Snow Water Equivalent Retrieval Using Active and Passive Microwave Observations

This paper implements a newly developed combined active and passive algorithm for the retrieval of snow water equivalent (SWE) by using three- channel active and two- channel passive observations. First, passive microwave observations at 19 and 37Â GHz are used to determine the scattering albedo of...

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Published in:Water Resources Research
Main Authors: Zhu, Jiyue, Tan, Shurun, Tsang, Leung, Kang, Do‐hyuk, Kim, Edward
Format: Article in Journal/Newspaper
Language:unknown
Published: International Society for Optics and Photonics 2021
Subjects:
combine active and passive
scattering albedo
snow water equivalent (SWE)
Natural Resources and Environment
Science
Annals of Glaciology
Online Access:https://hdl.handle.net/2027.42/168354
https://doi.org/10.1029/2020WR027563
id ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/168354
record_format openpolar
institution Open Polar
collection University of Michigan: Deep Blue
op_collection_id ftumdeepblue
language unknown
topic combine active and passive
scattering albedo
snow water equivalent (SWE)
Natural Resources and Environment
Science
spellingShingle combine active and passive
scattering albedo
snow water equivalent (SWE)
Natural Resources and Environment
Science
Zhu, Jiyue
Tan, Shurun
Tsang, Leung
Kang, Do‐hyuk
Kim, Edward
Snow Water Equivalent Retrieval Using Active and Passive Microwave Observations
topic_facet combine active and passive
scattering albedo
snow water equivalent (SWE)
Natural Resources and Environment
Science
description This paper implements a newly developed combined active and passive algorithm for the retrieval of snow water equivalent (SWE) by using three- channel active and two- channel passive observations. First, passive microwave observations at 19 and 37Â GHz are used to determine the scattering albedo of snow. An a priori scattering albedo is obtained by averaging over time series observations. Second, 13.3Â GHz is introduced to formulate a three- channel (9.6, 13.3, and 17.2Â GHz) radar algorithm which reduces effects of background scattering from the snow- soil interface, and improves SWE retrieval. In the algorithm, the bicontinuous dense media radiative transfer (DMRT- Bic) is used to compute look- up tables (LUTs) of both radar backscatter and radiometer brightness temperatures (TBs) of the snowpack. To accelerate the retrieval, a parameterized model is derived from LUT by regression training, which links backscatter to the scattering albedo at 9.6Â GHz or 13.3Â GHz and to SWE. The volume scattering of snow is obtained by subtracting the background scattering from radar observations. SWE is then retrieved through a cost function that is guided by the a priori scattering albedo obtained from the passive microwave observations. The proposed algorithm, along with the active- only version, is evaluated against the Finnish Nordic Snow Radar Experiment (NoSREx) data set measured in 2009- 2013. The combined active- passive algorithm achieves root mean square errors (RSME) less than 27Â mm and correlation coefficients above 0.68 for 2009- 2010, RMSE less than 21Â mm and correlation above 0.85 for 2010- 2011, and RMSE less than 40Â mm and correlation above 0.38 for 2012- 2013.Key PointsSnow water equivalent retrieval using X (9.6Â GHz) and upper Ku band (17.2Â GHz) radar observations is improved by adding lower Ku- band (13.3Â GHz) dataPassive observations are used to obtain scattering albedos, which improves the radar retrieval algorithm performanceThe resulting combined active and passive algorithm is validated against ...
