Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing

Various remote sensing products are used to identify spatial-temporal trends in snow cover in river basins originating in the Himalayas and adjacent Tibetan-Qinghai plateau. It is shown that remote sensing allows detection of spatial-temporal patterns of snow cover across large areas in inaccessible terrain, providing useful information on a critical component of the hydrological cycle. Results show large variation in snow cover between years while an increasing trend from west to east is observed. Of all river basins the Indus basin is, for its water resources, most dependent on snow and ice melt and large parts are snow covered for prolonged periods of the year. A significant negative winter snow cover trend was identified for the upper Indus basin. For this basin a hydrological model is used and forced with remotely sensed derived precipitation and snow cover. The model is calibrated using daily discharges from 2000 to 2005 and stream flow in the upper Indus basin can be predicted with a high degree of accuracy. From the analysis it is concluded that there are indications that regional warming is affecting the hydrology of the upper Indus basin due to accelerated glacial melting during the simulation period. This warming may be associated with global changes in air temperature resulting from anthropogenic forcings. This conclusion is primarily based on the observation that the average annual precipitation over a five year period is less than the observed stream flow and supported by positive temperature trends in all seasons.     

Classification and snow line detection for glacial areas using the polarimetric SAR image

Snow cover and glaciers are sensitive indicators of the environment. The vast spatial coverage of remote sensing data, coupled with the tough conditions in areas of interest has made remote sensing a particularly useful tool in the field of glaciology. Compared to optical images, synthetic aperture radar (SAR) data are hardly influenced by clouds. This is important because glacial areas are usually under cloud cover.The Dongkemadi glacier in the Qinghai-Tibetan plateau was selected as the study area for this paper. We use polarimetric SAR (PolSAR) image for classification on and around the glacier. The contrast between ice and wet snow is remarkable, but it is difficult to distinguish the ice from the ground on SAR images due to similar backscatter characteristics in former research. In our study, we found that this distinction can be achieved by target decomposition. Support Vector Machines (SVMs) are performed to classify the glacier areas using the selected features. The glacial areas are classified into six parts: wet snow, ice, river outwash, soil land, rocky land and others. The PolSAR-Target decomposition-SVMs (PTS) method is proven to be efficient, with an overall classification accuracy of 91.1% and a kappa coefficient of 0.875. Moreover, 86.63% of the bare ice and 96.76% of the wet snow are correctly classified. The classification map acquired using the PTS method also helps to determine the snow line, which is an important concept in glaciology.     

多源遥感驱动的SRM模型在缺资料地区的研究及应用

以MODIS雪盖、风云静止卫星降水、GLDAS气温等多源数据,作为传统SRM模型的输入参数,构建多源遥感驱动的SRM融雪径流模型,并在缺资料地区——青藏高原的年楚河流域进行融雪过程的径流模拟。研究表明融雪后期的瞬时降雪很大程度上影响了插值后积雪覆盖率的精度,在插值的时候考虑降水和气温,排除瞬时积雪干扰,改进线性插值获得每天的积雪覆盖率,可以提高模型模拟精度;遥感驱动的SRM模型在缺资料地区年楚河适用性较好,Nash-Sutcliffe系数(NSE)达到0.681,体积差(Dv)为-0.17%,均方根误差(RMSE)为9.678,模型模拟的精度较高。研究结果可为高寒地区生态水文模型研究提供重要参考,同时可为SRM模型在其他流域尤其是缺资料地区融雪径流计算中的应用提供有效支撑。     

Analysis of ERS-1 Synthetic Aperture Radar data from Nordaustlandet,Svalbard

Study of the Earth's terrestrial ice masses (glaciers, ice caps and ice sheets), especially the seasonal variation of different surface conditions such as dry snow, wet snow and bare ice, is of particular importance in relation to possible climatic change. Synoptic monitoring techniques using visible and near-infrared satellite imagery are severely limited by the prevalence of cloud cover in the polar regions, and winter observations are impossible as a result of the absence of solar radiation. Consequently, considerable attention is now being focused on the use of imaging radar in the study of large ice masses. In this paper, we present and interpret a time-series of C-band synthetic aperture radar images acquired using the ERS-1 satellite from the Austfonna ice cap in eastern Svalbard. Winter imagery shows little variability, most of the ice cap having a uniform and high (approximately – 3dB) backscatter attributed to ice lenses or to a large effective grain size. Summer imagery shows considerable topographically-related detail, and backscatter values typically 5 to l0 dB less than in winter, which can be explained on the basis of surface scattering from wet snow. However, the marginal areas of the ice cap show a clearly defined zone of high ( –5dB) backscatter in mid- to late-August. It is proposed that this corresponds to the bare ice zone, the high backscatter values being due to scattering from crevasses and meltwater channels, and that the inner boundary of the zone of enhanced backscatter indicates the position of the transient snow line.     

