X-Band Polarimetric Sar Copolar Phase Difference for Fresh Snow Depth Estimation in the Northwestern Himalayan Watershed

Majumdar, Sayantan; Thakur, Praveen K.; Chang, Ling; Kumar, Shashi

doi:10.1109/igarss.2019.8898884

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…Kothi areas where several Differential Global Positioning System (DGPS) measurements were acquired using the Leica Viva GS 10 (Leica Geosystems AG, 2012) with adequate horizontal positional accuracies (∼7 cm) (Majumdar et al, 2019a). Due to the complex terrains, most of the DGPS readings had been obtained through the kinematic mode (Luo et al, 2014).…”

Section: Field Visitmentioning

confidence: 99%

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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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Section: Field Visitmentioning

confidence: 99%

“…The corresponding H/α plane plot in Figure 4.3(c) shows that the quad-pol approach is able to fully classify the winter-time image. However, since the summer-time image is having only HH and VV channels, the dual-pol method has been used to properly compare the respective scattering mechanisms (Majumdar et al, 2019a).…”

Section: Scattering Mechanismsmentioning

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

Self Cite

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“…Dai et al (2012) proposed using a priori snow characteristics such as density, temperature of the layered snowpack and its grain size to retrieve snow depth/SWE data from passive microwave brightness temperature. Majumdar et al (2019) worked with X-band SAR to estimate the fresh snow depth in the presence of complex topographical features containing significant uncertainty sources. Patil et al (2020) employed bistatic TerraSAR-X quad polarization data to retrieve snow depth using PolInSAR coherences and depicted the linear relationship between Snow depth and Hv polarization.…”

Section: Introductionmentioning

confidence: 99%

Machine learning model for snow depth estimation using a multisensory ubiquitous platform

et al. 2022

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Snow depth estimation is an important parameter that guides several hydrological applications and climate change prediction. Despite advances in remote sensing technology and enhanced satellite observations, the estimation of snow depth at local scale still requires improved accuracy and flexibility. The advances in ubiquitous and wearable technology promote new prospects in tackling this challenge. In this paper, a wearable IoT platform that exploits pressure and acoustic sensor readings to estimate and classify snow depth classes using some machine-learning models have been put forward. Significantly, the results of Random Forest classifier showed an accuracy of 94%, indicating a promising alternative in snow depth measurement compared to in situ, LiDAR, or expensive large-scale wireless sensor network, which may foster the development of further affordable ecological monitoring systems based on cheap ubiquitous sensors.

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“…The depth of fresh snow shows a strong correlation with CPD and structural anisotropy (expressed by an axial ratio of snow grain) of the medium. A map of SD has been generated using the CPD model by [10] at a spatial resolution of ∼0.36 km 2 . Over the complex terrain with a rugged surface, the spatial resolution of SD and SWE has to be improved to capture the local variability.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Snow Depth and Snow Water Equivalent Using Dual Polarization SAR Data

2020

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This paper deals with the retrieval of snow depth (SD) and snow water equivalent (SWE) using dual-polarization (HH-VV) synthetic aperture radar (SAR) data. The effect of different snowpack conditions on the SD and SWE inversion accuracy was demonstrated by using three TerraSAR-X acquisitions. The algorithm is based on the relationship between the SD, the co-polar phase difference (CPD), and particle anisotropy. The Dhundi observatory in the Indian Himalaya was selected as a validation test site where a field campaign was conducted for ground truth measurements in January 2016. Using the field measured values of the snow parameters, the particle anisotropy has been optimized and provided as an input to the SD retrieval algorithm. A spatially variable snow density ( ρ s ) was used for the estimation of the SWE, and a temporal resolution of 90 m was achieved in the inversion process. When the retrieval accuracy was tested for different snowpack conditions, it was found that the proposed algorithm shows good accuracy for recrystallized dry snowpack without distinct layering and low wetness (w). The statistical indices, namely, the root mean square error (RMSE), the mean absolute difference (MAD), and percentage error (PE), were used for the accuracy assessment. The algorithm was able to retrieve SD with an average MAE and RMSE of 6.83 cm and 7.88 cm, respectively. The average MAE and RMSE values for SWE were 17.32 mm and 21.41 mm, respectively. The best case PE in the SD and the SWE retrieval were 8.22 cm and 18.85 mm, respectively.

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X-Band Polarimetric Sar Copolar Phase Difference for Fresh Snow Depth Estimation in the Northwestern Himalayan Watershed

Cited by 4 publications

References 6 publications

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

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

Machine learning model for snow depth estimation using a multisensory ubiquitous platform

Retrieval of Snow Depth and Snow Water Equivalent Using Dual Polarization SAR Data

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