format Article in Journal/Newspaper
author Zhu, Jiyue
Tan, Shurun
Tsang, Leung
Kang, Do‐hyuk
Kim, Edward
author_facet Zhu, Jiyue
Tan, Shurun
Tsang, Leung
Kang, Do‐hyuk
Kim, Edward
author_sort Zhu, Jiyue
title Snow Water Equivalent Retrieval Using Active and Passive Microwave Observations
title_short Snow Water Equivalent Retrieval Using Active and Passive Microwave Observations
title_full Snow Water Equivalent Retrieval Using Active and Passive Microwave Observations
title_fullStr Snow Water Equivalent Retrieval Using Active and Passive Microwave Observations
title_full_unstemmed Snow Water Equivalent Retrieval Using Active and Passive Microwave Observations
title_sort snow water equivalent retrieval using active and passive microwave observations
publisher International Society for Optics and Photonics
publishDate 2021
url https://hdl.handle.net/2027.42/168354
https://doi.org/10.1029/2020WR027563
genre Annals of Glaciology
genre_facet Annals of Glaciology
op_relation Zhu, Jiyue; Tan, Shurun; Tsang, Leung; Kang, Do‐hyuk
Kim, Edward (2021). "Snow Water Equivalent Retrieval Using Active and Passive Microwave Observations." Water Resources Research 57(7): n/a-n/a.
0043-1397
1944-7973
https://hdl.handle.net/2027.42/168354
doi:10.1029/2020WR027563
Water Resources Research
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Markus, T., & Cavalieri, D. J. ( 1998 ). Snow depth distribution over sea ice in the Southern Ocean from satellite passive microwave data. Antarctic Sea Ice: Physical Processes, Interactions and Variability, 74, 19 - 39.
Meta, A., Imbembo, E., Trampuz, C., Coccia, A., & De Luca, G. ( 2012 ). A selection of meta sensing airborne campaigns at L- , X- and Ku band. EUSAR 2012; 9th European Conference on Synthetic Aperture Radar, Nuremberg, Germany (pp. 414 - 417 ).
National Research Council. ( 2007 ). Earth science and applications from space: National imperatives for the next decade and beyond. National Academies Press.
Oh, Y., Sarabandi, K., & Ulaby, F. T. ( 1992 ). An empirical model and an inversion technique for radar scattering from bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 30 ( 2 ), 370 - 381. https://doi.org/10.1109/36.134086
Pan, J., Durand, M. T., Vander Jagt, B. J., & Liu, D. ( 2017 ). Application of a Markov Chain Monte Carlo algorithm for snow water equivalent retrieval from passive microwave measurements. Remote Sensing of Environment, 192, 150 - 165. https://doi.org/10.1016/j.rse.2017.02.006
Pettinato, S., Santi, E., Brogioni, M., Paloscia, S., Palchetti, E., & Xiong, C. ( 2013 ). The potential of COSMO- SkyMed SAR images in monitoring snow cover characteristics. IEEE Geoscience and Remote Sensing Letters, 10 ( 1 ), 9 - 13. https://doi.org/10.1109/LGRS.2012.2189752
Proksch, M., Mätzler, C., Wiesmann, A., Lemmetyinen, J., Schwank, M., Löwe, H., & Schneebeli, M. ( 2015 ). MEMLS3&a: Microwave Emission Model of Layered Snowpacks adapted to include backscattering. Geoscientific Model Development, 8, 2611 - 2626. https://doi.org/10.5194/gmd-8-2611-2015
Pulliainen, J., & Hallikainen, M. ( 2001 ). Retrieval of regional snow water equivalent from space- borne passive microwave observations. Remote Sensing of Environment, 75 ( 1 ), 76 - 85. https://doi.org/10.1016/s0034-4257(00)00157-7
Saberi, N., Kelly, R., Flemming, M., & Li, Q. ( 2020 ). Review of snow water equivalent retrieval methods using spaceborne passive microwave radiometry. International Journal of Remote Sensing, 41 ( 3 ), 996 - 1018. https://doi.org/10.1080/01431161.2019.1654144
Shah, R., Xu, X., Yueh, S., Chae, C. S., Elder, K., Starr, B., & Kim, Y. ( 2017 ). Remote sensing of snow water equivalent using P- band coherent reflection. IEEE Geoscience and Remote Sensing Letters, 14 ( 3 ), 309 - 313. https://doi.org/10.1109/lgrs.2016.2636664
