基于多源数据的青藏高原雪深重建

青藏高原地形复杂,积雪时空分布异质性较强且大部分地区积雪较薄,而被动微波遥感因其空间分辨率低以及雪深反演中的不确定性,极大地限制了其反演青藏高原雪深的精度。本文尝试将多源遥感数据以及与积雪模型（SnowModel）相结合,来重建更高质量的青藏高原雪深数据。首先,利用MODIS积雪面积比例产品,根据构建的积雪衰减曲线以及经验的融合规则对低分辨率被动微波雪深进行了降尺度;然后,结合MODIS/被动微波融合雪深数据和SnowModel对研究区进行雪深数据同化实验;最后,利用地面站实测雪深数据对MODIS/被动微波融合雪深以及同化输出雪深的精度进行了分析和对比。结果表明,基于数据同化方法得到的雪深数据更接近地面观测雪深值,通过均方根误差以及相关系数的对比,同化雪深结果优于MODIS/被动微波融合雪深结果。     

基于MODIS和AMSR-E反演京津冀地区大气水汽含量及比较

为了长时间、大范围获取水汽数值,利用2005~2008年光学遥感的MODIS近红外、红外水汽产品,以及微波遥感AMSR-E数据,2种方法反演水汽。微波AMSR-E亮温数据采用Merritt N.Deeter(2007)亮温极化差方法,选取18.7GHz和23.8GHz 2个波段,得到AMSR-E升轨、降轨大气水汽数值。以京津冀地区为研究区域,通过地统计相关性分析、时间序列分析、年际间变化分析,可知2种方法4种资料反演的大气水汽数值的R2都达到0.95,时间分布符合中国雨带移动规律,空间分布不均。MODIS数据反演值比AMSR-E值要低,得到2种方法反演水汽的各自优缺点。     

一种考虑雪粒径变化的积雪面积反演算法

光谱混合分析能够提取亚像元信息,被广泛地应用于遥感影像目标探测之中。本文针对MODIS积雪遥感影像,基于光谱混合分析框架,利用渐进辐射传输模型建立不同粒径大小的雪反射率光谱库,提出了一种考虑端元变化及二次辐射的雪盖面积反演算法。此算法首先利用渐进辐射传输模型建立不同粒径大小积雪的反射率光谱库,然后使用序贯最大角凸锥方法获取植被、土壤与岩石、阴影的光谱库。在建立各种地物反射率光谱库之后,利用均方根误差最小的方法获取最优端元组合。在此基础上,考虑端元独立辐射以及积雪与其它地物的二次辐射过程,利用稀疏光谱混合模型获取积雪面积与雪粒径大小。实验结果表明：此方法能够同时反演雪粒径与积雪面积,反演的雪粒径相比单波段的渐进辐射传输模型小,反演的积雪面积相比MOD10A1产品精度略微提高。     

土壤水分微波反演方法进展和发展趋势

土壤水分是连接地表水循环和能量循环的关键参量,精确获取该参量对于理解气候变化、地表水文过程、地气间能量交换机理等具有重要意义。微波遥感由于其较为合适的探测深度和坚实的理论基础在观测地表浅层土壤水分上具有很大优势,结合反演方法可以获取空间连续的土壤水分含量,有助于更加客观认知土壤水分的时空演变机理。随着微波遥感数据的不断丰富,多种微波遥感土壤水分反演方法相继涌现,为了更好地了解其发展和趋势,本文总结了当前土壤水分微波反演常用的卫星遥感数据并分析其发展趋势,后从主动微波反演、被动微波反演和多源协同反演3个方面梳理了各类土壤水分微波反演方法的原理、发展和优缺点,最终总结出目前微波遥感土壤水分反演方法的发展趋势：即土壤水分微波反演方法的时空普适性逐渐增强、面向高时空分辨率的土壤水分微波协同反演方法快速发展以及土壤水分微波反演方法的智能化水平不断提高。     

