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1.
近15年天山地区积雪时空变化遥感研究   总被引:2,自引:0,他引:2  
准确监测天山地区积雪的时空变化信息对合理利用水资源及区域气候变化研究具有重要意义。采用基于三次样条函数的去云算法对2001~2015年天山地区的逐日MODIS积雪面积比例产品进行了去云处理,在此基础上分析了近15年天山地区积雪的时空分布及其变化特征。结果表明:(1)积雪年内变化经历从9月开始累积到翌年2月开始消融的过程,1月底积雪面积最大(超过60%),7~8月面积最小(约1.5%);春、夏、秋季中央天山的积雪覆盖率最高,而冬季最高的是北天山;(2)积雪覆盖面积呈现强烈的年际波动特征;研究区夏、冬季的积雪面积总体上呈下降趋势,而春、秋季的积雪面积呈增加趋势;(3)26.39%的地区积雪日数呈下降趋势(5.09%为显著下降),显著下降的地区主要分布在中央天山以及东天山的东部地区;34.26%的地区积雪日数呈增加趋势(2.8%为显著增加),显著增加的地区主要分布在北天山以及东天山西部。  相似文献   

2.
结合Terra和Aqua卫星的积雪产品,获取2001~2008年东北-内蒙古地区逐年积雪日数分布,并利用此数据对比Terra卫星积雪数据获取的逐年积雪日数。结果表明随海拔的升高,双星与单颗卫星积雪日数差异呈现明显增加的趋势。整个东北-内蒙古地区双星积雪日数平均高出单颗卫星积雪日15 d,但与台站积雪日数对比发现,双星积雪日数平均仍然偏低27 d。这说明,利用Terra和Aqua双卫星积雪监测数据能明显改善山区云对遥感监测的影响,同时也可以减少降雪初期和消融期由于积雪消融较快带来的积雪漏测,但不足以消除云等因素的影响。考虑到获取的2001~2006年台站年积雪日数与MODIS年积雪日数与有良好的统计关系,利用两者建立的线性统计关系修正整个东北-内蒙古地区的MODIS积雪日数,能够很好地消除云等因素带来的MODIS双卫星积雪日数偏小的问题,修正后台站与双星积雪日数之间的绝对误差由原来的27 d减小到18 d。  相似文献   

3.
利用1980~2019年中国长时间序列的AVHRR逐日无云积雪面积产品和气象站实测雪深资料计算积雪日数、积雪初日、积雪终日、积雪期、雪深等积雪物候参数,研究积雪物候的时空分布变化,同时结合ECMWF-ERA5再分析资料和GIMMS NDVI3g数据集分别提取气象因子(气温、降水)和植被因子(返青期、枯黄期、生长期),探究北疆积雪物候变化对气象因子和植被因子的响应。结果表明:北疆近40 a间的平均积雪日数为81.62 d/a,73%的区域为稳定积雪区,积雪初日在11月、终日在3月,积雪期为每年11月初至次年3月底4月初;空间上呈现不均匀分布,其中阿勒泰山地区、天山地区、大部分塔城盆地和额尔齐斯谷地区为主要积雪区,1980~2019年间北疆积雪覆盖面积比例、积雪日数和积雪期逐年降低,积雪初日基本没变,但积雪终日显著提前;ECMWF-ERA5再分析资料表明1980~2019年北疆积雪期降水量无明显变化,但积雪覆盖面积比例显著降低,说明降雪区雪深可能增加,这与北疆气象站实测雪深逐渐增加结果相吻合;平均气温与积雪期积雪覆盖面积比例、积雪日数、积雪期长度相关性较大,呈现显著负相关,积雪期降水量与积雪物候参数呈现正相关;积雪物候及其气候效应引起北疆自然植被返青期显著提前,植被生长期延长的特征。  相似文献   

