Snow physical parameters estimation using space-based Synthetic Aperture Radar

Thakur, Praveen K.; Garg, Pradeep; Garg, Rahul; Mani, Sneh; Pandit, Ankur; Kumar, Sanjeev

doi:10.1080/10106049.2012.672477

Cited by 33 publications

(26 citation statements)

References 40 publications

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“…The effect that backscattering from a smooth wet surface is much lower than from rough ground was used to detect wet snow and to predict melt water runoff [23]- [26]. Algorithms based on the polarimetric backscatter signal were developed and used for snow wetness and snow density determination [27], [28].…”

Section: Introductionmentioning

confidence: 99%

Snow Height Determination by Polarimetric Phase Differences in X-Band SAR Data

Leinß

Parrella

Hajnsek

2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The copolar phase difference (CPD) between VV and HH polarization of X-band SAR acquisitions shows a significant dependence on the depth of fresh snow. Phase differences of 5-15 deg/10 cm fresh snow were determined at a frequency of 9.65 GHz by comparing spatial and temporal variations of snow depth (SD) with the CPD. Spatial correlations were derived from snow transect measurements during January 2012 and TanDEM-X acquisitions. Temporal correlations were derived from weather station data and TerraSAR-X acquisitions between December 2012 and April 2013. All measurements were done at a test field near the city Sodankylae, Finland. To explain the observed CPD, a model derives birefringent properties from the microstructure of snow, which is described as aligned elliptical particles. The microscopic description is based on computer tomography observations. Different incidence angles were analyzed in consistency with the model. The temporal evolution of the CPD was linked to the temperature-gradient-driven recrystallization process. Sudden increases in the CPD indicate fresh snow. Slow decreases indicate the subsequent recrystallization process. The background signal of wet soil was considered and causes a small negative offset to the CPD. A quantitative determination of the depth of fresh snow is possible, because the specific CPD per meter of snow can be estimated. Spatial resolutions below are achievable with sensors such as TerraSAR-X or TanDEM-X. This paper presents a theoretical relationship between the microstructure of snow and the CPD and relates the CPD theoretically and empirically to the depth of fresh snow.

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Section: Introductionmentioning

confidence: 99%

Snow Height Determination by Polarimetric Phase Differences in X-Band SAR Data

Leinß

Parrella

Hajnsek

2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Remote sensing has played an important and critical role in regular mapping, monitoring and deriving physical and dynamical properties of snow and glaciers as most of these regions are inaccessible (Kulkarni et al 2007; Thakur et al 2012;Frey et al 2012; Gantayat et al 2014). In first part of this study inland mountain valley type of glaciers in Indian North West Himalaya has been studied for using radar remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Ice sheet features identification, glacier velocity estimation, and glacier zones classification using high-resolution optical and SAR data

et al. 2016

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Ice sheet features, glacier velocity estimation and glacier zones or facies classification are important research activities highlighting the dynamics of ice sheets and glaciers in Polar Regions and in inland glaciers. The Cband inSAR data is of ERS 1/2 tandem pairs with one day interval for spring of 1996 and L-band PolinSAR data of ALOS-PALSAR-2 for spring of 2015 is used in glacier velocity estimation. Glacier classification is done using multi-temporal C-and L-band SAR data and also with single date full polarization and hybrid polarization data. In first part, a mean displacement of 9 cm day-1 was recorded using SAR interferometric technique using ERS 1/2 tandem data of 25-26 March 1996. Previous studies using optical data based methods has shown that Gangotri glacier moves with an average displacement of 4 cm and 6 cm day-1. As present results using ERS 1/2 data were obtained for one day interval, i.e.

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“…In addition, the operating cost of LiDAR is sufficiently high and is also weather dependent (Deems et al, 2013). As a result, spaceborne SAR systems benefit from substantial coverage (globally available), cloud insensitivity, all-day operability and are extensively used to measure the snow physical properties sufficiently at high spatial resolutions (Moreira et al, 2013;Thakur et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Snow Depth and Snow Water Equivalent Estimation in the Northwestern Himalayan Watershed using Spaceborne Polarimetric SAR Interferometry

Majumdar¹,

Thakur²,

Chang³

et al. 2019

Preprint

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Snow depth (SD) and Snow Water Equivalent (SWE) constitute essential physical properties of snow and find extensive usage in the hydrological modelling domain. However, the prominent impact of the hydrometeorological conditions and difficult terrain conditions inhibit accurate measurement of the SD and SWE— an ongoing research problem in the cryosphere paradigm. In this context, spaceborne synthetic aperture radar (SAR) systems benefit from global coverage at sufficiently high spatial and temporal resolutions. Still, existing polarimetric and interferometric SAR techniques are susceptible to high volume scattering resulting from the increased snow grain sizes due to the standing (or old) snow formation driven by the temperature induced snow metamorphosis process. Hence, to model this volume decorrelation, the polarimetric SAR interferometry (Pol-InSAR) technique can be effectively applied. In this work, the standing snow depth (SSD) and its corresponding standing snow water equivalent (SSWE) are estimated using the single-baseline Pol-InSAR based hybrid Digital Elevation Model (DEM) differencing and coherence amplitude inversion model. To achieve this, six TerraSAR-X, TanDEM-X Coregistered Single look Slant range Complex (CoSSC) bistatic quad-pol acquisitions between December 2015 and January 2016 over Dhundi (situated in the Beas watershed, northwestern Himalayas, India) are used. Due to the associated problems of model parameter tuning, complex topographical conditions, and limited ground-truth measurements, appropriate sensitivity analyses have been carried out for the parameter optimisation. Furthermore, the uncertainty sources are identified by performing a summer (June 8, 2017) and wintertime (January 8, 2016) comparative analysis of the study area which quantitatively highlights the changes in the percentages of the surface and volume scatterings. Evidently, the improved model displays sufficiently high overall SSD accuracy with coefficient of determination (R^2) ≈ 0.96, Mean Absolute Error (MAE) ≈ 1.61 cm, and Root Mean Square Error (RMSE) ≈ 2.16 cm. Additionally, the respective SSWEs have been calculated by assuming a fixed snow density for each epoch wherein the overall error metrics are R^2 ≈ 0.71, MAE ≈ 5.19 mm, and RMSE ≈ 6.84 mm. Therefore, this research successfully demonstrates the practicability of the improved Pol-InSAR model for SD estimation over rugged terrains.

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Snow physical parameters estimation using space-based Synthetic Aperture Radar

Cited by 33 publications

References 40 publications

Snow Height Determination by Polarimetric Phase Differences in X-Band SAR Data

Snow Height Determination by Polarimetric Phase Differences in X-Band SAR Data

Ice sheet features identification, glacier velocity estimation, and glacier zones classification using high-resolution optical and SAR data

Snow Depth and Snow Water Equivalent Estimation in the Northwestern Himalayan Watershed using Spaceborne Polarimetric SAR Interferometry

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