GNSS-R-Based Snow Water Equivalent Estimation with Empirical Modeling and Enhanced SNR-Based Snow Depth Estimation
| Autor: | Xin Chang, Jiancheng Li, Qi Wang, Yunwei Li, Kegen Yu, Taoyong Jin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Science 0211 other engineering and technologies Empirical modelling Satellite system 02 engineering and technology Snow 01 natural sciences snow water equivalent (SWE) Global Navigation Satellite System reflectometry (GNSS-R) Signal-to-noise ratio GNSS applications SNOTEL snow depth fusion empirical model General Earth and Planetary Sciences Environmental science Cryosphere Reflectometry 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Remote Sensing, Vol 12, Iss 3905, p 3905 (2020) Remote Sensing; Volume 12; Issue 23; Pages: 3905 |
| ISSN: | 2072-4292 |
| Popis: | Snow depth and snow water equivalent (SWE) are two parameters for measuring snowfall. By exploiting the Global Navigation Satellite System reflectometry (GNSS-R) technique and thousands of existing GNSS Continuous Operating Reference Stations (CORS) deployed in the cryosphere, it is possible to improve the temporal and spatial resolutions of the SWE measurement. In this paper, a fusion model for combining multi-satellite SNR (Signal to Noise Ratio) snow depth estimations is proposed, which uses peak spectral powers associated with each of the snow depth estimations. To simplify the estimation of SWE, the complete snowfall period over a winter season is split into snow accumulation, transition, and melting period in accordance with the variation characteristics of snow depth and SWE. By extensively using in situ snow depth and SWE observations recorded by snow telemetry network (SNOTEL) and regression analysis, three empirical models are developed to describe the relationship between snow depth and SWE for the three periods, respectively. Based on the snow depth fusion model and the SWE empirical models, an SWE estimation algorithm is proposed. Three data sets recorded in different environments are used to test the proposed method. The results demonstrate that there exists good agreement between the in situ SWE measurements and the SWE estimates produced by the proposed method; the root-mean-square error of SWE estimations is smaller than 6 cm when the SWE is up to 80 cm. |
| Databáze: | OpenAIRE |
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