MODIS_LST与AMSR-E_BT的相关性及地表温度反演

本文以2007年和2008年MODIS每日地表温度（LST）数据及AMSR-E地表亮温（BT）数据为研究对象,结合土地覆盖类型数据,统计分析MODIS_LST与AMSR-E_BT在不同土地覆盖类型、频率和极化方式条件下的相关性。结果表明,频率在18.7、23.8和36.5 GHz的AMSR-E-BT与MODIS_LST的相关性较大,且在垂直极化通道上的相关性较在水平极化上大;不同土地覆盖类型,与MODIS_LST相关性较大所对应的AMSR-E微波通道不同。同时,考虑混合像元问题对相关性的影响,对25种不同地物类型组合下MODIS_LST与AMSR-E-BT的相关性进行统计分析,发现混合像元中地物类型越多,则二者相关性越小。最后,采用多元线性回归分析法,根据不同土地覆盖类型建立反演回归模型,对部分研究区域MODIS-LST进行反演,误差平均在±3.15 K以内,与不考虑下垫面覆盖的模型比较,反演MODIS_LST精度平均提高了1.5 K。     

CE-318太阳光度计及探空数据反演水汽含量与MODIS近红外水汽产品对比

大气水汽含量是遥感定量反演的重要参数。本文利用CE-318太阳光度计,反演了2014年6月至2016年5月南宁市的大气水汽含量,分析其季节变化特征,并将其与探空数据、MODIS近红外水汽产品数据进行相关性分析。结果表明：（1）南宁市大气水汽含量季节变化特征明显：夏季高达4~6 g/cm²,而冬季则只有2 g/cm²,与南宁地处亚热带地区有关。夏季该地区季风气候盛行,大气水汽含量高,冬季季风气候减弱,大气相对干燥。（2）CE-318太阳光度计反演的大气水汽含量（PW）与探空数据获取的水汽含量之间存在良好的线性相关,相关系数为0.877,平均绝对误差为0.42 g/cm²,平均相对误差为10.96%;而MODIS近红外波段反演的水汽精度较低,平均绝对误差为0.74 g/cm²,平均相对误差为18.74%。     

基于LTDR AVHRR和MODIS观测的全球长时间序列叶面积指数遥感反演

叶面积指数是描述土壤-植被-大气之间物质和能量交换的关键参数,获取大区域长时间序列叶面积指数有助于研究气候变化条件下植被的响应及反馈。本文利用MODIS观测和经过重新处理的地表长时间数据集（Land Long Term Data Record）LTDR AVHRR数据,生成了全球1981-2012年叶面积指数数据。算法通过建立二者之间像元级关系,利用高质量MODIS观测约束历史AVHRR数据的反演,这有助于减小2种存在显著差别传感器反演结果的不一致性,也有助于提高AVHRR反演质量。首先算法利用高质量MODIS地表反射率反演2000-2012年叶面积指数,然后利用多年每8 d的LTDR AVHRR地表反射率数据计算简单比植被指数（Simple Ratio,SR）,利用SR平均值和MODIS LAI平均值建立像元级AVHRR SR-MODIS LAI关系。在此基础上,实现1981-1999年AVHRR LAI反演,最终得到全球1981-2012年叶面积指数数据。本算法反演的AVHRR和MODIS LAI与全球植被的空间分布吻合,能表征主要生物群系类型的季节变化特征,2个数据集一致性较好,并且与NASA MODIS LAI标准产品（MOD15A2）的空间分布和季节变化曲线吻合较好。     

基于背景知识的全球长时间序列反照率反演

全球范围时空连续的长时间序列地表反照率,对气候模拟与陆面过程研究具有重要意义。针对现有地表反照率产品普遍存在大量的数据缺失、有效反演比例低和时间序列短的问题,本文以多年MODIS和AVHRR数据,通过构建背景知识库进行高时间分辨率的AVHRR和MODIS数据的BRDF参数反演,实现MODIS与AVHRR数据在像元尺度上的定量融合,生成了全球时空连续长时间序列的地表反照率产品。首先,通过假设不同年份同一时期的地表状态不变,利用多年同一时期的MODIS和AVHRR观测数据构造多角度方向反射率,基于BRDF模型反演得到窄波段反照率;然后,通过宽波-窄波转换,得到MODIS的宽波段反照率;最后,结合AVHRR长时间序列优势及MODIS数据多光谱的特点,对二者进行定量融合,生成具有高度一致性长时间序列地表反照率产品。验证结果表明,本文地表反照率产品在地表异质性较小时与SURFRAD地面实测反照率之间具有非常好的一致性,在无积雪覆盖时与MODIS反照率产品之间吻合良好。本文的地表反照率产品无时空缺失,且时间覆盖率得到了极大的提高,能支持气候模式模拟与陆面过程模型进行近30 a来的地气系统模拟研究。     