Shi, J., & Dozier, J. ( 2000 ). Estimation of snow water equivalence using SIR- C/X- SAR. II. Inferring snow depth and particle size. IEEE Transactions on Geoscience and Remote Sensing, 38 ( 6 ), 2475 - 2488. https://doi.org/10.1109/36.885196
Staudinger, M., Stahl, K., & Seibert, J. ( 2014 ). A drought index accounting for snow. Water Resources Research, 50, 7861 - 7872. https://doi.org/10.1002/2013WR015143
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Sturm, M., Goldstein, M. A., & Parr, C. ( 2017 ). Water and life from snow: A trillion dollar science question. Water Resources Research, 53, 3534 - 3544. https://doi.org/10.1002/2017WR020840
Sturm, M., Holmgren, J., & Liston, G.E. ( 1995 ). A seasonal snow cover classification system for local to global applications. Journal of Climate, 8, 1261 - 1283. https://doi.org/10.1175/1520-0442(1995)008<1261:ASSCCS>2.0.CO;2
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Thompson, A., & Kelly, R. ( 2019 ). Observations of a coniferous forest at 9.6 and 17.2 GHz: Implications for SWE retrievals. Remote Sensing, 11 ( 1 ), 6. https://doi.org/10.3390/rs11010006
Tsang, L., Kong, J. A., & Ding, K. H. ( 2004 ). Scattering of electromagnetic waves: Theories and applications (Vol. 27 ). John Wiley & Sons.
Ulaby, F. T., & Stiles, W. H. ( 1980 ). The active and passive microwave response to snow parameters: 2. Water equivalent of dry snow. Journal of Geophysical Research, 85 ( C2 ), 1045 - 1049. https://doi.org/10.1029/jc085ic02p01045
Xiong, C., & Shi, J. ( 2017 ). The potential for estimating snow depth with QuikScat data and a snow physical model. IEEE Geoscience and Remote Sensing Letters, 14 ( 7 ), 1156 - 1160. https://doi.org/10.1109/LGRS.2017.2701808
Xu, X., Tsang, L., & Yueh, S. ( 2012 ). Electromagnetic models of co/crosspolarization of bicontinuous/DMRT in radar remote sensing of terrestrial snow at X- and Ku- band for CoReH2O and SCLP applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5 ( 3 ), 1024 - 1032. https://doi.org/10.1109/JSTARS.2012.2190719
Zhu, J., Tan, S., King, J., Derksen, C., Lemmetyinen, J., & Tsang, L. ( 2018 ). Forward and inverse radar modeling of terrestrial snow using SnowSAR data. IEEE Transactions on Geoscience and Remote Sensing, 56 ( 12 ), 7122 - 7132. https://doi.org/10.1109/TGRS.2018.2848642
Bales, R. C., Molotch, N. P., Painter, T. H., Dettinger, M. D., Rice, R., & Dozier, J. ( 2006 ). Mountain hydrology of the western United States. Water Resources Research, 42, W08432. https://doi.org/10.1029/2005WR004387
Broxton, P. D., Van Leeuwen, W. J., & Biederman, J. A. ( 2019 ). Improving snow water equivalent maps with machine learning of snow survey and lidar measurements. Water Resources Research, 55 ( 5 ), 3739 - 3757. https://doi.org/10.1029/2018wr024146
Chang, A. T. C., Foster, J. L., & Hall, D. K. ( 1987 ). Nimbus- 7 SMMR derived global snow cover parameters. Annals of Glaciology, 9, 39 - 44. https://doi.org/10.1017/s0260305500000355
Chang, W., Ding, K. H., Tsang, L., & Xu, X. ( 2016 ). Microwave scattering and medium characterization for terrestrial snow with QCA- Mie and bicontinuous models: Comparison studies. IEEE Transactions on Geoscience and Remote Sensing, 54 ( 6 ), 3637 - 3648. https://doi.org/10.1109/TGRS.2016.2522438
Chang, W., Tan, S., Lemmetyinen, J., Tsang, L., Xu, X., & Yueh, S. H. ( 2014 ). Dense media radiative transfer applied to SnowScat and SnowSAR. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 ( 9 ), 3811 - 3825. https://doi.org/10.1109/JSTARS.2014.2343519
Cline, D., Elder, K., Davis, B., Hardy, J., Liston, G. E., Imel, D., et al. ( 2003 ). Overview of the NASA cold land processes field experiment (CLPX- 2002). In Microwave Remote Sensing of the Atmosphere and Environment III (Vol. 4894, pp. 361 - 372 ). International Society for Optics and Photonics.
