A method to estimate the thickness of the sea ice of the Bohai Sea is proposed using geostationary ocean color imager (GOCI) data and then applied to the dynamic monitoring of the sea ice thickness in the Bohal Sea ...A method to estimate the thickness of the sea ice of the Bohai Sea is proposed using geostationary ocean color imager (GOCI) data and then applied to the dynamic monitoring of the sea ice thickness in the Bohal Sea during the winter of 2014 to 2015. First of all, a model is given between the GOCI shortwave broadband albedo and the reflectance of each band with high temporal resolution GOCI data. Then, the relationship model between the sea ice thickness and the GOCI shortwave broadband albedo is established and applied to the thickness extraction of the sea ice in the Bohai Sea. Finally, the sea ice thickness extraction method is tested by the results based on the MODIS data, thermodynamic empirical models (Lebedev and Zubov), and the in situ ice thickness data. The test results not only indicated that the sea ice thickness retrieval method based on the GOCI data was a good correlation (r2〉0.66) with the sea ice thickness retrieved by the MODIS and thermodynamic empirical models, but also that the RMS is only 6.82 cm different from the thickness of the sea ice based on the GOCI and in situ data.展开更多
海洋卫星COMs-1((Communication,Ocean&Meteorological Satellite-1)上携带的GOCI(Geostationary Ocean Color Imager)传感器以海洋监测为主,也具备较好的陆地监测潜力,但传感器陆地辐射特性存在偏差。为改善GOCI陆地辐射特性,基于M...海洋卫星COMs-1((Communication,Ocean&Meteorological Satellite-1)上携带的GOCI(Geostationary Ocean Color Imager)传感器以海洋监测为主,也具备较好的陆地监测潜力,但传感器陆地辐射特性存在偏差。为改善GOCI陆地辐射特性,基于MODIS数据,对GOCI可见光和近红外波段开展交叉辐射定标,弥补场地定标成本较高、定标参数更新周期长的不足,拓展其陆地定量遥感监测能力.交叉辐射定标中,考虑了GOCI和MODIS传感器相应波段光谱响应函数之间的匹配;通过辐射传输模拟,订正两传感器观测角度对辐射定标的影响;通过选取两传感器同一过境时刻的数据,降低太阳角度对辐射定标的影响,提高交叉定标精度.通过MODIS数据模拟的GOCI相应波段的表观辐亮度与GOCI实测结果比对,R^2大于0.88。对定标结果进行初步验证,表明交叉辐射定标后,GOCI陆地上的辐射特性满足基本的定量遥感需求。展开更多
黄海呈现独有的地形条件,且该海域的潮波运动独具特征。本文利用静止海洋水色成像仪(Geostationary Ocean Color Imager, GOCI)遥感反演和俄勒冈州立大学(Oregon State University,OSU)潮流模式分别获取了黄海海域的海表流场,基于该海...黄海呈现独有的地形条件,且该海域的潮波运动独具特征。本文利用静止海洋水色成像仪(Geostationary Ocean Color Imager, GOCI)遥感反演和俄勒冈州立大学(Oregon State University,OSU)潮流模式分别获取了黄海海域的海表流场,基于该海域独特的潮波系统提出并识别潮波干涉区,进而对GOCI反演的流场做潮流提取,并对两种潮流数据作分区可用性评价,通过实测的漂流浮标数据验证评估。结果表明:利用GOCI反演和OSU潮流模式获取的海表流场具有一定程度的可靠性,GOCI反演的海表流场的流速平均相对大小误差值为0.77,OSU潮流模式获取的海表流场流速平均相对大小误差值为0.49;在靠近潮波干涉区的黄海中部海域,GOCI潮流数据与实测数据在方向上的一致性要优于OSU潮流数据,两者平均角度误差值分别为48.45°和63.10°;在远离潮波干涉区的黄海近岸海域,OSU潮流数据与实测数据在速度大小和方向上的一致性要优于GOCI潮流数据。展开更多
The unavoidable nature of Ulva prolifera mixed pixel in low-resolution remote sensing images would result in rough boundary of U.prolifera patches,omission of tiny patches,and overestimation of coverage area.The decom...The unavoidable nature of Ulva prolifera mixed pixel in low-resolution remote sensing images would result in rough boundary of U.prolifera patches,omission of tiny patches,and overestimation of coverage area.The decomposition of U.prolifera mixed pixel addresses the issue of coverage area overestimation,and the remaining problems can be alleviated by subpixel mapping(SPM).Due to the drift and dissipation of U.prolifera,a suitable SPM method is the single image-based unsupervised method.However,the method has difficulties in detail reconstruction,insufficient learning of spectral information,and SPM error introduced by abundance deviation.Therefore,we proposed a multiple-feature decision fusion SPM(MFDFSPM)method.It involves three branches to obtain the spatial,abundance,and spectral features of U.prolifera while considers multi-feature information using the fusion strategy.Experiments on the Geostationary Ocean Color Imager images in the Yellow Sea of China indicate that the MFDFSPM overperforms several typical U.prolifera SPM methods in higher accuracy and stronger robustness in both SPM and abundance calculation,which produced subpixel map with more detailed spatial information and less noise.展开更多
