Detection of small-scale roughness and refractive index of sea ice in passive satellite microwave remote sensing

Polar ice masses and sheets are sensitive indicators of climate change. Small-scale surface roughness significantly impacts the microwave emission of the sea ice/snow surface; however, published results of surface roughness measurements of sea ice are rare. Knowing the refractive index is important to discriminate between objects. In this study, the small-scale roughness and refractive index over sea ice are estimated with AMSR-E observations and a unique method. Consequently, the small-scale surface roughness of 0.25 cm to 0.5 cm at AMSR-E 6.9 GHz shows reasonable agreement with the results of known observations, ranging from 0.2 cm to 0.6 cm for the sea ice in the Antarctic and Arctic regions. The refractive indexes are retrieved from 1.6 to 1.8 for winter, from 1.2 to 1.4 for summer in the Arctic and the Antarctic, which are similar to those of the sea ice and results from previous studies. This research shows the physical characteristics of the sea ice edges and melting process. Accordingly, this investigation provides an effective procedure for retrieving the small-scale roughness and refractive index of sea ice and snow. Another advantage of this study is the ability to distinguish sea ice from the sea surface by their relative small-scale roughness.     

Narrowband-to-broadband albedo conversion for glacier ice and snow: equations based on modeling and ranges of validity of the equations

In this paper, we propose equations for narrowband-to-broadband (NTB) albedo conversion for glacier ice and snow for four types of satellite sensors: thematic mapper (TM), advanced very high resolution radiometer (AVHRR), moderate resolution imaging spectroradiometer (MODIS), and multi-angle imaging spectroradiometer (MISR). We do this on the basis of spectral albedos and incident spectral irradiances generated with radiative-transfer models of the (sub-)surface (a two-stream model) and the atmosphere, respectively. First, we establish equations for reference values of atmospheric components and the surface elevation. These equations describe measurements with root-mean-square differences of ∼0.016. We then show that the “reference equations” also perform well when total ozone and aerosol optical depth are changed with respect to the reference. The negative effect of humidity and elevation variations on the performance of the equations can be eliminated by adding a correction term. We argue that narrowband albedos are much less sensitive to variations in the incident spectral irradiance than broadband albedos. Hence, our conclusions about the effects of variations in atmospheric composition and elevation are also valid for equations for NTB conversion proposed in other papers.     

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.     

Cloud detection in Landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index threshold value decision

This work presents a new algorithm designed to detect clouds in satellite visible and infrared (IR) imagery of ice sheets. The approach identifies possible cloud pixels through the use of the normalized difference snow index (NDSI). Possible cloud pixels are grown into regions and edges are determined. Possible cloud edges are then matched with possible cloud shadow regions using knowledge of the solar illumination azimuth. A scoring index quantifies the quality of each match resulting in a classified image. The best value of the NDSI threshold is shown to vary significantly, forcing the algorithm to be iterated through many threshold values. Computational efficiency is achieved by using sub-sampled images with only minor degradation in cloud-detection performance. The algorithm detects all clouds in each of eight test Landsat-7 images and makes no incorrect cloud classifications.     

Rapid change of snow surface properties at Vostok, East Antarctica, revealed by altimetry and radiometry

We present results of snow surface properties using the ENVISAT dual frequency altimeter at S (3.2 GHz) and Ku (13.6 GHz) bands and the AMSR-E microwave radiometer at frequencies ranging between 6 and 36 GHz in the Vostok region, East Antarctica. The altimetric time series observed between 2002 and 2008 show variations at 3 different time scales (daily, seasonal and inter-annual), that correlate directly with variations in the snow surface properties. In this study we focus on the analysis of the rapid daily event, occurring on February 14th 2005, that created a jump of the backscatter coefficient of up to 5.3 dB at the S band and 2.5 dB at the Ku band. The ratio of V/H-polarization brightness temperature slowly decreased in December and January 2005, and suddenly increased on February 14th 2005.The origin of this rapid event is investigated using AWS data from Vostok station, altimetric and radiometric data simultaneously. Both snow surface density and roughness are found to vary during this event. This event is shown to be synchronous with strong wind occuring during a period of anomalous wind direction, and the presence of surface hoar. These particular conditions certainly modified the snow surface roughness and thus impacted the altimetric signal. We finally investigate the impact of this event on the calculation of the regional ice-sheet mass-balance using different corrections of height with echo shape variations. It is shown to be negligible only if the full echo shape correction (Legresy et al., 2006) is used.     

