共查询到15条相似文献，搜索用时 93 毫秒
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基于GOCI数据渤海海冰厚度算法研究 总被引：2，自引：0，他引：2
提出一种基于GOCI数据提取渤海海冰厚度方法并将其应用于2014年-2015年冬季渤海海冰厚度动态变化监测。首先基于高时间分辨率的GOCI数据建立GOCI短波宽带反射率与各波段反射率模型,然后建立海冰厚度与GOCI短波宽带反射率模型,并将此模型应用于渤海海冰厚度监测,最后通过基于MODIS数据、热动力学模型（Lebedev和Zubov模型）反演获得的海冰厚度以及实测海冰厚度数据对实验结果进行验证。实验结果表明：基于GOCI数据建立海冰厚度模型所反演的海冰厚度与基于MODIS数据反演的海冰厚度以及Lebedev和Zubov模型具有较高相关性（R2>0.86）,而且反演结果接近实测数据（RMS为6.82 cm）。 相似文献
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渤海AVHRR多通道海冰密集度反演算法试验研究 总被引：2，自引：1，他引：1
为了得到更精确的渤海海冰密集度反演参数,采用辽东湾不同类型海冰ASD实测数据,在分析光谱特征的基础上,针对NOAA/AVHRR数据确定出合适海冰密集度反演算法阈值。继而,基于线性光谱混合模型的多通道反演算法进行了一系列算法试验。同时实现了基于LandSat5-TM数据的渤海海冰密集度场反演,并利用该结果与AVHRR单通道和多通道算法得到的海冰密集度反演结果进行比对分析。定量误差分析结果表明,当单通道算法或组合算法中包含1通道时,与Landsat5-TM反演结果的平均误差为正值,包含2通道且不包含1通道时,平均误差为负值;同时使用这两个通道较只包含其一的各种组合算法的平均误差明显偏小;在各种组合算法中,1245四个通道组合反演的海冰密集度结果误差最小,可应用于渤海AVHRR数据海冰密集度反演。 相似文献
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利用“北京一号”小卫星全色和多光谱影像对冬季渤海海冰进行了监测。根据多光谱谱段特征和现场测试的海冰光谱之间的对应关系, 建立了卫星影像冰水识别、渤海海冰外缘线的模型, 估算了海冰面积及判别海冰发展趋势;对卫星影像数据进行监督分类, 建立了相应的分类模型;根据海冰类型的不同将海冰区域进行了划分, 并作为对海冰灾害严重程度的判别依据;以2007年度冬季为例, 对渤海海冰的分布面积进行了统计, 与海冰监测业务中使用的MODIS数据反演结果进行了对比, 对比结果显示“北京一号”卫星可以为海洋监测和预报服务部门提供高分辨率海冰遥感信息, 并为我国海冰灾害的防范提供可靠有效的资料。 相似文献
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MODIS渤海海冰遥感资料反演 总被引：9，自引：0，他引：9
鉴于渤海海冰监测和预报对海冰卫星遥感数字化产品的迫切需求,本文利用MODIS的1B级数据进行渤海海冰参数反演,提供海冰遥感图像和海冰密集度、冰厚数值产品,作为渤海海冰监测和海冰数值预报初始场的重要信息来源,以及海冰预报质量检验的参考依据之一。反演结果表明,其各通道对海冰性质有很好的反映,资料信号比较稳定,对不同密集度和厚度的冰有较好的区分,相对NOAA/AVHRR和HY-1A资料有更好的实际应用价值;Terra/MODIS和HY-1A/COCTS海冰遥感反演结果对比也为HY-1A系列卫星海冰遥感的改进和提高提供有益的启示。 相似文献
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为解决现场调查数据覆盖不足的问题,利用卫星遥感数据(Landsat TM和ETM+)对桑沟湾海域的海冰厚度进行了反演。与Zubov模型计算结果相比,本反演结果与之接近(相关系数0.89)。由遥感影像提取结果看出,桑沟湾海冰厚度随时间和空间变化明显。在轻冰年份,桑沟湾基本无冰。在偏重冰年和重冰年份,桑沟湾出现大量浮冰,并且海冰在水动力和风应力的作用下,呈现由近岸到离岸冰厚不断减小的趋势。重冰年份桑沟湾南侧由于受潮汐和风力推动作用下发生挤压变形,近岸出现平均冰厚较大的海冰(20 cm),桑沟湾中部也出现平均厚度约5~10 cm的海冰。 相似文献
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星载SSM／I微波遥感渤海海冰的辐射特征分析 总被引：2，自引：0，他引：2
根据我国渤海区域星载微波遥感ＤＭＳＰ ＳＳＭ／Ｉ１９９４￣１９９６年的观察数据的研究,指出现有的星载微波遥感海冰的数据标定和验证ＴＢＩ和ＯＳＩ算法是不适用的。用２２和８５ＧＨｚ的散射指数ＳＩ和１９ＧＨｚ的极化指数ＰＩ,讨论了渤海区域海冰的主要辐射特征,用矢量辐射传输理论数值模拟了ＳＩ、ＰＩ与海冰覆盖及其厚度变化的定量关系,用ＳＩ－ＰＩ图规划了渤海区域性海冰的识别方法,给出了渤海海冰的空间分布及其辐 相似文献
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根据我国渤海区域星载微波遥感ＤＭＳＰＳＳＭ／Ｉ１９９４～１９９６年的观察数据的研究,指出现有的星载微波遥感海冰的数据标定和验证ＴＢＩ和ＯＳＩ算法是不适用的。用２２和８５ＧＨｚ的散射指数ＳＩ和１９ＧＨｚ的极化指数ＰＩ,讨论了渤海区域海冰的主要辐射特征,用矢量辐射传输理论数值模拟了ＳＩ、ＰＩ与海冰覆盖及其厚度变化的定量关系,用ＳＩ－ＰＩ图规划了渤海区域性海冰的识别方法,给出了渤海海冰的空间分布及其辐射特征的时间序列变化。 相似文献
10.