基于动态NDSI阈值的每日积雪监测方法

准确掌握积雪覆盖信息对于气象、水文和全球气候变化研究都具有重要的意义。遥感技术在进行大范围、高频率的积雪覆盖监测中发挥着重要的作用。目前,SNOMAP算法是用于积雪遥感监测最普遍的技术手段,其核心是利用固定阈值的归一化差分积雪指数（Normalized Difference Snow Index,NDSI）进行积雪识别,但这种方法忽略了积雪光谱信息的时相变化,会产生积雪监测的误差。本文提出了一种动态NDSI阈值方法,以纯永久积雪像元的平均NDSI值作为参照系调整固定的NDSI阈值,从而削减影像光谱值波动对积雪识别的影响。以三江源地区作为研究区域,将基于每日MODIS数据进行积雪监测最佳的NDSI阈值与同日纯永久积雪像元的平均NDSI值作线性回归,通过每日纯永久积雪像元平均NDSI值的变化来调整用于积雪识别的NDSI阈值。结果表明：① 基于每日MODIS数据进行积雪覆盖监测最佳的NDSI阈值与同日纯永久积雪像元的平均NDSI值之间存在较好的线性关系,决定系数R ²达到0.86;② 三江源地区动态NDSI阈值的范围为0.29~0.37,其平均值在0.33左右,说明MODIS全球积雪面积产品中将NDSI阈值取为0.40会低估三江源地区的积雪面积;③ 与采用固定NDSI阈值0.33的监测方法相比,动态NDSI阈值法近似率、总体分类精度和F值的平均值分别提高了5.17%、0.70%、1.14%。     

北京市热岛效应时空变化的HJ-1B监测分析

 本文利用2008-2011年HJ-1B/CCD可见光-近红外数据,以及HJ-1B/IRS热红外数据,采用遥感算法反演北京市地表温度,并用MODIS地表温度产品对反演结果进行了初步验证。同时分析了北京市热岛效应的年际、年内变化趋势。另利用热场变异指数分析其空间分布特征,以及NDVI、NDBI与城市下垫面对热岛效应的影响。结果表明:(1)2008-2010年北京市热岛强度总体呈上升趋势,2011年有所缓解,4年热岛强度分别为:5.2℃、5.2℃、9.2℃、8.2℃;(2)北京市2010年四季存在明显热岛现象,夏季最强,春、秋次之,冬季最弱,四季热岛强度分别为8.2℃、9.4℃、9.2℃、4.3℃;(3)2008-2011年北京市热岛空间分布特征表明,房山区和大兴区的南部热岛效应逐年缓解,2011年昌平区热岛效应比前3年明显,植被和水体形成城市冷岛;(4)地表温度与NDVI呈明显负相关,与NDBI呈正相关,城市热岛效应与下垫面类型存在明显相关性。     

Retrieval of Snow Depth on Sea Ice in the Arctic Using the FengYun-3B Microwave Radiation Imager

