Spatial-temporal variability of snow cover over the Amur River Basin inferred from MODIS daily snow products in recent decades

MODIS snow products MOD10A1\MYD10A1 provided us a unique chance to investigate snow cover as well as its spatial-temporal variability in response to global changes from regional and global perspectives. By means of MODIS snow products MOD10A1\MYD10A1 derived from an extensive area of the Amur River Basin, mainly located in the Northeast part of China, some part in far east area of the former USSR and a minor part in Republic of Mongolia, the reproduced snow datasets after removal of cloud effects covering the whole watershed of the Amur River Basin were generated by using 6 different cloud-effect-removing algorithms. The accuracy of the reproduced snow products was evaluated with the time series of snow depth data observed from 2002 to 2010 within the Chinese part of the basin, and the results suggested that the accuracies for the reproduced monthly mean snow depth datasets derived from 6 different cloud-effect-removing algorithms varied from 82% to 96%, the snow classification accuracies (the harmonic mean of Recall and Precision) was higher than 80%, close to the accuracy of the original snow product under clear sky conditions when snow cover was stably accumulated. By using the reproduced snow product dataset with the best validated cloud-effect-removing algorithm newly proposed, spatial-temporal variability of snow coverage fraction (SCF), the date when snow cover started to accumulate (SCS) as well as the date when being melted off (SCM) in the Amur River Basin from 2002 to 2016 were investigated. The results indicated that the SCF characterized the significant spatial heterogeneity tended to be higher towards East and North but lower toward West and South over the Amur River Basin. The inter-annual variations of SCF showed an insignificant increase in general with slight fluctuations in majority part of the basin. Both SCS and SCM tended to be slightly linear varied and the inter-annual differences were obvious. In addition, a clear decreasing trend in snow cover is observed in the region. Trend analysis (at 10% significance level) showed that 71% of areas between 2,000 and 2,380 m a.s.l. experienced a reduction in duration and coverage of annual snow cover. Moreover, a severe snow cover reduction during recent years with sharp fluctuations was investigated. Overall spatial-temporal variability of Both SCS and SCM tended to coincide with that of SCF over the basin in general.     

基于GF-1的青藏高原东南部积雪分布及MODIS积雪产品适用性研究

青藏高原东南部海拔高,地形复杂,云量大,准确掌握该地区的积雪分布特征对于积雪灾害防治非常重要。论文以2013—2019年冬季积雪积累期云量符合要求的35景高分一号(GF-1)影像为基础,将全色影像和多光谱影像融合为2 m分辨率影像,通过目视解译获取了研究区积雪的空间分布特征,结合改进后的30 m分辨率SRTM DEM,探讨了地形对积雪分布的影响。结果表明：积雪像元在研究区范围内占比为33.1%。积雪的垂直分布特征明显：积雪在高程带4000~5000 m(高海拔)处分布较集中,积雪面积占比为18.1%;在高程带0~2000 m、2000~3000 m和6000~7000 m处积雪面积占比均不到0.1%。积雪在北坡、东北坡的分布比例较高,均为15%以上;在南坡、西坡、西南坡、东南坡分布比例较低,均为10%左右。将基于GF-1影像获取的积雪分布分别与同日获取的根据MODIS V6积雪产品计算的积雪比例(MODIS FSC)和积雪分布的对比表明,64.4%的MODIS FSC像元绝对误差不超过10%,MODIS积雪分布产品对含雪像元的漏分率和误分率平均为33.8%和32.7%,说明MODIS积雪产品在研究区的精度还具有较高的不确定性,其对低覆盖积雪反演的精度较差。这表明利用MODIS积雪产品研究青藏高原东南部积雪的时空变化特征时还需要对其积雪反演算法进行改进,同时亟需加强地面观测和基于多源遥感数据的积雪研究。研究结果可为青藏高原东南部雪冰灾害防治提供支撑。     

