{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:48:39Z","timestamp":1775472519285,"version":"3.50.1"},"reference-count":186,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T00:00:00Z","timestamp":1676419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring snowpack depth is essential in many applications at regional and global scales. Space-borne passive microwave (PMW) remote sensing observations have been widely used to estimate snow depth (SD) information for over four decades due to their responsiveness to snowpack characteristics. Many approaches comprised of static and dynamic empirical models, non-linear, machine-learning-based models, and assimilation approaches have been developed using spaceborne PMW observations. These models cannot be applied uniformly over all regions due to inherent limitations in the modelling approaches. Further, the global PMW SD products have masked out in their coverage critical regions such as the Himalayas, as well as very high SD regions, due to constraints triggered by prevailing topographical and snow conditions. Therefore, the current review article discusses different models for SD estimation, along with their merits and limitations. Here in the review, various SD models are grouped into four types, i.e., static, dynamic, assimilation-based, and machine-learning-based models. To demonstrate the rationale behind these drawbacks, this review also details various causes of uncertainty, and the challenges present in the estimation of PMW SD. Finally, based on the status of the available PMW SD datasets, and SD estimation techniques, recommendations for future research are included in this article.<\/jats:p>","DOI":"10.3390\/rs15041052","type":"journal-article","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T03:09:21Z","timestamp":1676430561000},"page":"1052","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Passive Microwave Remote Sensing of Snow Depth: Techniques, Challenges and Future Directions"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6632-6893","authenticated-orcid":false,"given":"Srinivasarao","family":"Tanniru","sequence":"first","affiliation":[{"name":"Hydro-Remote Sensing Applications (H-RSA) Group, Department Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8602-1934","authenticated-orcid":false,"given":"RAAJ","family":"Ramsankaran","sequence":"additional","affiliation":[{"name":"Hydro-Remote Sensing Applications (H-RSA) Group, Department Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India"},{"name":"Interdisciplinary Program in Climate Studies, Indian Institute of Technology Bombay, Mumbai 400076, India"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"130","DOI":"10.3389\/feart.2018.00130","article-title":"Snow Cover Change as a Climate Indicator in Brunswick Peninsula, Patagonia","volume":"6","author":"Aguirre","year":"2018","journal-title":"Front. Earth Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100","DOI":"10.3389\/feart.2020.00100","article-title":"Changes in Climatology, Snow Cover, and Ground Temperatures at High Alpine Locations","volume":"8","author":"Bender","year":"2020","journal-title":"Front. Earth Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1080\/10106049.2018.1469675","article-title":"Snow Cover Area Change and Its Relations with Climatic Variability in Kashmir Himalayas, India","volume":"34","author":"Ahmed","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"137","DOI":"10.5194\/essd-7-137-2015","article-title":"A Long-Term Northern Hemisphere Snow Cover Extent Data Record for Climate Studies and Monitoring","volume":"7","author":"Estilow","year":"2015","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_5","unstructured":"Lemke, P., Ren, J., Alley, R.B., Allison, I., Carrasco, J., Flato, G., Fujii, Y., Kaser, G., Mote, P., and Thomas, R.H. (2007). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4245","DOI":"10.1175\/1520-0442(2001)014<4245:SAFASC>2.0.CO;2","article-title":"Snow-Albedo Feedback and Seasonal Climate Variability over North America","volume":"14","author":"Yang","year":"2001","journal-title":"J. Clim."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1080\/02626667.2019.1599486","article-title":"Effects of Snow-Depth Change on Spring Runoff in Cryosphere Areas of China","volume":"64","author":"Liu","year":"2019","journal-title":"Hydrol. Sci. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.5194\/tc-11-1647-2017","article-title":"Assimilation of Snow Cover and Snow Depth into a Snow Model to Estimate Snow Water Equivalent and Snowmelt Runoff in a Himalayan Catchment","volume":"11","author":"Stigter","year":"2017","journal-title":"Cryosphere"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1029\/2002RG000123","article-title":"Snow Avalanche Formation","volume":"41","author":"Schweizer","year":"2003","journal-title":"Rev. Geophys."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Beniston, M. (1997). Climatic Change at High Elevation Sites, Springer.","DOI":"10.1007\/978-94-015-8905-5_1"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1038\/nclimate2237","article-title":"Consistent Increase in High Asia\u2019s Runoff Due to Increasing Glacier Melt and Precipitation","volume":"4","author":"Lutz","year":"2014","journal-title":"Nat. Clim. Change"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"177","DOI":"10.3389\/feart.2019.00177","article-title":"Near Real-Time Measurement of Snow Water Equivalent in the Nepal Himalayas","volume":"7","author":"Kirkham","year":"2019","journal-title":"Front. Earth Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1002\/2015RG000481","article-title":"Measurement of the Physical Properties of the Snowpack","volume":"53","author":"Kinar","year":"2015","journal-title":"Rev. Geophys."},{"key":"ref_14","first-page":"942","article-title":"Snow Depth Estimation in the Indian Himalaya Using Multi-Channel Passive Microwave Radiometer","volume":"108","author":"Singh","year":"2015","journal-title":"Curr. