Abstract
The MOD10A1 daily snow cover product offers a fine temporal resolution, which is particularly important for time-critical applications. Although the reliability of this product was mostly verified by ground measurements before use, the inadequate representativeness of point-scale observations may lead to errors in the assessment of MOD10A1. In this study, based on the Google Earth Engine, MOD10A1 was thoroughly evaluated by a total of 20 131 binary snow maps generated from 30 m Landsat imagery for the 2000–2016 snow seasons in three typical snow regions across China. The three typical snow regions included Northern Xinjiang, Northeast China, and the Tibetan Plateau. In general, the accuracy of MOD10A1 under clear sky conditions is 86.5% based on ground observations from meteorological stations and 90.3% based on Landsat imagery. Compared with ground measurements, snow cover derived from Landsat images is in better agreement with MOD10A1 due to a better spatial match. The error analyses also indicate that 1) compared with land cover, topography has larger effects on the accuracy of MOD10A1 over the three snow regions with statistically significant negative relationships, particularly in Northern Xinjiang ( R = −0.604, P R = 0.58, P < 0.05) based on the Mann–Kendall test.
Highlights
S plays an immensely critical role in the ecosystem cycle [1], [2], climate variation [3], water supply [4], [5], and freezing disaster occurrences [6]
By leveraging Google Earth Engine (GEE)’s extensive data archive and high-performance computing capability, we efficiently evaluated the accuracy of MOD10A1 based on finer-resolution imagery over a long-term period without heavy data downloading, eliminated the spatial mismatch issue caused by point-scale observations, and provided a comprehensive analysis on the accuracy of MOD10A1 over the three typical snow regions in China
Among the two statistical metrics, the highest FS value is in Northern Xinjiang (94.5%), and the lowest FS value appears on the Tibetan Plateau (61.6%); the Tibetan Plateau exhibits the highest overall accuracy (OA) value (96.6%), and Northeast China shows the lowest OA value (91.7%)
Summary
S plays an immensely critical role in the ecosystem cycle [1], [2], climate variation [3], water supply [4], [5], and freezing disaster occurrences [6]. Sources of increasing concern are the potential effects of climate change on snow-covered area (SCA) and snowmelt runoff, the linkage is difficult to quantify. The MODIS snow cover data have gained widespread application owing to its fine temporal resolution [17]–[20]. The MODIS snow cover data are the basis for creating or improving various integrated snow cover products [22]–[24], which are cloud free for mapping spatially continuous SCA. The assessment of MODIS snow cover data is crucial for making full use of them in scientific studies and practical applications, as well as accumulating knowledge on how they can be further improved
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