基于FY-3D/MERSI-Ⅱ归一化积雪指数和MOD10A1的精度对比分析

风云三号D星（FY-3D）是我国新一代极轨气象卫星,中分辨率光谱成像仪Ⅱ（MERSI-Ⅱ）是其携带的核心传感器之一,MERSI-Ⅱ实现了云、气溶胶、水汽、陆地表面特性、海洋水色等大气、陆地、海洋参量的高精度定量反演。选取2018年7、8月无云时相的FY-3D/MERSI-Ⅱ数据对天山中段终年积雪进行归一化积雪指数（NDSI）的计算。结合高分辨率Landsat-8影像,利用混淆矩阵对FY-3D/MERSI-Ⅱ数据计算结果与同期MODIS日积雪产品数据MOD10A1进行精度对比分析。结果表明：FY-3D/MERSI-Ⅱ图像平均总体精度为0.855,MOD10A1图像平均总体精度为0.820,FY-3D/MERSI-Ⅱ积雪覆盖提取平均总体精度比MOD10A1积雪覆盖提取平均总体精度高0.035。FY-3D/MERSI-Ⅱ的Kappa系数平均值为0.659,MOD10A1的Kappa系数平均值为0.558,FY-3D/MERSI-Ⅱ的Kappa系数平均值大于MOD10A1的Kappa系数平均值。故FY-3D/MERSI-Ⅱ数据提取积雪覆盖面积精度更高,更接近高分辨率Landsat-8影像目视解译结果。     

基于MODIS与AMSR-E数据的中国6大牧区草原积雪遥感监测研究

内蒙古、新疆、西藏、青海、甘肃和四川的草原区这6大牧区是中国重要的畜牧业生产基地,也是雪灾频发的区域,及时、准确地获取6大牧区雪情时空特征对于防灾减灾,指导畜牧业生产有着重要的现实意义。光学遥感与微波遥感各具优缺点,综合运用MODIS和AMSR-E数据构建草原积雪遥感监测模型,以日为监测单元,以旬为多日合成时段,对中国6大牧区在2008年10月上旬至2009年3月下旬间的草原积雪覆盖范围进行监测,并对监测结果进行检验,以此说明MODIS与AMSR-E数据在雪灾监测方面协同监测的可行性,为其他雪盖遥感监测研究提供参考。     

青藏高原积雪对全球变暖的响应

根据６０个地面基本气象台站１９５７－１９９２年逐日雪深观测记录，用统计模式检验了青藏高原积雪变化趋势，证明近３６年来高原积雪变化呈普遍增加趋势，并且与北球冬季气温呈正相关，高原积雪的增加与北半球温带低地春季积雪面积自８０年代后期的减少形成了鲜明的对比，与两个大陆冰盖雪积累率的增加相一致。     

基于MODIS积雪产品的高亚洲融雪末期雪线高度遥感监测

以2001—2016年逐日MODIS积雪产品为主要数据源,在高亚洲区域发展了大尺度融雪末期雪线高度的遥感提取方法,并对其2001—2016年的时空变化特征进行了分析。提取方法首先对逐日的MODIS积雪覆盖率产品进行去云处理,获得积雪覆盖日数（SCD）数据集;并用冰川年物质平衡观测数据、融雪末期Landsat数据对提取终年积雪的MODIS SCD阈值进行率定;最后以MODIS SCD提取的终年积雪面积结合地形“面积—高程”曲线实现大尺度融雪末期雪线高度信息的提取。结果表明：① 高亚洲融雪末期雪线高度的空间异质性较强,总体上呈南高北低的纬度地带性分布规律;并因受山体效应的影响,雪线高度由高海拔地区向四周呈环形逐渐降低的特点。② 高亚洲2001—2016年融雪末期雪线高度总体上表现为明显的增加趋势。在744个30 km的监测格网中,24.2%的格网雪线高度呈显著增加,而仅0.9%的格网呈显著下降。除兴都库什、西喜马拉雅外,其他地区雪线高度均表现为升高趋势,显著上升的地区主要分布在天山、喜马拉雅中东部和念青唐古拉山等,其中以东喜马拉雅升高最为显著（8.52 m yr ^-1）。③ 夏季气温是影响高亚洲融雪末期雪线高度变化的主要因素,两者具有显著的正相关关系（R = 0.64,P < 0.01）。     

塔克拉玛干沙漠腹地冬季积雪下垫面地表反照率及土壤温湿度变化特征

利用塔克拉玛干沙漠大气环境观测试验站西站10 m梯度探测系统气象和辐射观测数据,分析了塔中积雪下垫面地表反照率、土壤温度、土壤湿度的变化特征及其相互关系。结果表明：塔中积雪覆盖期间地表反照率0.18~0.97,日均值为0.60;有积雪覆盖的地表反照率日变化更偏向反"J"型,呈现出上午大于傍晚的形态,平均早晚较差为0.13;积雪使0~40 cm深度土壤温度下降,积雪消融后土壤湿度增大使各层土壤温度趋于接近,并使0、10、20 cm深度的土壤温度日变幅呈减小趋势,减小幅度分别为41%、39%、39%;积雪地表反照率与地表温度表现出负相关关系,反照率越高地表温度越低,二者相关系数为-0.71;积雪地表反照率与5 cm深度土壤湿度负相关,高地表反照率对应低土壤湿度,低地表反照率对应高土壤湿度,二者相关系数为-0.74。     

