HJ-1B的积雪参数反演研究

积雪参数如雪盖、雪深是标示气候变化的敏感因子,具有较高时空分辨率的HJ-1B卫星是专门用于灾害监测与评估业务的国产卫星之一,开展以HJ-1B为主的积雪参数反演对于我国国产卫星的理论研究与深入应用具有重要意义。本文针对积雪参数反演,根据HJ卫星CCD和IRS传感器的数据特征,深入分析积雪等典型地物的光谱特性,而后针对同时具有HJ-1B/CCD、IRS数据和只有CCD或者IRS传感器数据3种情况展开积雪信息提取方法研究;在进行浅雪区雪深反演时,利用两种不同的统计回归模型进行交叉验证、研究对比。研究结果表明,3种情况下提取出的积雪精度都达到了80%以上,以第1种情况提取精度最高,两种统计模型反演出的浅雪区雪深的一致度达到83%左右,说明HJ星数据能较好地反演雪盖及浅雪区的雪深,能满足实际应用的需要。     

天山典型林带积雪的多角度遥感识别

天山中段的山地针叶林带很大程度上影响了该地区整体卫星雪盖的识别精度,多角度卫星遥感技术的发展为林区积雪识别提供了新的途径。本文选取了2000年4月至2001年6月,10个时段研究区内无云覆盖的(Multi-angle Imaging Spectro Radiometer)MISR多角度数据,首先对红光波段不同角度观测结果组成的角度谱图像进行非监督分类,以确定天山林带的分布区域,然后在玛纳斯河中下游与那拉提山东部选取典型像元,分析这些像元红光波段各角度反射率在林区不同积雪覆盖状况下的表现差异。研究发现,若林区存在积雪,0°,±26.1°,±45.6°五个观测角度反射率的平均值大于0.1,在部分降雪月份,后向45.6°观测的反射率大于天顶方向观测的2.5倍。根据这一结论,给出基于MISR数据的研究区不同时段的积雪识别结果。结果表明,MISR红光波段对林区积雪反应敏感,不同角度观测的反射率在林区有雪和无雪时差异较大,故可利用多角度遥感信息进行林区积雪识别。     

HJ-1B卫星遥感影像的积雪识别

针对国产卫星HJ-1B数据积雪像元识别的问题,该文分析了归一化差分积雪指数法和改进的归一化差分积雪指数法的优缺点,并根据积雪与其他地物的光谱特征变化幅度的差异性,提出了一个仅用HJ-CCD数据作为数据源的积雪识别方法。实验中,选取了两块不同特征的影像进行试验,以神经网络分类结合目视解译方法的提取结果作为标准进行精度评价。结果表明,该文提出的方法操作简单,能快速、准确地识别区域积雪覆盖面积。     

MODIS数据在积雪检测中的应用

积雪作为影响环境的一个因素,是非常重要的。自1999年Terra卫星升空以来,MODIS数据在环境监测的各个方面得到了广泛的应用。由于MODIS数据的高光谱、高空间分辨率、高时间分辨率等特征,越来越多地应用到积雪监测方面。本文就MODIS数据的积雪检测算法进行了探讨,对森林中雪的检测以及云和雪的区分进行了大量的研究。结果显示：MODIS数据对积雪检测非常有效。     

FY-2C积雪判识方法研究

介绍了利用FY-2C资料进行积雪判识的原理,在阈值法基础上的辅助因子函数积雪判识方法以及相应的FY-2C积雪判识结果精度验证分析等。一般较为常用的卫星遥感积雪判识方法为简单阈值法,由于其带有一定的随机性,很难客观反映下垫面条件差异对阈值选取的影响。以阈值法为基础,将所使用的主要变量以函数形式表达,以海拔高度、地理位置、季节、土地覆盖类型等作为阈值函数的变量,通过大量采样建立起多种阈值函数,从而实现随时空特点变化的阈值实时计算。该方法用于FY-2C积雪判识,较好地解决了FY-2C全圆盘范围内广大区域不同下垫面类型下的实时积雪监测。通过与NOAA-17人机交互积雪判识结果对比分析,该方法的积雪判识精度可达85%左右。     

基于遥感卫星影像的积雪范围自动化提取

积雪是地球表面重要的覆盖物,也是全球大气与地表相互作用的重要影响因素.本文以资源三号和高景一号卫星遥感影像为主要数据源,将天山中段地区作为主要研究区,分别采用差值指数阈值法和影像亮度分割法,获取遥感影像积雪范围.对比分析可知,利用影像亮度分割法获取积雪范围的结果更准确.利用遥感手段识别积雪范围,对开展智能化地物识别具有重大意义,同时,对开展智能遥感应用也具有重要的应用价值.     