Snow on sea ice is a sensitive indicator of climate change because it plays an important role regulating surface and near surface air temperatures. Given its high albedo and low thermal conductivity, snow cover is considered a key reason for amplified warming in polar regions. This study focuses on retrieving snow depth on sea ice from brightness temperatures recorded by the Microwave Radiation Imager(MWRI) on board the FengYun(FY)-3 B satellite. After cross calibration with the Advanced Microwave Scanning Radiometer-EOS(AMSR-E) Level 2 A data from January 1 to May 31, 2011, MWRI brightness temperatures were used to calculate sea ice concentrations based on the Arctic Radiation and Turbulence Interaction Study Sea Ice(ASI) algorithm. Snow depths were derived according to the proportional relationship between snow depth and surface scattering at 18.7 and 36.5 GHz. To eliminate the influence of uncertainties in snow grain sizes and sporadic weather effects, seven-day averaged snow depths were calculated. These results were compared with snow depths from two external data sets, the IceBridge ICDIS4 and AMSR-E Level 3 Sea Ice products. The bias and standard deviation of the differences between the MWRI snow depth and IceBridge data were respectively 1.6 and 3.2 cm for a total of 52 comparisons. Differences between MWRI snow depths and AMSR-E Level 3 products showed biases ranging between-1.01 and-0.58 cm, standard deviations from 3.63 to 4.23 cm, and correlation coefficients from 0.61 to 0.79 for the different months.     

2002-2012年青藏高原积雪物候变化及其对气候的响应

积雪是地表最活跃的自然要素之一,其动态变化对气候、环境以及人类生活都产生了重要影响。本文利用MODIS积雪产品和IMS雪冰产品,首先通过Terra、Aqua双星合成和临近日合成去除MODIS积雪产品中的部分云像元,再与IMS融合,获取了青藏高原2002-2012年逐日无云积雪覆盖产品,并逐像元计算每个水文年的积雪覆盖日数（SCD）、积雪开始期（SCS）和积雪结束期（SCE）,分析了不同生态分区积雪的时空变化特征,以及积雪开始期和结束期与温度、降水的关系。结果表明：青藏高原积雪分布存在明显的空间差异,南部喜马拉雅山脉和念青唐古拉山地区以及西部帕米尔高原和喀喇昆仑山脉为SCD的2个高值区,年均积雪日数在200 d以上。18.1%的区域SCS表现出明显的提前趋势,主要集中在青藏高原中东部;羌塘高原南部、念青唐古拉山西段以及川西地区有显著推迟趋势,占高原面积的8.5%。23.2%的区域SCE显著推迟,主要集中在果洛那曲高寒区、昆仑山区和念青唐古拉山地区;而仅有6.9%的区域表现出提前趋势,主要分布在高原西南部。总体上,不同生态单元内积雪开始与结束期受温度、降水的影响差异很大,表现出不同的空间格局与演变趋势。     

Comparative analysis of microwave brightness temperature data in Northeast China using AMSR-E and MWRI products

With such significant advantages as all-day observation, penetrability and all-weather coverage, passive microwave remote sensing technique has been widely applied in the research of global environmental change. As the satellite-based passive microwave remote sensor, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) loaded on NASA’s (National Aeronautics and Space Administration of USA) Aqua satellite has been popularly used in the field of microwave observation. The Microwave Radiation Imager (MWRI) loaded on the Chinese FengYun-3A (FY-3A) satellite is an AMSR-E-like conical scanning microwave sensor, but there are few reports about MWRI data. This paper firstly proposed an optimal spatial position matching algorithm from rough to exact for the position matching between AMSR-E and MWRI data, then taking Northeast China as an example, comparatively analyzed the microwave brightness temperature data derived from AMSR-E and MWRI. The results show that when the antenna footprints of the two sensors are filled with either full water, or full land, or mixed land and water with approximate proportion, the errors of brightness temperature between AMSR-E and MWRI are usually in the range from −10 K to +10 K. In general, the residual values of brightness temperature between the two microwave sensors with the same spatial resolution are in the range of ±3 K. Because the spatial resolution of AMSR-E is three times as high as that of MWRI, the results indicate that the quality of MWRI data is better. The research can provide useful information for the MWRI data application and microwave unmixing method in the future.     

