Because of the distinctive vertical climate and topography gradients in the alpine region, the snow cover of the Tienshan Mountains possesses complex spatiotemporal heterogeneity, particularly during the melting process. Quantifying the environmental factors is therefore crucial to understanding the melting process and for predicting and managing snowmelt runoff. Herein, the snow cover area, grain size, and contamination extent were determined to characterize the detailed melting status based on surface reflectance data of MOD09A1 in the central Tienshan Mountains from 2013 to 2017. The environmental factors collected include relief (elevation, slope, and aspect); meteorology (surface air temperature, land surface temperature, solar radiation, and wind speed); and land surface vegetation. Analysis of the geodetector results indicated the following. (1) Patterns of changes in the overall dominant environmental variables were consistent for the pre-, mid-, and post-melting periods defined according to the decline of snow cover area over five years. (2) The overall major environmental factors were wind speed and radiation (pre-period), land surface temperature and elevation (mid-period), and elevation and land surface types (post-period), respectively. (3) Regional distinctions were detected of the dominant environmental factors. In the pre-melting period, the effects of solar radiation and wind speed were noticeable in the north and south regions, respectively. The effects of elevation, land surface temperature, and land cover types became more prominent in all regions during the mid- and post-melting periods. (4) Interaction between the major environmental factors was significantly enhanced on both the overall and regional scales, thus affecting the snow-melting process. Finally, the energy distribution mismatch resulted in the snowmelt. Multiple environmental factors substantially affect heat redistribution at different spatiotemporal scales, resulting in the snowmelt as a complex manifestation of the factors and their interactions. The findings highlight regional differences in various environmental factors of the melting process and offer a theoretical foundation for the melting process at various scales over multiple years.
Detecting the variations in snow cover aging over undulating alpine regions is challenging owing to the complex snow-aging process and shadow effect from steep slopes. This study proposes a novel snow-cover status index, namely shadow-adjusted snow-aging index (SASAI), portraying the integrated aging process within the Manas River Basin in northwest China. The Environment Satellites HJ-1A/B optical images and in-field measurements were used during the snow ablation and accumulation periods. The in-field measurements provide a reference for building a candidate library of snow-aging indicators. The representative aging samples for training and validation were obtained using the proposed time-gap searching method combined with the target zones established based on the altitude of snowline. An analytic hierarchy process was used to determine the snow-aging index (SAI) using multiple optimal snow-aging indicators. After correction by the extreme value optimization algorithm, the SASAI was finally corrected for the effects of shading and assessed. This study provides both a flexible algorithm that indicates the characteristics of snow aging and speculation on the causes of the aging process. The separability of the SAI/SASAI and adaptability of this algorithm on multiperiod remote sensing images further demonstrates the applicability of the SASAI to all the alpine regions. cloud removal techniques, and spatiotemporal data fusion techniques [8][9][10][11]. For example, moderate resolution imaging spectroradiometer (MODIS) data are broadly employed as a reliable data source for detecting the extent of snow cover owing to its high spatial, time, and spectral resolutions [12,13]. Its sensitivity to factors such as aerosol optical properties are also explored in alpine areas [14]. Further, the "subpixel snow-cover information", "empirical relationship assumptions" [15,16], and "spectral unmixing" [17] models have been extensively applied for the inversion of the fractional snow cover. Transient snowline altitude and glacier elevation can be extracted by combining optical and synthetic aperture radar (SAR) imagery as well as DEM data [18]. Passive microwave-based models can penetrate clouds and provide measurements in shadowed regions and hence, are useful for inversion of the snow depth [19,20], snow water equivalent (SWE) [21], and snow cover storage [22]. Based on this, the composite snow cover products ESA GlobSnow SWE dataset [23] and snow data assimilation system (SNODAS) [24] were produced.Microscopically, SAP mainly refers to physical metamorphism as a variation in the grain size and particle structure [1][2][3][4]. The accumulation of pollution in snow, increasing liquid water content, and surface roughness are also typical symptoms of SAP (these also influence snow metamorphism) [5,6]. Extensive research has been conducted on retrieving the snow grain size using "scaled band area" algorithm and the MODIS snow-covered area grain size (MODSCAG) model [15]. On the basis of the quasi-crystalline approximation (QCA) th...
Because of the distinctive vertical climate and topography gradients in the alpine region, the snow cover of the Tienshan Mountains possesses complex spatial-temporal heterogeneity especially during the melting process. Quantifying the environmental factors is therefore crucial to understanding the melting process and for prediction and management of snowmelt runoff. In this study, the snow cover area, grain size, and contamination extent were determined to characterize the detailed melting status based on surface reflectance data of MOD09A1 in the central Tienshan Mountains from 2013 to 2017. The environmental factors collected include relief (elevation, slope, and aspect), meteorology (air temperature, land surface temperature, solar radiation, and wind speed), and land surface vegetation. Analysis of the Geo-detector results indicated the following. 1) The patterns of change in the overall dominant environmental variables were consistent for the pre-, mid-, and post-melting periods defined according to the decline of snow cover area over five years. 2) The overall major environmental factors were wind speed and radiation (pre-period), land surface temperature and elevation (mid-period), and elevation and land surface (post-period), respectively. 3) Regional distinctions were found in the spatiotemporal heterogeneity of the dominant environmental factors. In the pre-melting period, the effects of solar radiation and wind speed were noticeable in the north and south regions, respectively. The effects of elevation, land surface temperature, and land cover types became more prominent in all regions during the mid- and post-melting periods. 4) Interaction between the major environmental factors was significantly enhanced on both the overall and regional scales, thus affecting the snow melting process. Finally, the snowmelt is a response to an energy distribution mismatch. Multiple environmental factors substantially affect heat redistribution at different spatiotemporal scales, resulting in snow melt as a complex manifestation of the factors and their interactions. The findings highlighted regional differences in the various environmental factors of the melting process and offer a theoretical foundation for melting process at various scales over multiple years.
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