共查询到20条相似文献，搜索用时 15 毫秒
1.
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. 相似文献
2.
ABSTRACTSnow 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. 相似文献
3.
Radar remote sensing has great potential to determine the extent and properties of snow cover. Availability of space-borne sensor dual-polarization C-band data of environmental satellite- (ENVISAT-) advanced synthetic aperture radar (ASAR) can enhance the accuracy in measurement of snow physical parameters as compared with single polarization data measurement. This study shows the capability of C-band synthetic aperture radar (SAR) data for estimating dry snow density over snow covered rugged terrain in Himalayan region. The snow density is an important parameter for the snow hydrology and avalanche forecasting related studies. An algorithm has been developed for estimating snow density, based on snow volume scattering and snow-ground scattering components. The radar backscattering coefficients of both horizontal–horizontal (hh) and vertical–vertical (vv) polarization and incidence angle are used as inputs in the algorithm to provide the snow dielectric constant which can be used to derive snow density using Looyenga's semi-empirical formula. Comparison was made between snow density estimated from algorithm using ENVISAT-ASAR hh and vv polarization data and the measured field value. The mean absolute error between estimated and measured snow density was found to be 0.024 g/cm3. 相似文献
4.
Praveen K. Thakur S. P. Aggarwal G. Arun Sahil Sood A. Senthil Kumar Sneh Mani D. P. Dobhal 《Journal of the Indian Society of Remote Sensing》2017,45(3):525-539
Snow physical properties, snow cover and glacier facies are important parameters which are used to quantify snowpack characteristics, glacier mass balance and seasonal snow and glacier melt. This study has been done using C-band synthetic aperture radar (SAR) data of Indian radar imaging satellite, radar imaging satellite-1 (RISAT)-1, to estimate the seasonal snow cover and retrieve snow physical properties (snow wetness and snow density), and glacier radar zones or facies classification in parts of North West Himalaya (NWH), India. Additional SAR data used are of Radarsat-2 (RS-2) satellite, which was used for glacier facies classification of Smudra Tapu glacier in Himachal Pradesh. RISAT-1 based snow cover area (SCA) mapping, snow wetness and snow density retrieval and glacier facies classification have been done for the first time in NWH region. SAR-based inversion models were used for finding out wet and dry snow dielectric constant, dry and wet SCA, snow wetness and snow density. RISAT-1 medium resolution scan-SAR mode (MRS) in HV polarization was used for first time in NWH for deriving time series of SCA maps in Beas and Bhagirathi river basins for years 2013–2014. The SAR-based inversion models were implemented separately for RISAT-1 quad pol. FRS2, for wet snow and dry snow permittivity retrieval. Masks for layover and shadow were considered in estimating final snow parameters. The overall accuracy in terms of R2 value comes out to be 0.74 for snow wetness and 0.72 for snow density based on the limited ground truth data for subset area of Manali sub-basin of Beas River up to Manali for winter of 2014. Accuracy for SCA was estimated to be 95 % when compared with optical remote sensing based SCA maps with error of ±10 %. The time series data of RISAT-1 MRS and hybrid data in RH/RV mode based decompositions were also used for glacier radar zones classification for Gangotri and Samudra Tapu glaciers. The various glaciers radar zones or facies such as debris covered glacier ice, clean or bare glacier ice radar zone, percolation/refreeze radar zone and wet snow, ice wall etc., were identified. The accuracy of classified maps was estimated using ground truth data collected during 2013 and 2014 glacier field work to Samudra Tapu and Gangotri glaciers and overall accuracy was found to be in range of 82–90 %. This information of various glacier radar zones can be utilized in marking firn line of glaciers, which can be helpful for glacier mass balance studies. 相似文献
5.
6.
《International Journal of Digital Earth》2013,6(1):78-90
Abstract Information of snow cover (SC) over Himalayan regions is very important for regional climatological and hydrological studies. Precise monitoring of SC in the Himalayan region is essential for water supply to hydropower stations, irrigation requirements, and flood forecasting. Microwave remote sensing has all weather, day and night earth observation capability unlike optical remote sensing. In this study, spaceborne synthetic aperture radar interferometric (InSAR) coherence analysis is used to monitor SC over Himalayan rugged terrain. The feasibility of monitoring SC using synthetic aperture radar (SAR) interferometry depends on the ability to maintain coherence over InSAR pair acquisition time interval. ERS-1/2 InSAR coherence and ENVISAT ASAR InSAR coherence images are analyzed for SC mapping. Data sets of winter and of snow free months of the Himalayan region are taken for interferogram generation. Coherence images of the available data sets show maximum decorrelation in most of the area which indicates massive snowfall in the region in the winter season and melting in the summer. Area showing coherence loss due to decorrelation is mapped as a snow-covered area. The result is validated with field observations of snow depth and it is found that standing snow is inversely related to coherence in the Himalayan region. 相似文献
7.