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.jhydrol.2018.04.027","article-title":"Remote Sensing, Hydrological Modeling and in Situ Observations in Snow Cover Research: A Review","volume":"561","author":"Dong","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"617","DOI":"10.5194\/tc-5-617-2011","article-title":"Variability of Snow Depth at the Plot Scale: Implications for Mean Depth Estimation and Sampling Strategies","volume":"5","author":"Fassnacht","year":"2011","journal-title":"Cryosphere"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.1002\/hyp.9355","article-title":"Subgrid Variability of Snow Water Equivalent at Operational Snow Stations in the Western USA","volume":"27","author":"Meromy","year":"2013","journal-title":"Hydrol. Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kongoli, C., Key, J., and Smith, T.M. (2019). Mapping of Snow Depth by Blending Satellite and In-Situ Data Using Two-Dimensional Optimal Interpolation-Application to AMSR2. Remote Sens., 11.","DOI":"10.3390\/rs11243049"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1007\/s10651-020-00461-5","article-title":"Statistical Methods for Forecasting Daily Snow Depths and Assessing Trends in Inter-Annual Snow Depth Dynamics","volume":"27","author":"Woody","year":"2020","journal-title":"Environ. Ecol. Stat."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.5194\/tc-10-1075-2016","article-title":"Mapping Snow Depth in Alpine Terrain with Unmanned Aerial Systems (UASs): Potential and Limitations","volume":"10","author":"Buhler","year":"2016","journal-title":"Cryosphere"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2022.01.020","article-title":"Direct Photogrammetry with Multispectral Imagery for UAV-Based Snow Depth Estimation","volume":"186","author":"Maier","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1017\/S0260305500200736","article-title":"Nimbus-7 SMMR Derived Global Snow Cover Parameters","volume":"9","author":"Chang","year":"1987","journal-title":"Ann. Glaciol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/S0034-4257(97)00085-0","article-title":"Comparison of Snow Mass Estimates from a Prototype Passive Microwave Snow Algorithm, a Revised Algorithm and a Snow Depth Climatology","volume":"62","author":"Foster","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_24","first-page":"307","article-title":"The AMSR-E Snow Depth Algorithm: Description and Initial Results","volume":"29","author":"Kelly","year":"2009","journal-title":"J. Remote Sens. Soc. Jpn."},{"key":"ref_25","first-page":"4157","article-title":"Uncertainty Analysis for Evaluating the Accuracy of Snow Depth Measurements","volume":"12","author":"Lee","year":"2015","journal-title":"Hydrol. Earth Syst. Sci. Discuss."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.rse.2018.03.008","article-title":"Support Vector Regression Snow-Depth Retrieval Algorithm Using Passive Microwave Remote Sensing Data","volume":"210","author":"Xiao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"112630","DOI":"10.1016\/j.rse.2021.112630","article-title":"Improving Snow Depth Estimation by Coupling HUT-Optimized Effective Snow Grain Size Parameters with the Random Forest Approach","volume":"264","author":"Yang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_28","unstructured":"Shi, L., Qiu, Y., Lemmetyinen, J., and Shi, J. (August, January 28). Atmospheric Correction of Passive Microwave Brightness Temperature on the Estimation of Snow Depth. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"229","DOI":"10.5194\/tc-9-229-2015","article-title":"Snow Depth Mapping in High-Alpine Catchments Using Digital Photogrammetry","volume":"9","author":"Marty","year":"2015","journal-title":"Cryosphere"},{"key":"ref_30","unstructured":"Hall, D.K., Kelly, R.E., Foster, J.L., and Chang, A.T. (2006). Encyclopedia of Hydrological Sciences, John Wiley & Sons, Ltd.. Chapter 55."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.3189\/002214311796406077","article-title":"Recent Advances in Remote Sensing of Seasonal Snow","volume":"56","author":"Nolin","year":"2010","journal-title":"J. Glaciol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.coldregions.2008.07.002","article-title":"Assessing the Applicability of Terrestrial Laser Scanning for Spatial Snow Depth Measurements","volume":"54","author":"Prokop","year":"2008","journal-title":"Cold Reg. Sci. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2166\/nh.1987.0001","article-title":"Microwave Remote Sensing of Snowpack Properties: Potential and Limitations","volume":"18","author":"Bernier","year":"1987","journal-title":"Hydrol. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/B978-0-12-409548-9.10358-6","article-title":"Snow Properties from Passive Microwave","volume":"4","author":"Hallikainen","year":"2018","journal-title":"Compr. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3794","DOI":"10.1109\/JSTARS.2014.2323199","article-title":"Snow Height Determination by Polarimetric Phase Differences in X-Band SAR Data","volume":"7","author":"Leinss","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8739598","DOI":"10.1155\/2017\/8739598","article-title":"Estimating Snow Depth and Snow Water Equivalence Using Repeat-Pass Interferometric SAR in the Northern Piedmont Region of the Tianshan Mountains","volume":"2017","author":"Li","year":"2017","journal-title":"J. Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4629","DOI":"10.1038\/s41467-019-12566-y","article-title":"Snow Depth Variability in the Northern Hemisphere Mountains Observed from Space","volume":"10","author":"Lievens","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mahmoodzada, A.B., Varade, D., and Shimada, S. (2020). Estimation of Snow Depth in the Hindu Kush Himalayas of Afghanistan during Peakwinter and Early Melt Season. Remote Sens., 12.","DOI":"10.3390\/rs12172788"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Patil, A., Singh, G., and R\u00fcdiger, C. (2020). Retrieval of Snow Depth and Snow Water Equivalent Using Dual Polarization SAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12071183"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1080\/2150704X.2020.1779373","article-title":"Snow Depth and Snow Water Equivalent Retrieval Using X-Band PolInSAR Data","volume":"11","author":"Patil","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"9","DOI":"10.3189\/2015AoG69A886","article-title":"Snow Thickness Retrieval from L-Band Brightness Temperatures: A Model Comparison","volume":"56","author":"Maass","year":"2015","journal-title":"Ann. Glaciol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3501","DOI":"10.1029\/2000GL012484","article-title":"Penetration Depth of Interferometric Synthetic-Aperture Radar Signals in Snow and Ice","volume":"28","author":"Rignot","year":"2001","journal-title":"Geophys. Res. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/0273-1177(81)90389-6","article-title":"Microwave response of snow","volume":"1","author":"Ulaby","year":"1981","journal-title":"Adv. Space Res"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tsai, Y.L.S., Dietz, A., Oppelt, N., and Kuenzer, C. (2019). Remote Sensing of Snow Cover Using Spaceborne SAR: A Review. Remote Sens., 11.","DOI":"10.3390\/rs11121456"},{"key":"ref_45","unstructured":"Amlien, J. (2022, November 01). Remote Sensing of Snow with Passive Microwave Radiometers. A Review of Current Algorithms; Report no 1019, Norsk Regnesentral, 2008. ISBN 978-82-539-0529-7. Available online: https:\/\/vdocuments.mx\/remote-sensing-of-snow-with-passive-microwave-radiometers-6-remote-sensing-of-snow.html?page=1."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1080\/15481603.2021.1946938","article-title":"Recent Advances in the Remote Sensing of Alpine Snow: A Review","volume":"58","author":"Awasthi","year":"2021","journal-title":"GIScience Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4094","DOI":"10.1080\/01431161.2011.640964","article-title":"Remote Sensing of Snow\u2014A Review of Available Methods","volume":"33","author":"Dietz","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1080\/01431161.2019.1654144","article-title":"Review of Snow Water Equivalent Retrieval Methods Using Spaceborne Passive Microwave Radiometry","volume":"41","author":"Saberi","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Cho, E., Tuttle, S., and Jacobs, J. (2017). Evaluating Consistency of Snow Water Equivalent Retrievals from Passive Microwave Sensors over the North Central U.S.: SSM\/I vs. SSMIS and AMSR-E vs. AMSR2. Remote Sens., 9.","DOI":"10.3390\/rs9050465"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lemmetyinen, J., Derksen, C., Rott, H., Macelloni, G., King, J., Schneebeli, M., Wiesmann, A., Lepp\u00e4nen, L., Kontu, A., and Pulliainen, J. (2018). Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements. Remote Sens., 10.","DOI":"10.3390\/rs10020170"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1080\/01431161.2010.483482","article-title":"Global Estimates of Snow Water Equivalent from Passive Microwave Instruments: History, Challenges and Future Developments","volume":"31","author":"Clifford","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3313","DOI":"10.1016\/j.rse.2011.07.014","article-title":"Snowpack and Runoff Generation Using AMSR-E Passive Microwave Observations in the Upper Helmand Watershed, Afghanistan","volume":"115","author":"Vuyovich","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"e2021WR030119","DOI":"10.1029\/2021WR030119","article-title":"Solving Challenges of Assimilating Microwave Remote Sensing Signatures with a Physical Model to Estimate Snow Water Equivalent","volume":"57","author":"Merkouriadi","year":"2021","journal-title":"Water Resour. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1080\/20964471.2022.2032998","article-title":"Daily Snow Water Equivalent Product with SMMR, SSM\/I and SSMIS from 1980 to 2020 over China","volume":"6","author":"Jiang","year":"2022","journal-title":"Big Earth Data"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3517","DOI":"10.1016\/j.rse.2011.08.014","article-title":"Estimating Northern Hemisphere Snow Water Equivalent for Climate Research through Assimilation of Space-Borne Radiometer Data and Ground-Based Measurements","volume":"115","author":"Takala","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1175\/2010JHM1202.1","article-title":"Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes","volume":"11","author":"Sturm","year":"2010","journal-title":"J. Hydrometeorol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/0273-1177(89)90491-2","article-title":"Snow Dielectric Measurements","volume":"9","author":"Denoth","year":"1989","journal-title":"Adv. Space Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1660","DOI":"10.1088\/0022-3727\/21\/11\/522","article-title":"Dielectric Properties of Fresh-Water Ice at Microwave Frequencies","volume":"21","year":"1988","journal-title":"J. Phys. D Appl. Phys."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1017\/S0022143000031415","article-title":"Microwave Emission from Snow and Glacier Ice","volume":"16","author":"Chang","year":"1976","journal-title":"J. Glaciol."},{"key":"ref_60","first-page":"1","article-title":"Analysis of the Effects of Snowpack Properties on Satellite Microwave Brightness Temperature and Emissivity Data","volume":"1","author":"Lakhankar","year":"2012","journal-title":"J. Remote Sens. GIS"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1080\/014311699213613","article-title":"Passive Microwave Data for Snow-Depth and Snow-Extent Estimations in the Himalayan Mountains","volume":"20","author":"Saraf","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","unstructured":"Chang, A.T.C., Hall, D.K., Foster, J.L., Rango, A., and Schmugge, T.J. (2022, November 15). Studies of Snowpack Properties by Passive Microwave Radiometry, Available online: https:\/\/ntrs.nasa.gov\/citations\/19790008308."},{"key":"ref_63","first-page":"241","article-title":"Microwave Properties of Ice and Snow","volume":"Volume 227","author":"Schmitt","year":"1998","journal-title":"Solar System Ices. Astrophysics and Space Science Library"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0034-4257(99)00046-2","article-title":"Microwave Emission Model of Layered Snowpacks","volume":"70","author":"Wiesmann","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.rse.2004.09.012","article-title":"Quantifying the Uncertainty in Passive Microwave Snow Water Equivalent Observations","volume":"94","author":"Foster","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"145","DOI":"10.3189\/172756408787814690","article-title":"Snow Depth Derived from Passive Microwave Remote-Sensing Data in China","volume":"49","author":"Che","year":"2008","journal-title":"Ann. Glaciol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1134\/S0001433818030076","article-title":"Cosmos-243 as the Starting Point for the Development of Microwave Radiometry Methods of the Earth\u2019s Atmosphere and Surface","volume":"54","author":"Gorbunov","year":"2018","journal-title":"Izv. Atmos. Ocean. Phys."