基于MODIS数据的我国天山典型区积雪特征研究

准确监测天山地区积雪面积和积雪日数对合理利用水资源及分析区域气候变化有重要意义。MODIS每日积雪产品可以为大面积快速积雪制图与监测提供依据,但因云量较高成为其应用的瓶颈。利用结合MODIS产品的时间与空间信息有效地减少了云对MODIS积雪产品的影响,并利用改进的MODIS积雪数据和DEM分析2002~2009年天山地区积雪面积和积雪日数的变化特征。结果表明:积雪频率总体上随着海拔升高而增大;不同坡向积雪面积差异明显,西北坡积雪覆盖率最高,北坡、西坡和东北坡次之,南坡和东南坡的积雪覆盖率最低;2006~2008年研究区积雪面积出现低值,年内最大积雪面积呈逐年减少的趋势;随着海拔下降,积雪日数逐渐变小,天山南部地区积雪日数仅为40 d以下;积雪日数大的区域年际积雪日数变化相对稳定,积雪日数少于40 d的区域积雪日数的变异系数最大,年际积雪日数变化不稳定。     

青藏高原MODIS积雪面积比例产品的精度验证与去云研究

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。     

2000~2010年吉林省积雪时空变化特征及其与气候的关系

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。     

C波段SAR山区积雪面积提取研究

合成孔径雷达(SAR)不仅具有穿云透雾,全天候观测地表的能力,而且可穿透地表覆盖一定深度获取地表覆盖物内部特征信息。利用2011年10景ENVISAT\|ASAR可变极化模式精细图像(ASA_APP_1P)数据,分析比较了黑河上游祁连山冰沟流域不同时段积雪SAR后向散射特性,应用同期的MODIS积雪面积产品确定研究区积雪的累积和消融背景信息。研究表明:由于融雪期积雪含水量上升,SAR图像后向散射系数相比干雪或无雪图像明显降低,经过分析认为广泛应用的-3 dB阈值会明显低估湿雪覆盖范围,-2 dB阈值更适合该地区湿雪面积参数提取。山区积雪融化过程中低海拔区域积雪融化而高海拔山区积雪仍可能为干雪,在提取湿雪像元的基础上,根据Sigmoid函数阈值获取的像元湿雪百分比及DEM信息来提取干雪像元,最终获取整个流域积雪面积信息。通过与Landsat ETM+图像积雪面积分类结果进行比较,总体精度达到78%。积雪累积和消融背景信息的分析表明:误差主要源于流域东北部与西北部低海拔区域积雪快速消融。     

近15年天山地区积雪时空变化遥感研究

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。     

基于MODIS数据的积雪监测

通过对遥感卫星资料中云和雪的光谱特征的分析,提出利用中分辨率成像光谱仪(MODIS)红外、可见光谱段数据进行云、雪监测和分离的方法;并提供监测实例来说明利用MODIS数据可进行积雪监测。     

风云三号积雪覆盖产品评估

由于积雪在地球气候系统和水文循环中调节能量和水交换的特定作用,准确地估计积雪分布和制作高质量的积雪产品对短期气候预测以及水文管理至关重要。中国气象局国家卫星气象中心从2009年开始生成风云三号卫星积雪覆盖率(MULSS多仪器融合数据)产品,为了检验产品算法和为积雪产品在气候研究中的应用提供客观依据,有必要对积雪产品的精度进行评估。以MODIS MOD10C1(MYD10C1)全球日积雪覆盖数据集为参考,基于总精度、Heidke技巧评分等5项检验指标,主要对2010~2014年的风云三号积雪产品进行评估,并进一步分析不同时间尺度积雪覆盖率精度的偏差分布。总体而言,风云三号的卫星积雪产品都与MODIS产品保持了较好的时空一致性。如在积雪季节,风云MULSS积雪产品与MODIS产品的空间分布和时间演变相对统一;但是,可能受到云检测的处理的差异的影响,在融雪期二者的有无雪一致性略有下降。此外,两个产品的积雪覆盖率偏差有明显的年际、季节和月变化,从2012年开始,风云三号MULSS积雪产品相对MODIS的偏差由在中国北部偏高转变为在全国范围内的偏低,从积雪期到融雪期,偏差明显减小。从月的时间尺度来说,东北及新疆北部地区都是积雪变化的敏感区域,青藏高原地区受到地形影响,积雪常年保持,偏差稳定。     

雅砻江地区无云积雪覆盖产品的制作

MODIS积雪产品在晴空下积雪识别精度很高,但其受云污染导致数据缺失严重。IMS和SWE数据虽为无云产品,但受分辨率的限制积雪监测精度有待提高。以青藏高原东部雅砻江流域及周边地区为例,通过合成MODIS每日积雪覆盖产品、邻近日分析法以及改进的SNOWL判别法对云像素进行重分类,然后用IMS或者SWE无云积雪数据对中间生成的片雪再分类,制作了除云后的逐日无云积雪覆盖产品。再用目视解译法将从HJ-1B卫星影像中提取的积雪覆盖信息作为观测"真值",对无云积雪覆盖产品进行分类精度评估。结果表明:通过算法的改进,提高了该产品与观测数据的积雪一致率和总体分类精度,总体上解决了因云污染导致的数据缺失,IMS和SWE积雪监测精度不足的问题。     