Distribution of winter-spring snow over the Tibetan Plateau and its relationship with summer precipitation in Yangtze River

The distribution of winter-spring snow cover over the Tibetan Plateau(TP) and its relationship with summer precipitation in the middle and lower reaches of Yangtze River Valley(MLYRV) during 2003–2013 have been investigated with the moderate-resolution imaging spectrometer(MODIS) Terra data(MOD10A2) and precipitation observations. Results show that snow cover percentage(SCP) remains approximately 20% in winter and spring then tails off to below 5% with warmer temperature and snow melt in summer. The lower and highest percentages present a declining tendency while the middle SCP exhibits an opposite variation. The maximum value appears from the middle of October to March and the minimum emerges from July to August. The annual and winter-spring SCPs present a decreasing tendency. Snow cover is mainly situated in the periphery of the plateau and mountainous regions, and less snow in the interior of the plateau, basin and valley areas in view of snow cover frequency(SCF) over the TP. Whatever annual or winter-spring snow cover, they all have remarkable declining tendency during 2003–2013, and annual snow cover presents a decreasing trend in the interior of the TP and increasing trend in the periphery of the TP. The multi-year averaged eight-day SCP is negatively related to mean precipitation in the MLYRV. Spring SCP is negatively related to summer precipitation while winter SCP is positively related to summer precipitation in most parts of the MLYRV. Hence, the influence of winter snow cover on precipitation is much more significant than that in spring on the basis of correlation analysis. The oscillation of SCF from southeast to northwest over the TP corresponds well to the beginning, development and cessation of the rain belt in eastern China.     

基于MODIS数据中国天山积雪面积时空变化特征分析

基于2011-2015年MOD10A2积雪产品和气象数据,通过几何校正、去云预处理,应用归一化差分积雪指数算法等获取中国境内天山山区积雪覆盖面积数据,分析了积雪面积的时空变化特征及与气温降水的关系。结果表明：（1）年内积雪面积呈单峰变化,9月开始积累,次年1月达峰值,3月气温回暖消融加速,至7月最小。春秋季波动较大但没有明显的增减趋势,夏季积雪面积最小,冬季最大且呈减小趋势。（2）2001-2015年积雪覆盖面积整体上呈减少趋势,积雪覆盖率最大值的波动比最小值的波动更加剧烈。（3）积雪覆盖率随着海拔升高而增大,海拔<1 500 m区域积雪覆盖率低于10%,海拔>4 500 m以上区域平均可达70%,为常年稳定积雪区。积雪覆盖率在西北坡最高,南坡最低。（4）年均气温升高是积雪覆盖面积减小的主因,年积雪覆盖面积变化与年降水量变化保持一致的下降趋势。     

新疆典型流域积雪遥感信息系统数据库的建立

天山山区冬季积雪深厚,稳定积雪期较长,利用积雪遥感图象可以动态监测大面积的积雪变化,有效地调查大范围的积雪资源状况,积雪遥感监测和积雪遥感制图涉及多方面的资料,积雪数据库是积雪遥感监测系统必不可少的一部分。本文就新疆典型流域积雪遥感信息系统数据库的建立,数据库之间的数据格式转换,接口,可视化界面等进行讨论。     

基于MODIS数据的蒙古高原积雪时空变化研究

利用Terra卫星和Aqua卫星提供的2002年9月1日~2017年5月31日每日积雪覆盖产品MOD10C1和MYD10C1,提取蒙古高原积雪日数、积雪面积、积雪初日及积雪终日信息,得到蒙古高原积雪特征分布和变化趋势,同时,结合蒙古高原108个地面气象观测站的气温资料,分析研究区积雪变化特征和气温的关系。结果表明：（1）蒙古高原平均积雪日数在60～90 d之间,积雪初日主要分布在315～335 d之间,积雪终日大多集中在31～61 d之间,蒙古高原东部地区积雪初日有明显的提前趋势,西南地区积雪终日有明显的提前趋势。（2）积雪面积在积雪季内呈 “单峰型”,1月份为积雪面积最大月,年均积雪面积呈微弱的下降趋势。（3）最大积雪覆盖面积与温度具有明显的相关性,稳定积雪覆盖区的临界温度大概介于-11~-8 ℃之间。（4）温度是影响积雪特征变化的重要因素。     