依据2015年GOCI(geostationary ocean color imager)卫星影像反演的悬浮体浓度数据,分析了山东半岛沿岸海域表层悬浮体质量浓度和锋面月变化特征,揭示该海域悬浮体的分布特征和扩散格局,并结合风速、波高以及海表温度数据,对其控制因素...依据2015年GOCI(geostationary ocean color imager)卫星影像反演的悬浮体浓度数据,分析了山东半岛沿岸海域表层悬浮体质量浓度和锋面月变化特征,揭示该海域悬浮体的分布特征和扩散格局,并结合风速、波高以及海表温度数据,对其控制因素进行初步探讨。结果显示:研究区内悬浮体质量浓度整体表现为冬季最高,春秋次之,夏季最低的分布特征;悬浮体扩散过程可以划分为4个阶段,冬季稳定外输,春季向岸退缩,夏季近岸贮存,秋季向外扩散。此外,山东半岛近岸存在一条悬浮体质量浓度高于10 mg/L的浑浊带,该浑浊带同样表现出季节变化,它在秋季开始形成,其悬浮体含量、幅宽及延伸范围在冬季达到最大,春季减弱,夏季消失。研究认为山东半岛沿岸海域的表层悬浮体来源主要是海底沉积物的再悬浮。风场、海浪以及沿岸流的强弱变化对悬浮体分布和输运的季节变化有重要的控制作用:风场和海浪影响海水混合搅拌强度,改变海底沉积物再悬浮作用的临界深度,进而影响表层海水悬浮体浓度,致使悬浮体浓度与风浪的月际变化趋势基本一致;沿岸流携带高浓度悬浮体沿山东半岛输运形成沿岸浑浊带,沿岸流的强度变化直接控制浑浊带的季节变化。展开更多
海洋水色遥感技术是新兴的探测技术,能够通过遥感平台上搭载的探测器对海表水色信息进行探测进而获取海洋信息。随着极地轨道卫星发展逐渐成熟,地球静止卫星也进入了发展阶段,开启了水色遥感技术的新时期。本文基于地球同步卫星的优势特...海洋水色遥感技术是新兴的探测技术,能够通过遥感平台上搭载的探测器对海表水色信息进行探测进而获取海洋信息。随着极地轨道卫星发展逐渐成熟,地球静止卫星也进入了发展阶段,开启了水色遥感技术的新时期。本文基于地球同步卫星的优势特点,着重总结了第一颗地球静止水色卫星GOCI(geostationary ocean color imager)的主要参数信息及标准水色算法,同时对各算法的应用范围、计算公式及各方法的联系进行了简要的介绍,并针对其实际应用进行了讨论,验证了GOCI数据的实用性,最后指出了GOCI在目前研究过程中存在的问题,并对第二代地球同步遥感器进行简要论述,以期为海洋探测研究提供更多的技术支持。展开更多
结合GOCI(Geostationary Ocean Color Imager)传感器的波段特征,基于缨帽变换方法设计了一种简单有效的绿潮指数(TCT-GTI)算法。结合目视判断的绿潮识别结果,将TCT-GTI算法与两种已有的遥感算法(AFAI和IGAG算法)监测结果进行对比发现,TC...结合GOCI(Geostationary Ocean Color Imager)传感器的波段特征,基于缨帽变换方法设计了一种简单有效的绿潮指数(TCT-GTI)算法。结合目视判断的绿潮识别结果,将TCT-GTI算法与两种已有的遥感算法(AFAI和IGAG算法)监测结果进行对比发现,TCT-GTI算法的绿潮遥感监测结果精度较高,具有较高的可信度。将TCT-GTI算法应用到2017年多景GOCI影像,对中国黄海海域绿潮信息进行提取,同时分析绿潮覆盖面积的日变化特征,研究2017年绿潮暴发事件的漂移轨迹。研究结果表明,绿潮覆盖面积在中午12:00达到最大,这可能是光合作用等因素的影响。2017年,绿潮暴发事件经历了西北至东北的漂移路径,由江苏盐城外海海域向西北方向漂移至南黄海东部,然后继续向东北方向移动,抵达山东半岛南岸,逐渐消亡。展开更多
基金The National Natural Science Foundation of China under contract No.41306193the Research and Development Special Foundation for Public Welfare Industry under of China contract No.201105016the Basic Research of First Institute of Oceanography,State Oceanic Administration under contract No.GY2014T03
文摘A method to estimate the thickness of the sea ice of the Bohai Sea is proposed using geostationary ocean color imager (GOCI) data and then applied to the dynamic monitoring of the sea ice thickness in the Bohal Sea during the winter of 2014 to 2015. First of all, a model is given between the GOCI shortwave broadband albedo and the reflectance of each band with high temporal resolution GOCI data. Then, the relationship model between the sea ice thickness and the GOCI shortwave broadband albedo is established and applied to the thickness extraction of the sea ice in the Bohai Sea. Finally, the sea ice thickness extraction method is tested by the results based on the MODIS data, thermodynamic empirical models (Lebedev and Zubov), and the in situ ice thickness data. The test results not only indicated that the sea ice thickness retrieval method based on the GOCI data was a good correlation (r2〉0.66) with the sea ice thickness retrieved by the MODIS and thermodynamic empirical models, but also that the RMS is only 6.82 cm different from the thickness of the sea ice based on the GOCI and in situ data.