NDSI与NDFSI结合的山区林地积雪制图方法

积雪是冰冻圈的重要组成部分,因其在可见光波段的高反射率、低导热率的特性以及大面积的覆盖,成为全球辐射平衡的重要决定因子。在中纬度的干旱和半干旱山区,季节性的冰雪融水是春季河川径流的主要补给水源,山区积雪分布的变化对融雪期河流径流量的波动具有重要影响。当前的积雪产品在下垫面为山区林地时会低估积雪面积,从而影响了山区水文过程模拟的精度。本文基于Landsat OLI影像,采用归一化差值积雪指数NDSI和归一化差值林地积雪指数NDFSI相结合的方法,对春季融雪期的阿尔泰山区泰加林地进行积雪识别,并采用海拔高度、温度、以及对应的高分数据对提取结果进行了定量分析。结果表明,采用NDSI进行积雪识别时,山区林地的积雪会被大量漏分;对林地像元采用NDFSI阈值法可以区分林地中是否有积雪分布。NDSI和NDFSI相结合的积雪识别方法操作简单,不需要提供森林分布图等辅助数据,可以有效提高山区林地复杂环境下积雪制图的精度。     

基于MODIS影像的内蒙古草原积雪监测

光学遥感源MODIS具有高光谱分辨率、高时间分辨率、高空间分辨率、全球范围内免费接收等优势,被广泛应用于洪涝、干旱、森林草原火灾、雪灾等自然灾害的动态监测领域。MODIS数据用于内蒙古草原积雪监测,提取积雪信息在国内尚属空白。本文利用MODIS L1B 500m分辨率数据,经过几何校正、去"双眼皮"预处理,根据归一化差分积雪指数(NDSI)算法和综合阈值判别法对内蒙古自治区2008年1月下旬大范围降雪进行积雪信息提取,制作积雪覆盖图。利用内蒙古生态与农业气象中心发布的雪情遥感监测信息验证积雪覆盖图的准确度。验证结果表明,MODIS数据用于大范围积雪监测非常有效。     

小流域积雪时空变化特征对比研究

利用Terra和Aqua卫星提供的2002~2012年MODIS 8d积雪分类产品MOD10A2和MYD10A2生成双星8d合成产品MOYD10A2,并进行精度评价。在此基础上,提取了嫩江与辽河流域积雪覆盖率、相对积雪日数和归一化积雪指数,并对比分析了嫩江与辽河流域的积雪时空变化特征。结果表明:(1)合成后的图像大大降低了云层对积雪产品的影响,更好地恢复了云下积雪遥感信息,提高了积雪遥感指数的精度;(2)嫩江、辽河流域积雪高峰期均出现在11月中下旬到次年4月中上旬,每年的积雪覆盖率高峰期和变化趋势基本保持一致,但嫩江流域的积雪覆盖率明显高于辽河流域;受地理位置和海陆分布的影响,嫩江流域积雪天数呈由东北向西南方向减小的趋势,且积雪天数明显多于辽河流域;(3)MODIS 8d积雪分类产品可在长时间、大范围积雪监测中发挥作用。     

干雪深度反演的同极化相位差模型

积雪深度是积雪的重要结构参数,获取高精度雪深空间分布信息对于流域尺度水资源管理、气候变化研究和灾害预报等具有重要意义.本文以新疆阿尔泰山南坡克兰河上游为研究区,利用C波段全极化GF-3数据及地面同步观测数据,根据VV与HH极化信号在积雪中折射率不同导致相位差异的原理,使用Maxwell-Garnett方程构建同极化相位差(co-pol arized phase difference,CPD)的正演模型,并基于CPD与雪深关系构建了雪深反演模型.通过对具有不同积雪条件的浅雪区与深雪区分别进行雪深反演,获得雪深空间分布信息.同时对反演不确定性进行了分析,并与已有方法进行比较,研究结果表明:①假定研究区积雪各向异性介电常数恒定的理想情况下,CPD仅是雪深的函数,可用半经验的线性模型反演雪深,反演精度的高低与计算CPD过程中使用的滤波器的窗口大小有关,浅雪区的最优滤波窗口为59×59像元,反演精度R为0.83,RMSE为2.72 cm,深雪区的最优滤波窗口为33×33像元,反演精度R为0.54,RMSE为11.69 cm;②雪深反演误差与坡度显著相关,随着坡度的增加,雪深的反演误差呈现出显著增加的趋势,雪深反演不确定性受雪层变质程度、含水量及卫星入射角观测几何条件影响,反演方法对于干燥、雪层变质结晶程度低、均质的积雪及具有大入射角的SAR卫星有更好的适用性;③对比已有基于CPD模型的雪深反演方法,本文方法已经将反演所需要的参数减少为遥感获取的CPD数据,以及进行模型拟合的实测雪深数据,反演精度更高.研究表明CPD模型反演山区雪深空间分布是有效和可行的,研究成果为山区雪深遥感反演提供了新思路.     

