Consistency and Accuracy Assessment of Snow Cover Products from Terra, Aqua, SNPP and JPSS-1 Satellites
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The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard Joint Polar Satellite System (JPSS) satellites will replace the Moderate-Resolution Imaging Spectroradiometer (MODIS) to prolong data recording in the future. Therefore, it is a fundamental task to analyze the consistency and assess the accuracy of the snow cover products retrieved from the two sensors. In this study, snow cover products from MODIS/Terra, MODIS/Aqua, VIIRS/SNPP and VIIRS/JPSS-1, were evaluated in terms of Normalized Difference Snow Index (NDSI) consistency and accuracy assessment using higher resolution images of Landsat and Sentinel-2 snow cover products. Paired comparisons were performed among the four products in five major snow distribution regions over the world: Northeast China (NE), Northwest China (NW), the Qinghai–Tibet Plateau (QT), Northern America (NA), and European Union (EU). The two categories of snow products are utilized: The L3 Daily Tiled products, referenced by their Earth Science Data Type (ESDT) names of VJ110A1, VNP10A1, MOD10A1, MYD10A1, and L3 Daily Cloud-Gap-Filled (CGF) products, VJ110A1F, VNP10A1F, MOD10A1F, MYD10A1F. The important conclusions demonstrated as follows.(1) During the snow season, the four types of 10A1 snow products demonstrated good consistency, with higher R values and smaller BIAS under clear sky. VIIRS exhibited a higher snow cover percentage than MODIS. By combining the four 10A1snow products, it is effective and feasible to produce cloud-free snow products.(2) The consistency of the four 10A1F snow products was lower than that of the 10A1 products under clear skies. SNPP showed good consistency with JPSS-1, and the same to TERRA with AQUA.(3) In the 10A1F products based on the previous day's clear-sky cloud-filling algorithm, VJ1 and VNP products exhibited larger fluctuations compared to MOD and MYD products. Among the 10A1F products, the smaller fluctuations and higher snow cover percentage of MODIS, along with a cloud persistence duration higher than VIIRS, led to an overestimation in MODIS's 10A1F snow products.(4) The snow-cloud confusion is existing both in products with the same sensor and with different sensors for the 10A1products, and the latter is much larger than the former, the percentage of which is approximately 10% in the five regions.(5) High-resolution snow product validation indicates that VIIRS has higher accuracy in both snow products than MODIS. (6) The newest JPSS-1 snow cover products display good agreement with that of SNPP. The pixels with the flag of ‘no decision’ in VNP10A1, MOD10A1, MYD10A1 are labelled as land, waterbody, and mostly clouds in VJ110A1 product, respectively.               Above all, in spite of existing sensor differences affecting consistency of snow cover products, the paired comparisons indicated that under clear skies, the four snow products exhibit good consistency, with higher consistency observed in snow products from the same sensor. The evaluations by higher resolution snow products assured the high accuracy. It is effective and feasible to produce cloud-free snow products considering the overestimation of 10A1F products.The 2011–2012 snow season across the lower 48 states was seen by most snow lovers as, simply stated, a dud. Despite some notable, even rather remarkable snow events, seasonal snowfall totals were b...
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contains the snow water equivalent snow_depth and snow density measurements made by hyd 3 with the canadian snow sampler in 1996
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The snow disaster often takes place in the north of Xinjiang.So it is of great significance to exactly monitor the snow distribution and snow depth in the northern Xinjiang,which can provide scientific basis for snow disaster prevention and reduction.In recent years,NDSI is mainly used to abstract the snow cover with MODIS data.The NDSI is a spectral ratio that takes advantage of the spectral difference of snow in short-wave infrared and visible spectral bands.It can only discern one pixel into snow or other features,and can not satisfy accurate drainage basin snow cover mapping and snow parameter extracting.In this study,linear spectrum mixing model was used to abstract snow fraction in the north of Xinjiang.Then we established the relationship between snow fraction and NDSI and evaluated whether NDSI can be used to estimate the cover rate of snow within a 250m pixel.The result showed that they had good linear relationship.The mean absolute error for 25 true measured points was 0.06.Moreover,we analyzed the correlation between snow depth and the reflected spectrum of snow and compared the true measured snow reflected spectrum with the image reflected spectrum.The most sensitive bands to snow depth were chosen.At last,the snow depth-inversing model was built.
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Abstract Observations of the snow depth at 21 sites at Resolute were made twice weekly during the winter of 1957–58. As a result of these observations, and of other observations on snow made for the National Research Council, it is shown that the snow depth and the water content of the snow did not continue to increase during the winter as the snow fell. Rather the strong winds eroded the snow surface and the increase in depth was irregular and relatively slow. Furthermore, the observations on the density of the snow cover lead to the conclusion that attempts to measure the density in similar regions with an accuracy greater than ± 0.05 g. cm. −3 are not warranted.
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