Identifying sky conditions in Iran from MODIS Terra and Aqua cloud products

Clouds can influence climate through many complex interactions within the hydrological cycle. Due to the important effects of cloud cover on climate, it is essential to study its variability over certain geographical areas. This study provides a spatial and temporal distribution of sky conditions, cloudy, partly cloudy, and clear days, in Iran. Cloud fraction parameters were calculated based on the cloud product(collection 6_L2) obtained from the Moderate Resolution Imaging Spectroradiometer(MODIS) sensors on board the Terra(MOD06) and Aqua(MYD06) satellites. The cloud products were collected daily from January 1, 2003 to December 31, 2014(12 years) with a spatial resolution of 5 km × 5 km. First, the cloud fraction data were converted into a regular geographic coordinate network over Iran. Then, the estimations from both sensors were analyzed. Results revealed that the maximum annual frequency of cloudy days occurs along the southern shores of the Caspian Sea, while the minimum annual frequency occurs in southeast Iran. On average, the annual number of cloudy and clear-sky days was 88 and 256 d from MODIS Terra, as compared to 96 and 244 d from MODIS Aqua. Generally, cloudy and partly cloudy days decrease from north to south, and MODIS Aqua overestimates the cloudy and partly cloudy days compared to MODIS Terra.     

Numerical simulation of seasonal snow in Tianshan Mountains

Snowfall in the Tianshan Mountains in China is frequent during winter;thus,avalanches have become a severe issue in snow-covered areas.Accumulation and metamorphosis,as well as hydrothermal exchanges with the environment,considerably affect the stability of snow on slopes.Therefore,a hydrothermal model of snow cover and its underlying surfaces must be developed on the basis of meteorological data to predict and help manage avalanches.This study adopted the conceptual model of snow as a porous medium and quantitatively analysed its internal physical processes on the basis of the thermal exchanges amongst its components.The effects of local meteorological factors on snow structure and the redistribution of energy and mass inside the snow cover in the Tianshan Mountains were simulated.Simulation results showed that deformation as a result of overlying snow and sublimation of snow cover at the bottom is the main cause of density variation in the vertical profile of snow cover.Temperature drives water movement in snow.The low-density area of the bottom snow is the result of temperature gradient.The simulation results of the long-term snow internal mass distribution obtained by the method established in this study are highly consistent with the actual observed trend of variation.Such consistency indicates an accurate simulation of the physical characteristics of snow cover in small and microscale metamorphism in the Tianshan Mountains during the stable period.     

Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network

It is more difficult to retrieve land surface temperature(LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2(AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System(AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4° and 1.9° when five frequencies(ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.     

An Assessment of Snow Cover Duration Variability Among Three Basins of Songhua River in Northeast China Using Binary Decision Tree

The dynamics of snow cover differs greatly from basin to basin in the Songhua River of Northeast China, which is attributable to the differences in the topographic shift as well as changes in the vegetation and climate since the hydrological year (HY) 2003. Daily and flexible multi-day combinations from the HY 2003 to 2014 were produced using Moderate Resolution Imaging Spectroradiometer (MODIS) from Terra and Aqua remote sensing satellites for the snow cover products in the three basins including the Nenjiang River Basin (NJ), Downstream Songhua River Basin (SD) and Upstream Songhua River Basin (SU). Snow cover duration (SCD) was derived from flexible multiday combination each year. The results showed that SCD was significantly associated with elevation, and higher SCD values were found out in the mountainous areas. Further, the average SCDs of NJ, SU and SD basins were 69.43, 98.14 and 88.84 d with an annual growth of 1.36, 2.04 and 2.71 d, respectively. Binary decision tree was used to analyze the nonlinear relationships between SCD and six impact factors, which were successfully applied to simulate the spatial distribution of depth and water equivalent of snow. The impact factors included three topographic factors (elevation, aspect and slope), two climatic factors (precipitation and air temperature) and one vegetation index (Normalized Difference Vegetation Index, NDVI). By treating yearly SCD values as dependent variables and six climatic factors as independent variables, six binary decision trees were built through the combination classification and regression tree (CART) with and without the consideration of climate effect. The results from the model show that elevation, precipitation and air temperature are the three most influential factors, among which air temperature is the most important and ranks first in two of the three studied basins. It is suggested that SCD in the mountainous areas might be more sensitive to climate warming, since precipitation and air temperature are the major factors controlling the persistence of snow cover in the mountainous areas.     