Detecting Arctic snow and ice cover with FY-1D global data

Chinese meteorological satellite FY-1D can obtain global data from four spectral channels which include visible channel(0.58-0.68 μm) and infrared channels(0.84-0.89 μm,10.3-11.3 μm,11.5-12.5 μm).2366 snow and ice samples,2024 cloud samples,1602 land samples and 1648 water samples were selected randomly from Arctic imageries.Land and water can be detected by spectral features.Snow-ice and cloud can be classified by textural features.The classifier is Bayes classifier.By synthesizing five d ays classifying result of Arctic snow and ice cover area,complete Arctic snow and ice cover area can be obtained.The result agrees with NOAA/NESDIS IMS products up to 70%.     

青藏高原积雪分布与变化特征

本文对青藏高原ＳＭＭＲ修积雪深度、ＮＯＡＡ周积雪面积、地面台站积雪深度进行了分析。结果表明青藏高原东西两侧多雪与腹地少雪形成鲜明对比,高原东部是高原积雪年际变化最显著的地区,它主导了整个高原积雪的年际变化,并且与西部多雪区年际波动呈反位相关系。从６０年代到８０年代积雪年际波动幅度有明显增加趋势,积雪变化具有３年左右准周期。随着全球变暖,青藏高原积雪将会有所增加。     

祁连山区季节性积雪资源的气候分析*

本文采用1986年10月至1988年9月NOAA—9、10两颗卫星的AVHRR资料标准化后反演的积雪参量,对照祈连山区26个气象站1951—1988年逐日雪深、雪密度和积雪日数的资科,修正卫星反演的平均值。得到高分辨率多年平均雪深和雪盖频率的空间分布。从而估算出各流域平均雪储量,并与降雪量和春季融雪径流作对此分析。     

RS与GIS支持下的草地雪灾监测试验研究

冬季降雪是影响我国北方草原牧区畜牧业发展的重要因子。在草地雪灾监测和危险性评价过程中，确定积雪区域和积雪深度与牧草高度之间的关系是关键步骤。通过在内蒙古锡林郭勒草原的试验研究，可以认为：应用ＮＯＡＡ／ＡＶＨＲＲ气象卫星资料确定积雪分布范围、应用地面气象站观测的降雪量资料及由ＤＥＭ生成的地形参数确定积雪深 是在较大范围内进行雪灾监测的有效方法。     

基于HJ-1B数据的雪盖提取方法研究——以军塘湖流域为例

 HJ-1A、1B卫星具有较高的时间和空间分辨率,适合小流域尺度的积雪动态监测研究。本文基于HJ-1B数据,选取军塘湖流域,针对同时具有HJ-1B/CCD、IRS数据和只有HJ-1B/CCD数据两种情况展开雪盖提取方法研究。对于第一种情况,因研究区南端有大面积森林覆盖,会影响雪像元识别,选用[WTBX]NDSI[WTBZ]和[WTBX][STBX]S3[WTBZ][STBZ]两种雪盖指数,并利用[WTBX]NDVI[WTBZ]或TM影像反演的林区辅助判识积雪。结果表明:当有植被信息辅助分类时,两种雪盖指数均能较好提取出森林覆盖区的积雪,且提取结果基本一致,精度较高。对于第二种情况,因无法计算雪盖指数,采用光谱与纹理信息结合的SVM法提取雪盖,提取的面积和精度与上述方法相比略低,但很接近,说明在缺少[WTBX]IRS[WTBZ]数据的情况下,仅利用CCD仍可提取出较为准确的雪盖,满足实际应用需求。     

Comparison and analysis of snow cover data based on different definitions of snow cover days

