Preparation And Application Of Daily Cloud-Free MODIS Snow Cover Products Over Northeast China

Snow is a significant climatic factor influencing various aspects such as surface radiation balance,global hydrological processes,and ecological environment.Additionally,snow serves as a crucial freshwater resource with essential implications for human livelihood and production.Therefore,precise monitoring of snow distribution and changes holds substantial value in studying global climate change.The Northeast region of China is recognized as one of the country’s primary stable snow regions and a key agricultural production area.Investigating the seasonal dynamics of snow accumulation and melt in this region is of utmost importance for crop production.In this study,an analysis was conducted on the cloud-contaminated pixels present in the MODIS(Moderate Resolution Imaging Spectroradiometer)V6 snow product,specifically the Snow＿cover＿class dataset,in the Northeast region.During the winter months(December,January,and February),noticeable issues of excessive cloud masking were observed in the forested regions of the Da Hinggan Mountains,Xiao Hinggan Mountains,and Changbai Mountains.This misclassification of significant forest snow cover as clouds resulted in significant data gaps in the Normalized Differential Snow Index(NDSI)of the MODIS snow product.To address this limitation,an optimal threshold for the green band was identified to effectively differentiate forest snow from cloud-contaminated pixels,thereby mitigating the problem of excessive cloud masking.For the remaining cloud-contaminated pixels,a novel Spatiotemporal Cube Cloud Removal Algorithm based on NDSI Similarity(STNSI)was developed to reconstruct and fill the missing NDSI values.The algorithm employed a standardized Euclidean distance measure between the NDSI of a central pixel and its neighboring pixels as a similarity criterion.This measure facilitated the adjustment of the NDSI values of the neighborhood pixels based on the error offset with the central pixel,resulting in the generation of high-precision,daily cloud-free NDSI time series.Furthermore,optimal NDSI thresholds for different land surface types were determined using ground-based snow depth data,enabling the production of binary snow cover products.The study analyzed the spatiotemporal variations of snow in the Northeast region of China and quantitatively evaluated snow cover stability using the proposed Snow Cover Duration Index(SDI).The key findings of this study are as follows:(1)The current MODIS V6 snow product,which utilizes the Normalized Differential Snow Index(NDSI),exhibits notable issues of excessive cloud masking in the Northeast region of China,particularly in forested areas.This significantly hampers the product’s applicability in studying snow spatiotemporal variations.The study identified the green band as a discriminative factor between clouds and forest snow,as cloud-contaminated pixels typically exhibit a reflectance greater than 0.4 in the green band,while forest snow pixels exhibit lower reflectance.(2)By integrating terra and aqua,decision tree classification,and the STNSI algorithm,it was possible to generate high-precision,cloud-free NDSI snow products with continuous long-term time series.Accuracy evaluation of the cloud removal process using cloud assumption tests revealed average correlation coefficients,root mean square errors,and absolute errors of 0.96,0.10,and 0.08,respectively,when comparing the cloud-free images with the original true images.Optimal NDSI thresholds of 0 for forest areas and 0.09 for non-forest areas were determined,resulting in the production of daily binary snow cover products.(3)Snow cover in Northeast China exhibits distinct spatial and temporal variations.The mountainous regions,including Da Hinggan Mountains,Xiao Hinggan Mountains,and Changbai Mountains,have higher Snow Cover Days(SCD),while the central plain region has lower SCD values.Regions with SCD exceeding 180 days are mainly concentrated in the northernmost part of Northeast China,while extensive areas of the central plain region have SCD below 60 days.Snow cover distribution remains relatively stable over the years,with similar spatial patterns.Additionally,snow cover area(SCA)in Northeast China shows significant latitudinal zonation,with higher SCA at higher latitudes and lower SCA at lower latitudes.Higher latitude areas experience earlier snow cover onset and later snowmelt compared to lower latitude regions.MannKendall and Theil-Sen Median analyses were employed to examine interannual variations in Northeast China’s snow cover.The results indicated that the area with an increasing trend in accumulated snow days accounts for 52.33%,while the area with a decreasing trend accounts for 43.61%,and the area with insignificant changes accounts for 4.06%.(4)In Northeast China,snow cover is virtually absent at the regional scale in September and October,with monthly mean SDI mostly below 5 days.From November onward,the accumulation of snow cover days increases,with the majority of monthly mean SDI falling between 5 and 10 days.December,January,and February are considered the stable snow period,with January having the highest snow cover days.In January,10.64% of the region experiences continuous snow cover for an entire month,resulting in an SDI of 31 days.Snow cover gradually decreases in March,and the monthly mean SDI begins to decline throughout the region,reaching nearly no snow cover by April,with the area where the monthly mean SDI is below 10 days recovering to 96.74%.Forested areas consistently exhibit higher SCD and monthly mean SDI compared to non-forested areas.