东北地区MODIS亚像元积雪覆盖率反演及验证

以中巴资源卫星数据作为地面“真值”影像,根据东北地区地理环境与气候特点对Salomoson亚像元积雪覆盖率模型参数进行修正,反演东北地区MODIS像元积雪覆盖率,并用不同方案对模型的稳定性和精度进行分析。研究结果表明,经修正后的Salomoson亚像元积雪覆盖率反演模型对不同地貌--景观单元具有稳定性,其中较小的波动源于积雪物理性质差异、大气效应、积雪影像分类误差及影像配准误差。在东北平原区,NDSI值在0.52~0.65时,模型反演精度高,但反演雪盖率总体偏低,主要是由NDSI基于对波段反射率的非线性转换引起的;雪盖率高估的像元主要分布在城区外围以及农村居民点,而覆盖城区、乡、镇以及居民点之间道路的像元雪盖率误差小,其原因是人类活动频率影响像元内积雪组分与非积雪组分的光谱特性的差异程度。与MODIS雪产品进行对比分析,积雪覆盖率提供较传统雪盖制图更加丰富的信息,然而对林区冠层下积雪覆盖二者均未给出准确估计。     

青藏高原积雪对气候反馈的初步研究

积雪具有很高的反照率,能反射回绝大部分的太阳短波辐射;同时,积雪是热的不良导体,其热阻隔性会抑制地表的长波辐射。因此,积雪的积累和消融会强烈地改变大气层顶的辐射平衡,进而对气候产生反馈。采用ERA-Interim再分析资料和MODIS去云积雪产品,通过改进的偏辐射扰动思想,对青藏高原地区2001~2010年积雪影响下大气层顶的辐射能量收支状况进行模拟,计算对应的积雪辐射强迫,并在此基础上估算积雪反馈。结果表明:研究区99.5%以上地区的大气层顶辐射平衡为负,即积雪对气候存在正的辐射强迫,年平均辐射强迫为3.97 W·m-2。时空分布特征表明,积雪辐射强迫的年际差异不大,但空间差异很大,其空间分布与积雪覆盖率有很强的正相关关系,在绝大多数情况下,短波反照率辐射强迫对积雪辐射强迫起着决定性作用,且青藏高原的积雪反馈强度约为9.35 W·m-2·℃-1。     

View angle effects on MODIS snow mapping in forests

Binary snow maps and fractional snow cover data are provided routinely from MODIS (Moderate Resolution Imaging Spectroradiometer). This paper investigates how the wide observation angles of MODIS influence the current snow mapping algorithm in forested areas. Theoretical modeling results indicate that large view zenith angles (VZA) can lead to underestimation of fractional snow cover (FSC) by reducing the amount of the ground surface that is viewable through forest canopies, and by increasing uncertainties during the gridding of MODIS data. At the end of the MODIS scan line, the total modeled error can be as much as 50% for FSC. Empirical analysis of MODIS/Terra snow products in four forest sites shows high fluctuation in FSC estimates on consecutive days. In addition, the normalized difference snow index (NDSI) values, which are the primary input to the MODIS snow mapping algorithms, decrease as VZA increases at the site level. At the pixel level, NDSI values have higher variances, and are correlated with the normalized difference vegetation index (NDVI) in snow covered forests. These findings are consistent with our modeled results, and imply that consideration of view angle effects could improve MODIS snow monitoring in forested areas.     

MODIS和VEGETATION雪盖产品在北疆的验证及比较

雪盖产品的准确性评估对于水文模型中的遥感应用具有重要的意义,利用北疆47个气象站实测雪深资料,并将气象站根据海拔和下垫面进行分类,对我国可使用的3种光学遥感雪盖产品MOD10A1、MOD10A2和VGT-S10雪盖产品进行验证。研究表明,MOD10A1、MOD10A2和VGT-S10雪盖产品识别总体精度分别为91.3%、90.6%和87.9%,3种产品在农田、草地、城镇和建筑用地总体精度更高 |在稀疏灌木林、裸地与稀疏植被识别总体精度较低,特别是在山区,3种产品识别精度均较低,分别为66.3%、75.7%和61.9%。进一步统计3种雪盖产品的错分误差、漏分误差,发现3种产品错分误差都比较小,但在山区站的漏分误差比较严重,分别为32.4%、21.7%和36.3%,3种产品在山区都低估了雪盖面积。3种不同时间分辨率的雪盖产品云影响率分别为61.8%、7.6%和1.8%。最后将MODIS合成与VGT-S10时间分辨率相同的雪盖产品,并对两种产品在积雪积累期和消融期进行相互比较,比较发现MODIS识别精度要优于VGT-S10雪盖产品,3种产品中VGT-S10由于合成天数最多,所以雪盖产品受云的影响最小。     

