whereas SMOS descending decreased by 0.01. For in situ sensors, the difference is less pronounced, with only a significant change in correlation of 0.04 for SM SMOS ascending. The results indicate that the use of microwave-based input for temperature and filtering is a viable and preferred alternative to the use of land surface models in soil moisture climate data records from passive microwave sensors.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/rs13132480</doi><orcidid>https://orcid.org/0000-0003-1697-8321</orcidid><orcidid>https://orcid.org/0000-0002-6655-0588</orcidid><orcidid>https://orcid.org/0000-0003-3796-149X</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2072-4292 |
| ispartof | Remote sensing (Basel, Switzerland), 2021-07, Vol.13 (13), p.2480 |
| issn | 2072-4292 2072-4292 |
| language | eng |
| recordid | cdi_hal_primary_oai_HAL_hal_04790215v1 |
| source | DOAJ Directory of Open Access Journals; MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals |
| subjects | Algorithms Calibration Climate change Climate models Climatic data Continental interfaces, environment Datasets effective temperature Filtration Freeze-thawing LPRM Microwave sensors passive microwave radiometry Radiometers Remote sensing Satellites Sciences of the Universe Sensors SMAP SMOS Soil moisture Soil temperature Temperature Tropical environments Vegetation |
| title | L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T21%3A55%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=L-Band%20Soil%20Moisture%20Retrievals%20Using%20Microwave%20Based%20Temperature%20and%20Filtering.%20Towards%20Model-Independent%20Climate%20Data%20Records&rft.jtitle=Remote%20sensing%20(Basel,%20Switzerland)&rft.au=van%20der%20Schalie,%20Robin&rft.date=2021-07-01&rft.volume=13&rft.issue=13&rft.spage=2480&rft.pages=2480-&rft.issn=2072-4292&rft.eissn=2072-4292&rft_id=info:doi/10.3390/rs13132480&rft_dat=%3Cproquest_doaj_%3E2549628183%3C/proquest_doaj_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2549628183&rft_id=info:pmid/&rft_doaj_id=oai_doaj_org_article_bf79b46179b44a90b2260ce2ce19ea5a&rfr_iscdi=true |