{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:35:28Z","timestamp":1760229328978,"version":"build-2065373602"},"reference-count":62,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171317","42090014","2021NTST02","2021YFB3900104","2019QZKK0206"],"award-info":[{"award-number":["42171317","42090014","2021NTST02","2021YFB3900104","2019QZKK0206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities","award":["42171317","42090014","2021NTST02","2021YFB3900104","2019QZKK0206"],"award-info":[{"award-number":["42171317","42090014","2021NTST02","2021YFB3900104","2019QZKK0206"]}]},{"name":"National Key Research and Development Program of China","award":["42171317","42090014","2021NTST02","2021YFB3900104","2019QZKK0206"],"award-info":[{"award-number":["42171317","42090014","2021NTST02","2021YFB3900104","2019QZKK0206"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program","award":["42171317","42090014","2021NTST02","2021YFB3900104","2019QZKK0206"],"award-info":[{"award-number":["42171317","42090014","2021NTST02","2021YFB3900104","2019QZKK0206"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Snow depth estimation with passive microwave (PM) remote sensing is challenged by spatial variations in the Earth\u2019s surface, e.g., snow metamorphism, land cover types, and topography. Thus, traditional static snow depth retrieval algorithms cannot capture snow thickness well. In this study, we present a new operational retrieval algorithm, hereafter referred to as the pixel-based method (0.25\u00b0 \u00d7 0.25\u00b0 grid-level), to provide more accurate and nearly real-time snow depth estimates. First, the reference snow depth was retrieved using a previously proposed model in which a microwave snow emission model was coupled with a machine learning (ML) approach. In this process, an effective grain size (effGS) value was optimized by utilizing the snow microwave emission model, and then the nonlinear relationship between snow depth and multiple predictive variables, e.g., effGS, longitude, elevation, and brightness temperature (Tb) gradients, was established with the ML technique to retrieve reference snow depth data. To select a robust and well-performing ML approach, we compared the performance of widely used support vector regression (SVR), artificial neural network (ANN) and random forest (RF) algorithms over China. The results show that the three ML models performed similarly in snow depth estimation, which was attributed to the inclusion of effGS in the training samples. In this study, the RF model was used to retrieve the snow depth reference dataset due to its slightly stronger robustness according to our comparison of results. Second, the pixel-based algorithm was built based on the retrieved reference snow depth dataset and satellite Tb observations (18.7 GHz and 36.5 GHz) from Advanced Microwave Scanning Radiometer 2 (AMSR2) during the 2012\u20132020 period. For the pixel-based algorithm, the fitting coefficients were achieved dynamically pixel by pixel, making it superior to the traditional static methods. Third, the built pixel-based algorithm was verified using ground-based observations and was compared to the AMSR2, GlobSnow-v3.0, and ERA5-land products during the 2012\u20132020 period. The pixel-based algorithm exhibited an overall unbiased root mean square error (unRMSE) and R2 of 5.8 cm and 0.65, respectively, outperforming GlobSnow-v3.0, with unRMSE and R2 values of 9.2 cm and 0.22, AMSR2, with unRMSE and R2 values of 18.5 cm and 0.13, and ERA5-land, with unRMSE and R2 values of 10.5 cm and 0.33, respectively. However, the pixel-based algorithm estimates were still challenged by the complex terrain, e.g., the unRMSE was up to 17.4 cm near the Tien Shan Mountains. The proposed pixel-based algorithm in this study is a simple and operational method that can retrieve accurate snow depths based solely on spaceborne PM data in comparatively flat areas.<\/jats:p>","DOI":"10.3390\/rs14122800","type":"journal-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T23:55:24Z","timestamp":1655078124000},"page":"2800","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Comparison of Machine Learning-Based Snow Depth Estimates and Development of a New Operational Retrieval Algorithm over China"],"prefix":"10.3390","volume":"14","author":[{"given":"Jianwei","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9847-9034","authenticated-orcid":false,"given":"Lingmei","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2726-771X","authenticated-orcid":false,"given":"Jinmei","family":"Pan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6163-2912","authenticated-orcid":false,"given":"Jiancheng","family":"Shi","sequence":"additional","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5930-3183","authenticated-orcid":false,"given":"Shengli","family":"Wu","sequence":"additional","affiliation":[{"name":"National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6811-8425","authenticated-orcid":false,"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Fangbo","family":"Pan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1038\/nature04141","article-title":"Potential impacts of a warming climate on water availability in snow-dominated regions","volume":"438","author":"Barnett","year":"2005","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1038\/s41558-020-0746-8","article-title":"Agricultural risks from changing snowmelt","volume":"10","author":"Qin","year":"2020","journal-title":"Nat. Clim. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3534","DOI":"10.1002\/2017WR020840","article-title":"Water and life from snow: A trillion dollar science question","volume":"53","author":"Sturm","year":"2017","journal-title":"Water Resour. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1038\/s41586-020-2258-0","article-title":"Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018","volume":"581","author":"Pulliainen","year":"2020","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1038\/s41558-021-01074-x","article-title":"Climate change decisive for Asia\u2019s snow meltwater supply","volume":"11","author":"Kraaijenbrink","year":"2021","journal-title":"Nat. Clim. Chang."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1699","DOI":"10.1016\/j.rse.2010.02.019","article-title":"Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data","volume":"114","author":"Derksen","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s00382-013-1774-0","article-title":"On the persistent spread in snow-albedo feedback","volume":"42","author":"Qu","year":"2014","journal-title":"Clim. Dyn."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tsang, L., Durand, M., Derksen, C., Barros, A.P., Kang, D.H., Lievens, H., Marshall, H.P., Zhu, J., Johnson, J., and King, J. (2021). Review Article: Global Monitoring of Snow Water Equivalent Using High Frequency Radar Remote Sensing. Cryosphere Discuss., in review.","DOI":"10.5194\/tc-2021-295"},{"key":"ref_9","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_10","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":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"39","DOI":"10.3189\/S0260305500200736","article-title":"Nimbus-7 SMMR derived global snow cover parameters","volume":"9","author":"Chang","year":"1987","journal-title":"Ann. Glaciol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.rse.2005.02.014","article-title":"Evaluation of passive microwave snow water equivalent retrievals across the boreal forest tundra transition of western Canada","volume":"96","author":"Derksen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_13","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_14","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_15","first-page":"531","article-title":"Improvement of snow depth retrieval for FY3B-MWRI in China","volume":"44","author":"Jiang","year":"2014","journal-title":"Sci. China Earth Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, J., Jiang, L., Wu, S., Wang, G., Wang, J., and Liu, X. (2019). Development of a Snow Depth Estimation Algorithm over China for the FY-3D\/MWRI. Remote Sens., 11.","DOI":"10.3390\/rs11080977"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.rse.2007.02.034","article-title":"A parameterized multiple-scattering model for microwave emission from dry snow","volume":"111","author":"Jiang","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"W12524","DOI":"10.1029\/2012WR012133","article-title":"Coupling the snow thermodynamic model SNOWPACK with the microwave emission model of layered snowpacks for subarctic and arctic snow water equivalent retrievals","volume":"48","author":"Langlois","year":"2012","journal-title":"Water Resour. Res."},{"key":"ref_19","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_20","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_21","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.5194\/gmd-11-2763-2018","article-title":"SMRT: An active-passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)","volume":"11","author":"Picard","year":"2018","journal-title":"Geosci. Model Dev."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.rse.2017.02.006","article-title":"Application of a Markov Chain Monte Carlo algorithm for snow water equivalent retrieval from passive microwave measurements","volume":"192","author":"Pan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_24","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_25","first-page":"1","article-title":"Exploiting the ANN Potential in Estimating Snow Depth and Snow Water Equivalent from the Airborne SnowSAR Data at X- and Ku-Bands","volume":"99","author":"Santi","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.5194\/tc-12-1579-2018","article-title":"Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan","volume":"12","author":"Bair","year":"2018","journal-title":"Cryosphere"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","unstructured":"Wang, J., Forman, B.A., and Xue, Y. (2020). Exploration of synthetic terrestrial snow mass estimation via assimilation of amsr-e brightness temperature spectral differences using the catchment land surface model and support vector machine regression. Water Resour. Res., e2020WR027490.","DOI":"10.1002\/essoar.10502498.1"},{"key":"ref_29","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_30","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_31","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_32","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1002\/2016WR018878","article-title":"Estimating snow water equivalent in a Sierra Nevada watershed via spaceborne radiance data assimilation","volume":"53","author":"Li","year":"2017","journal-title":"Water Resour. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6488","DOI":"10.1029\/2017WR022219","article-title":"Estimating snow mass in North America through assimilation of Advanced Microwave Scanning Radiometer brightness temperature observations using the Catchment land surface model and support vector machines","volume":"54","author":"Xue","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2017WR022132","article-title":"Simulation and assimilation of passive microwave data using a snowpack model coupled to a well-calibrated radiative transfer model over North-Eastern Canada","volume":"54","author":"Larue","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","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_36","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_37","first-page":"1","article-title":"Time Series X- and Ku-Band Ground-Based Synthetic Aperture Radar Observation of Snow-Covered Soil and Its Electromagnetic Modeling","volume":"60","author":"Xiong","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.rse.2014.09.016","article-title":"Simulating seasonally and spatially varying snow cover brightness temperature using HUT snow emission model and retrieval of a microwave effective grain size","volume":"156","author":"Lemmetyinen","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3172","DOI":"10.1109\/JSTARS.2016.2614158","article-title":"Atmospheric and Forest Decoupling of Passive Microwave Brightness Temperature Observations Over Snow-Covered Terrain in North America","volume":"10","author":"Xue","year":"2017","journal-title":"IEEE J. Select. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_40","first-page":"1","article-title":"The Influence of Thermal Properties and Canopy-Intercepted Snow on Passive Microwave Transmissivity of a Scots Pine","volume":"99","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1080\/17538947.2021.1950852","article-title":"The influence of tree transmissivity variations in winter on satellite snow parameter observations","volume":"14","author":"Li","year":"2021","journal-title":"Int. J. Digit. Earth"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2969","DOI":"10.5194\/tc-15-2969-2021","article-title":"Impact of dynamic snow density on GlobSnow snow water equivalent retrieval accuracy","volume":"15","author":"Luojus","year":"2021","journal-title":"Cryosphere"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_44","unstructured":"Tedesco, M., Jeyaratnam, J., and Kelly, R. (2015). NRT AMSR2 Daily L3 Global Snow Water Equivalent EASE-Grids, NASA LANCE AMSR2 at the Global Hydrology Resource Center Distributed Active Archive Center."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks Editor","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_48","first-page":"281","article-title":"Support vector method for function approximation, regression estimation and signal processing","volume":"7","author":"Vapnik","year":"1997","journal-title":"Adv. Neural Inf. Processing Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112316","DOI":"10.1016\/j.rse.2021.112316","article-title":"Improving satellite retrieval of oceanic particulate organic carbon concentrations using machine learning methods","volume":"256","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/72.97934","article-title":"A general regression neural network","volume":"2","author":"Specht","year":"1991","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0167-7012(00)00201-3","article-title":"Artificial neural networks: Fundamentals, computing, design, and application","volume":"43","author":"Basheer","year":"2000","journal-title":"J. Microbiol. Methods"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3355","DOI":"10.1016\/j.rse.2011.07.018","article-title":"Fractional snow cover mapping through artificial neural network analysis of modis surface reflectance","volume":"115","author":"Dobreva","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3739","DOI":"10.1029\/2018WR024146","article-title":"Improving snow water equivalent maps with machine learning of snow survey and lidar measurements","volume":"55","author":"Broxton","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1109\/TGRS.2018.2854625","article-title":"Daily river discharge estimates by merging satellite optical sensors and radar altimetry through artificial neural network","volume":"57","author":"Tarpanelli","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"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","first-page":"12","article-title":"Snow Cover Identification with SSM\/I Data in China","volume":"18","author":"Li","year":"2007","journal-title":"J. Appl. Meteorol. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1002\/wat2.1140","article-title":"Estimating the spatial distribution of snow water equivalent in the world\u2019s mountains","volume":"3","author":"Dozier","year":"2016","journal-title":"WIREs Water"},{"key":"ref_58","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_59","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_60","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rse.2016.06.018","article-title":"The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo","volume":"184","author":"Painter","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"5079","DOI":"10.5194\/tc-15-5079-2021","article-title":"Performance assessment of radiation-based field sensors for monitoring the water equivalent of snow cover (SWE)","volume":"15","author":"Royer","year":"2021","journal-title":"Cryosphere"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.rse.2017.01.022","article-title":"Snow depth from ICESat laser altimetry-a test study in southern norway","volume":"191","author":"Treichler","year":"2017","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2800\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:27:54Z","timestamp":1760138874000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2800"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,10]]},"references-count":62,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14122800"],"URL":"https:\/\/doi.org\/10.3390\/rs14122800","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,6,10]]}}}