| The fifth assessment report of the Intergovernmental Panel on Climate Change(IPCC)pointed out that there is no doubt that the global warming trend has accelerated in recent years.As one of the most active natural elements in the cryosphere,snow has the most sensitive feedback to climate change.China has a vast territory and a wide range of snow cover.In the context of global climate change,in-depth research on the temporal and spatial variations of snow and the dynamic response of snow to climate change have important research significance in the aspect the sustainable use of snow water resources,ecological environment protection,environmental protection,regional climate change and natural disaster prediction.Based on the characteristics of optical remote sensing with high temporal and spatial resolution and passive microwave remote sensing not be affected by bad weather,we combined the MODIS standard daily snow cover products MOD10A1 and MYD10A1 with the passive microwave remote sensing data of the AMSR-E SWE product and the data of the multi-source remote sensing product IMS to develop a new snow cover mapping algorithm,and generated daily cloud-free snow cover product for China from 2000 to 2018.At the same time,based on AMSR2 brightness temperature products and optical Landsat data,a multi-factor snow depth downscaling algorithm based on a multi-factor approach and snow cover mapping in forested areas are respectively proposed.On this basis,based on the climate data of national meteorological stations,the raster datasets of China's regional temperature and precipitation from 2000 to 2018 are generated,and the temporal and spatial distribution characteristics of snow,temperature and precipitation in China under different scales during 2000-2018 are systematically analyzed,as well as the dynamic trend of interannual and seasonal changes.Finally,the Person correlation analysis method is used to study the dynamic response relationship of snow to temperature and precipitation changes.The results of this study indicate:(1)The MOD10A1 and MYD10A1 images are greatly affected by clouds and cannot be directly used for snow monitoring.The cloud removal algorithm in this study can achieve the goal of completely removing cloud pixels,but different land cover types can affect the accuracy of the product.Overall,the MODIS daily cloud-free snow products obtained in this study have a high overall accuracy,with the average Kappa being 0.581,which is close to a high consistency.By completely removing cloud interference,we effectively improved the capability to correctly monitor snow cover range at a large scale.(2)In the Tibetan Plateau,snow depth is greatly affected by geographic location,snow-covered days,terrain and brightness temperature difference.By inputting multiple variables,the accuracy of snow depth downscaling model is greatly improved,and the best model is the power model.Overall,the downscaled snow depth datasets proposed in this study has a high accuracy,with root mean square error(RMSE)and mean absolute error of 2.00 cm and 0.25 cm respectively,both of which are better than other existing snow depth products.Furthermore,the downscaled snow depth datasets exhibit good accuracy(RMSE = 0.58 cm)in shallow snow areas where snow depth is less than 3 cm,which is very meaningful.(3)The NDFSI index has good potential to detect snow cover in forested areas with the aid of NDVI index.The threshold value of NDFSI and NDVI is set to be 0.35 and 0.25,respectively.Compared with the snow cover measured by Landsat 8 OLI images,the average BIAS and FAR values of this results are 1.24 and 14.34%,which are reduced by 2.09 and 33.72%,respectively.The overall accuracy of 80.67% is reached,which is improved by 22.89%.The snow classification scheme combining the NDFSI,NDVI and NDSI indexes based on MODIS data used in this work is simple and very effective in improving automatic snow cover mapping in the typical forested areas of Northeast China.(4)The distribution of average annual snow cover days and annual snow depth in China from 2000 to 2018 has certain characteristics of latitude and altitude zonality,that is,areas with more snow cover days and larger snow depth have relatively high latitude.From 2000 to 2018,the average snow cover days in spring and summer showed a decreasing trend in China,while the average snow cover days in autumn and winter showed an increasing trend.The inter-annual variation of snow depth is slightly different.The average snow depth in China in spring,autumn and winter shows an increasing trend.On the whole,the annual snow cover days and snow depth of seasonal snow cover areas in China show an increasing trend,while the annual snow cover days and snow depth in high latitude areas and high-altitude mountainous areas show a decreasing trend.The regional average temperature in China from 2000 to 2018 has obvious altitude zonality in spatial distribution,that is,regions with higher altitudes tend to have lower temperatures.The distribution of annual precipitation in China has obvious differences from north to south,and the overall spatial distribution shows a decreasing distribution pattern from the southeast coast to the northwest inland.From 2000 to 2018,both temperature and precipitation in China showed a fluctuating upward trend,and the trend rates were-0.024°C/year and 4.065 mm/year,respectively.(5)From 2000 to 2018,there are obvious regional differences in the response of snow variations to temperature and precipitation in China.On the whole,in Northeast China,precipitation is the leading factor for the increase in snow in this area.In the parts of southern China,temperature is the leading factor in the increase of snow in this area.Combined with the seasonal variations of temperature,it can be seen that the annual decrease in temperature in winter and spring in southern China has caused the main reason for the increase in snow.In the northern region of Xinjiang,China,the annual decrease in precipitation is the leading factor in the decreasing trend of snow.In the eastern part of the Tibetan Plateau,the annual increase in precipitation is the leading factor in the increasing trend of snow cover,and the climate in the eastern part of the Tibetan Plateau is developing towards a warm and humid trend.In the southern part of the plateau,the decrease in snow is the result of the combined effect of rising temperature and decrease in precipitation.The decrease in precipitation has resulted in insufficient water vapor sources and reduced snow accumulation,and snow melting is further accelerated due to the warming temperature.As a result,the snow has been decreasing year by year,and the climate in this area is developing towards warming and drying. |
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