{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:07:24Z","timestamp":1772554044177,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T00:00:00Z","timestamp":1639612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["80NSSC21K0545"],"award-info":[{"award-number":["80NSSC21K0545"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006196","name":"Jet Propulsion Lab","doi-asserted-by":"publisher","award":["1664013"],"award-info":[{"award-number":["1664013"]}],"id":[{"id":"10.13039\/100006196","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sea-ice contamination in the antenna field of view constitutes a large error source in retrieving sea-surface salinity (SSS) with the spaceborne Soil Moisture Active Passive (SMAP) L-band radiometer. This is a major obstacle in the current NASA\/Remote Sensing Systems (RSS) SMAP SSS retrieval algorithm in regards to obtaining accurate SSS measurements in the polar oceans. Our analysis finds a strong correlation between 8-day averaged SMAP L-band brightness temperature (TB) bias and TB measurements from the Advanced Microwave Scanning Radiometer (AMSR2) in the C-through Ka-band frequency range for sea-ice contaminated ocean scenes. We show how this correlation can be employed to develop: (1) a discriminant analysis that is able to reliably flag the SMAP observations for sea-ice contamination and (2) subsequently remove the sea-ice contamination from the SMAP observations, which results in significantly more accurate SMAP SSS retrievals near the sea-ice edge. We provide a case study that evaluates the performance of the proposed sea-ice flagging and correction algorithm. Our method is also able to detect drifting icebergs, which go often undetected in many available standard sea-ice products and thus result in spurious SMAP SSS retrievals.<\/jats:p>","DOI":"10.3390\/rs13245120","type":"journal-article","created":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T21:32:40Z","timestamp":1639690360000},"page":"5120","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["SMAP Salinity Retrievals near the Sea-Ice Edge Using Multi-Channel AMSR2 Brightness Temperatures"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5488-1566","authenticated-orcid":false,"given":"Thomas","family":"Meissner","sequence":"first","affiliation":[{"name":"Remote Sensing Systems, 444 Tenth Street, Suite 200, Santa Rosa, CA 95401, USA"}]},{"given":"Andrew","family":"Manaster","sequence":"additional","affiliation":[{"name":"Remote Sensing Systems, 444 Tenth Street, Suite 200, Santa Rosa, CA 95401, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111769","DOI":"10.1016\/j.rse.2020.111769","article-title":"Sea surface salinity estimates from spaceborne L-band radiometers: An overview of the first decade of observation (2010\u20132019)","volume":"242","author":"Reul","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kao, H.-Y., Lagerloef, G., Lee, T., Melnichenko, O., Meissner, T., and Hacker, P. (2018). Assessment of Aquarius Sea Surface Salinity. Remote Sens., 10.","DOI":"10.3390\/rs10091341"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tang, W., Yueh, S., Yang, D., Fore, A., Hayashi, A., Lee, T., Fournier, S., and Holt, B. (2018). The Potential and Challenges of Using Soil Moisture Active Passive (SMAP) Sea Surface Salinity to Monitor Arctic Ocean Freshwater Changes. Remote Sens., 10.","DOI":"10.3390\/rs10060869"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Olmedo, E., Gabarr\u00f3, C., Gonz\u00e1lez-Gambau, V., Mart\u00ednez, J., Ballabrera-Poy, J., Turiel, A., Portabella, M., Fournier, S., and Lee, T. (2018). Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions. Remote Sens., 10.","DOI":"10.3390\/rs10111772"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112027","DOI":"10.1016\/j.rse.2020.112027","article-title":"New insights into SMOS sea surface salinity retrievals in the Arctic Ocean","volume":"249","author":"Supply","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"243","DOI":"10.3389\/fmars.2019.00243","article-title":"Satellite Salinity Observing System: Recent Discoveries and the Way Forward","volume":"6","author":"Vinogradova","year":"2019","journal-title":"Front. Mar. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Meissner, T., Wentz, F.J., and Le Vine, D.M. (2021, October 30). Aquarius Salinity Retrieval Algorithm Theoretical Basis Document (ATBD). End of Mission Version. RSS Technical Report 120117. Available online: images.remss.com\/papers\/tech_reports\/2017\/Meissner_AQ_ATBD_EOM_final.pdf.","DOI":"10.56236\/RSS-be"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Heygster, G., Huntemann, M., Ivanova, N., Saldo, R., and Pedersen, L.T. (2014, January 13\u201318). Response of passive microwave sea ice concentration algorithms to thin ice. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6947266"},{"key":"ref_9","unstructured":"Meier, W.N., Fetterer, F., Savoie, M., Mallory, S., Duerr, R., and Stroeve, J. (2021, October 30). NOAA\/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 3. Available online: https:\/\/nsidc.org\/data\/g02202\/versions\/3."},{"key":"ref_10","unstructured":"Danish Meteorological Institute Global Sea Ice Concentration (AMSR-2) | OSI SAF (2021, October 30). Processed by the OSI-SAF Hybrid Dynamic Algorithm. Available online: https:\/\/osi-saf.eumetsat.int\/products\/osi-408."},{"key":"ref_11","unstructured":"Meissner, T., Wentz, F.J., Manaster, A., and Lindsley, R. (2021, October 30). NASA\/RSS SMAP Level 2C Sea Surface Salinity V4.0 Validated Dataset, Available online: https:\/\/podaac.jpl.nasa.gov\/dataset\/SMAP_RSS_L2_SSS_V4."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fournier, S., Lee, T., Tang, W., Steele, M., and Olmedo, E. (2019). Evaluation and Intercomparison of SMOS, Aquarius, and SMAP Sea Surface Salinity Products in the Arctic Ocean. Remote Sens., 11.","DOI":"10.3390\/rs11243043"},{"key":"ref_13","unstructured":"Meissner, T., Wentz, F.J., Manaster, A., and Lindsley, R. (2021, October 30). NASA\/RSS SMAP Salinity: Version 4.0 Validated Release: Release Notes, Algorithm Theoretical Basis Document (ATBD), Validation, Data Format Specification. Available online: https:\/\/data.remss.com\/smap\/SSS\/Release_V4.0.pdf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Meissner, T., Wentz, F.J., and Vine, D.M.L. (2018). The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases. Remote Sens., 10.","DOI":"10.3390\/rs10071121"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6499","DOI":"10.1002\/2014JC009837","article-title":"The emission and scattering of L-band microwave radiation from rough ocean surfaces and wind speed measurements from the Aquarius sensor","volume":"119","author":"Meissner","year":"2014","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_16","unstructured":"Ashcroft, P., and Wentz, F.J. (2021, October 30). AMSR Level 2A Algorithm Theoretical Basis Document (ATBD). RSS Technical Report 121599B-1. Available online: https:\/\/images.remss.com\/papers\/rsstech\/2000_121599B-1_Wentz_AMSR_Level2A_Algorithm_ATBD.pdf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1109\/36.58966","article-title":"Optimum interpolation of imaging microwave radiometer data","volume":"28","author":"Poe","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wentz, F.J. (2021, October 30). AMSR-2 Air-Sea Essential Climate Variables RSS Version 8.2. Available online: https:\/\/images.remss.com\/papers\/tech_reports\/2021\/AMSR-2_Air-Sea_Essential_Climate_Variables_RSS_Version_8.2.pdf.","DOI":"10.56236\/RSS-bi"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1002\/2015RS005858","article-title":"Atmospheric absorption model for dry air and water vapor at microwave frequencies below 100 GHz derived from spaceborne radiometer observations: Atmospheric Absorption Model","volume":"51","author":"Wentz","year":"2016","journal-title":"Radio Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3004","DOI":"10.1109\/TGRS.2011.2179662","article-title":"The Emissivity of the Ocean Surface Between 6 and 90 GHz Over a Large Range of Wind Speeds and Earth Incidence Angles","volume":"50","author":"Meissner","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wentz, F.J., and Meissner, T. (2021, October 30). AMSR Ocean Algorithm Theoretical Basis Document (ATBD). RSS Technical Report 121599A-1. Available online: images.remss.com\/papers\/rsstech\/2000_121599A-1_Wentz_AMSR_Ocean_Algorithm_ATBD_Version2.pdf.","DOI":"10.56236\/RSS-af"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1836","DOI":"10.1109\/TGRS.2004.831888","article-title":"The complex dielectric constant of pure and sea water from microwave satellite observations","volume":"42","author":"Meissner","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"64","DOI":"10.5670\/oceanog.2009.39","article-title":"US GODAE: Global Ocean Prediction with the HYbrid Coordinate Ocean Model (HYCOM)","volume":"22","author":"Chassignet","year":"2009","journal-title":"Oceanography"},{"key":"ref_24","unstructured":"(2021, October 20). Global Ocean Forecasting System GOFS 3.1: 41-layer HYCOM + NCODA Global 1\/12\u00b0 Analysis. Available online: https:\/\/www.hycom.org\/data\/glby0pt08\/expt-93pt0."