冰沟流域积雪面积比例与雪水当量关系初探

像元尺度上积雪面积比例与雪水当量的关系是将积雪遥感面积数据引入水文模型的有效手段。以冰沟流域为例,利用合成孔径雷达ENVISAT-ASAR数据反演得到积雪面积、雪水当量信息,分析了500m像元尺度上积雪面积比例与雪水当量的关系。结果表明:1在积雪面积比例未达到全覆盖饱和状态,雪水当量和积雪面积比例呈正相关关系,积雪面积比例控制着雪水当量的最大值,但由于受到地形的影响,关系不显著;2当考虑地形因子影响,即将坡度、坡向、海拔、积雪面积比例与雪水当量进行多元线性回归,回归系数的显著性水平均小于0.05,相关系数(r)达到0.841。因此,在高分辨率地形因子已知的情况下,结合遥感积雪数据,可建立良好的积雪面积比例和雪水当量之间的关系,有利于高分辨率积雪面积比例数据在寒区分布式水文模型中的应用。     

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。     

Empirical relationship between near-IR reflectance of melting seasonal snow and environmental temperature in a Himalayan basin

The snow-melting zone forms a site of marked physical-hydrological processes. A basic requirement for monitoring such zones is the data on temperature distribution, for which the method of environmental lapse rate and remote sensing with thermal infrared scanners have been used earlier. In this study, repetitive multispectral image data sets acquired from IRS-LISS-III satellite sensor have been used for snow mapping in the Gangotri glacier, Himalayas, and topographically corrected reflectance values have been computed using a digital elevation model. Temperature measurements at a field station have been used for estimating temperature distribution in the snow-covered basin on the corresponding dates. It is shown here that in the melting seasonal snow zone, the topographically corrected mean near-IR reflectance systematically decreases from higher to lower elevation levels, and can be broadly related to the environmental temperature distribution in a restricted range of about 0-5 °C. As a large number of satellites routinely provide data in the reflected infrared range, this method appears to have a reasonable potential, at least to provide surrogate data for filling-in gaps in the database, with possible applications in environmental surveillance, runoff modeling, climatic modeling and numerous other snowfield studies.     

Activities at the Norwegian Hydrotechnical Laboratory

Abstract

Applications of AVHRR and optical satellite data at the Norwegian Hydrotechnical Laboratory (NHL) include studies of mesoscale eddies in the Norwegian Coastal Current, glacial outflow in fjords, snow hydrology, ice mapping and the motion of polar (atmospheric) lows. Geometrical parameters of mesoscale eddies were gathered from NOAA infrared images for the period 1981-1985 in connection with the study of large surface currents at the Troll Field. An algorithm for computing the phase speed of eddies along the coast was developed in order to estimate the magnitude of the surface currents. In situ, multi-frequency optical measurements in fjords have been tested against multi-frequency Landsat Thematic Mapper images. Surface fjord water laden with glacial till may have a visibility less than 10 cm. Estimates of the amounts of sediments in the surface layer may be made by remote sensing. Flow patterns of the colder melt water in fjords are also revealed by infrared images. A collaboration with the Norwegian Water Resources Board and the Norwegian aerial surveying firm Fjellanger Wideree is in progress to apply AVHRR remote sensing of snow fields to hydrologic models. Ice mapping is performed with data from NOAA, Landsat and SPOT satellites and stereo photogrammetry. NHL has joined several institutions in the U.K. and Canada for this study. Objectives include ice geometries, extent and forecasting. The motion of polar lows is one of the few all-weather applications of AVHRR data at NHL. The work was a joint venture with the Norwegian Meteorologic Institute.     