Identification of snow cover regimes through spatial and temporal clustering of satellite microwave brightness temperatures

Areas of similar ecology are often delineated based on homogenous topography, temperature, and land cover. Once delineated, these zones become the basis for a wide variety of scientific research and management activities. For instance, in Canada, ecozones are commonly utilized ecological management units delineated using geographic, topographic, and climatic information aided by spring and summer vegetation conditions. Snow cover has an influence on local and regional hydrological conditions and climate, as well as on animal habitats. As such, we posit that inclusion of winter conditions, incorporating spatial- and temporal-variation in snow cover is an additional element for consideration when delineating areas with homogenous conditions. In our analysis we use satellite passive microwave brightness temperatures from 19 years of Special Sensor Microwave/Imager (SSM/I) measurements to produce a daily time-series on snow cover, and demonstrate how these data can be used to delineate areas of similar winter conditions. We use splines and curve fitting to generalize the dense time-series (of over 6900 days) to a set of metrics, and select three for use in cluster-based generalization of snow cover regimes: annual maximum difference between 37 and 19 GHz SSM/I measurements (with differences in magnitudes indicative of snow accumulation), variation of 37-19 GHz brightness temperatures (indicative of snow cover variability), and variation in the rate of brightness temperature change during the snow melt season (indicative of seasonal change). Our results indicate that these metrics produce spatial units that are unique, and not captured by conventional ecological management units, while also producing spatial units that cohere to those generated from summer conditions. Spatial units that are found to have spatial cohesion between summer and winter data sources are located in regions where the amount of snow tends to be low, and snow cover variability minimal. We propose that snow cover regimes may be used to augment typical vegetation-based ecological zonations or to provide insights on hydrology and animal habitat conditions. Inclusion of winter conditions is especially important when areal delineations are used to monitor impacts of climate change, and as a baseline for monitoring changes in snow cover amount, extent, and/or distribution.     

Observed and modelled effects of ice lens formation on passive microwave brightness temperatures over snow covered tundra

Immediately before an April 2007 snow survey and passive microwave radiometer field campaign in the Northwest Territories, Canada, a rain-on-snow event deposited a thin (~ 3 mm) continuous layer of ice on the surface of the snowpack. At eight sites the brightness temperature (T_b) of the undisturbed snow pack was measured with a multi-frequency dual polarization (6.9, 19, 37, and 89 GHz) ground based radiometer system. The ice lens was then carefully removed and the T_bs were measured again. The individual V-pol channels and the 37 V − 19 V difference were largely unaffected by the presence of the ice lens, exhibiting a systematic shift of about 3 K. In comparison, the ice lens had a considerable effect on the H-pol T_b at all frequencies, with a mean difference (ice lens present − ice lens removed) of − 9 K (± 5.3 K) at 6.9 GHz, − 40 K (± 11.3 K) at 19 GHz, − 33 K (± 7.6 K) at 37 GHz, and − 19 K (± 8.0 K) at 89 GHz. The effect of the ice lens on H-pol measurements was also observed with spaceborne data from the Advanced Microwave Scanning Radiometer (AMSR-E) satellite data.Simulations of T_b were produced for each site using a new two layer formulation of the Helsinki University of Technology (HUT) snow emission model. The ice lens was used as the top layer and the underlying snowpack considered as a homogenous second layer. The agreement between observations and simulations was variable, with agreement strongest at 19 GHz. A comparison with simulations produced using the Microwave Emission Model of Layered Snowpacks (MEMLS) suggests HUT model uncertainty is related not to the ice lens, but to difficulties in simulating emission from deep snow. Overall, the observations and simulations suggest H-pol measurements are capable of detecting new ice layers across the tundra snowpack, while V-pol measurements are more appropriate for snow water equivalent (SWE) retrievals due to their relative insensitivity to ice layers.     

Detection limits of coral reef bleaching by satellite remote sensing: Simulation and data analysis

Monitoring of coral reef bleaching has hitherto been based on regional-scale, in situ data. Larger-scale trends, however, must be determined using satellite-based observations. Using both a radiative transfer simulation and an analysis of multitemporal Landsat TM images, the ability of satellite remote sensing to detect and monitor coral reef bleaching is examined. The radiative transfer simulation indicates that the blue and green bands of Landsat TM can detect bleaching if at least 23% of the coral surface in a pixel has been bleached, assuming a Landsat TM pixel with a resolution of 30×30 m on shallow (less than 3 m deep) reef flats at Ishigaki Island, Japan. Assuming an area with an initial coral coverage of 100% and in which all corals became completely bleached, the bleaching could be detected at a depth of up to 17 m. The difference in reflectance of shallow sand and corals is compared by examining multitemporal Landsat TM images at Ishigaki Island, after normalizing for variations in atmospheric conditions, incident light, water depth, and the sensor's reaction to the radiance received. After the normalization, a severe bleaching event when 25-55% of coral coverage was bleached was detected, but a slight bleaching event when 15% of coral coverage was bleached was not detected. The simulation and data analysis agreed well with each other, and identified reliable limits for satellite remote sensing for detecting coral reef bleaching. Sensitivity analysis on solar zenith angle, aerosol (visibility) and water quality (Chl a concentration) quantified the effect of these factors on bleaching detection, and thus served as general guidelines for detecting coral reef bleaching. Spatial misregistration resulted in a high degree of uncertainty in the detection of changes at the edges of coral patches mainly because of the low (∼30 m) spatial resolution of Landsat TM, indicating that detection of coral reef bleaching by Landsat TM is limited to extremely severe cases on a large homogeneous coral patch and shallow water depths. Satellite remote sensing of coral reef bleaching should be encouraged, however, because the development and deployment of advanced satellite sensors with high spatial resolution continue to progress.     

