Snow Water Equivalent Retrieval Using Active and Passive Microwave Observations

Abstract

Recent climate change has significantly altered cold land hydrological cycles, where the freshwater is highly dependent upon the presence of the snow and ice in high altitude and latitude regions (

SWE Retrieval Algorithms Based on the Parameterized Bi-continuous DMRT Model without Priors on Grain Size or Scattering Albedo

“…S now water equivalent (SWE) is an important parameter of relevance for hydrological, climatological, and meteorological applications and active microwave remote sensing has great potential to monitor snow cover and retrieve SWE. X-and Kuband radar measurements [1][2][3][4][5][6], provide a viable pathway to produce SWE information at the temporal and spatial scales necessary to advance operational environmental prediction, climate monitoring, and water resource management across the northern hemisphere. The ESA Cold Regions Hydrology High-Resolution Observatory (CoReH 2 O) mission (dual-frequency X-and Ku-band; Rott et al, 2010 [3]) was a major impetus.…”

“…Physically based retrieval algorithms were also developed in [4]. We developed an algorithm (Zhu et al algorithm) in our previous two papers [1,2] based on X and Ku band volume scattering which surements (co-polarized X and Ku band) and several parameters of snow microstructure and roughness. Thus, it is important to develop an effective SWE retrieval algorithm based on two measurements.…”

“…Algorithms for SWE retrieval such as Rott et al 2010 [3], Shi, 2006 [5], Cui et al 2016 [24], and Xiong et al 2014 [25], 2016 [26] have used a cost function-based approach. The algorithms in our previous two papers (Zhu et al, 2018 [1], 2021 [2]) also adopt the cost function approach. A reason for using the cost function approach is that the Φ, usually has more parameters than the radar observations meaning that q is larger than n. Because of this, a constraint in terms of some prior information is input.…”

“…The two algorithms are validated with 4 sets of airborne data and 3 years of tower time series measurements. The two algorithms are based on improvements of the previous algorithm published in the previous two papers [1,2]. The physical model is the bi-continuous DMRT model, and a parametrization is carried out over a look-up table of DMRT results.…”

Quantitative Evaluation of the Soil Signal Effect on the Correlation between Sentinel-1 Cross Ratio and Snow Depth

“…In addition, physical model-based SD retrieval methods have been developed in recent years [20,21]. The principle is to use the model to describe the surface scattering under the snow cover and to remove the background scattering from the radar observations to obtain the snow volume scattering [22]. Furthermore, some studies have established the relationship between SD and snow thermal resistance, and retrieved the SD using the change in soil dielectric constant [23,24].…”