卫星遥感雪盖制图方法对比与分析

利用ＬａｎｄｓａｔＴＭ、ＮＯＡＡ／ＡＶＨＲＲ和中分辨率成像光谱仪（ＭＯＤＩＳ）三个平台传感器的遥感数据,分别使用训练样本监督分类、阈值数字信号统计、雪盖指数方法制作雪盖图和提取积雪面积。结果表明：不同传感器遥感图像因时相和时空分辨率的差异,提取积雪信息的有效方法有所不同。但基于反射特性的雪盖指数计算法具有普遍的实际操作性意义,即雪盖制图精度高,分类合理,是提取积雪信息的最佳技术手段;当使用监督积雪分类时,只有取得精确的信号文件,分类结果才是可信的;而阈值数字信号统计的雪的阈值确定具有很大的经验性和随机性,但对数据不完整或只有单波段时也不失为有效和简便的途径;山影补偿处理法基本可以消除地形阴影的影响;而去云后其覆盖下的积雪恢复技术值得进一步讨论。     

黑河综合遥感联合试验研究进展:概述

“黑河综合遥感联合试验”是在我国典型的内陆河流域--黑河流域开展的大型航空、卫星遥感和地面同步观测试验。28个单位的280多名科研人员、研究生和工程技术人员参加了试验。介绍了2008年加强试验期以来的研究工作进展。“黑河综合遥感联合试验”最主要的成果是一套多尺度、高质量的综合观测数据集,该数据集已正式对外发布,十分有力地支持了一系列生态、水文、定量遥感模型的发展、改进和验证,试验所期望突破“数据瓶颈”的目标已经实现。此外,已在积雪参数提取、地表冻融微波遥感、森林结构参数的遥感反演、蒸散发观测与遥感估算、土壤水分反演、生物物理参数和生物化学参数反演、水文气象观测、尺度推绎、流域水文模拟和同化应用等方面取得了丰富的研究成果。试验亮点包括蒸散发观测及其遥感模型改进、机载激光雷达的应用、多角度热红外传感器研制和应用;航空遥感在获取高质量、高分辨率数据方面依然具有不可替代的作用。     

Passive and active airborne microwave remote sensing of snow cover

The presence of snow cover affects the regional energy and water balance, thus having a significant impact on the global climate system. Temporal knowledge of the onset of snow melt and snow water equivalent (SWE) values are important variables in the prediction of flooding, as well as water resource applications such as reservoir management and agricultural activities. Microwave remote sensing techniques have been effective for monitoring snow pack parameters (snow extent, depth, water equivalent, wet/dry state). Coincident ground data, airborne polarimetric C-band (5.3 GHz) Synthetic Aperture Radar (SAR) and passive microwave radiometer data (19, 37 and 85 GHz) were collected on four dates (1 December 1997, 6 March 1998, 12 March 1998 and 9 March 1999) over two flight lines in Eastern Ontario, Canada. The multitemporal, multi-sensor data were analysed for changes in SAR polarimetric signatures and microwave brightness temperatures as a function of changing snow pack parameters. Results indicate that certain parameters such as linear polarizations and pedestal height are sensitive to changes in snow pack parameters, and respond differently to various snow conditions. SWE values derived from the passive microwave brightness temperatures compare well with ground measurements, with the exception of low snow volume and in the presence of significant ice layers.     

卫星微波遥感受水成物影响及其敏感度分析研究

对卫星微波遥感水成物辐射效应的理解在云参数反演以及数值预报中使用受云和降水影响卫星资料等诸多应用中都是十分重要的。使用快速辐射传输模式CRTM,通过中尺度数值模式WRF预报输出的水成物信息,分析了云水、雨水、冰、雪和霰5类水成物对卫星微波遥感的影响及水成物辐射效应给卫星微波观测计算带来的误差特征,设计敏感性试验研究了卫星微波遥感对水成物含量、有效粒子半径和垂直分布层次的敏感度。结果表明:卫星微波遥感在多个探测频率上受水成物的影响较大,云水和雨水的影响主要以增温为主,雨水的影响幅度要大于云水,在影响最大的AMSUA 2通道,云水和雨水的增温分别可达到8 K和17 K左右。冰、雪和霰散射效应对卫星微波遥感的影响则主要以降温为主;相比之下,霰的影响量级最大,雪次之,两种水成物对微波遥感影响最大的通道为AMUSB的2通道,可达到-18 K和-9 K左右,冰的作用微乎其微。卫星微波遥感探测通道对水成物含量的敏感与受水成物辐射效应影响对应一致。卫星微波遥感不受云水和冰水粒子有效半径变化的影响,对雨、雪和霰大降水粒子的有效粒子半径较为敏感。同时,敏感性和影响程度随探测频率的不同呈现出影响的复杂性。此外,卫星微波探测对水成物垂直分布层次的敏感性表现为对水成物敏感探测通道的变化,其中AMSUB 3~5通道对高层水物质,包括冰、雪和霰垂直廓线的变化最为敏感。     

