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Sciences in Cold and Arid Regions ›› 2022, Vol. 14 ›› Issue (2): 79-90.doi: 10.3724/SP.J.1226.2022.21046.

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Estimating snow depth or snow water equivalent from space

LiYun Dai1,Tao Che1,2()   

  1. 1.Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    2.Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2021-05-27 Accepted:2021-11-25 Online:2022-04-30 Published:2022-04-25
  • Contact: Tao Che E-mail:chetao@lzb.ac.cn
  • Supported by:
    the National Key Research and Development Program of China(Grand 2020YFA0608501);the National Natural Science Foundation of China(Grand 42171143);the CAS 'Light of West China' Program(E029070101)

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Abstract:

Satellite remote sensing is widely used to estimate snow depth and snow water equivalent (SWE) which are two key parameters in global and regional climatic and hydrological systems. Remote sensing techniques for snow depth mainly include passive microwave remote sensing, Synthetic Aperture Radar (SAR), Interferometric SAR (InSAR) and Lidar. Among them, passive microwave remote sensing is the most efficient way to estimate large scale snow depth due to its long time series data and high temporal frequency. Passive microwave remote sensing was utilized to monitor snow depth starting in 1978 when Nimbus-7 satellite with Scanning Multichannel Microwave Radiometer (SMMR) freely provided multi-frequency passive microwave data. SAR was found to have ability to detecting snow depth in 1980s, but was not used for satellite active microwave remote sensing until 2000. Satellite Lidar was utilized to detect snow depth since the later period of 2000s. The estimation of snow depth from space has experienced significant progress during the last 40 years. However, challenges or uncertainties still exist for snow depth estimation from space. In this study, we review the main space remote sensing techniques of snow depth retrieval. Typical algorithms and their principles are described, and problems or disadvantages of these algorithms are discussed. It was found that snow depth retrieval in mountainous area is a big challenge for satellite remote sensing due to complicated topography. With increasing number of freely available SAR data, future new methods combing passive and active microwave remote sensing are needed for improving the retrieval accuracy of snow depth in mountainous areas.

Key words: snow depth, snow water equivalent, remote sensing, satellite

Figure 1

(a) Schematic diagram of microwave emission transfer process of snowpack; (b) Variations in brightness temperatures at 5 frequencies with increasing snow depth at constant snow characteristics"

Table 1

Monthly error coefficient ε in different snow types (Foster et al., 2005)"

MonthSnow types
TundraAlpineTaigaMarinePrairieEphemeral
Oct.-0.20-0.10-0.20-0.20-0.20-0.20
Nov.0.10-0.10-0.20-0.20-0.20-0.20
Dec.0.150.050.05-0.15-0.10-0.20
Jan.0.200.050.05-0.150.05-0.20
Feb.0.250.050.05-0.150.15-0.20
Mar.0.300.050.05-0.150.20-0.20
Apr.0.300.050.25-0.150.20-0.20
May0.300.100.25-0.150.20-0.20

Table 2

Error coefficient γ in different forest cover fraction (Foster et al., 2005)"

ItemForest cover fraction
5%15%25%35%45%55%65%75%85%95%
Error coefficients0.050.050.100.150.200.250.300.400.500.50

Figure 2

Flowchart of SWE retrieval from PM in GlobSnow algorithm (Pullianen, 2006)"

Table 3

Monthly average offsets to remove the influence of variation in grain size with a snow season (Che et al., 2008)"

MonthItem
SMMRSSM/I
Oct.-3.64-4.18
Nov.-3.08-3.58
Dec.-1.91-1.93
Jan.-0.190.29
Feb.1.512.15
Mar.2.653.31
Apr.3.323.80

Figure 3

Backscatter coefficient response to SWE at 9 GHz (Ulaby and Stiles, 1980)"

Figure 4

Geometry of microwave refraction in dry snowpack. The solid line (ΔRs +ΔRa ) is the microwave path in dry snowpack, and the dashed line ΔR0 is the microwave path in the snow-free condition. SD is snow depth"

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