Cochand, M., Christe, P., Ornstein, P., & Hunkeler, D. ( 2019 ). Groundwater storage in high alpine catchments and its contribution to streamflow. Water Resources Research, 55, 2613 - 2630. https://doi.org/10.1029/2018WR022989
Cui, Y., Xiong, C., Lemmetyinen, J., Shi, J., Jiang, L., Peng, B., et al. ( 2016 ). Estimating snow water equivalent with backscattering at X and Ku band based on absorption loss. Remote Sensing, 8 ( 6 ), 505. https://doi.org/10.3390/RS8060505
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spelling ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/168354 2024-09-15T17:40:02+00:00 Snow Water Equivalent Retrieval Using Active and Passive Microwave Observations Zhu, Jiyue Tan, Shurun Tsang, Leung Kang, Do‐hyuk Kim, Edward 2021-07 application/pdf https://hdl.handle.net/2027.42/168354 https://doi.org/10.1029/2020WR027563 unknown International Society for Optics and Photonics Wiley Periodicals, Inc. Zhu, Jiyue; Tan, Shurun; Tsang, Leung; Kang, Do‐hyuk Kim, Edward (2021). "Snow Water Equivalent Retrieval Using Active and Passive Microwave Observations." Water Resources Research 57(7): n/a-n/a. 0043-1397 1944-7973 https://hdl.handle.net/2027.42/168354 doi:10.1029/2020WR027563 Water Resources Research Rott, H., Yueh, S. H., Cline, D. W., Duguay, C., Essery, R., Haas, C., et al. ( 2010 ). Cold regions hydrology high- resolution observatory for snow and cold land processes. Proceedings of the IEEE, 98 ( 5 ), 752 - 765. https://doi.org/10.1109/JPROC.2009.2038947 Markus, T., & Cavalieri, D. J. ( 1998 ). Snow depth distribution over sea ice in the Southern Ocean from satellite passive microwave data. Antarctic Sea Ice: Physical Processes, Interactions and Variability, 74, 19 - 39. Meta, A., Imbembo, E., Trampuz, C., Coccia, A., & De Luca, G. ( 2012 ). A selection of meta sensing airborne campaigns at L- , X- and Ku band. EUSAR 2012; 9th European Conference on Synthetic Aperture Radar, Nuremberg, Germany (pp. 414 - 417 ). National Research Council. ( 2007 ). Earth science and applications from space: National imperatives for the next decade and beyond. National Academies Press. Oh, Y., Sarabandi, K., & Ulaby, F. T. ( 1992 ). An empirical model and an inversion technique for radar scattering from bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 30 ( 2 ), 370 - 381. https://doi.org/10.1109/36.134086 Pan, J., Durand, M. T., Vander Jagt, B. J., & Liu, D. ( 2017 ). Application of a Markov Chain Monte Carlo algorithm for snow water equivalent retrieval from passive microwave measurements. Remote Sensing of Environment, 192, 150 - 165. https://doi.org/10.1016/j.rse.2017.02.006 Pettinato, S., Santi, E., Brogioni, M., Paloscia, S., Palchetti, E., & Xiong, C. ( 2013 ). The potential of COSMO- SkyMed SAR images in monitoring snow cover characteristics. IEEE Geoscience and Remote Sensing Letters, 10 ( 1 ), 9 - 13. https://doi.org/10.1109/LGRS.2012.2189752 Proksch, M., Mätzler, C., Wiesmann, A., Lemmetyinen, J., Schwank, M., Löwe, H., & Schneebeli, M. ( 2015 ). MEMLS3&a: Microwave Emission Model of Layered Snowpacks adapted to include backscattering. Geoscientific Model Development, 8, 2611 - 2626. https://doi.org/10.5194/gmd-8-2611-2015 Pulliainen, J., & Hallikainen, M. ( 2001 ). Retrieval of regional snow water equivalent from space- borne passive microwave observations. Remote Sensing of Environment, 75 ( 1 ), 76 - 