FY-3/VIRR资料积雪多阈值综合判识方法研究

利用卫星遥感监测积雪分布相比地面观测具有明显优势,目前基于FY-3卫星数据在积雪监测方面的研究较少。借鉴现有积雪卫星遥感监测算法,研究出适用于FY-3/VIRR资料的积雪判识方法,利用归一化积雪指数和多波段综合阈值实现积雪判识,提取积雪信息生成区域二值化积雪分布图。通过实例分析验证算法有效可行,并与MODIS积雪产品MOD10及其L1B数据NDSI判识结果进行对比,说明算法判识结果良好。研究表明,FY-3卫星数据可作为积雪遥测的可靠资料来源,可延用于积雪监测与灾害预警业务系统中,促进国产卫星数据的应用与推广。     

Assimilation of meteorological and remote sensing data for snowmelt runoff forecasting

A versatile data assimilation scheme for remote sensing snow cover products and meteorological data was developed, aimed at operational use for short-term runoff forecasting. Spatial and temporal homogenisation of the various input data sets is carried out, including meteorological point measurements from stations, numerical weather predictions, and snow maps from satellites. The meteorological data are downscaled to match the scale of the snow products, derived from optical satellite images of MODIS and from radar images of Envisat ASAR. Snow maps from SAR and optical imagery reveal systematic differences which need to be compensated for use in snowmelt models. We applied a semi-distributed model to demonstrate the use of satellite snow cover data for short-term runoff forecasting. During the snowmelt periods 2005 and 2006 daily runoff forecasts were made for the drainage basin Ötztal (Austrian Alps) for time lags up to 6 days. Because satellite images were obtained intermittently, prognostic equations were applied to predict the daily snow cover extent for model update. Runoff forecasting uncertainty is estimated by using not only deterministic meteorological predictions as input, but also 51 ensemble predictions of the EPS system of the European Centre for Medium Range Weather Forecast. This is particularly important for water management tasks, because meteorological forecasts are the main error source for runoff prediction, as confirmed by simulation studies with modified input data from the various sources. Evaluation of the runoff forecasts reveals good agreement with the measurements, confirming the usefulness of the selected data processing and assimilation scheme for operational use.     

卫星遥感海冰监测技术在河北省近海海域的应用

卫星遥感技术已成为海冰监测的重要技术手段之一。介绍了应用EOS系列卫星、HY-1B卫星和HJ-1A/B卫星等遥感数据提取河北省近海海域海冰分布、海冰类型、海冰厚度等海冰信息的方法。重点研究了应用HJ-1A/B卫星遥感数据进行海冰分类,及根据海冰类型与厚度的对应关系估算海冰厚度的方法,并以2013年1月17日的卫星遥感数据为例,提取海冰信息,制作相关海冰专题产品。结果表明:利用多种卫星遥感数据进行海冰监测,既可相互补充又能提高监测效率和准确率,制作更精细化的海冰监测产品,为河北海洋管理部门制定防灾减灾措施提供重要依据。     

卫星雪盖制图中的一些技术问题

介绍了几种主要卫星资料用于雪盖制图的不同时、空分辫率及应用特点，阐述了雪与云的区分技术、雪盖范围的匀绘与面积估计技术，最后指出了雪盖制图技术的一些新发展。     

基于CUAHSI-HIS的水文遥感监测系统设计

针对当前水文遥感监测系统遥感图像清晰度较低,造成水文遥感监测准确率下降的问题,提出了基于CUAHSI-HIS的水文遥感监测系统;在硬件系统设计上,按照水文特征数据调配审核内容,划分硬件操作模块,选用ARM单片机作为水文遥感监测传感器,采用AT91SAM9X35-CU微处理器芯片,通过CC2530F256RHAR微处理芯片完成信息处理,利用供电电流传导方式特征转换水文信息,设计系统采集模块,集中编写与采集监测流域内部水文特征,根据CUAHSI-HIS对流域水文特征进行水文数据调整,并按照调整后的水文数据对监测系统软件平台进行管理,加强对平台的管理,整理平台改造信息,执行软件平台系统改造指令;实验结果表明,基于CUAHSI-HIS的水文遥感监测系统遥感图像清晰度较高,能够有效提高监测准确率。     

Spatial and temporal variations of summer surface temperatures of high-arctic tundra on Svalbard — Implications for MODIS LST based permafrost monitoring