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. 相似文献
8.
Abstract Estimation of snowmelt runoff is very important in the Western Himalayan rivers in India where it is required to plan for hydropower generation and the water management during the non‐monsoon season. An attempt has been made to estimate snowmelt runoff on a 10 day average basis in Beas Basin up to Pandoh Dam during May, 1998 and November, 1999 using a Snowmelt Runoff Model (SRM), which is a degree day method. The input parameters for the model are derived from existing maps, satellite data, metrological and hydrological data. The relief of the basin is divided into 12 elevation zones of 500 m each. The temperature was extrapolated to these elevation zones using temperature lapse rate calculated using the observed temperature at seven stations within the basin. Snow covered area in the basin was determined using Indian Remote Sensing Satellites IRS ‐ 1C / 1D Wide Field Sensor (WIFS). The runoff from the snow covered area and snow free area was separately calculated in each elevation zone. The model parameter degree‐day factor is taken from literature and runoff coefficients for snow and rain are derived using the observed data. The total discharge at the dam site is computed by a weighted sum of runoff components from all the elevation zones. There is a good agreement between the observed and computed runoff with a coefficient of determination of 0.854 and the difference in volume is + 4.6 %. 相似文献
9.
A radiative transfer model is used to simulate the sea ice radar altimeter effective scattering surface variability as a function of snow depth and density. Under dry snow conditions without layering these are the primary snow parameters affecting the scattering surface variability. The model is initialized with in situ data collected during the May 2004 GreenIce ice camp in the Lincoln Sea (73/spl deg/W; 85/spl deg/N). Our results show that the snow cover is important for the effective scattering surface depth in sea ice and thus for the range measurement, ice freeboard, and ice thickness estimation. 相似文献
10.
将GNSS-R/IR技术的应用领域拓展到地表冻融状态的监测中，本文利用冻融土混合介质介电常数模型计算土壤介电常数，采用双站全极化相干反射率模型和随机粗糙面散射模型，分别计算了经冻融土反射的GPS相干反射量的镜像反射率，以及GPS非相干反射分量的漫散射特性。模拟分析了冻融转换时，GPS多路径信息（GNSS-IR）以及包含漫散射信号的延迟多普勒图（GNSS-R）的变化特征。理论研究表明冻融转换过程中，地表介电常数的变化导致GPS多路径信息和延迟多普勒图的明显变化。本文从散射机理上揭示了利用GNSS-R和GNSS-IR遥感进行地表冻融特性监测的理论依据。 相似文献
11.
Praveen K. Thakur Pradeep K. Garg Shiv P. Aggarwal Rahul D. Garg Sneh Mani 《Journal of the Indian Society of Remote Sensing》2013,41(4):933-945
The current study has used Synthetic Aperture Radar (SAR) satellite data to estimate the Snow Cover Area (SCA) in Manali watershed of Beas River in Northwest Himalayas of Himachal Pradesh, India. SAR data used in this study is of Radarsat-2 (RS2) and Environmental Satellite (ENVISAT), Advanced Synthetic Aperture Radar (ASAR). The SAR preprocessing was done with SAR image processing tools for converting raw SAR images into calibrated geo-coded backscatter images. Maps for forest, built area, layover and shadow were created and used for masking snow cover in these areas. The backscattering ratio of wet snow to reference image threshold method with value range from ?2 to ?3 db was used to estimate wet SCA for study area. In this technique, if the threshold is too high (≥-2 db) wet SCA is overestimated and if it is too low (≤-3db), this method underestimates the SCA. The wet SCA is under/over estimated (+6 % to?8 % on average) in late spring season due to the inherent terrain and SAR imaging effects of layover/foreshortening and shadow and also due to the masking of forest areas. Overall, the SCA derived from SAR data matches well when compared with total SCA derived from cloud free optical remote sensing data products, especially during wet season. 相似文献
12.
多时相ASAR数据的地表覆盖分类研究 总被引：2，自引：1，他引：1
本文选择了位于念青唐古拉山脉西段,覆盖范围大约100×100km2的区域,使用四个不同时期内的ASAR图像数据进行地表覆盖分类的研究。研究结果表明,虽然同种类型的地物在同一景雷达图像上的后向散射系数存在一定的差异,但是其后向散射系数随时间的变化规律却是一致的。根据地物后向散射系数的这种时相特征,我们对研究区的地表覆盖进行了分类,结果显示使用该方法能有效地区分草原、草甸、裸岩、水体、终年积雪等。 相似文献
13.
