Snow Grain-Size Estimation Over Mountainous Areas From MODIS Imagery

Wang, Jiangeng; Xia, Feng; Xiao, Pengfeng; Ye, Nan; Zhang, Xueliang; Cheng, Yinyi

doi:10.1109/lgrs.2017.2775207

Cited by 9 publications

(9 citation statements)

References 21 publications

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“…In this study, the powers of 1/e were tested, too. With the penetration depth considered, the new t 0e f f and γ e f f was recalculated using Equation ( 15) and used to retrieve effective p ex using Equation (10).…”

Section: Effective P Ex With the Consideration Of Penetration Depthmentioning

confidence: 99%

“…Although the snow microstructure parameters, for example, the geometric grain size, specific surface area, and autocorrelation function, are measurable using different instruments [6][7][8], when it comes to the remote sensing signal simulation or snow parameter retrieval based on the theory of forward models, the idea of an effective microstructure parameter (or simply called the effective grain size) was widely used and defined as the fitted value of a snow microstructure parameter that gives the same signal as the remote sensing observations. For example, the effective microstructure parameter at the snow's surface was fitted using the observed reflectance of visible to shortwave infrared (SWIR) sensors in [9][10][11], and the effective microstructure parameter at the microwave bands was fitted based on single-layer snow radiative transfer models in [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the Effective Microstructure Parameter of the Microwave Emission Model of Layered Snowpack for Multiple-Layer Snow

Pan

Shi

2021

Remote Sensing

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Natural snow, one of the most important components of the cryosphere, is fundamentally a layered medium. In forward simulation and retrieval, a single-layer effective microstructure parameter is widely used to represent the emission of multiple-layer snowpacks. However, in most cases, this parameter is fitted instead of calculated based on a physical theory. The uncertainty under different frequencies, polarizations, and snow conditions is uncertain. In this study, we explored different methods to reduce the layered snow properties to a set of single-layer values that can reproduce the same brightness temperature (TB) signal. A validated microwave emission model of layered snowpack (MEMLS) was used as the modelling tool. Multiple-layer snow TB from the snow’s surface was compared with the bulk TB of single-layer snow. The methods were tested using snow profile samples from the locally validated and global snow process model simulations, which follow the natural snow’s characteristics. The results showed that there are two factors that play critical roles in the stability of the bulk TB error, the single-layer effective microstructure parameter, and the reflectivity at the air–snow and snow–soil boundaries. It is important to use the same boundary reflectivity as the multiple-layer snow case calculated using the snow density at the topmost and bottommost layers instead of the average density. Afterwards, a mass-weighted average snow microstructure parameter can be used to calculate the volume scattering coefficient at 10.65 to 23.8 GHz. At 36.5 and 89 GHz, the effective microstructure parameter needs to be retrieved based on the product of the snow layer transmissivity. For thick snow, a cut-off threshold of 1/e is suggested to be used to include only the surface layers within the microwave penetration depth. The optimal method provides a root mean squared error of bulk TB of less than 5 K at 10.65 to 36.5 GHz and less than 10 K at 89 GHz for snow depths up to 130 cm.

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Section: Effective P Ex With the Consideration Of Penetration Depthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of the Effective Microstructure Parameter of the Microwave Emission Model of Layered Snowpack for Multiple-Layer Snow

Pan

Shi

2021

Remote Sensing

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“…Snow has distinct characteristics in the optical (visible and near infrared) wavelength for different grain sizes. Moreover, the reflectance of MODIS Channel 5 presents a larger changing range caused by different snow grain sizes compared to that of other channels and is thus used to estimate the snow grain size in this study [27].…”

Section: Estimating Snow Grain Sizementioning

confidence: 99%

“…Threshold SGS is the threshold of declined snow grain size for detecting snowfall event. We set Threshold SGS as 100 µm to avoid the influence of retrieval errors, due to the root mean squared error (RMSE) of the estimated snow grain size from MODIS image being 80.42 µm [27]. If one of the rules is satisfied, it indicates that there was a snowfall event at the pixel between day n and day n + CPC + 1.…”

Section: Detecting Snowfall Eventsmentioning

confidence: 99%

“…All of these forms of metamorphism result in the increase of snow grain size [22,23]. Nowadays, space-borne sensors in the visible, near infrared, and shortwave infrared spectrums have been used to estimate SGS at large spatial scales with moderate-to-high spatial resolutions [24][25][26][27]. The change of SGS over a region may provide information on snowfall events through a sudden decrease in grain size as new snow apparently has smaller grains.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Snowfall Events over Mountainous Areas Using Optical Imagery

Wang

Zhang

Cheng

et al. 2018

Water

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Snowfall over mountainous areas not only has important implications on the water cycle and the Earth’s radiation balance, but also causes potentially hazardous weather. However, snowfall detection remains one of the most difficult problems in modern hydrometeorology. We present a method for detecting snowfall events from optical satellite data for seasonal snow in mountainous areas. The proposed methodology is based on identifying expanded snow cover or suddenly declined snow grain size using time series images, from which it is possible to detect the location and time of snowfall events. The methodology was tested with Moderate Resolution Imaging Spectroradiometer (MODIS) daily radiance data for an entire hydrologic year from July 2014 to June 2015 in the mountainous area of the Manas River Basin, Northwest China. The study evaluated the recordings of precipitation events at eighteen meteorological stations in the study area prove the effectiveness of the proposed method, showing that there was more liquid precipitation in the second and third quarter, and more solid precipitation in the first and fourth quarter.

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