The ground surface temperature is one of the key parameters that determine the thermal regime of permafrost soils in arctic regions. Due to remoteness of most permafrost areas, monitoring of the land surface temperature (LST) through remote sensing is desirable. However, suitable satellite platforms such as MODIS provide spatial resolutions that cannot resolve the considerable small-scale heterogeneity of the surface conditions characteristic for many permafrost areas. This study investigates the spatial variability of summer surface temperatures of high-arctic tundra on Svalbard, Norway. A thermal imaging system mounted on a mast facilitates continuous monitoring of approximately 100 × 100 m of tundra with a wide variability of different surface covers and soil moisture conditions over the entire summer season from the snow melt until fall. The net radiation is found to be a control parameter for the differences in surface temperature between wet and dry areas. Under clear-sky conditions in July, the differences in surface temperature between wet and dry areas reach up to 10 K. The spatial differences reduce strongly in weekly averages of the surface temperature, which are relevant for the soil temperature evolution of deeper layers. Nevertheless, a considerable variability remains, with maximum differences between wet and dry areas of 3 to 4 K. Furthermore, the pattern of snow patches and snow-free areas during snow melt in July causes even greater differences of more than 10 K in the weekly averages. Towards the end of the summer season, the differences in surface temperature gradually diminish. Due to the pronounced spatial variability in July, the accumulated degree-day totals of the snow-free period can differ by more than 60% throughout the study area. The terrestrial observations from the thermal imaging system are compared to measurements of the land surface temperature from the MODIS sensor. During periods with frequent clear-sky conditions and thus a high density of satellite data, weekly averages calculated from the thermal imaging system and from MODIS LST agree within less than 2 K. Larger deviations occur when prolonged cloudy periods prevent satellite measurements. Furthermore, the employed MODIS L2 LST data set contains a number of strongly biased measurements, which suggest an admixing of cloud top temperatures.We conclude that a reliable gap filling procedure to moderate the impact of prolonged cloudy periods would be of high value for a future LST-based permafrost monitoring scheme. The occurrence of sustained subpixel variability of the summer surface temperature is a complicating factor, whose impact needs to be assessed further in conjunction with other spatially variable parameters such as the snow cover and soil properties.     

Application of incoherent target decomposition theorems to classify snow cover over the Himalayan region

Snow cover is an important parameter for hydrological modelling and climate change modelling. Various methods are available only for wet snow-cover mapping using conventional synthetic aperture radar (SAR) data. Total snow (wet + dry) cover mapping with SAR data is still a topical research area. Therefore, incoherent target decomposition theorems have been implemented on fully polarimetric SAR data to characterize the scattering of various targets. Further classification techniques – both unsupervised and supervised – have been applied for accurate mapping of total snow cover. For this purpose, Advanced Land Observing Satellite – phased array-type L-band SAR (ALOS–PALSAR) data (12 May 2007) have been analysed for snow classification of glaciated terrain in and around Badrinath region in Himalaya. An ALOS-Advanced Visible and Near Infrared Radiometer (AVNIR)-2 image (6 May 2007) was also used to provide assistance in the selection of different training classes. It has been found that the application of incoherent target decomposition theorems such as H/A/α and four-component scattering mechanism models are good for extracting the desired information of snow cover from fully polarimetric PALSAR data. Finally, based on these target decomposition theorems and the Wishart classifier, PALSAR data have been classified into snow or non-snow cover, and the user accuracy of snow classes was found to be better than the user accuracy of other classes. Hence, the application of incoherent target decomposition theorems with full polarimetric ALOS-PALSAR data is useful for snow-cover mapping.     

Synergistic approach for mapping debris-covered glaciers using optical-thermal remote sensing data with inputs from geomorphometric parameters

Debris cover on glacier boundaries critically impedes the global inventorying of glaciers and confounds most of the techniques developed for semi-automated mapping of glaciers. Debris on the glacier (referred as supraglacial debris) and that occurring outside the glacier boundaries (referred as periglacial debris) being derived from a common source, i.e. the valley rock, tend to have a similar spectral response in the reflection region which renders them mutually indistinguishable. However, there exist temperature differences between them. This aspect has been considered in this remote sensing based study to distinguish between the supraglacial and periglacial debris in a test area in the Chenab basin, Himalayas, by inclusion of thermal infrared (TIR) bands in remote sensing data processing. A synergistic multisensor approach for the delineation of debris-covered glacier boundaries is used here which integrates the inputs from thermal (TERRA-ASTER sensor) and optical (IRS-P6-AWiFS sensor) remote sensing data, multispectral classification techniques and the DEM derived geomorphometric parameters. The results of this study corroborate earlier findings on utilization of temperature differences as one of the parameters in glacial studies. The proposed synergistic approach therefore appears useful in accurate mapping of debris-covered glaciers in the Himalayan region.