Investigating spatiotemporal snow cover variability via cloud-free MODIS snow cover product in Central Alborz Region

Developing a Cloud-Reduced MODIS Surface Reflectance Product for Snow Cover Mapping in Mountainous Regions

“…Due to the absence of in situ measurements, we used a validation methodology presented by Gafurov and Bardossy [15] and improved by Dariane et al [21]. This method is based on assuming selected images with lowest cloudiness as ground truth and filling them with a cloud mask that is borrowed from among highly clouded images.…”

“…The presented methodology showed an accuracy of 92% during snow season. Combination of different methods was investigated by several studies [14,15,20,21]. They applied a set of spatial, temporal and multi-sensor methods in a sequence-based algorithm to reduce cloud coverage.…”

“…The results indicated that combination of methods could remove clouds efficiently with an accuracy of 95%. Considering seasonal variations in accuracy of different cloud removal methods [20], Dariane et al [21] developed a cloud removal algorithm to combine different methods to reduce cloudiness along with maintaining the accuracy of snow mapping. Their algorithm was able to remove 94% of clouds and preserve the accuracy by 93%.…”

Gap-Filling of a MODIS Normalized Difference Snow Index Product Based on the Similar Pixel Selecting Algorithm: A Case Study on the Qinghai–Tibetan Plateau

“…The proposed method can fill cloud gaps without a significant loss of accuracy, especially during snow cover transition periods (autumn and spring), which may provide more accurate cloud-free NDSI data for climate change and energy balance studies. distribution and its spatiotemporal changes has become the focus of numerous studies [5][6][7][8][9][10][11][12][13], such that the timely and accurate acquisition of snow distribution information has become critical [9,14,15].Satellite images acquired using remote sensing can provide continuous spatiotemporal information on snow coverage over long time series and on a global scale, which is advantageous to a large number of researchers [16][17][18][19][20][21][22]. Among other widely-utilized snow cover assessment methods, MODIS products have become one of the main data sources for ice and snow research due to their global coverage, long time series (i.e., the databases are currently updated and have been maintained since 2000), high spatial (e.g., 500 m) and temporal (e.g., daily) resolutions, and free access, which allow for real-time, accurate, and large-scale snow cover variation monitoring [14].…”

“…The specific rules are the following [14]: if a pixel is cloudy in one product, but cloud-free in another (Aqua or Terra), the cloudy pixel will be updated using the classification of the cloud-free pixel. Temporal filtering is another popular temporal method that directly replaces cloudy pixels using information from previous or subsequent day (or days) pixels [21]. However, since snow cover is assumed to remain constant throughout a given temporal interval, the accuracy in snow-transitional periods is lower than that in snow-stable periods [47].Furthermore, the spatial filter (SF) and snow line (SNOWL) approaches are among the main spatial methods, of which the most common is the SF [39,40,48].…”