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1109\/TGRS.1982.350411","article-title":"Snow-Cover Parameters Retrieved from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) Data","volume":"GE-20","author":"Kunzi","year":"1982","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0165-232X(82)90019-2","article-title":"Snow Water Equivalent Estimation by Microwave Radiometry","volume":"5","author":"Chang","year":"1982","journal-title":"Cold Reg. Sci. Technol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1007\/s11430-013-4798-8","article-title":"Improvement of Snow Depth Retrieval for FY3B-MWRI in China","volume":"57","author":"Jiang","year":"2014","journal-title":"Sci. China Earth Sci."},{"key":"ref_71","first-page":"915","article-title":"Snow Depth Estimation Using a Lookup Table Method Based on MEMLS","volume":"24","author":"Liu","year":"2014","journal-title":"Gaojishu Tongxin\/Chin. High Technol. Lett."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"29663","DOI":"10.1029\/96JD03327","article-title":"Snow Parameters Derived from Microwave Measurements during the BOREAS Winter Field Campaign","volume":"102","author":"Chang","year":"1997","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1109\/JSTARS.2020.2970738","article-title":"Retrieving Snow Depth Information from AMSR2 Data for Qinghai-Tibet Plateau","volume":"13","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Wei, P., Zhang, T., Zhou, X., Yi, G., Li, J., Wang, N., and Wen, B. (2021). Reconstruction of Snow Depth Data at Moderate Spatial Resolution (1 km) from Remotely Sensed Snow Data and Multiple Optimized Environmental Factors: A Case Study over the Qinghai-Tibetan Plateau. Remote Sens., 13.","DOI":"10.3390\/rs13040657"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1002\/hyp.1020","article-title":"A Passive Microwave Snow Depth Algorithm with a Proxy for Snow Metamorphism","volume":"16","author":"Josberger","year":"2002","journal-title":"Hydrol. Process."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"8076","DOI":"10.1029\/2002RS002648","article-title":"Development of a Passive Microwave Global Snow Depth Retrieval Algorithm for Special Sensor Microwave Imager (SSM\/I) and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) Data","volume":"38","author":"Kelly","year":"2003","journal-title":"Radio Sci."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Dai, L., Che, T., Xie, H., and Wu, X. (2018). Estimation of Snow Depth over the Qinghai-Tibetan Plateau Based on AMSR-E and MODIS Data. Remote Sens., 10.","DOI":"10.3390\/rs10121989"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1109\/36.763302","article-title":"HUT Snow Emission Model and Its Applicability to Snow Water Equivalent Retrieval","volume":"37","author":"Pulliainen","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1029\/1999RS002270","article-title":"Dense Media Radiative Transfer Theory Based on Quasicrystalline Approximation with Applications to Passive Microwave Remote Sensing of Snow","volume":"35","author":"Tsang","year":"2000","journal-title":"Radio Sci."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.5194\/gmd-6-1061-2013","article-title":"Simulation of the Microwave Emission of Multi-Layered Snowpacks Using the Dense Media Radiative Transfer Theory: The DMRT-ML Model","volume":"6","author":"Picard","year":"2013","journal-title":"Geosci. Model Dev."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Tedesco, M., and Jeyaratnam, J. (2016). A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures. Remote Sens., 8.","DOI":"10.3390\/rs8121037"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.rse.2015.09.009","article-title":"Comparison of Passive Microwave Brightness Temperature Prediction Sensitivities over Snow-Covered Land in North America Using Machine Learning Algorithms and the Advanced Microwave Scanning Radiometer","volume":"170","author":"Xue","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1016\/j.rse.2014.10.016","article-title":"Improved Snow Depth Retrieval by Integrating Microwave Brightness Temperature and Visible\/Infrared Reflectance","volume":"156","author":"Liang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Nikraftar, Z., Hasanlou, M., and Esmaeilzadeh, M. (2016, January 12\u201319). Novel Snow Depth Retrieval Method Using Time Series SSMI Passive Microwave Imagery. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences\u2014ISPRS Archives, Prague, Czech Republic.","DOI":"10.5194\/isprsarchives-XLI-B8-525-2016"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Hu, Y., Che, T., Dai, L., and Xiao, L. (2021). Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere. Remote Sens., 13.","DOI":"10.3390\/rs13071250"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1175\/JHM502.1","article-title":"Feasibility Test of Multifrequency Radiometric Data Assimilation to Estimate Snow Water Equivalent","volume":"7","author":"Durand","year":"2006","journal-title":"J. Hydrometeorol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1109\/TGRS.2009.2036910","article-title":"Dynamic Approaches for Snow Depth Retrieval from Spaceborne Microwave Brightness Temperature","volume":"48","author":"Tedesco","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"3695","DOI":"10.1016\/j.rse.2011.09.008","article-title":"Correcting for the Influence of Frozen Lakes in Satellite Microwave Radiometer Observations through Application of a Microwave Emission Model","volume":"115","author":"Lemmetyinen","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.rse.2013.12.009","article-title":"Assimilating Passive Microwave Remote Sensing Data into a Land Surface Model to Improve the Estimation of Snow Depth","volume":"143","author":"Che","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1175\/JHM-D-16-0102.1","article-title":"Improving the Radiance Assimilation Performance in Estimating Snow Water Storage across Snow and Land-Cover Types in North America","volume":"18","author":"Kwon","year":"2017","journal-title":"J. Hydrometeorol."