Snow depth and snow water equivalent retrieval using X-band PolInSAR data

Patil, Akshay; Mohanty, Shradha; Singh, Gulab

doi:10.1080/2150704x.2020.1779373

Cited by 19 publications

(10 citation statements)

References 20 publications

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“…A critical factor in SAR interferometry is the baseline estimation, where efforts are made to ensure that the baseline is maintained within the critical limit [27]. A poor baseline usually results in the loss of interference, and subsequently, lower precision of the interferometric phase [17]. The interferogram and the coherence map were generated using two Sentinel-1 SLC master and slave images as defined previously [28].…”

Section: Background On the Estimation Of Snow Depthmentioning

confidence: 99%

“…Thus, to account for these issues, limiting the local incidence angle to an appropriate range for the investigations is highly recommended. In this case, the limits for the local incidence angle, i.e., the lower and the upper bounds, 15 and 75 degrees, respectively, for Equation (14), are determined from the observation of the normal probability plot of the local incidence angle shown in Figure 7 [17,20]. Table 2 summarizes the information available for investigation in the context of the number of pixels available for analysis on the SCA, layover shadow, local incidence angle mask, and the overall mask for the February and March 2019 datasets.…”

Section: Masking For Sca and Layover-shadow Pixelsmentioning

confidence: 99%

“…However, Lidar data is usually affected by the weather conditions and is typically applied at a regional scale [15]. Polarimetric and interferometric synthetic aperture radar (SAR) techniques have been effectively used for the modeling of snow depth and SWE [16,17]. However, such techniques are affected by the presence of moisture in the snowpack.…”

Section: Introductionmentioning

confidence: 99%

“…SAR interferometry has been widely used in the literature [17] for the investigation of surface subsidence and or upliftment. Subsequently, in the literature, Differential Interferometric SAR (DInSAR) data has been effectively utilized for the modeling of snow depth and SWE [18,19].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Snow Depth in the Hindu Kush Himalayas of Afghanistan during Peak Winter and Early Melt Season

2020

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The Pamir ranges of the Hindu Kush regions in Afghanistan play a substantial role in regulating the water resources for the Middle Eastern countries. Particularly, the snowmelt runoff in the Khanabad watershed is one of the critical drivers for the Amu River, since it is a primary source of available water in several Middle Eastern countries in the off monsoon season. The purpose of this study is to devise strategies based on active microwave remote sensing for the monitoring of snow depth during the winter and the melt season. For the estimation of snow depth, we utilized a multi-temporal C-band (5.405 GHz) Sentinel-1 dual polarimetric synthetic aperture radar (SAR) with a differential interferometric SAR (DInSAR)-based framework. In the proposed approach, the estimated snowpack displacements in the vertical transmit-vertical receive (VV) and vertical transmit-horizonal receive (VH) channels were improved by incorporating modeled information of snow permittivity, and the scale was enhanced by utilizing snow depth information from the available ground stations. Two seasonal datasets were considered for the experiments corresponding to peak winter season (February 2019) and early melt season (March 2019). The results were validated with the available nearest field measurements. A good correlation determined by the coefficient of determination of 0.82 and 0.57, with root mean square errors of 2.33 and 1.44 m, for the peak winter and the early melt season, respectively, was observed between the snow depth estimates and the field measurements. Further, the snow depth estimates from the proposed approach were observed to be significantly better than the DInSAR displacements based on the correlation with respect to the field measurements.

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Section: Background On the Estimation Of Snow Depthmentioning

confidence: 99%

Section: Masking For Sca and Layover-shadow Pixelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Snow Depth in the Hindu Kush Himalayas of Afghanistan during Peak Winter and Early Melt Season

2020

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“…Numerous studies also applied active microwave remote sensing to the goal of estimating snow properties. Examples include estimation of snow cover extent [15]- [17], snow wetness [18], and snow water equivalent (SWE) [19]- [22]. According to various radiative transfer models, such as the Microwave Emission Model of Layered Snowpacks 3 [23], [24] and active (MEMLS 3&a) [25], and dense media radiative transfer (DMRT) models (see [26] and references therein), the frequency-dependent backscattering coefficient changes with variations in the snow and the subnivean layer's state parameters (SPs), such as snow wetness, microstructure, density, height, and, consequently, SWE and ground permittivity.…”

Section: Introductionmentioning

confidence: 99%

Wideband Backscattering From Alpine Snow Cover: A Full-Season Study

Naderpour

Schwank

Houtz

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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This articleexperimentally investigates relationships between copol backscattering at a wide range of frequencies (L-to Ka-bands) and snow-ground state parameters (SPs) in different evolution phases during the full winter cycle of 2019/2020. Backscattering coefficients from 1 to 40 GHz, in situ snow-ground SPs, and meteorological data are measured at the Davos-Laret Remote Sensing Field Laboratory (Switzerland). Relative strengths of the snow-ground system's three primary scattering elements (air-snow interface, snow volume, and snow-ground interface) on backscattering are assessed. An anticorrelation between reasonably high snow wetness and backscattering coefficient is found, especially at higher microwave frequencies. For small amounts of snow wetness, backscatter coefficients at L-and S-bands are intensified via increasing snow volume and snow surface scattering. Snow-ground SPs influence backscattering according to their characteristic time scales of temporal evolution. Under dry snow conditions and at low and intermediate frequencies, ground permittivity is the major influencer of backscatter at a time scale of roughly two weeks. Snowfall is the major influencer of backscatter at a time scale of a few hours to a few days. The findings of this article are valuable to the development of retrieval algorithms using machine learning while maintaining a grasp on the ongoing physical processes. Another key message is that multifrequency active microwave measurements are critical to maximize the number of retrievable SPs and their estimation accuracy. For example, while Ka-band performs well in the detection of snow cover, L-band measurements are more responsive to changes of snow water equivalent (SWE) under moist or wet snow conditions.

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