高分辨率遥感卫星数据传输中信道编码技术分析与研究

根据RS编译码原理,结合高分辨率遥感卫星在轨影像实际数据压缩情况,提出将RS(255,243)和RS(34,26)两种编码算法进行联合使用的方案,以解决数据传输中误码问题,仿真试验证明该方法在解决有干扰条件下的星地数据传输时,可有效防止和减少误码扩散。     

基于微波和可见光遥感的冬小麦土壤墒情反演

土壤墒情是水文学、气象学及农业科学研究领域中的一个重要指标参数,对气候、农业、旱情监测都具有极为重要的意义。以河南省广利灌区冬小麦为研究对象,以水云模型为基础,利用Landsat-8和Sentinel-1 A数据计算双层衰减因子、植被和土壤的后向散射系数。采用RBF神经网络拟合土壤的后向散射系数与土壤含水量的关系,通过实测监测点对反演的结果进行精度验证,结果表明:相关系数的平方为0.754 5,均方根误差为0.022,结果良好,可为灌区冬小麦土壤墒情的评估提供参考。     

基于卫星遥感及数据库同步的气象灾害监测预警系统设计

为缩小预警监测指标与测试指标之间的数值差,实现对气象灾害现象的准确监测,设计基于卫星遥感及数据库同步的气象灾害监测预警系统。在中心站体系内建立自动监测站局域网络,按照实际响应需求,连接影像显示模块与预警、响应模块,完成监测预警系统主站部分的设计。求解遥感影像数据集,根据卫星信息堆叠系数求解结果,定义核函数任务映射表达式,实现基于卫星遥感技术的气象灾害监测信息处理。建立数据库模型,通过分析中间件性能需求的方式,确定监测数据的XML同步处理结果,完成数据库同步中间件的搭建,结合相关设备元件,实现基于卫星遥感及数据库同步的气象灾害监测预警系统设计。实验结果表明,卫星遥感及数据库同步处理技术可以有效控制预警监测指标与测试指标之间的数值差,其最大取值结果不超过0.26,符合精准监测气象灾害现象的应用需求。     

The application of satellite remote sensing and spatial proximity analysis techniques to observations on the grazing of oilseed rape by roe deer

In many areas of northern Europe rapeseed is the only botanical species for vegetable oil production. Rapeseed in many areas has become the most dominant breakcrop stabilizing rotations with a high proportion of cereals. The area sown to the crop has expanded dramatically in the last 20 years, with the Scottish crop rising from less than 100ha in 1980 to over 40000 ha in the 1990s.

During periods of severe winter weather, and thus a reduction in the availability of foodstuffs, it has been suggested there is an increased risk of roe deer poisoning from oilseed rape. This is especially true where there is a high proportion of the crop grown in close proximity to roe deer woodland habitats. Whereas agricultural census data can provide satisfactory estimates of the total area of oilseed rape in say a given administrative parish, precise spatial information which can be related to deer habitats is not readily available. A technique is illustrated which combines conventional feeding range analysis data, satellite remote sensing and spatial proximity analysis techniques, in an effort to examine the potential magnitude of such poisoning within the Grampian Region.     

Integrated use of satellite remote sensing,GIS, and ground spectroscopy techniques for monitoring olive oil mill waste disposal areas on the island of Crete,Greece

Olive oil mill wastes (OOMW) constitute a major pollution factor in olive-growing regions and an important problem to be solved for the agricultural industry. Olive oil mill wastes are normally deposited in tanks, or directly into the soil or even on adjacent torrents, rivers, and lakes, posing a high risk of environmental pollution in regard to public health. This study aims to develop integrated satellite remote sensing, geographical information systems (GIS), and ground spectroscopy methodologies to detect and monitor OOMW disposal areas on the island of Crete, Greece in the Southeastern Mediterranean. More than 1000 disposal tanks were mapped through an extended global positioning system (GPS) survey that took place throughout the island. Satellite images of both high (IKONOS) and medium (Landsat 8 OLI (Operational Land Imager)) resolution were preprocessed and analysed by applying geometric, radiometric, and atmospheric corrections. A library with a spectral signature of OOMW including both different time periods and satellite sensors was developed. At the same time, ground spectroscopy campaigns were carried out and a complementary spectral signature library was developed. The narrow band reflectance of ground measurements was recalculated using the relative response filters of the corresponding satellite sensors. Both libraries were compared for their accuracy through statistical approaches and the optimum spectral range for detecting OOMW areas was estimated. Subsequently, further auxiliary image-processing techniques such as image fusion, linear spectral unmixing (LSU), false-colour composites (FCCs), image classification, and principal component analysis (PCA) were applied to satellite images to enhance OOMW patterns, and an innovative OOMW detection index for Landsat 8 was developed. In addition, several vegetation indices were applied and compared in regard to their efficiency in detecting waste ponds. Finally an integrated, semi-automatic methodology was developed in the GIS environment employing classification algorithms for the detection of waste ponds. This study highlights the potential of satellite remote sensing, GIS, and ground spectroscopy in the semi–automatic detection of OOMW disposal areas in the context of the Mediterranean landscape.