In order to analyze the differences between the two snow cover data, the snow cover data of 884 meteorological stations in China from 1951 to 2005 are counted. The data include days of visual snow observation, snow depth, and snow cover durations, which vary according to different definitions of snow cover days. Two series of data, as defined by "snow depth" and by "weather observation," are investigated here. Our results show that there is no apparent difference between them in east China and the Xinjiang region, but in northeast China and the Tibetan Plateau the "weather observation" data vary by more than 10 days and the "snow depth" data vary by 0.4 cm. Especially in the Tibetan Plateau, there are at least 15 more days of "weather observation" snow in most areas (sometimes more than 30 days). There is an obvious difference in the snow cover data due to bimodal snowfall data in the Tibetan Plateau, which has peak snowfalls from September to October and from April to May. At those times the temperature is too high for snow cover formation and only a few days have trace snow cover. Also, the characteristics and changing trends of snow cover are analyzed here based on the snow cover data of nine weather stations in the northeast region of the Tibetan Plateau, by the Mann-Kendall test. The results show significantly fewer days of snow cover and shorter snow durations as defined by "snow depth" compared to that as defined by "weather observation." Mann-Kendall tests of both series of snow cover durations show an abrupt change in 1987.     

2000—2019年新疆积雪终日时空变化特征

基于Google Earth Engine（GEE）遥感云平台,利用2000—2019年MODIS积雪产品资料提取和计算新疆积雪终日信息,利用趋势分析,变异系数等方法分析了新疆积雪终日时空变化特征和变化趋势。结果表明：（1） 新疆积雪终日以天山为界,天山以北长于南部,山区为积雪终日的高值区,盆地为积雪终日的低值区。北疆准噶尔盆地和伊犁河谷积雪终日在75~114 d之间,南疆塔里木盆地在0~31 d之间属于低值区。阿尔泰山脉、天山山脉和昆仑山脉区域在224~365 d之间属于高值区。（2） 南疆和北疆积雪终日有明显的时空差异,2000—2019年北疆准噶尔盆地和高海拔山脉地区积雪终日有明显的推迟趋势,推迟幅度达到14 d,占新疆总面积的8%。南疆塔里木盆地和东疆区域有明显的提前趋势,提前幅度达到16 d约占新疆总面积的44%。塔里木盆地和准噶尔盆地具有相反的变化趋势。（3） 新疆积雪终日年际变化差异显著,天山中段和北疆积雪终日出现不稳定状况,天山中段2002—2009年总体上呈现“M”型的特点,即多年积雪消融日年均值中出现明显的波峰和波谷,北疆2009—2019年积雪终日有较大的年际变化呈现出不稳定状况,出现明显的波峰和波谷,年际变化较大。     

MODIS雪深反演数学模型验证及分析

在MODIS卫星遥感积雪监测的基础上,利用雪深反演数学模型、积雪指数NDSI和多光谱阈值等相结合的方法,对2004年以来新疆北疆地区的积雪分布状况进行了反演和计算,并利用2004年11月～2005年3月冬季北疆地区气象台站雪深数据和2004年12月～2006年1月加密野外实测雪深数据,对反演雪深数据进行了验证及分析,北疆各地除塔城地区反演精度为83.2%以外,其它地区反演精度达85.2%以上,平均反演精度达86.2%;野外实测数据验证反演精度达92%以上。     

古尔班通古特沙漠积雪覆盖、沙尘天气特征及其相互关系

利用TERRA/MODIS MOD10A2雪盖产品数据和地面观测积雪日数、冻土深度和沙尘天气日数等数据,从不同时间尺度分析古尔班通古特沙漠地表积雪覆盖与沙尘天气的特征及其相互关系。结果表明:①沙尘天气主要发生在4—10月,春季（4—5月）沙尘天气最多,夏秋季逐渐减少。从年际变化看,20世纪80年代前,沙尘天气发生日数呈逐年增加趋势,而积雪日数增减波动较大,二者间关系不明显,80年代后,沙尘天气逐年减少,积雪日数呈波动增加趋势。②冬春季积雪覆盖率、≥1 cm积雪日数、≥5 cm积雪日数、≥10 cm积雪日数与翌年春季沙尘天气发生均呈显著负相关关系,冬春季≥1 cm积雪日数每超过常年平均积雪日数1 d,翌年春季沙尘天气日数则减少4.3 d,而平均冻土深度与沙尘天气呈显著正相关关系。③积雪覆盖使沙漠地表形成冷源性下垫面和近地层逆温层结,增加了大气稳定度,同时春季积雪消融增加了土壤湿度,为荒漠植被生长提供充足的水分,使表层土壤为强风提供沙尘的可能性降低,从而对沙尘天气的发生起到阻碍、消弱作用。