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"857","DOI":"10.5194\/tc-11-857-2017","article-title":"Mapping Snow Depth within a Tundra Ecosystem Using Multiscale Observations and Bayesian Methods","volume":"11","author":"Wainwright","year":"2017","journal-title":"Cryosphere"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Kwon, Y., Forman, B.A., Ahmad, J.A., Kumar, S.V., and Yoon, Y. (2019). Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia. Remote Sens., 11.","DOI":"10.3390\/rs11192265"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"e2020WR029010","DOI":"10.1029\/2020WR029010","article-title":"Improving Snow Estimates Through Assimilation of MODIS Fractional Snow Cover Data Using Machine Learning Algorithms and the Common Land Model","volume":"57","author":"Hou","year":"2021","journal-title":"Water Resour. Res."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"4237","DOI":"10.1080\/01431160701874595","article-title":"Snow Depth Estimation over North-Western Indian Himalaya Using AMSR-E","volume":"29","author":"Das","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1017\/S0260305500014270","article-title":"Preliminary Analysis of Snow Microwave Radiometry Using the SSM\/I Passive-Microwave Data: The Case of La Grande River Watershed (Quebec)","volume":"25","author":"Bernier","year":"1997","journal-title":"Ann. Glaciol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"2701","DOI":"10.1016\/j.rse.2008.01.001","article-title":"The Contribution of AMSR-E 18.7 and 10.7 GHz Measurements to Improved Boreal Forest Snow Water Equivalent Retrievals","volume":"112","author":"Derksen","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1080\/01431168308948536","article-title":"& K. Microwave Remote Sensing of Snow Cover","volume":"4","author":"Schanda","year":"1983","journal-title":"Int. J. Remote Sens."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/0273-1177(89)90496-1","article-title":"Microwave Emission of Snow-Covered and Snow-Free Terrain from Satellite Measurements","volume":"9","author":"Aschbacher","year":"1989","journal-title":"Adv. Space Res."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"111268","DOI":"10.1016\/j.rse.2019.111268","article-title":"AMSR2 Snow Depth Downscaling Algorithm Based on a Multifactor Approach over the Tibetan Plateau, China","volume":"231","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.rse.2004.06.012","article-title":"Siberia Snow Depth Climatology Derived from SSM\/I Data Using a Combined Dynamic and Static Algorithm","volume":"93","author":"Grippa","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1109\/TGRS.2010.2041357","article-title":"Multiple-Layer Adaptation of HUT Snow Emission Model: Comparison with Experimental Data","volume":"48","author":"Lemmetyinen","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2011.08.029","article-title":"Snow Depth and Snow Water Equivalent Estimation from AMSR-E Data Based on a Priori Snow Characteristics in Xinjiang, China","volume":"127","author":"Dai","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s12524-016-0578-1","article-title":"Snow Depth Inversion Using the Localized HUT Model Based on FY-3B MWRI Data in the Farmland of Heilongjiang Province, China","volume":"45","author":"Wu","year":"2017","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Gu, L., Fan, X., Li, X., and Wei, Y. (2019). Snow Depth Retrieval in Farmland Based on a Statistical Lookup Table from Passive Microwave Data in Northeast China. Remote Sens., 11.","DOI":"10.3390\/rs11243037"},{"key":"ref_105","unstructured":"Kelly, R., Li, Q., and Saberi, N. (August, January 28). \u2019The AMSR2 Satellite-Based Microwave Snow Algorithm (SMSA): A New Algorithm for Estimating Global Snow Accumulation. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1876","DOI":"10.1109\/TGRS.2020.3004594","article-title":"The Use of a Monte Carlo Markov Chain Method for Snow-Depth Retrievals: A Case Study Based on Airborne Microwave Observations and Emission Modeling Experiments of Tundra Snow","volume":"59","author":"Saberi","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Saberi, N., and Kelly, R. (2014, January 13\u201318). An Evaluation of DMRT-ML for AMSR2 Estimates of Snow Depth. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6946840"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1109\/TGRS.2006.872087","article-title":"Retrieval of Dry-Snow Parameters from Microwave Radiometric Data Using a Dense-Medium Model and Genetic Algorithms","volume":"44","author":"Tedesco","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"2503","DOI":"10.1029\/2008GL035214","article-title":"Radiance Assimilation Shows Promise for Snowpack Characterization","volume":"36","author":"Durand","year":"2009","journal-title":"Geophys. Res. Lett."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.5194\/tc-12-2287-2018","article-title":"A Particle Filter Scheme for Multivariate Data Assimilation into a Point-Scale Snowpack Model in an Alpine Environment","volume":"12","author":"Piazzi","year":"2018","journal-title":"Cryosphere"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1016\/j.advwatres.2005.08.004","article-title":"Assimilating Remotely Sensed Snow Observations into a Macroscale Hydrology Model","volume":"29","author":"Andreadis","year":"2006","journal-title":"Adv. Water Resour."},{"key":"ref_112","unstructured":"Graf, T., Koike, T., Li, X., Hirai, M., and Tsutsui, H. (August, January 31). Assimilating Passive Microwave Brightness Temperature Data into a Land Surface Model to Improve the Snow Depth Predictability. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Denver, CO, USA."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.advwatres.2013.02.005","article-title":"Assimilating Satellite-Based Snow Depth and Snow Cover Products for Improving Snow Predictions in Alaska","volume":"54","author":"Liu","year":"2013","journal-title":"Adv. Water Resour."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Helmert, J., \u015eorman, A.\u015e., Montero, R.A., de Michele, C., de Rosnay, P., Dumont, M., Finger, D.C., Lange, M., Picard, G., and Potopov\u00e1, V. (2018). Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST Harmosnow Survey. Geosciences, 8.","DOI":"10.3390\/geosciences8120489"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"8045","DOI":"10.1029\/2018WR023190","article-title":"Direct Insertion of NASA Airborne Snow Observatory-Derived Snow Depth Time Series Into the ISnobal Energy Balance Snow Model","volume":"54","author":"Hedrick","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"10684","DOI":"10.1002\/2014JD022012","article-title":"Assimilating MODIS-Based Albedo and Snow Cover Fraction into the Common Land Model to Improve Snow Depth Simulation with Direct Insertion and Deterministic Ensemble Kalman Filter Methods","volume":"119","author":"Xu","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.jhydrol.2015.12.015","article-title":"Combining Snowpack Modeling and Terrestrial Laser Scanner Observations Improves the Simulation of Small Scale Snow Dynamics","volume":"533","author":"Revuelto","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1175\/2008JHM871.1","article-title":"A Simple Data Assimilation System for Complex Snow Distributions (SnowAssim)","volume":"9","author":"Liston","year":"2008","journal-title":"J. Hydrometeorol."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"103066","DOI":"10.1016\/j.coldregions.2020.103066","article-title":"Gap-Filling Snow-Depth Time-Series with Kalman Filtering-Smoothing and Expectation Maximization: Proof of Concept Using Spatially Dense Wireless-Sensor-Network Data","volume":"175","author":"Avanzi","year":"2020","journal-title":"Cold Reg. Sci. Technol."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s12517-021-06699-y","article-title":"Assimilation of D-InSAR Snow Depth Data by an Ensemble Kalman Filter","volume":"14","author":"Yang","year":"2021","journal-title":"Arab. J. Geosci."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1002\/2016WR019092","article-title":"Improving Physically Based Snow Simulations by Assimilating Snow Depths Using the Particle Filter","volume":"53","author":"Magnusson","year":"2017","journal-title":"Water Resour. Res."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1029\/2018WR023400","article-title":"Particle Filter Data Assimilation of Monthly Snow Depth Observations Improves Estimation of Snow Density and SWE","volume":"55","author":"Smyth","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2019.03.016","article-title":"Estimating Alpine Snow Depth by Combining Multifrequency Passive Radiance Observations with Ensemble Snowpack Modeling","volume":"226","author":"Kim","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/JSTARS.2010.2040462","article-title":"Assessment of the NASA AMSR-E SWE Product","volume":"3","author":"Tedesco","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.rse.2006.01.002","article-title":"Mapping of Snow Water Equivalent and Snow Depth in Boreal and Sub-Arctic Zones by Assimilating Space-Borne Microwave Radiometer Data and Ground-Based Observations","volume":"101","author":"Pulliainen","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1175\/JHM-D-15-0021.1","article-title":"Quantifying the Added Value of Snow Cover Area Observations in Passive Microwave Snow Depth Data Assimilation","volume":"16","author":"Kumar","year":"2015","journal-title":"J. Hydrometeorol."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"7091","DOI":"10.1002\/2013JD021329","article-title":"Assimilation of MODIS Snow Cover through the Data Assimilation Research Testbed and the Community Land Model Version 4","volume":"119","author":"Zhang","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"2637","DOI":"10.5194\/hess-21-2637-2017","article-title":"Role of Forcing Uncertainty and Background Model Error Characterization in Snow Data Assimilation","volume":"21","author":"Kumar","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Zhu, L., Zhang, Y., Wang, J., Tian, W., Liu, Q., Ma, G., Kan, X., and Chu, Y. (2021). Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13040584"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1007\/s11769-008-0356-2","article-title":"Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and in-Situ Data\u2014A Case Study in Qinghai-Tibet Plateau","volume":"18","author":"Cao","year":"2008","journal-title":"Chin. Geogr. Sci."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/s00521-009-0320-9","article-title":"Comparison of Artificial Neural Network and Combined Models in Estimating Spatial Distribution of Snow Depth and Snow Water Equivalent in Samsami Basin of Iran","volume":"19","author":"Tabari","year":"2010","journal-title":"Neural. Comput. Appl."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.rse.2003.12.002","article-title":"Artificial Neural Network-Based Techniques for the Retrieval of SWE and Snow Depth from SSM\/I Data","volume":"90","author":"Tedesco","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1007\/s12517-020-05642-x","article-title":"Snow Depth Retrieval from Passive Microwave Imagery Using Different Artificial Neural Networks","volume":"13","author":"Zaerpour","year":"2020","journal-title":"Arab. J. Geosci."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1109\/36.239907","article-title":"Retrieval of Snow Parameters by Iterative Inversion of a Neural Network","volume":"31","author":"Davis","year":"1993","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1109\/36.175336","article-title":"Inversion of Snow Parameters from Passive Microwave Remote Sensing Measurements by a Neural Network Trained with a Multiple Scattering Model","volume":"30","author":"Tsang","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"10241","DOI":"10.1109\/TGRS.2019.2932732","article-title":"Evaluation of Brightness Temperature Sensitivity to Snowpack Physical Properties Using Coupled Snow Physics and Microwave Radiative Transfer Models","volume":"57","author":"Kang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1109\/72.870038","article-title":"On Overfitting, Generalization, and Randomly Expanded Training Sets","volume":"11","author":"Karystinos","year":"2000","journal-title":"IEEE Trans Neural Netw"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"212","DOI":"10.3389\/feart.2019.00212","article-title":"Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia","volume":"7","author":"Ahmad","year":"2019","journal-title":"Front. Earth Sci."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Xue, Y., and Forman, B.A. (2017, January 23\u201328). Integration of Satellite-Based Passive Microwave Brightness Temperature Observations and an Ensemble-Based Land Data Assimilation Framework to Improve Snow Estimation in Forested Regions. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8126958"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"5384","DOI":"10.1002\/hyp.13951","article-title":"Random Forests as a Tool to Understand the Snow Depth Distribution and Its Evolution in Mountain Areas","volume":"34","author":"Revuelto","year":"2020","journal-title":"Hydrol. Process."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"84007","DOI":"10.1088\/1748-9326\/abfe8d","article-title":"Improving the Snowpack Monitoring in the Mountainous Areas of Sweden from Space: A Machine Learning Approach","volume":"16","author":"Zhang","year":"2021","journal-title":"Environ. Res. Lett."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.5194\/tc-14-1763-2020","article-title":"Snow Depth Estimation and Historical Data Reconstruction over China Based on a Random Forest Machine Learning Approach","volume":"14","author":"Yang","year":"2020","journal-title":"Cryosphere"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Yang, J., Jiang, L., Pan, J., Shi, J., Wu, S., Wang, J., and Pan, F. (2022). Comparison of Machine Learning-Based Snow Depth Estimates and Development of a New Operational Retrieval Algorithm over China. Remote Sens., 14.","DOI":"10.3390\/rs14122800"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1038\/s41597-021-00939-2","article-title":"GlobSnow v3.0 Northern Hemisphere Snow Water Equivalent Dataset","volume":"8","author":"Luojus","year":"2021","journal-title":"Sci. Data"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"2001705","DOI":"10.1109\/LGRS.2022.3226204","article-title":"Mountain Snow Depth Retrieval from Optical and Passive Microwave Remote Sensing Using Machine Learning","volume":"19","author":"Xiong","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"127027","DOI":"10.1016\/j.jhydrol.2021.127027","article-title":"Development of a Fine-Resolution Snow Depth Product Based on the Snow Cover Probability for the Tibetan Plateau: Validation and Spatial\u2013Temporal Analyses","volume":"604","author":"Yan","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"1933","DOI":"10.5194\/tc-11-1933-2017","article-title":"Evaluation of Snow Cover and Snow Depth on the Qinghai-Tibetan Plateau Derived from Passive Microwave Remote Sensing","volume":"11","author":"Dai","year":"2017","journal-title":"Cryosphere"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"3052","DOI":"10.1002\/hyp.8253","article-title":"A New Approach of Dynamic Monitoring of 5-Day Snow Cover Extent and Snow Depth Based on MODIS and AMSR-E Data from Northern Xinjiang Region","volume":"26","author":"Yu","year":"2012","journal-title":"Hydrol. Process."},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Wei, Y., Li, X., Li, L., Gu, L., Zheng, X., Jiang, T., and Li, X. (2022). An Approach to Improve the Spatial Resolution and Accuracy of AMSR2 Passive Microwave Snow Depth Product Using Machine Learning in Northeast China. Remote Sens., 14.","DOI":"10.3390\/rs14061480"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1080\/07055900.1998.9649605","article-title":"Spatial and Temporal Variability of Canadian Monthly Snow Depths, 1946\u20131995","volume":"36","author":"Brown","year":"2010","journal-title":"Atmos. -Ocean"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1175\/1520-0442(1995)008<1261:ASSCCS>2.0.CO;2","article-title":"A Seasonal Snow Cover Classification System for Local to Global Applications","volume":"8","author":"Sturm","year":"1995","journal-title":"J. Clim."},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Xiao, X., Zhang, T., Zhong, X., and Li, X. (2020). Spatiotemporal Variation of Snow Depth in the Northern Hemisphere from 1992 to 2016. Remote Sens., 12.","DOI":"10.3390\/rs12172728"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s40641-020-00159-7","article-title":"Data Assimilation Improves Estimates of Climate-Sensitive Seasonal Snow","volume":"6","author":"Girotto","year":"2020","journal-title":"Curr. Clim. Change Rep."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1002\/(SICI)1099-1085(199808\/09)12:10\/11<1611::AID-HYP684>3.0.CO;2-4","article-title":"Measurements and Modelling of Snow Interception in the Boreal Forest","volume":"12","author":"Hedstrom","year":"1998","journal-title":"Hydrol. Process."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1109\/36.563276","article-title":"Influence of Land-Cover Category on Brightness Temperature of Snow","volume":"35","author":"Kurvonen","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"9088","DOI":"10.1002\/2013WR014734","article-title":"Comparison of Passive Microwave and Modeled Estimates of Total Watershed SWE in the Continental United States","volume":"50","author":"Vuyovich","year":"2014","journal-title":"Water Resour. Res."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.rse.2016.06.005","article-title":"Estimation of Snow Depth from Passive Microwave Brightness Temperature Data in Forest Regions of Northeast China","volume":"183","author":"Che","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/TGRS.2005.860208","article-title":"Sensitivity of Passive Microwave Snow Depth Retrievals to Weather Effects and Snow Evolution","volume":"44","author":"Markus","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.advwatres.2012.08.010","article-title":"Small Scale Spatial Variability of Snow Density and Depth over Complex Alpine Terrain: Implications for Estimating Snow Water Equivalent","volume":"55","author":"Fassnacht","year":"2013","journal-title":"Adv. Water Resour."},{"key":"ref_160","first-page":"57","article-title":"Towards the Definition of Optimum Sensor Specifications for Microwave Remote Sensing of Snow","volume":"20","author":"Good","year":"1982","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.2012.2234468","article-title":"Evaluating Passive Microwave Radiometry for the Dynamical Transition from Dry to Wet Snowpacks","volume":"52","author":"Kang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.rse.2017.06.016","article-title":"Effect of Spatial Variability of Wet Snow on Modeled and Observed Microwave Emissions","volume":"198","author":"Vuyovich","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1029\/JC085iC02p01037","article-title":"The Active and Passive Microwave Response to Snow Parameters: 1. Wetness","volume":"85","author":"Stiles","year":"1980","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1109\/TAP.1986.1143757","article-title":"Dielectric Properties of Snow In the 3 to 37 GHz Range","volume":"AP-34","author":"Hallikainen","year":"1986","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"307","DOI":"10.3189\/S026030550001301X","article-title":"Discrimination of a Wet Snow Cover Using Passive Microwave Satellite Data","volume":"17","author":"Walker","year":"1993","journal-title":"Ann. Glaciol."