85. https://doi.org/10.1016/s0034-4257(00)00157-7 Saberi, N., Kelly, R., Flemming, M., & Li, Q. ( 2020 ). Review of snow water equivalent retrieval methods using spaceborne passive microwave radiometry. International Journal of Remote Sensing, 41 ( 3 ), 996 - 1018. https://doi.org/10.1080/01431161.2019.1654144 Shah, R., Xu, X., Yueh, S., Chae, C. S., Elder, K., Starr, B., & Kim, Y. ( 2017 ). Remote sensing of snow water equivalent using P- band coherent reflection. IEEE Geoscience and Remote Sensing Letters, 14 ( 3 ), 309 - 313. https://doi.org/10.1109/lgrs.2016.2636664 Shi, J., & Dozier, J. ( 2000 ). Estimation of snow water equivalence using SIR- C/X- SAR. II. Inferring snow depth and particle size. IEEE Transactions on Geoscience and Remote Sensing, 38 ( 6 ), 2475 - 2488. https://doi.org/10.1109/36.885196 Staudinger, M., Stahl, K., & Seibert, J. ( 2014 ). A drought index accounting for snow. Water Resources Research, 50, 7861 - 7872. https://doi.org/10.1002/2013WR015143 Sturm, M. ( 2015 ). White water: Fifty years of snow research in WRR and the outlook for the future. Water Resources Research, 51, 4948 - 4965. https://doi.org/10.1002/2015WR017242 Sturm, M., Goldstein, M. A., & Parr, C. ( 2017 ). Water and life from snow: A trillion dollar science question. Water Resources Research, 53, 3534 - 3544. https://doi.org/10.1002/2017WR020840 Sturm, M., Holmgren, J., & Liston, G.E. ( 1995 ). A seasonal snow cover classification system for local to global applications. Journal of Climate, 8, 1261 - 1283. https://doi.org/10.1175/1520-0442(1995)008<1261:ASSCCS>2.0.CO;2 Tague, C., & Grant, G. E. ( 2009 ). Groundwater dynamics mediate low- flow response to global warming in snow- dominated alpine regions. Water Resources Research, 45, W07421. https://doi.org/10.1029/2008WR007179 Tan, S., Chang, W., Tsang, L., Lemmetyinen, J., & Proksch, M. ( 2015 ). Modeling both active and passive microwave remote sensing of snow using dense media radiative transfer (DMRT) theory with multiple scattering and backscattering enhancement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 ( 9 ), 4418 - 4430. https://doi.org/10.1109/JSTARS.2015.2469290 Thompson, A., & Kelly, R. ( 2019 ). Observations of a coniferous forest at 9.6 and 17.2 GHz: Implications for SWE retrievals. Remote Sensing, 11 ( 1 ), 6. https://doi.org/10.3390/rs11010006 Tsang, L., Kong, J. A., & Ding, K. H. ( 2004 ). Scattering of electromagnetic waves: Theories and applications (Vol. 27 ). John Wiley & Sons. Ulaby, F. T., & Stiles, W. H. ( 1980 ). The active and passive microwave response to snow parameters: 2. Water equivalent of dry snow. Journal of Geophysical Research, 85 ( C2 ), 1045 - 1049. https://doi.org/10.1029/jc085ic02p01045 Xiong, C., & Shi, J. ( 2017 ). The potential for estimating snow depth with QuikScat data and a snow physical model. IEEE Geoscience and Remote Sensing Letters, 14 ( 7 ), 1156 - 1160. https://doi.org/10.1109/LGRS.2017.2701808 Xu, X., Tsang, L., & Yueh, S. ( 2012 ). Electromagnetic models of co/crosspolarization of bicontinuous/DMRT in radar remote sensing of terrestrial snow at X- and Ku- band for CoReH2O and SCLP applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5 ( 3 ), 1024 - 1032. https://doi.org/10.1109/JSTARS.2012.2190719 Zhu, J., Tan, S., King, J., Derksen, C., Lemmetyinen, J., & Tsang, L. ( 2018 ). Forward and inverse radar modeling of terrestrial