Cristian Flueraru Gheorghe Stancalie Vasile Craciunescu Elena Savin 《Transactions in GIS》2007,11(6):927-941
The MODIS snowcover product is one of many geophysical products derived from MODIS data. A cross‐validation of the MODIS snowcover daily products with data obtained from the meteorological network stations was conducted for the entire territory of Romania. The validation time interval covered the period between 29 October, 2004 and 1 May, 2005. The overall accuracy for the whole set of cloud‐free useful data proved to be 95%. The validation time interval included the three common snow situations: (1) late autumn months where 37.1% of the initial set of the data was used, and the overall accuracy was 98.6%; (2) the “winter” months where the clouds reduced the set of useful data – 31.75%– and the overall accuracy was 93.7%; and (3) the months of February and March which returned the highest accuracy (> 95%). Additionally, a cross‐validation using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) high‐resolution imagery was carried out. Furthermore, the MODIS, meteorological data and ASTER data were integrated into a Geographic Information System (GIS) environment to perform flexible and comprehensive cross‐checking followed by a thematic analysis based on additional sets of data such as digital elevation models (DEMs) and land‐cover datasets. 相似文献
14.
15.
Abstract Ikonos panchromatic and multispectral satellite data were acquired in October 2000 and August 2002 for a test area along US Highway 2, the southern border of Glacier National Park (GNP), Montana, USA. The research goals were to map snow avalanche paths and to characterize vegetation patterns in selected paths for longitudinal (i.e., source, track, and runout) and transverse (i.e., inner, flanking, outer) zones as part of a study of forest dynamics and nutrient flux from paths into terrestrial and aquatic systems. In some valleys, as much as 50 percent of the area may be covered by snow avalanche paths, and as such, serve as an important carbon source servicing terrestrial and aquatic ecosystems. Snow avalanches move woody debris down‐slope by snapping, tipping, trimming, and excavating branches, limbs, and trees, and by injuring and scaring trees that remain in‐place. Further, snow avalanches alter the vegetation structure on paths through secondary plant succession of disturbed areas. Contrast and edge enhancements, Normalized Difference Vegetation Index (NDVI), and the Tasseled Cap greenness and wetness transformations were used to examine vegetation patterns in selected paths that were affected by high magnitude snow avalanches during the winter of 2001-2002. Using image transects organized in longitudinal patterns in paths and in forests, and transects arranged in transverse patterns across the sampled paths, the Tasseled Cap transforms (and NDVI values) were plotted and assessed. Preliminary results suggest that NDVI patterns are different for paths and forests, and Tasseled Cap greenness and wetness patterns are different for longitudinal and transverse zones that describe the morphology of snow avalanche paths. The differentiation of paths from the background forest and the characterization of paths by morphometric zones through remote sensing has implications for mapping forest disturbances and dynamics over time and for large geographic areas and for modeling nutrient flux in terrestrial and aquatic systems. 相似文献
16.
V. D. Mishra P. Mathur R. P. Singh 《Journal of the Indian Society of Remote Sensing》2005,33(3):381-393
Snow cover is an important variable for climatic and hydrologic models due to its effect on surface albedo, energy, and mass
balance. Satellite observations successfully provide a global and comprehensive hemispheric-scale record of the short-term,
as well as inter-seasonal variations in snow cover. Passive microwave sensors provide an excellent method to monitor temporal
and spatial variations in large-scale snow cover parameters, overcoming problems of cloud cover. Using microwave remote sensing
data, snow parameters (snow surface temperature, snow water equivalence, scattering index, emissivity, snow depth) have been
retrieved to integrate with the snow cover simulation model developed by SASE for avalanche risk assessment on regional basis.
Multispectral and multitemporal brightness temperature data obtained from the Special Sensor Microwave Imager (SSM/I), flown
onboard the DMSP satellites, for the period November 2000 to April 2001 and from November 2001 to February 2002 have been
analysed. A comparative data set on snow measurements and meteorological observations of a region covering large area of Pir-Panjal
and the Greater Himalayan range, available on near real time basis from SASE field observatories were also used. Model calculations
were carried out to study the effects of atmospheric transmission on the microwave radiation emitted from the snow covered
and snow free ground and atmosphere. The sensitivity of combinations of the SSM/I channels at 19, 37 and 85 GHz, in both horizontal
and vertical polarizations, in respect to snow depth, surface temperature of the snowpack have been carried out. Decision
rule based algorithms are developed to identify snow cover and non-snow area. 相似文献
17.