4.
基于MODIS数据的玛纳斯河山区雪盖时空分布分析   总被引:2,自引:0,他引:2       下载免费PDF全文
基于2000~2010年的MODIS/Terra积雪8 d合成数据(MOD10A2)与DEM数据,通过计算和分析积雪频率与积雪覆盖率,研究了新疆玛纳斯河山区雪盖的时空分布特征。结果表明:① 研究区一月份积雪覆盖丰富,积雪频率高值区主要分布在北部中低山地区、南部中海拔地区以及清水河与塔西河的河源地区;四月与十月的雪盖分布规律相似,总体上积雪频率随高程上升而上升;七月份只有少部分高山区域被积雪覆盖;② 积雪频率始终保持较高水平的区域是玛纳斯河、金沟河、清水河以及塔西河的河源高山地区,而玛纳斯河流域中上游的河谷地区则始终保持较低水平;③ 一月份,1 400 m以下地区的积雪覆盖率超过95%,随着高程上升,迅速下降至2 600 m的最低值约41%,此后逐渐上升至5 000 m以上80%左右;④ 一月、四月和十月份积雪覆盖率在大部分高程带上均表现为北坡、东北坡和西北坡最高,东坡和西坡次之,南坡、东南坡和西南坡最低的规律;七月份各高程带的雪盖分布没有明显的坡向差异。  相似文献   

5.
青藏高原MODIS积雪面积比例产品的精度验证与去云研究   总被引:1,自引:0,他引:1  
MODIS积雪产品的精度验证和去云处理是积雪监测研究的基础。首先利用青藏高原典型地区的ETM+数据作为“真值”影像,对MODIS积雪面积比例(FSC)产品在无云条件下的精度进行验证,发展了一个基于三次样条函数插值的去云算法,并采用基于“云假设”的检验和地面站积雪覆盖日数(SCD)检验两种方法对去云算法的精度进行了分析评价。结果表明:MODIS FSC产品在青藏高原地区具有较高的精度,与FSC“真值”相比,其平均绝对误差、均方根误差以及相关系数分别为0.098、0.156和0.916;去云算法能够有效地获取云遮蔽像元的FSC信息,平均绝对误差为0.092,用新生成的无云MODIS FSC产品计算得到的SCD与地面观测值具有较高的一致性(87.03%),平均绝对误差为3.82 d。  相似文献   

6.
利用Terra卫星提供的2000年10月1日到2010年4月30日每日雪覆盖产品MOD10A1,提取研究区积雪覆盖指数SCI、积雪日数SCD、积雪初日SCOD及积雪终日SCMD遥感信息,结合同期吉林省界内23个地面气象观测站的同期气温和降水资料,分析该区积雪的变化特征与气温和降水的关系。结果表明:① 吉林省大部分地区积雪日数为30~90 d,东部山区积雪持续时间长、积雪初日日期早以及积雪终日日期晚,中西部地区变化情况相反;② 积雪覆盖指数SCI呈波浪式变化,与积雪季气温呈负相关;③ 积雪日数与气温呈反相关、与降水量呈正相关,与积雪季气温、夏季降水量的相关系数分别为-0.7407、0.6875;积雪初日情况相反,与积雪季气温、夏季平均气温为0.743、0.5479;积雪终日与气温呈反相关、与降水量呈正相关,与积雪季气温、夏季降水量为-0.5214、0.4647。积雪指数均对气温的变化更敏感,气温升高导致积雪初日推迟、积雪终日提前,从而使积雪日数减小;积雪季降水量的增加有利于积雪日数增大,而积雪终日的推迟有利于夏季降水量的增加。  相似文献   

7.
祁连山区积雪类型丰富、判识复杂,是中国积雪研究的典型区域。因此,精确地监测祁连山区积雪面积变化及其时空演变,对祁连山区生态环境和社会经济发展等具有重要意义。FY-3C MULSS利用多阈值积雪指数模型提供全球日积雪覆盖产品,FY-4A AGRI传感器每15~60 min提供一景覆盖全球的多光谱影像。基于FY-4A AGRI高时间分辨率的特征,构建适合于FY-4A号数据的动态多阈值多时相云隙间积雪识别方法,很大程度上减小了云对光学数据识别积雪造成的影响,并结合FY-3C MULSS积雪覆盖日产品较高空间分辨率的优势,融合得到去除云后的FY3C4积雪覆盖数据。利用Landsat 8 OLI卫星数据对融合后的积雪数据进行对比验证,结果表明融合FY-3C和FY-4A后的数据能更好地判识祁连山区的积雪覆盖情况。以MODIS MOD10A2积雪产品为真实值,随机检验了2018年3月~2019年3月融合后数据的积雪判识精度,发现无云情况下方法的总体精度可达到85.25%。进一步研究发现祁连山区积雪面积在海拔、气候和坡向等因素的影响下时空分布极不均匀,总体呈现出冬春季节大于夏秋季节,以及东部积雪面积大于西部积雪面积的特征。  相似文献   