snow using SnowSAR data. IEEE Transactions on Geoscience and Remote Sensing, 56 ( 12 ), 7122 - 7132. https://doi.org/10.1109/TGRS.2018.2848642 Bales, R. C., Molotch, N. P., Painter, T. H., Dettinger, M. D., Rice, R., & Dozier, J. ( 2006 ). Mountain hydrology of the western United States. Water Resources Research, 42, W08432. https://doi.org/10.1029/2005WR004387 Broxton, P. D., Van Leeuwen, W. J., & Biederman, J. A. ( 2019 ). Improving snow water equivalent maps with machine learning of snow survey and lidar measurements. Water Resources Research, 55 ( 5 ), 3739 - 3757. https://doi.org/10.1029/2018wr024146 Chang, A. T. C., Foster, J. L., & Hall, D. K. ( 1987 ). Nimbus- 7 SMMR derived global snow cover parameters. Annals of Glaciology, 9, 39 - 44. https://doi.org/10.1017/s0260305500000355 Chang, W., Ding, K. H., Tsang, L., & Xu, X. ( 2016 ). Microwave scattering and medium characterization for terrestrial snow with QCA- Mie and bicontinuous models: Comparison studies. IEEE Transactions on Geoscience and Remote Sensing, 54 ( 6 ), 3637 - 3648. https://doi.org/10.1109/TGRS.2016.2522438 Chang, W., Tan, S., Lemmetyinen, J., Tsang, L., Xu, X., & Yueh, S. H. ( 2014 ). Dense media radiative transfer applied to SnowScat and SnowSAR. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 ( 9 ), 3811 - 3825. https://doi.org/10.1109/JSTARS.2014.2343519 Cline, D., Elder, K., Davis, B., Hardy, J., Liston, G. E., Imel, D., et al. ( 2003 ). Overview of the NASA cold land processes field experiment (CLPX- 2002). In Microwave Remote Sensing of the Atmosphere and Environment III (Vol. 4894, pp. 361 - 372 ). International Society for Optics and Photonics. Cochand, M., Christe, P., Ornstein, P., & Hunkeler, D. ( 2019 ). Groundwater storage in high alpine catchments and its contribution to streamflow. Water Resources Research, 55, 2613 - 2630. https://doi.org/10.1029/2018WR022989 Cui, Y., Xiong, C., Lemmetyinen, J., Shi, J., Jiang, L., Peng, B., et al. ( 2016 ). Estimating snow water equivalent with backscattering at X and Ku band based on absorption loss. Remote Sensing, 8 ( 6 ), 505. https://doi.org/10.3390/RS8060505 Derksen, C., Lemmetyinen, J., King, J., Garnaud, C., Belair, S., Lapointe, M., et al ( 2018 ). A new dual- frequency Ku- band radar mission concept for cryosphere applications. Paper presented at 12th European Conference on Synthetic Aperture Radar Electronic Proceedings, 04- 07 June, 2018. Aachen, Germany. Déry, S. J., Stahl, K., Moore, R. D., Whitfield, P. H., Menounos, B., & Burford, J. E. ( 2009 ). Detection of runoff timing changes in pluvial, nival, and glacial rivers of western Canada. Water Resources Research, 45, W04426. https://doi.org/10.1029/2008WR006975 Diffenbaugh, N. S., Swain, D. L., & Touma, D. ( 2015 ). Anthropogenic warming has increased drought risk in California. Proceedings of the National Academy of Sciences, 112 ( 13 ), 3931 - 3936. https://doi.org/10.1073/pnas.1422385112 Ding, K. H., Xu, X., & Tsang, L. ( 2010 ). Electromagnetic scattering by bicontinuous random microstructures with discrete permittivities. IEEE Transactions on Geoscience and Remote Sensing, 48 ( 8 ), 3139 - 3151. https://doi.org/10.1109/TGRS.2010.2043953 Drinkwater, M. R., Long, D. G., & Bingham, A. W. ( 2001 ). Greenland snow accumulation estimates from satellite radar scatterometer data. Journal of Geophysical Research, 106 ( D24 ), 33935 - 