全球MODIS冰雪反照率产品在定量遥感中有着广泛应用,但由于该产品的业务化算法是建立在表征植被—土壤系统基础上的罗斯表层(RT)李氏稀疏互易核(LSR)的二向性反射分布函数(BRDF)模型(简称为RTLSR),因此该模型对冰雪的二向性反射及反照率的反演能力有待评估。本文基于地球反射极化和方向测量仪(POLDER)的多角度冰雪反射率数据,综合评估了RTLSR模型在表征冰雪二向反射及反演反照率等方面的能力。为量化评估结果,本研究基于渐进辐射传输(ART)模型,从POLDER冰雪数据中筛选出高质量数据,使用ART模型拟合的高质量结果作为参考,比较结果表明:(1)在表征冰雪方向性散射方面,RTLSR模型整体拟合精度较低。在1020 nm波段,其均方根误差(RMSE)最大可达到0.0498,相较于ART模型的拟合结果偏高了约53.70%;(2)在反演冰雪反照率方面,RTLSR模型与ART模型反演结果也存在差别,其决定系数为0.529,均方根误差为0.0333,偏差为-0.0274,基于RTLSR模型的反演结果低估了ART模型的反演结果。为了使核驱动模型能更准确地表征冰雪BRDF特征和反演反照率,该模型需要针对冰雪散射特点进行进一步的发展。 相似文献
18.
城市交通网络分形维数的不确定性估计、控制与分析 总被引：1，自引：0，他引：1
长度-半径维数模型作为描述城市交通网络复杂不确定性现象的一种分形分维方法,其自身存在的不确定性往往被忽视,且相关研究更是鲜见报道。故针对该模型在分形维数测算全过程中存在的不确定性问题,本文率先开展了系统剖析、定量估计和质量控制研究。首先对数据源、矢量化处理、测算中心、尺度选择、以及分维数模型估计等一系列环节进行了不确定性估计与分析,其中首次给出了分形维数在一定置信水平下的不确定性度量区间,并依据误差传播理论对误差的传递和累积进行了描述;然后着重提出了基于LMed S(Least Median of Squares)的质量控制方法。最后通过对拉萨市的算例实验表明:道路的矢量化过程、测算中心和测算尺度的选择都会导致分维的不确定性;并在对数据质量进行控制的基础上,通过置信区间对长度-半径维数模型的不确定性进行了在一定概率水平下的首次度量;同时结合区域现状对研究结果给出了合乎实际的解释。本文在描述表征不确定性问题的分形几何和分形维数的基础上,系统地揭示了其自身不确定性的本质,不仅进一步丰富了分形分维理论,为控制其质量奠定理论基础,而且可为城市交通网络分形维数的地学应用提供可靠的科学依据。 相似文献
19.
星载微波SSM/Ⅰ遥感在中国东北华北农田的辐射特征分析 总被引：2，自引：1，他引：1
本文研究了星载微波ＳＳＭ／Ⅰ１９９６年在中国东北华北平原农田上７个通道辐射亮度温度（ＴＢ）的遥感数据,提出用几个通道ＴＢ组合的散射指数和极化指数来分析中国平原地区农田的微波辐射特征,及其随生长季节的时间性变化。星载ＳＳＭ／Ⅰ数据可以监视农作物的生长和平原地区地面湿度的变化。本文还给出了大气和农作物地表矢量辐射传输的数值模拟结果。 相似文献
20.
Anil V Kulkarni S S Randhawa B P Rathore I M Bahuguna R K Sood 《Journal of the Indian Society of Remote Sensing》2002,30(4):221-228
Himalayan region has high concentrations of mountain glaciers. Large extent of this region is covered by seasonal snow during winter. Runoff generates from melting of these snow and glaciers is one of the important sources of water for the Himalayan Rivers. Glaciers and snowfields are distributed throughout the Himalayas and form a source of numerous streams. Due to steep slopes, all such streams have potential sites for hydropower generation. If this potential is fully utilized, it will help in generating power from environmentally friendly Run-of-River (RoR) hydropower stations. Considering these aspects, a stream flow simulation model was developed for small streams. This will help in estimation of average seasonal unrestricted hydropower potential of snow and glaciated streams for winter, summer, monsoon and autumn seasons. Information generated through remote sensing technique as glacier, permanent snow cover, seasonal snow cover, altitude of snow and glaciers were used in conjunction with daily maximum and minimum temperature, rainfall and discharge. The model was developed for Malana nala located in Parbati River basin near Kullu in Himachal Pradesh. It was validated at adjacent Tosh nala in the same basin. Seasonal runoff computed from the model is comparable with observed data for all seasons except Monsoon. Good results in autumn, winter and summer seasons demonstrates usefulness of runoff model to assess hydropower potential of snow and glaciated streams and therefore, the model was applied to ungauged Sorang Gad and Kirang Khad. In winter runoff was estimated as 1.8 and 1.69 cumecs for Kirang Khad and Sorang Gad, respectively. This is important, as viability of hydropower station depends upon winter stream runoff. These results suggest that the model is useful tool to assess initial estimate of hydropower potential for large number of snow and glaciated streams, for which no hydrological data is available. 相似文献