基于MODIS数据的玛纳斯河山区雪盖时空分布分析

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。     

Integrated assessment on multi-temporal and multi-sensor combinations for reducing cloud obscuration of MODIS snow cover products of the Pacific Northwest USA

MODIS (Moderate Resolution Imaging Spectroradiometer) snow cover products, of daily, freely available, worldwide spatial extent at medium spatial resolution, have been widely applied in regional snow cover and modeling studies, although high cloud obscuration remains a concern in some applications. In this study, various approaches including daily combination, adjacent temporal deduction, fixed-day combination, flexible multi-day combination, and multi-sensor combination are assessed to remove cloud obscuration while still maintain the temporal and spatial resolutions. The performance of the resultant snow cover maps are quantitatively evaluated against in situ observations at 244 SNOTEL stations over the Pacific Northwest USA during the period of 2006-2008 hydrological years. Results indicate that daily Terra and Aqua MODIS combination and adjacent temporal deduction can reduce cloud obscuration and classification errors although an annual mean of 37% cloud coverage remains. Classification errors in snow-covered months are actually small and tend to underestimate the snow cover. Primary errors of MODIS daily, fixed and flexible multi-day combination products occur during transient months. Flexible multi-day combination is an efficient approach to maintain the balance between temporal resolution and realistic estimation of snow cover extent since it uses two thresholds to control the combination processes. Multi-sensor combinations (daily or multi-day), taking advantage of MODIS high spatial resolution and AMSR-E cloud penetration ability, provide cloud-free products but bring larger image underestimation errors as compared with their MODIS counterparts.     

Accuracy assessment of MODIS,NOAA and IRS data in snow cover mapping under Himalayan conditions

Snow cover information is an essential parameter for a wide variety of scientific studies and management applications, especially in snowmelt runoff modelling. Until now NOAA and IRS data were widely and effectively used for snow‐covered area (SCA) estimation in several Himalayan basins. The suit of snow cover products produced from MODIS data had not previously been used in SCA estimation and snowmelt runoff modelling in any Himalayan basin. The present study was conducted with the aim of assessing the accuracy of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions. The total SCA was estimated using these three datasets for 15 dates spread over 4 years. The results were compared with ground‐based estimation of snow cover. A good agreement was observed between satellite‐based estimation and ground‐based estimation. The influence of aspect in SCA estimation was analysed for the three satellite datasets and it was observed that MODIS produced better results. Snow mapping accuracy with respect to elevation was tested and it was observed that at higher elevation MODIS sensed more snow and proved better at mapping snow under mountain shadow conditions. At lower elevation, IRS proved better in mapping patchy snow cover due to higher spatial resolution. The temporal resolution of MODIS and NOAA data is better than IRS data, which means that the chances of getting cloud‐free scenes is higher. In addition, MODIS has an automated snow‐mapping algorithm, which reduces the time and errors incorporated during processing satellite data manually. Considering all these factors, it was concluded that MODIS data could be effectively used for SCA estimation under Himalayan conditions, which is a vital parameter for snowmelt runoff estimation.     

Validation of daily MODIS snow cover maps of the Upper Rio Grande River Basin for the 2000-2001 snow year

Snow cover represents an important water resource for the Upper Rio Grande River Basin of Colorado and New Mexico. Accuracy assessment of MODIS snow products was accomplished using Geographic Information System (GIS) techniques. Daily snow cover maps produced from Moderate Resolution Imaging Spectroradiometer (MODIS) data were compared with operational snow cover maps produced by the National Operational Hydrologic Remote Sensing Center (NOHRSC) and against in situ Snowpack Telemetry (SNOTEL) measurements for the 2000-2001 snow season. Over the snow season, agreement between the MODIS and NOHRSC snow maps was high with an overall agreement of 86%. However, MODIS snow maps typically indicate a higher proportion of the basin as being snow-covered than do the NOHRSC snow maps. In particular, large tracts of evergreen forest on the western slopes of the San de Cristo Range, which comprise a large portion of the eastern margin of the basin, are more consistently mapped as snow-covered in the MODIS snow products than in the NOHRSC snow products. NOHRSC snow maps, however, typically indicate a greater proportion of the central portion of the basin, predominately in cultivated areas, as snow. Comparisons of both snow maps with in situ SNOTEL measurements over the snow season show good overall agreement with overall accuracies of 94% and 76% for MODIS and NOHRSC, respectively. A lengthened comparison of MODIS against SNOTEL sites, which increases the number of comparisons of snow-free conditions, indicates a slightly lower overall classification accuracy of 88%. Errors in mapping extra snow and missing snow by MODIS are comparable, with MODIS missing snow in approximately 12% of the cases and mapping too much snow in 15% of the cases. The majority of the days when MODIS fails to map snow occurs at snow depths of less than 4 cm.     

A comparison of optical-band based snow extent products during spring over North America

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.     

Assessment of FY-3 Snow Cover Product

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.