8.
利用MOD10A2积雪产品分析了2000~2014年青藏高原(以下简称高原)积雪面积和覆盖率的时空分布和变化特点。主要结论如下:1近15a高原年平均积雪面积减少趋势不明显,但季节差异很大,秋季积雪面积略显上升趋势,其他3个季节略有减少趋势,其中夏季减少趋势相对较明显;积雪面积变化与同期气温之间存在负相关关系,且与最高气温的关系更为密切;2过去15a高原积雪覆盖率变化趋势的空间差异明显。青海南部至藏北羌塘高原北部及西南喜马拉雅山脉北麓增加趋势较明显,其中青海南部覆盖范围最广,而念青唐古拉山中西段、喜马拉雅山东段、高原东南地势较低区域和西北部存在较明显的减少趋势,其中那曲东南减少最为明显;3高原积雪覆盖率的年际变率空间差异同样很大,总体上与高原平均积雪覆盖分布相似,即高寒内陆和周边的高大山脉及其周边地区是积雪年际差异明显区域,且主要是由春秋两季的年际变率导致的,高原积雪年际变率较大区域是高原主要的牧区和雪灾频发区,是高原积雪监测和防灾减灾的重点地区。  相似文献   

9.
植被吸收利用太阳光合有效辐射比率反映了植被固碳释氧能力,根据青藏高原GIMMS NDVI3g(1982~2015年)和MODIS NDVI(2001~2015年)数据,采用非线性半理论半经验模型进行FPAR反演及时空变化分析。结果表明:①2001~2015年GIMMS NDVI3g和MODIS NDVI反演FPAR在空间分布上具有较高的一致性,相关系数为0.82(P<0.01),年际变化趋势一致至少6年的区域占80%;②青藏高原FPAR受坡度和海拔影响较大,其中15~35坡度FPAR变化最快,700~2 100 m海拔区间FPAR值最大;不同坡向对应的FPAR除南坡方向偏低外其他方向差异不大。③1982~2015年青藏高原四季FPAR时空变化研究中,冬季FPAR年际变化最明显,约78.5%的区域表现为增长趋势;秋季FPAR下降区域最多,但超过71.5%区域变化不显著;④基于MODIS NDVI和GIMMS NDVI两数据反演的所有植被类型的FPAR都在2012年间出现小幅度下降趋势,且不同植被类型FPAR的年际变化趋势各不相同。  相似文献   

10.
针对积雪观测站点稀少的问题,提出一种考虑海拔影响,能够融合MODIS积雪面积产品和站点观测的雪深空间插值方法,该方法利用去云后MODIS积雪面积产品构建的无积雪“虚拟站点”弥补站点分布不均匀和稀少的不足,利用泛协克里金插值方法考虑海拔对雪深的影响。利用北疆地区50个气象站点的逐日雪深观测资料、逐日MODIS积雪面积产品和AMSR-E被动微波雪水当量和雪深产品,对普通克里金、泛克里金、普通协克里金和泛协克里金插值结果进行了比较研究。研究结果表明:积雪覆盖范围较大时,站点雪深与海拔之间相关系数较大,利用泛协克里金插值结果精度高且稳定;否则利用普通克里金插值精度较高且稳定。通过增加“虚拟站点”,能够提高雪深插值精度,并在一定程度上修正了克里金插值中存在的平滑效应。
  相似文献   

11.
Monitoring the extent and pattern of snow cover in the dry, high altitude, Trans Himalayan region (THR) is significant to understand the local and regional impact of ongoing climate change and variability. The freely available Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover images, with 500 m spatial and daily temporal resolution, can provide a basis for regional snow cover mapping, monitoring and hydrological modelling. However, high cloud obscuration remains the main limitation. In this study, we propose a five successive step approach — combining data from the Terra and Aqua satellites; adjacent temporal deduction; spatial filtering based on orthogonal neighbouring pixels; spatial filtering based on a zonal snowline approach; and temporal filtering based on zonal snow cycle — to remove cloud obscuration from MODIS daily snow products. This study also examines the spatial and temporal variability of snow cover in the THR of Nepal in the last decade. Since no ground stations measuring snow data are available in the region, the performance of the proposed methodology is evaluated by comparing the original MODIS snow cover data with least cloud cover against cloud-generated MODIS snow cover data, filled by clouds of another densely cloud-covered product. The analysis indicates that the proposed five-step method is efficient in cloud reduction (with average accuracy of > 91%). The results show very high interannual and intra-seasonal variability of average snow cover, maximum snow extent and snow cover duration over the last decade. The peak snow period has been delayed by about 6.7 days per year and the main agropastoral production areas of the region were found to experience a significant decline in snow cover duration during the last decade.  相似文献   