用气象卫星资料监测积雪

本文简要叙述了利用气象卫星资料进行积雪监测的可行性和复杂性;以改进的甚高分辨率扫描辐射仪(AVHRR)资料为例综述了遥感监测积雪的原理、方法和资料处理过程;分析了计算结果,并探讨了未来积雪监测的发展。     

Sequence-based mapping approach to spatio-temporal snow patterns from MODIS time-series applied to Scotland

Snow cover and its monitoring are important because of the impact on important environmental variables, hydrological circulation and ecosystem services. For regional snow cover mapping and monitoring, the MODIS satellite sensors are particularly appealing. However cloud presence is an important limiting factor. This study addressed the problem of cloud cover for time-series in a boreal-Atlantic region where melting and re-covering of snow often do not follow the usual alpine-like patterns. A key requirement in this context was to apply improved methods to deal with the high cloud cover and the irregular spatio-temporal snow occurrence, through exploitation of space-time correlation of pixel values. The information contained in snow presence sequences was then used to derive summary indices to describe the time series patterns. Finally it was tested whether the derived indices can be considered an accurate summary of the snow presence data by establishing and evaluating their statistical relations with morphology and the landscape. The proposed cloud filling method had a good agreement (between 80 and 99%) with validation data even with a large number of pixels missing. The sequence analysis algorithm proposed takes into account the position of the states to fully consider the temporal dimension, i.e. the order in which a certain state appears in an image sequence compared to its neighbourhoods. The indices that were derived from the sequence of snow presence proved useful for describing the general spatio-temporal patterns of snow in Scotland as they were well related (more than 60% of explained deviance) with environmental information such as morphology supporting their use as a summary of snow patterns over time. The use of the derived indices is an advantage because of data reduction, easier interpretability and capture of sequence position-wise information (e.g. importance of short term fall/melt cycles). The derived seven clusters took into account the temporal patterns of the snow presence and they were well separated both spatially and according to the snow patterns and the environmental information. In conclusion, the use of sequences proved useful for analysing different spatio-temporal patterns of snow that could be related to other environmental information to characterize snow regimes regions in Scotland and to be integrated with ground measures for further hydrological and climatological analysis as baseline data for climate change models.     

Potential of multispectral reflectance for assessment of snow geophysical parameters in Solang valley in the lower Indian Himalayas

ABSTRACT

Snow geophysical parameters such as wetness, density and permittivity are a significant input in hydrological models and water resource management. In this paper, we utilize the triangle method based on a feature space developed with the near-infrared (NIR) reflectance and the Normalized Differenced Snow Index (NDSI) for the estimation of surface snow wetness, permittivity and density. The triangular feature space based on NIR reflectance and NDSI is parameterized to yield a linear relationship between the snow wetness and the NIR reflectance. Snow density and permittivity are derived based on the least squares solution of empirical relations based on the observations of surface snow wetness. The proposed methodology was evaluated using Sentinel-2 data, and the modeled snow geophysical parameters were validated with respect to field measurements. Based on the results, it was inferred that the NIR reflectance varies linearly with the liquid water content in the snow. A good agreement was determined between the modeled and measured parameters for wet snow conditions as observed by the coefficient of determination of 0.968, 0.521 and 0.969 for the snow wetness, density and permittivity (real part), respectively. The proposed approach can be significantly utilized with unmanned aerial sensors for monitoring of physical properties of fresh or wet snow and is thus expected to contribute considerably in hydrological applications and avalanche studies.     

中国主要牧区雪灾遥感监测评估模型研究

该文以中国雪灾高发区的西藏那曲地区为例，主要介绍了在试验区雪灾背景数据库支持下，利用ＮＯＡＡ／ＡＶＨＲＲ卫星遥感资料对雪灾进行判别，预测及对灾情损失进行综合评估的一些技术和方法，其内容包括：（１）用于雪灾危险等级分析与判定的判别模型；（２）用于灾情发展趋势模拟与预测的预测模型；（３）用于灾情损失综合评估与估算的评介模型。并运用这些模型对１９９５年２－３月发生在西藏那曲的雪灾进行实时分析，其结果和实     