渤海冬季海冰反照率变化 总被引：1，自引：1，他引：0
渤海海冰对于大尺度气候变化比较敏感,基于CLARA-A1-SAL数据分析了1992~2008年冬季（12、1、2月）渤海海冰区域反照率的时空变化,同时分析了海冰密集度、海冰外延线面积和海水表面温度的变化与海冰反照率的相互关系。渤海海冰区域反照率随时间波动变化且变化趋势不明显,趋势线斜率仅为0.0388%。年际变化在9.93%~14.5%之间,平均值为11.79%。海冰反照率在1999,2000和2005等重冰年的值明显高于其他年份,在1994,1998,2001和2006等轻冰年的值较低。从单个月份反照率来看,12月海冰反照率的增加趋势（趋势线斜率0.0988%）明显高于1月和2月,1月的海冰反照率平均值（12.9%）高于另外两个月份。海冰反照率和海冰密集度呈明显的正相关关系;和海表面温度呈负相关关系（显著性水平90%）。 相似文献
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The estimate of sea ice resources quantity in the Bohai Sea based on NOAA/AVHRR data 总被引：5，自引：4，他引：1
YUAN Shuai GU Wei XU Yingjun WANG Ping HUANG Shuqing LE Zhangyan CONG Jianou 《海洋学报(英文版)》2012,31(1):33-40
The research on sea ice resources is the academic base of sea ice exploitation in the Bohai Sea. According to the ice-water spectrum differences and the correlation between ice thickness and albedo, this paper comes up with a sea ice thickness inversion model based on the NOAA/AVHRR data. And then a sea ice resources quantity (SIQ) time series of Bohai Sea is established from 1987 to 2009. The results indicate that the average error of inversion sea ice thickness is below 30%. The maximum sea ice resources quantity is about 6 × 10 9 m 3 and the minimum is 1.3 × 10 9 m 3 . And a preliminary analysis has been made on the errors of the estimate of sea ice resources quantity (SIQ). 相似文献
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The HY-1A satellite is the first oceanic satellite of China. During the winter of 2002-2003, the data of the HY-1A were applied to the sea ice monitoring and forecasting for the Bohai Sea of China for the fhst time. The sea ice retrieval system of the HY-1A has been constructed. It receives 1B data from the satellite, outputs sea ice images and provides digital products of ice concentration, ice thickness and ice edge, which can be used as important information for sea ice monitoring and the initial fields of the numeric sea ice forecast and as one of the reference data for the sea ice forecasting verification. The sea ice retrieval system of the satellite is described, including its processes, methods and parameters. The retrieving results and their application to the sea ice monitoring and forecasting for the Bohai Sea are also discussed. 相似文献
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The HY-1A satellite is the first oceanic satellite of China. During the winter of 2002～2003, the data of the HY-1A were applied to the sea ice monitoring and forecasting for the Bohai Sea of China for the first time. The sea ice retrieval system of the HY-1A has been constructed. It receives 1B data from the satellite, outputs sea ice images and provides digital products of ice concentration, ice thickness and ice edge, which can be used as important information for sea ice monitoring and the initial fields of the numeric sea ice forecast and as one of the reference data for the sea ice forecasting verification. The sea ice retrieval system of the satellite is described, including its processes, methods and parameters. The retrieving results and their application to the sea ice monitoring and forecasting for the Bohai Sea are also discussed. 相似文献
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基于高光谱遥感的渤海海冰厚度半经验模型 总被引：1，自引：0，他引：1
Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness(SEMSIT) that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands(spectral dimension information) are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided. 相似文献
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基于MODIS热红外数据的渤海海冰厚度反演 总被引：3，自引：1，他引：2
Level ice thickness distribution pattern in the Bohai Sea in the winter of 2009–2010 was investigated in this paper using MODIS night-time thermal infrared imagery.The cloud cover in the imagery was masked out manually.Level ice thickness was calculated using MODIS ice surface temperature and an ice surface heat balance equation.Weather forcing data was from the European Centre for Medium-Range Weather Forecasts(ECMWF) analyses.The retrieved ice thickness agreed reasonable well with in situ observations from two off-shore oil platforms.The overall bias and the root mean square error of the MODIS ice thickness are –1.4 cm and 3.9 cm,respectively.The MODIS results under cold conditions(air temperature –10°C) also agree with the estimated ice growth from Lebedev and Zubov models.The MODIS ice thickness is sensitive to the changes of the sea ice and air temperature,in particular when the sea ice is relatively thin.It is less sensitive to the wind speed.Our method is feasible for the Bohai Sea operational ice thickness analyses during cold freezing seasons. 相似文献