12.
Using the observation snow cover data from Landsat TM and ETM+ from January 2000 to May 2001, the inter annual temporal and spatial characteristics of snow cover over middle Tianshan mountains are analyzed. Combining with digital elevation model (DEM) data, the distribution of snow cover in different terrain conditions and different altitude per-month are acquired. After analyzing the spatial distribution and temporal variation regulation of snow cover, it comes to a conclusion that the snow cover within year is correlated with altitude, aspect and slope. On the whole, the ratio of snow cover within year increases when the altitude increases and it decreases when the slope increases. The average height of snow cover boundary is high in summer and autumn but low in spring and winter. The difference of snow cover in aspect west and east is obvious in certain times, but the difference is less than that of the aspect north and south. This study provides a scientific support for utilization of water resource and the research of climate and environment in Tianshan Mountains.  相似文献   

13.
Accurate areal measurements of snow cover extent are important for hydrological and climate modeling. The traditional method of mapping snow cover is binary where a pixel is considered either snow-covered or snow-free. Fractional snow cover (FSC) mapping can achieve a more precise estimate of areal snow cover extent by estimating the fraction of a pixel that is snow-covered. The most common snow fraction methods applied to Moderate Resolution Imaging Spectroradiometer (MODIS) images have been spectral unmixing and an empirical Normalized Difference Snow Index (NDSI). Machine learning is an alternative for estimating FSC as artificial neural networks (ANNs) have been successfully used for estimating the subpixel abundances of other surfaces. The advantages of ANNs are that they can easily incorporate auxiliary information such as land cover type and are capable of learning nonlinear relationships between surface reflectance and snow fraction. ANNs are especially applicable to mapping snow cover extent in forested areas where spatial mixing of surface components is nonlinear. This study developed a multilayer feed-forward ANN trained through backpropagation to estimate FSC using MODIS surface reflectance, NDSI, Normalized Difference Vegetation Index (NDVI) and land cover as inputs. The ANN was trained and validated with higher spatial-resolution FSC maps derived from Landsat Enhanced Thematic Mapper Plus (ETM+) binary snow cover maps. Testing of the network was accomplished over training and independent test areas. The developed network performed adequately with RMSE of 12% over training areas and slightly less accurately over the independent test scenes with RMSE of 14%. The developed ANN also compared favorably to the standard MODIS FSC product. The study also presents a comprehensive validation of the standard MODIS snow fraction product whose performance was found to be similar to that of the ANN.  相似文献   

14.
Snow is an important land cover on the earth's surface. It is characterized by its changing nature. Monitoring snow cover extent plays a significant role in dynamic studies and prevention of snow-caused disasters in pastoral areas. Using NASA EOS Terra/MODIS snow cover products and in situ observation data during the four snow seasons from November 1 to March 31 of year 2001 to 2005 in northern Xinjiang area, the accuracy of MODIS snow cover mapping algorithm under varied snow depth and land cover types was analyzed. The overall accuracy of MODIS daily snow cover mapping algorithm in clear sky condition is high at 98.5%; snow agreement reaches 98.2%, and ranges from 77.8% to 100% over the 4-year period for individual sites. Snow depth (SD) is one of the major factors affecting the accuracy of MODIS snow cover maps. MODIS does not identify any snow for SD less than 0.5 cm. The overall accuracy increases with snow depth if SD is equal to or greater than 3 cm, and decreases for SD below 3 cm. Land cover has an important influence in the accuracy of MODIS snow cover maps. The use of MOD10A1 snow cover products is severely affected by cloud cover. The 8-day composite products of MOD10A2 can effectively minimize the effect of cloud cover in most cases. Cloud cover in excess of 10% occurs on 99% of the MOD10A1 products and 14.7% of the MOD10A2 products analyzed during the four snow seasons. User-defined multiple day composite images based on MOD10A1, with flexibilities of selecting composite period, starting and ending date and composite sequence of MOD10A1 products, have an advantage in effectively monitoring snow cover extent for regional snow-caused disasters in pastoral areas.  相似文献   