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1109\/36.481908","article-title":"Global Identification of Snowcover Using Ssm\/i Measurements","volume":"34","author":"Grody","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1002\/hyp.6076","article-title":"Uncertainty in Snow Mass Retrievals from Satellite Passive Microwave Data in Lake-Rich High-Latitude Environments","volume":"20","author":"Rees","year":"2006","journal-title":"Hydrol. Process."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.rse.2010.09.001","article-title":"Evaluation of the HUT Modified Snow Emission Model over Lake Ice Using Airborne Passive Microwave Measurements","volume":"115","author":"Gunn","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_169","unstructured":"Duguay, C., English, M.C., Profile, S., and Rees, A. (2005, January 7\u201310). Preliminary Assessment of the Impact of Lakes on Passive Microwave Snow Retrieval Algorithms in the Arctic. Proceedings of the 62nd Eastern Snow Conference Proceedings, Waterloo, ON, Canada."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/LGRS.2006.871744","article-title":"Atmospheric Correction of AMSR-E Brightness Temperatures for Dry Snow Cover Mapping","volume":"3","author":"Tedesco","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.rse.2006.10.024","article-title":"Identification of Atmospheric Influences on the Estimation of Snow Water Equivalent from AMSR-E Measurements","volume":"111","author":"Wang","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"7279","DOI":"10.1109\/TGRS.2016.2599013","article-title":"Snow Depth Retrieval Based on a Multifrequency Dual-Polarized Passive Microwave Unmixing Method from Mixed Forest Observations","volume":"54","author":"Gu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"2210","DOI":"10.1109\/JSTARS.2018.2815681","article-title":"Snow Depth Retrieval Based on a Multifrequency Passive Microwave Unmixing Method for Saline-Alkaline Land in the Western Jilin Province of China","volume":"11","author":"Gu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"4414","DOI":"10.1109\/JSTARS.2018.2870752","article-title":"Using a Linear Unmixing Method to Improve Passive Microwave Snow Depth Retrievals","volume":"11","author":"Liu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.rse.2005.04.010","article-title":"Factors Affecting Remotely Sensed Snow Water Equivalent Uncertainty","volume":"97","author":"Dong","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Yang, J., Jiang, L., Dai, L., Pan, J., Wu, S., and Wang, G. (2019). The Consistency of SSM\/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China. Remote Sens., 11.","DOI":"10.3390\/rs11161879"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1109\/LGRS.2011.2105243","article-title":"A First-Order Characterization of Errors from Neglecting Stratigraphy in Forward and Inverse Passive Microwave Modeling of Snow","volume":"8","author":"Durand","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"8899","DOI":"10.1080\/01431161.2011.591844","article-title":"Evaluation of Terrain Effect on Microwave Radiometer Measurement and Its Correction","volume":"32","author":"Guo","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1080\/01431160050030538","article-title":"Technical Note: Relief Effects for Passive Microwave Remote Sensing Technical Note Relief EOE Ects for Passive Microwave Remote Sensing","volume":"21","author":"Standley","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_180","doi-asserted-by":"crossref","unstructured":"Wang, P., Jiang, L., Zhang, L., and Guo, Y. (2010, January 25\u201330). Impact of Terrain Topography on Retrieval of Snow Water Equivalence Using Passive Microwave Remote Sensing. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5652279"},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1016\/j.rse.2009.08.003","article-title":"A Decision Tree Algorithm for Surface Soil Freeze\/Thaw Classification over China Using SSM\/I Brightness Temperature","volume":"113","author":"Jin","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"38","DOI":"10.3189\/172756402781817428","article-title":"Hemispheric-Scale Comparison and Evaluation of Passive-Microwave Snow Algorithms","volume":"34","author":"Armstrong","year":"2002","journal-title":"Ann. Glaciol."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.5194\/tc-14-2925-2020","article-title":"Snow Depth Mapping from Stereo Satellite Imagery in Mountainous Terrain: Evaluation Using Airborne Laser-Scanning Data","volume":"14","author":"Gascoin","year":"2020","journal-title":"Cryosphere"},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"159","DOI":"10.5194\/tc-16-159-2022","article-title":"Sentinel-1 Snow Depth Retrieval at Sub-Kilometer Resolution over the European Alps","volume":"16","author":"Lievens","year":"2022","journal-title":"Cryosphere"},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1002\/hyp.13415","article-title":"Understanding Subgrid Variability of Snow Depth at 1-Km Scale Using Lidar Measurements","volume":"33","author":"He","year":"2019","journal-title":"Hydrol. Process."},{"key":"ref_186","doi-asserted-by":"crossref","unstructured":"Hou, Y., Huang, X., and Zhao, L. (2022). Point-to-Surface Upscaling Algorithms for Snow Depth Ground Observations. Remote Sens., 14.","DOI":"10.3390\/rs14194840"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1052\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:36:06Z","timestamp":1760121366000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1052"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,15]]},"references-count":186,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15041052"],"URL":"https:\/\/doi.org\/10.3390\/rs15041052","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,15]]}}}