},{"key":"ref_25","unstructured":"(2021, October 20). Canada Meteorological Center GHRSST Level 4 CMC0.1deg Global Foundation Sea Surface Temperature Analysis (GDS version 2), Available online: https:\/\/podaac.jpl.nasa.gov\/dataset\/CMC0.1deg-CMC-L4-GLOB-v3.0."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6997","DOI":"10.1029\/2019JC015367","article-title":"A Near-Real-Time Version of the Cross-Calibrated Multiplatform (CCMP) Ocean Surface Wind Velocity Data Set","volume":"124","author":"Mears","year":"2019","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_27","unstructured":"Wentz, F.J., Scott, J., Hoffman, R., Leidner, M., Atlas, R., and Ardizzone, J. (2021, October 30). Remote Sensing Systems Cross-Calibrated Multi-Platform (CCMP) 6-hourly ocean vector wind analysis product on 0.25 deg grid, Version 2.0. Available online: www.remss.com\/measurements\/ccmp."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1175\/2010BAMS2946.1","article-title":"A Cross-calibrated, Multiplatform Ocean Surface Wind Velocity Product for Meteorological and Oceanographic Applications","volume":"92","author":"Atlas","year":"2011","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_29","unstructured":"(2021, October 30). National Centers for Environmental Prediction NCEP Data Products GFS and GDAS, Available online: https:\/\/www.nco.ncep.noaa.gov\/pmb\/products\/gfs\/."},{"key":"ref_30","unstructured":"(2021, October 30). Precipitation Processing System At NASA GSFC GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06, Available online: https:\/\/disc.gsfc.nasa.gov\/datacollection\/GPM_3IMERGHH_06.html."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7105","DOI":"10.1109\/TGRS.2016.2596100","article-title":"Sensitivity of Ocean Surface Salinity Measurements From Spaceborne L-Band Radiometers to Ancillary Sea Surface Temperature","volume":"54","author":"Meissner","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.rse.2006.05.013","article-title":"Improved retrieval of sea ice total concentration from spaceborne passive microwave observations using numerical weather prediction model fields: An intercomparison of nine algorithms","volume":"104","author":"Andersen","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.5194\/tc-9-1797-2015","article-title":"Inter-comparison and evaluation of sea ice algorithms: Towards further identification of challenges and optimal approach using passive microwave observations","volume":"9","author":"Ivanova","year":"2015","journal-title":"Cryosphere"},{"key":"ref_34","unstructured":"Tian, T., Tonboe, R., and Lavelle, J. (2021, December 08). The EUMETSAT OSI SAF AMSR-2 Sea Ice Concentration Algorithm Theoretical Basis Document (ATBD). Product OSI-408. Version 1.0. Available online: https:\/\/osisaf-hl.met.no\/sites\/osisaf-hl.met.no\/files\/baseline_document\/osisaf_cdop2_ss2_atbd_amsr2-sea-ice-conc_v1p1.pdf."},{"key":"ref_35","unstructured":"U.S. National Ice Center Antarctic Iceberg Tracking (2021, October 30). Available online: https:\/\/usicecenter.gov\/Resources\/AntarcticIcebergs."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Vazquez-Cuervo, J., Gentemann, C., Tang, W., Carroll, D., Zhang, H., Menemenlis, D., Gomez-Valdes, J., Bouali, M., and Steele, M. (2021). Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models. Remote Sens., 13.","DOI":"10.3390\/rs13050831"},{"key":"ref_37","unstructured":"Donlon, C. (2021, October 30). Copernicus Imaging Microwave Radiometer (CIMR) Mission Requirement Document. Available online: https:\/\/esamultimedia.esa.int\/docs\/EarthObservation\/CIMR-MRD-v3.0-20190930_Issued.pdf."},{"key":"ref_38","unstructured":"Duda, R.O., Hart, P.E., and Stork, D.G. (2001). Pattern Classification, Wiley. [2nd ed.]."},{"key":"ref_39","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer. Information Science and Statistics."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5120\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:50:01Z","timestamp":1760169001000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5120"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,16]]},"references-count":39,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13245120"],"URL":"https:\/\/doi.org\/10.3390\/rs13245120","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,16]]}}}