The recent developments in cloud removal approaches of MODIS snow cover product

Abstract

Abstract. The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, and the energy balance. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are the most popular datasets used in the community. However, for MODIS, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for MODIS snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating cloud-free MODIS snow cover products and to expose the development trends. The methods of generating cloud-free MODIS snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. The spatial methods and temporal methods remove the cloud cover of the snow product based on the spatial patterns and temporal changing correlation of the snowpack, respectively. The spatio-temporal methods utilize the spatial and temporal features of snow jointly. The multi-source fusion methods utilize the complementary information among different sources among optical observations, microwave observations, and station observations.

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

“…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]. Extensive studies [23][24][25][26][27][28] have demonstrated that MODIS products exhibit an excellent snow extraction performance, with an overall accuracy exceeding 90% under clear-sky conditions.…”

“…Extensive studies [23][24][25][26][27][28] have demonstrated that MODIS products exhibit an excellent snow extraction performance, with an overall accuracy exceeding 90% under clear-sky conditions. Nonetheless, cloud occlusion in MODIS snow cover products often leads to numerous data gaps, which hinders their promotion and adoption in environmental research.A large number of algorithms have been developed to improve the spatiotemporal continuity of MODIS snow products over the past decade [14,25,[29][30][31][32][33][34][35][36][37][38][39][40][41][42]. Traditional cloud removal algorithms can be divided into four types: temporal, spatial, spatiotemporal, and multi-source fusion methods [14].The Terra and Aqua combination (TAC) method is the simplest and most transparent of the temporal methods [40,[43][44][45] and has, thus, become the most popular temporal approach.…”

“…The TAC method can decrease the cloud-gap fraction (CGF) by up to 5-20% without significantly sacrificing the product accuracy [43,46]. 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]. This method consists of replacing a cloudy pixel by using four or eight neighboring non-cloud pixels [14]. The SNOWL method [13,38], which is also known as the snow transition elevation method [39,49], reclassifies the cloudy pixels as snow or land according to the snowpack elevation distribution; cloudy pixels above the snow line will be classified as snow, while those below the snow line will be classified as land.The spatiotemporal method combines the spatial and temporal methods alternately and successively [21].…”

An Integrated Shadow-Adjusted Snow-Aging Index for Alpine Regions

“…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].…”