基于Sentinel-2时序数据的山区积雪识别与面积变化

Snow cover identification and area change in mountainous regions based on Sentinel-2 time series data

积雪是全球气候变化的重要指示器,也是中国西北干旱地区重要的淡水资源。卫星遥感由于具有大面积短周期重复观测等特点,已经成为积雪识别与面积变化监测的重要手段。与Landsat TM相比,Sentinel-2影像具有更高的辐射分辨率、光谱分辨率和空间分辨率以及更高的时间分辨率,成为积雪研究的重要数据源。然而,在中小尺度,高分辨率卫星辐射亮度值不仅受大气水汽、气溶胶等大气衰减,受地形遮蔽、坡度、坡向等地形因素的影响,同时也受地表二向反射分布函数(BRDF)影响,从而使得地表信息失真。本文以中国天山中段为研究区,选择2017年5月—2018年4月共20时相Sentinel-2影像,基于数字高程模型(DEM),利用山地辐射传输模型考虑地表BRDF特性基础上对其同步进行大气校正与地形校正预处理,然后综合利用归一化差值积雪指数(NDSI)以及积雪在绿波段、近红外波段的反射率阈值法精确提取积雪覆盖信息,有效剔除了水体、浓密植被、阴影和辐射亮度较低像元对积雪识别的影响。同时,结合历史天气资料,重点分析了1年内研究区积雪面积变化特征。本研究对积雪消融引起的地表反射率和能量平衡变化以及陆地生态环境等研究具有重要意义。

Snow cover is an important indicator of global climate change,and also an important freshwater resource in the arid regions of northwest China.Satellite remote sensing is an important way for snow cover identification and area change monitoring due to its large-scale,short-period,and repeated observations.Compared with Landsat TM,Sentinel-2 image has advantages of higher radiation resolution,spectral resolution and spatial resolution,and higher time resolution,which undoubtedly is an important data source for snow research.At small and medium scales,the radiation brightness of high-resolution satellite is affected not only by atmospheric attenuation such as atmospheric water vapor and aerosol,by topographical factors such as terrain shading,slope,aspect,but also by the surface bidirectional reflectance distribution function(BRDF),which distorts the surface information.Based on the digital elevation model(DEM),the atmosphere correction and topographic correction preprocessing were carried out synchronously on the basis of the surface BRDF characteristics using the mountain radiative transmission model,with 20 Sentinel-2 images of the middle section of Tianshan Mountain in China from May 2017 to April 2018.Then,snow cover information was accurately extracted by the normalization difference snow index NDSI and the snow reflectance threshold method in the green and near-infrared bands.This method effectively eliminates the effects of water bodies,dense vegetation,shadows,and low radiance pixels on the recognition of snow.Moreover,combined with historical weather data,the variation characteristics of snow cover in the study area were analyzed within one year.Our results have great significance to the research on changes of surface reflectance and energy balance as well as terrestrial ecological environment caused by snow melting.