{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T21:38:31Z","timestamp":1779399511388,"version":"3.53.1"},"reference-count":54,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,1,25]],"date-time":"2018-01-25T00:00:00Z","timestamp":1516838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000844","name":"European Space Agency","doi-asserted-by":"publisher","award":["22829\/09\/NL\/JC"],"award-info":[{"award-number":["22829\/09\/NL\/JC"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000844","name":"European Space Agency","doi-asserted-by":"publisher","award":["22671\/09\/NL\/JA\/ef"],"award-info":[{"award-number":["22671\/09\/NL\/JA\/ef"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Current methods for retrieving SWE (snow water equivalent) from space rely on passive microwave sensors. Observations are limited by poor spatial resolution, ambiguities related to separation of snow microstructural properties from the total snow mass, and signal saturation when snow is deep (~&gt;80 cm). The use of SAR (Synthetic Aperture Radar) at suitable frequencies has been suggested as a potential observation method to overcome the coarse resolution of passive microwave sensors. Nevertheless, suitable sensors operating from space are, up to now, unavailable. Active microwave retrievals suffer, however, from the same difficulties as the passive case in separating impacts of scattering efficiency from those of snow mass. In this study, we explore the potential of applying active (radar) and passive (radiometer) microwave observations in tandem, by using a dataset of co-incident tower-based active and passive microwave observations and detailed in situ data from a test site in Northern Finland. The dataset spans four winter seasons with daily coverage. In order to quantify the temporal variability of snow microstructure, we derive an effective correlation length for the snowpack (treated as a single layer), which matches the simulated microwave response of a semi-empirical radiative transfer model to observations. This effective parameter is derived from radiometer and radar observations at different frequencies and frequency combinations (10.2, 13.3 and 16.7 GHz for radar; 10.65, 18.7 and 37 GHz for radiometer). Under dry snow conditions, correlations are found between the effective correlation length retrieved from active and passive measurements. Consequently, the derived effective correlation length from passive microwave observations is applied to parameterize the retrieval of SWE using radar, improving retrieval skill compared to a case with no prior knowledge of snow-scattering efficiency. The same concept can be applied to future radar satellite mission concepts focused on retrieving SWE, exploiting existing methods for retrieval of snow microstructural parameters, as employed within the ESA (European Space Agency) GlobSnow SWE product. Using radar alone, a seasonally optimized value of effective correlation length to parameterize retrievals of SWE was sufficient to provide an accuracy of &lt;25 mm (unbiased) Root-Mean Square Error using certain frequency combinations. A temporally dynamic value, derived from e.g., physical snow models, is necessary to further improve retrieval skill, in particular for snow regimes with larger temporal variability in snow microstructure and a more pronounced layered structure.<\/jats:p>","DOI":"10.3390\/rs10020170","type":"journal-article","created":{"date-parts":[[2018,1,25]],"date-time":"2018-01-25T12:25:49Z","timestamp":1516883149000},"page":"170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements"],"prefix":"10.3390","volume":"10","author":[{"given":"Juha","family":"Lemmetyinen","sequence":"first","affiliation":[{"name":"Finnish Meteorological Institute, Erik Palm\u00e9nin aukio 1, FI-00560 Helsinki, Finland"},{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chris","family":"Derksen","sequence":"additional","affiliation":[{"name":"Environment and Climate Change Canada, 4905 Dufferin Street, Toronto, ON M3H 5T4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helmut","family":"Rott","sequence":"additional","affiliation":[{"name":"ENVEO IT GmbH, F\u00fcrstenweg 176, A-6020 Innsbruck, Austria"},{"name":"Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, A-6020 Innsbruck, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9738-7939","authenticated-orcid":false,"given":"Giovanni","family":"Macelloni","sequence":"additional","affiliation":[{"name":"Institute of Applied Physics \u201cNello Carrara\u201d, Via Madonna del Piano, 10-50019 Sesto Fiorentino (FI), Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josh","family":"King","sequence":"additional","affiliation":[{"name":"Environment and Climate Change Canada, 4905 Dufferin Street, Toronto, ON M3H 5T4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2872-4409","authenticated-orcid":false,"given":"Martin","family":"Schneebeli","sequence":"additional","affiliation":[{"name":"WSL Institute for Snow and Avalanche Research SLF, Fl\u00fcelastrasse 11, CH-7260 Davos Dorf, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5889-2887","authenticated-orcid":false,"given":"Andreas","family":"Wiesmann","sequence":"additional","affiliation":[{"name":"GAMMA Remote Sensing Research and Consulting AG, Worbstr. 225, CH-3073 G\u00fcmligen, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leena","family":"Lepp\u00e4nen","sequence":"additional","affiliation":[{"name":"Finnish Meteorological Institute, Erik Palm\u00e9nin aukio 1, FI-00560 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Kontu","sequence":"additional","affiliation":[{"name":"Finnish Meteorological Institute, Erik Palm\u00e9nin aukio 1, FI-00560 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jouni","family":"Pulliainen","sequence":"additional","affiliation":[{"name":"Finnish Meteorological Institute, Erik Palm\u00e9nin aukio 1, FI-00560 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8037","DOI":"10.1175\/JCLI-D-15-0229.1","article-title":"Characterization of Northern Hemisphere snow water equivalent datasets, 1981\u20132010","volume":"28","author":"Mudryk","year":"2015","journal-title":"J. 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