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DOI	10.1016/j.rse.2020.111754
	Arctic and subarctic snow microstructure analysis for microwave brightness temperature simulations
	Vargel C.; Royer A.; St-Jean-Rondeau O.; Picard G.; Roy A.; Sasseville V.; Langlois A.
发表日期	2020
ISSN	00344257
卷号	242
英文摘要	Passive microwave (PMW) remote sensing has proven to be a useful approach to characterize the volume of seasonal snowpack in remote northern regions at the synoptic scale. Modeling emitted microwave brightness temperatures (TB) is made possible using a physical radiative transfer model that takes into account microstructural and stratigraphic structure of the snowpack. However, prescribing the microstructure remains a difficult task. This paper aims to find proper microstructure parametrization and the snow emission model formulation that best optimize TB simulations for Arctic and Subarctic snowpacks. Surfaced-based radiometric measurements in conjunction with in-situ snowpack characterization were used for testing different configurations based on the Snow Microwave Radiative Transfer model (SMRT), with two electromagnetic models (Dense Media Radiative Transfer Quasi Crystalline Approximation, DMRT, and Improved Born Approximation, IBA) and two microstructure description theories (Sticky Hard Sphere, SHS, and Exponential, Exp). We compare the performance of three configurations (DMRT-SHS, IBA-SHS and IBA-Exp) with a unique large dataset (119 snowpits with concomitant microwave ground-based radiometer observations) covering a wide range of Arctic and Subarctic snow types in Northern and Eastern Canada. Results show that the input measured microstructure parameters must be scaled up in order to better match simulated and observed TB at 11, 19, 37 and 89 GHz. We show that the IBA-Exp gives the best results, with a Root-Mean-Square Error (RMSE) lower by up to 30% for Subarctic snow and 24% for Arctic snow compare to the other model configurations we used. In addition, we undertake a complementary experiment on isolated homogeneous snow slabs to investigate the sensitivity of the scaling factor to snow microstructure. The retrieved microwave correlation length appears significantly different than the in-situ Debye correlation length. At high frequencies, the observed variability of these scaling factors with frequency and snowpack types means that density, SSA and estimated correlation length seem insufficient to appropriately fully characterize snow microstructure for microwave modeling purposes. © 2020 Elsevier Inc.
英文关键词	Arctic and subarctic snow; Canada; Correlation length; Microwave brightness temperature; Microwave radiative transfer of snow; SMRT model; Snow microstructure; SSA
语种	英语
scopus关键词	Large dataset; Luminance; Mean square error; Microstructure; Microwaves; Radiative transfer; Remote sensing; Stratigraphy; Temperature; Canada; Correlation lengths; Ground-based radiometers; Micro-structure parameters; Microwave brightness temperature; Microwave radiative transfer model; Quasicrystalline approximation; Radiometric measurements; Snow; brightness temperature; data set; microstructure; parameterization; radiative transfer; radiometer; remote sensing; snowpack; subarctic region; Arctic; Canada
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179354
作者单位	Centre de Recherches et d'Applications en Télédétection (CARTEL), Université de Sherbrooke, Québec, Canada; Centre d'Études Nordiques, Québec, Canada; Institut des Géosciences de l'Environnement, Université Grenoble Alpes-CNRS, Grenoble, France; Département des Sciences de l'Environnement, Université du Québec à Trois-Rivières, Québec, Canada
推荐引用方式 GB/T 7714	Vargel C.,Royer A.,St-Jean-Rondeau O.,et al. Arctic and subarctic snow microstructure analysis for microwave brightness temperature simulations[J],2020,242.
APA	Vargel C..,Royer A..,St-Jean-Rondeau O..,Picard G..,Roy A..,...&Langlois A..(2020).Arctic and subarctic snow microstructure analysis for microwave brightness temperature simulations.Remote Sensing of Environment,242.
MLA	Vargel C.,et al."Arctic and subarctic snow microstructure analysis for microwave brightness temperature simulations".Remote Sensing of Environment 242(2020).