东北典型林区雪深反演算法的验证与分析

积雪对自然环境和人类活动都有极其重要的影响。积雪参数（雪面积、雪深和雪水当量）反演对水文模型和气候变化研究有着实际的意义。然而,目前森林区的雪深遥感反演精度一直有待于进一步提高。东北地区是我国最大的天然林区和重要的季节性积雪区之一,本文利用FY3B卫星微波成像仪（MWRI）L1级亮温数据和L2级雪水当量数据,以及东北典型林区野外实测雪深数据,对Chang算法、NASA 96算法和FY3B雪深业务化反演算法进行了验证与分析。结果表明：在东北典型林区的雪深反演中,Chang算法和NASA 96算法反演的雪深波动都比较大,当森林覆盖度f≤0.6时,NASA 96算法表现比较好,均方根误差值在3种算法中较小,但当f >0.6时,NASA 96算法失真严重。当考虑纯森林像元（f=1）时,Chang算法低估了雪深47%。当f≤0.3时,FY3B业务化算法始终优于Chang算法。整体上,FY3B业务化算法相对稳定,具有较高的精度。     

Effects of snow cover on ground thermal regime: A case study in Heilongjiang Province of China

The important effects of snow cover to ground thermal regime has received much attention of scholars during the past few decades. In the most of previous research, the effects were usually evaluated through the numerical models and many important results are found. However, less examples and insufficient data based on field measurements are available to show natural cases. In the present work, a typical case study in Mohe and Beijicun meteorological stations, which both are located in the most northern tip of China, is given to show the effects of snow cover on the ground thermal regime. The spatial(the ground profile) and time series analysis in the extremely snowy winter of 2012–2013 in Heilongjiang Province are also performed by contrast with those in the winter of 2011–2012 based on the measured data collected by 63 meteorological stations. Our results illustrate the positive(warmer) effect of snow cover on the ground temperature(GT) on the daily basis, the highest difference between GT and daily mean air temperature(DGAT) is as high as 32.35℃. Moreover, by the lag time analysis method it is found that the response time of GT from 0 cm to 20 cm ground depth to the alternate change of snow depth has 10 days lag, while at 40 cm depth the response of DGAT is not significant. This result is different from the previous research by modeling, in which the response depth of ground to the alteration of snow depth is far more than 40 cm.     

Using Landsat images to monitor changes in the snow-covered area of selected glaciers in northern Pakistan

Landsat satellite images were used to map and monitor the snow-covered areas of four glaciers with different aspects(Passu: 36.473°N, 74.766°E;Momhil: 36.394°N, 75.085°E; Trivor: 36.249°N,74.968°E; and Kunyang: 36.083°N, 75.288°E) in the upper Indus basin, northern Pakistan, from 1990-2014. The snow-covered areas of the selected glaciers were identified and classified using supervised and rule-based image analysis techniques in three different seasons. Accuracy assessment of the classified images indicated that the supervised classification technique performed slightly better than the rule-based technique. Snow-covered areas on the selected glaciers were generally reduced during the study period but at different rates. Glaciers reached maximum areal snow coverage in winter and premonsoon seasons and minimum areal snow coverage in monsoon seasons, with the lowest snow-covered area occurring in August and September. The snowcovered area on Passu glacier decreased by 24.50%,3.15% and 11.25% in the pre-monsoon, monsoon and post-monsoon seasons, respectively. Similarly, the other three glaciers showed notable decreases in snow-covered area during the pre-and post-monsoon seasons; however, no clear changes were observed during monsoon seasons. During pre-monsoon seasons, the eastward-facing glacier lost comparatively more snow-covered area than the westward-facing glacier. The average seasonal glacier surface temperature calculated from the Landsat thermal band showed negative correlations of-0.67,-0.89,-0.75 and-0.77 with the average seasonal snowcovered areas of the Passu, Momhil, Trivor and Kunyang glaciers, respectively, during pre-monsoon seasons. Similarly, the air temperature collected from a nearby meteorological station showed an increasing trend, indicating that the snow-covered area reduction in the region was largely due to climate warming.