Reconstruction of Snow Depth Data at Moderate Spatial Resolution (1 km) from Remotely Sensed Snow Data and Multiple Optimized Environmental Factors: A Case Study over the Qinghai-Tibetan Plateau

Abstract

Snow depth distribution in the Qinghai-Tibetan plateau is important for atmospheric circulation and surface water resources. In-situ observations at meteorological stations and remote observation by passive microwave remote sensing technique are two main approaches for monitoring snow depth at regional or global levels. However, the meteorological stations are often scarce and unevenly distributed in mountainous regions because of inaccessibility, so are the in-situ snow depth measurements. Passive microwave remote sensing data can alleviate the unevenness issue, but accuracy and spatial (e.g., 25 km) and temporal resolutions are low; spatial heterogeneity in snow depth is thus hard to capture. On the other hand, optical sensors such as moderate resolution imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites can monitor snow at moderate spatial resolution (1 km) and high temporal resolution (daily) but only snow area extent, not snow depth. Fusing passive microwave snow depth data with optical snow area extent data provides an unprecedented opportunity for generating snow depth data at moderate spatial resolution and high temporal resolution. In this article, a linear multivariate snow depth reconstruction (LMSDR) model was developed by fusing multisource snow depth data, optical snow area extent data, and environmental factors (e.g., spatial distribution, terrain features, and snow cover characteristics), to reconstruct daily snow depth data at moderate resolution (1 km) for 16 consecutive hydrological years, taking Qinghai-Tibetan Plateau (QTP) as a case study. We found that snow cover day (SCD) and environmental factors such as longitude, latitude, slope, surface roughness, and surface fluctuation have a significant impact on the variations of snow depth over the QTP. Relatively high accuracy (root mean square error (RMSE) = 2.26 cm) was observed in the reconstructed snow depth when compared with in-situ data. Compared with the passive microwave remote sensing snow depth product, constructing a nonlinear snow depletion curve product with an empirical formula and fusion snow depth product, the LMSDR model (RMSE = 2.28 cm, R2 = 0.63) demonstrated a significant improvement in accuracy of snow depth reconstruction. The overall spatial accuracy of the reconstructed snow depth was 92%. Compared with in-situ observations, the LMSDR product performed well regarding different snow depth intervals, land use, elevation intervals, slope intervals, and SCD and performed best, especially when the snow depth was less than 3 cm. At the same time, a long-time snow depth series reconstructed based on the LMSDR model reflected interannual variations of snow depth well over the QTP.

Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications

“…Such frequently examined areas, which are not only covered as part of large-scale studies, but examined specifically, are for instance the Great Plains in North America [73,[82][83][84][85][86][87], sometimes as part of studies covering Central North America [46,75,[88][89][90][91][92], or the USA [93][94][95][96][97][98][99]. Another often-applied study area is the Tibetan Plateau in Asia [100][101][102][103][104][105], also sometimes as part of studies conducted over High-Mountain Asia (HMA) [106][107][108][109]. Also often examined is the Mackenzie-Basin in Canada [110][111][112][113][114] or Northwestern China [63,[115][116][117][118][119][120].…”

“…Moreover, it becomes apparent that the commencement of coverage frequently coincides with the launch of specific sensors. In addition to the aforementioned SMMR in 1978 (n = 10), this mainly applies to the Special Sensor Microwave/Imager (SSM/I, n = 10) in 1987 [91,109,140,159,167,185,188,189,[194][195][196][197], the Advanced Microwave Scanning Radiometer for Earth Observation (AMSR-E, n = 27) [61,82,88,101,107,110,111,129,131,137,142,[150][151][152]155,156,158,162,[172][173][174][175]177,179,198,199], and the Advanced Microwave Scanning Radiometer 2 (AMSR-2, n = 4) [103,126,200,201]. The average coverage duration is just under 9.5 years with the longest study covering 41 years (1978-2019) [123], while some studies only examine a few months or one winter season…”

“…What can be said, however, is that models tend to be used for a wider range of applications than static and dynamic algorithms. These include applications that are similar to snow monitoring using inversion algorithms [101,226], but also applications that combine SWE/SD with other fields such as meteorology by looking at snowstorms [215], or temperature prediction [155], or geophysics by analyzing droughts [97]. In addition, there were some studies that looked at the role that snow crystal size plays in the estimation of SWE/SD [177,227].…”

Snowpack Dynamics Influence Tree Growth and Signals in Tree Rings of Tianshan Mountain, Central Asia

“…The use of the annual tree ring index to reconstruct snow data can provide important reference information for understanding the history of changes in snowfall and predicting future ones [21]. Existing studies have shown a correlation between radial tree growth and snowpack [22][23][24][25], and reconstructions of snowpack metrics, such as snow depth and snow days, have been performed based on these data [18,[26][27][28][29][30][31]. However, only a limited number of studies have been conducted in Central Asia, and most of these studies were based on the results of analyses acquired from a relatively small number of sampling points and limited meteorological material.…”

Avalanche Susceptibility Mapping by Investigating Spatiotemporal Characteristics of Snow Cover Based on Remote Sensing Imagery along the Pemo Highway—A Critical Transportation Road in Tibet, China

“…Historical studies have indicated that snow is a key factor contributing to avalanche occurrences along the Pemo Highway [13][14][15][16]. Therefore, it is of great significance to assess avalanche susceptibility with snow as the main conditioning factor after obtaining the spatial and temporal characteristics of the snow cover along the Pemo Highway.…”