15.
We present the design, development, and testing of a new software package for generating snow cover maps. Using a custom inverse distance weighting method, we combine volunteer snow reports, cross-country ski track reports and station measurements to fill cloud gaps in the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product. The method is demonstrated by producing a continuous daily time step snow probability map dataset for the Czech Republic region. For validation, we checked the ability of our method to reconstruct MODIS snow cover under cloud by simulating cloud cover datasets and comparing estimated snow cover to actual MODIS snow cover. The percent correctly classified indicator showed accuracy between 80 and 90% using this method. The software is available as an R package. The output data sets are published on the HydroShare website for download and through a web map service for re-use in third-party applications.  相似文献   

16.
Due to the unique function that snow played in modulating energy and water exchanges in climate and hydrology system,it is important to estimate snow distribution and produce high quality products for short-term climate prediction and water resources management.National Satellite Meteorological Center publics FY-3 snow cover fraction product since 2009.It is necessary to evaluate the snow cover fraction product in order to verify the precision of retrieval algorithms and provide an objective evidences for climate studies.based on MODIS MOD10C1(MYD10C1) Global Daily Snow Cover Dataset,we carries out an evaluate of FY-3 snow cover fraction product from 2010 to 2014 based on five examine indexes,and analyses the bias distribution of snow cover fraction product in different time scales further.It is concluded that FY-3 snow product is a better time space consistency with MODIS MOD10C1(MYD10C1).For example,the consistency of two products is better in snow accumulation period,while it is reducing influenced by cloud detectionin snow melting time.At the same time,bias of snow cover fraction products have obviously changes in inter-annual time,seasonal and monthly.compares to MODIS products,FY-3 snow product is higher in North China,but it coverts to lower in whole China since 2012.Bias of two products decreases from snow accumulation period to snow melt period.In monthly time scale,North eastern China and north of Sinkiang area is sensitive area of snow variation.Bias is more stable because of Tibet Plateau is influenced by topography and covered with snow all the year.  相似文献   

17.
高时间分辨率的积雪判识对于新疆牧区农牧业发展和雪灾预警具有重要作用,针对已有积雪产品易受复杂地形地貌,下垫面类型以及云遮蔽的影响,导致积雪判识精度降低的问题,提出一种利用深度学习方法对风云4号A星多通道辐射扫描计(AGRI)数据与地理信息数据进行多特征时序融合的积雪判识方法:以多时相FY-4A/AGRI多光谱遥感数据,以及高程、坡向、坡度和地表覆盖类型等地形地貌信息作为模型输入,以Landsat 8 OLI提取的高空间分辨率积雪覆盖图作为“真值”标签,构建并训练基于卷积神经网络的积雪判识模型,从而有效区分新疆复杂地形与下垫面地区的云、雪以及无雪地表,最终得到逐小时积雪覆盖范围产品。经数据集和2019年地面气象站实测雪盖验证,该方法精度高于国际主流MODIS逐日积雪产品MOD10A1和MYD10A1,显著降低云雪误判率。  相似文献   

18.
We compare the performances of two widely used hemispheric scale snow products during April, May, and June over North America. The Interactive Multisensor Snow and Ice Mapping System (IMS), based primarily on optical-band remotely sensed images, is the latest incarnation of a product that dates back to the 1960s and has been used as input to operational weather forecasting models as well as for establishing the historical climatology of snow extent over land surfaces. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) has been used for numerous applications since it was launched aboard the Terra satellite platform in 1999. The MODIS snow product is based primarily on optical-band reflectances. We include in our analysis only observations that are largely unobstructed by clouds as determined using the MODIS cloud detection algorithm. Then, after removing the influences of terrain and projection errors, we identify regions and land surface types where discrepancies between these two products occur. We also compare IMS and MODIS to the snow reanalysis produced by the Canadian Meteorological Center (CMC).We find that on seasonal time scales, the most pronounced differences between the IMS and MODIS snow products occurs during the ablation season over North America. Our results corroborate earlier studies showing pronounced differences over the northern tundra in June, where MODIS appears to be in agreement with other observations; as well as differences in April and May in the boreal forest, where evidence suggests that both products may be biased (although MODIS biases may be smaller) in comparison with the CMC product (which is based on station observations). The influence of clouds may be a factor even though the analysis includes only clear days. Another possible explanation for these discrepancies involves the impact of numerous small lakes over the North American landscape on the interpretation of satellite retrievals in the visible band, although there are other potential sources of error in both products. For example, comparison to the CMC reanalysis suggests that MODIS may be overestimating snow during the ablation season in the boreal forest. The resolution of these discrepancies may affect our understanding of the seasonal snow cover cycle, the evaluation of and development of parameterization schemes for climate models, and the development of a climate data record for snow cover.  相似文献   

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