CBERS-02B星在轨测试数据国土资源应用评价

以中巴地球资源一号卫星02B星（CBERS-02B,以下简称为02B星）多光谱CCD数据和HR数据的2级产品为准,按照国土资源调查与监测的相关标准与技术规范,结合多光谱CCD和HR数据的影像质量、波段配准、几何畸变以及制图能力,从国土资源日常性调查业务、行政性监管与执法职能出发,对02B星在土地资源调查与监测、地质解译、矿化蚀变异常信息提取、地质灾害调查与监测、矿产资源开发状况调查与监测、区域生态地质环境调查等领域的遥感应用特点、关注的地类与地质要素的差异等方面开展应用评价。对02B星CCD和HR数据的国土资源调查与监测的应用能力进行了总结,对存在的问题进行了初步分析。该项研究对指导02B星数据的应用与后续星的研发具有重要意义。     

Synergy of in situ and space borne observation for snow depth mapping in the Swiss Alps

The Swiss Federal Institute for Snow and Avalanche Research in Davos (SLF) provides snow depth maps for Switzerland on a spatial resolution of 1 km × 1 km. These snow depth maps are derived from snow station measurements using a spatial interpolation method based on the dependency of snow depth and altitude. During a winter season the number of operating snow stations varies and the area-wide snow depth interpolation becomes increasingly difficult in spring. The objective of the study is to develop an operational and near-real time method to calculate snow depth maps using a combination of in situ snow depth measurements and the snow cover extent provided from space borne observations. The operational daily snow cover product obtained from the polar-orbiting NOAA-AVHRR satellite is used to gain an additional set of virtual snow stations to densify the in situ measurements for an improved spatial interpolation. The capacity of this method is demonstrated on selected days during winter 2005. Cross-validation tests are conducted to examine the quantitative accuracy of the synergetic interpolation method. The error estimators prove the decrease in error variance and increase of overall accuracy pointing out the high capacity of this new interpolation method that can be run in near real-time over a large horizontal domain at high horizontal resolution. A solid method for snow–no snow classification in the processing of the satellite data is essential to the quality of the snow depth maps.     

利用NOAA AVHRR数据研究北半球雪盖气候学特征

利用NOAA卫星图像,研究了北半球、欧亚、北美和青藏高原雪盖气候学特征及其变化趋势.指出北半球、欧亚和北美雪盖气候变化趋势基本一致,年均雪盖面积在1987年前后明显下降; 而青藏高原雪盖面积在1984年后明显下降,说明青藏高原雪盖的年际变化与北半球及欧亚、北美不完全一致.     

Development of a snow wetness inversion algorithm using polarimetric scattering power decomposition model

In this paper, a new snow wetness estimation model is proposed for full-polarimetric Synthetic Aperture Radar (SAR) data. Surface and volume are the dominant scattering components in wet-snow conditions. The generalized four component polarimetric decomposition with unitary transformation (G4U) based generalized surface and volume parameters are utilized to invert snow surface and volume dielectric constants using the Bragg coefficients and Fresnel transmission coefficients respectively. The snow surface and volume wetness are then estimated using an empirical relationship. The effective snow wetness is derived from the weighted averaged surface and volume snow wetness. The weights are derived from the normalized surface and volume scattering powers obtained from the generalized full-polarimetric SAR decomposition method. Six Radarsat-2 fine resolution full-polarimetric datasets acquired over Himachal Pradesh, India along with the near-real time in situ measurements were used to validate the proposed model. The snow wetness derived from the SAR data by the proposed model with in situ measurements indicated that the absolute error at 95% confidence interval is 1.3% by volume.     

Hyper-spectral data based investigations for snow wetness mapping

Spatial information on snow wetness content (SWC) is important for hydrology, climatology applications. Limited work is available on estimation of SWC using optical sensors. In present work, spectral signature characteristics of snow (~145 samples) acquired in winters of three years, using field spectral-radiometer (350–2500 nm) were correlated with synchronized SWC measurements. Correlation is found stronger in Near-Infra-Red (NIR) and Short-Wave-Infrared (SWIR) regions than Visible (VIS). Spectral peak width at 905 and 1240 nm is found negatively correlated with SWC, while positively correlated at 1025 nm. Asymmetry tends towards right as SWC increases and has stable positive correlations as compared to other characteristics. Sensitivity of widely used snow-related indices to SWC is also analyzed. Based on analysis, new ratio method at selected wavelengths is proposed to discriminate dry and wet snow zones using air/ground borne sensors. Proposed methodology is evaluated on air-borne hyper-spectral (AVIRIS-NG) data and 88% overall accuracy with kappa coefficient 77.6 observed after validation with reference observations.     

基于CBERS-2数据的新疆天山西部森林资源监测研究

探讨了利用CBERS-2数据监测新疆天山西部森林资源的遥感技术方法。在建立监测指标体系的基础上，通过遥感影像分析和地面调查，采用系统分析的方法提取森林资源信息，分析得出，新疆天山西部森林资源有林地及灌木林地所占比重最大，利用CBERS-2数据开展监测取得了较理想的效果。