33950. https://doi.org/10.1029/2001JD900107 Durand, M., Molotch, N. P., & Margulis, S. A. ( 2008 ). Merging complementary remote sensing datasets in the context of snow water equivalent reconstruction. Remote Sensing of Environment, 112 ( 3 ), 1212 - 1225. https://doi.org/10.1016/j.rse.2007.08.010 Ficke, A. D., Myrick, C. A., & Hansen, L. J. ( 2007 ). Potential impacts of global climate change on freshwater fisheries. Reviews in Fish Biology and Fisheries, 17 ( 4 ), 581 - 613. https://doi.org/10.1007/s11160-007-9059-5 Fowler, H. J., Kilsby, C. G., & O’Connell, P. E. ( 2003 ). 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Hydrological Processes, 11 ( 8 ), 903 - 924. https://doi.org/10.1002/(sici)1099-1085(19970630)11:8<903::aid-hyp511>3.0.co;2-7 IndexNoFollow combine active and passive scattering albedo snow water equivalent (SWE) Natural Resources and Environment Science Article 2021 ftumdeepblue https://doi.org/10.1029/2020WR02756310.1109/JPROC.2009.203894710.1002/2015WR01724210.3390/RS806050510.1002/(sici)1099-1085(19970630)11:8<903::aid-hyp511>3.0.co;2-710.1016/j.rse.2018.05.02810.3390/rs1002017010.1016/j.rse.2014.09.01610.5194/gi-5-403-201610. 2024-07-30T04:06:07Z This paper implements a newly developed combined active and passive algorithm for the retrieval of snow water equivalent (SWE) by using three- channel active and two- channel passive observations. First, passive microwave observations at 19 and 37 GHz are used to determine the scattering albedo of snow. An a priori scattering albedo is obtained by averaging over time series observations. Second, 13.3 GHz is introduced to formulate a three- channel (9.6, 13.3, and 17.2 GHz) radar algorithm which reduces effects of background scattering from the snow- soil interface, and improves SWE retrieval. In the algorithm, the bicontinuous dense media radiative transfer (DMRT- Bic) is used to compute look- up tables (LUTs) of both radar backscatter and radiometer brightness temperatures (TBs) of the snowpack. To accelerate the retrieval, a parameterized model is derived from LUT by regression training, which links backscatter to the scattering albedo at 9.6 GHz or 13.3 GHz and to SWE. The volume scattering of snow is obtained by subtracting the background scattering from radar observations. SWE is then retrieved through a cost function that is guided by the a priori scattering albedo obtained from the passive microwave observations. The proposed algorithm, along with the active- only version, is evaluated against the Finnish Nordic Snow Radar Experiment (NoSREx) data set measured in 2009- 2013. The combined active- passive algorithm achieves root mean square errors (RSME) less than 27 mm and correlation coefficients above 0.68 for 2009- 2010, RMSE less than 21 mm and correlation above 0.85 for 2010- 2011, and RMSE less than 40 mm and correlation above 0.38 for 2012- 2013.Key PointsSnow water equivalent retrieval using X (9.6 GHz) and upper Ku band (17.2 GHz) radar observations is improved by adding lower Ku- band (13.3 GHz) dataPassive observations are used to obtain scattering albedos, which improves the radar retrieval algorithm performanceThe resulting combined active and passive algorithm is validated against ... Article in Journal/Newspaper Annals of Glaciology University of Michigan: Deep Blue Water Resources Research 57 7

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