文摘海洋卫星COMs-1((Communication,Ocean&Meteorological Satellite-1)上携带的GOCI(Geostationary Ocean Color Imager)传感器以海洋监测为主,也具备较好的陆地监测潜力,但传感器陆地辐射特性存在偏差。为改善GOCI陆地辐射特性,基于MODIS数据,对GOCI可见光和近红外波段开展交叉辐射定标,弥补场地定标成本较高、定标参数更新周期长的不足,拓展其陆地定量遥感监测能力.交叉辐射定标中,考虑了GOCI和MODIS传感器相应波段光谱响应函数之间的匹配;通过辐射传输模拟,订正两传感器观测角度对辐射定标的影响;通过选取两传感器同一过境时刻的数据,降低太阳角度对辐射定标的影响,提高交叉定标精度.通过MODIS数据模拟的GOCI相应波段的表观辐亮度与GOCI实测结果比对,R^2大于0.88。对定标结果进行初步验证,表明交叉辐射定标后,GOCI陆地上的辐射特性满足基本的定量遥感需求。
文摘黄海呈现独有的地形条件,且该海域的潮波运动独具特征。本文利用静止海洋水色成像仪(Geostationary Ocean Color Imager, GOCI)遥感反演和俄勒冈州立大学(Oregon State University,OSU)潮流模式分别获取了黄海海域的海表流场,基于该海域独特的潮波系统提出并识别潮波干涉区,进而对GOCI反演的流场做潮流提取,并对两种潮流数据作分区可用性评价,通过实测的漂流浮标数据验证评估。结果表明:利用GOCI反演和OSU潮流模式获取的海表流场具有一定程度的可靠性,GOCI反演的海表流场的流速平均相对大小误差值为0.77,OSU潮流模式获取的海表流场流速平均相对大小误差值为0.49;在靠近潮波干涉区的黄海中部海域,GOCI潮流数据与实测数据在方向上的一致性要优于OSU潮流数据,两者平均角度误差值分别为48.45°和63.10°;在远离潮波干涉区的黄海近岸海域,OSU潮流数据与实测数据在速度大小和方向上的一致性要优于GOCI潮流数据。
基金Supported by the Shandong Provincial Natural Science Foundation of China(No.ZR2019MD023)the National Natural Science Foundation of China(No.41776182)。
文摘The unavoidable nature of Ulva prolifera mixed pixel in low-resolution remote sensing images would result in rough boundary of U.prolifera patches,omission of tiny patches,and overestimation of coverage area.The decomposition of U.prolifera mixed pixel addresses the issue of coverage area overestimation,and the remaining problems can be alleviated by subpixel mapping(SPM).Due to the drift and dissipation of U.prolifera,a suitable SPM method is the single image-based unsupervised method.However,the method has difficulties in detail reconstruction,insufficient learning of spectral information,and SPM error introduced by abundance deviation.Therefore,we proposed a multiple-feature decision fusion SPM(MFDFSPM)method.It involves three branches to obtain the spatial,abundance,and spectral features of U.prolifera while considers multi-feature information using the fusion strategy.Experiments on the Geostationary Ocean Color Imager images in the Yellow Sea of China indicate that the MFDFSPM overperforms several typical U.prolifera SPM methods in higher accuracy and stronger robustness in both SPM and abundance calculation,which produced subpixel map with more detailed spatial information and less noise.
文摘依据2015年GOCI(geostationary ocean color imager)卫星影像反演的悬浮体浓度数据,分析了山东半岛沿岸海域表层悬浮体质量浓度和锋面月变化特征,揭示该海域悬浮体的分布特征和扩散格局,并结合风速、波高以及海表温度数据,对其控制因素进行初步探讨。结果显示:研究区内悬浮体质量浓度整体表现为冬季最高,春秋次之,夏季最低的分布特征;悬浮体扩散过程可以划分为4个阶段,冬季稳定外输,春季向岸退缩,夏季近岸贮存,秋季向外扩散。此外,山东半岛近岸存在一条悬浮体质量浓度高于10 mg/L的浑浊带,该浑浊带同样表现出季节变化,它在秋季开始形成,其悬浮体含量、幅宽及延伸范围在冬季达到最大,春季减弱,夏季消失。研究认为山东半岛沿岸海域的表层悬浮体来源主要是海底沉积物的再悬浮。风场、海浪以及沿岸流的强弱变化对悬浮体分布和输运的季节变化有重要的控制作用:风场和海浪影响海水混合搅拌强度,改变海底沉积物再悬浮作用的临界深度,进而影响表层海水悬浮体浓度,致使悬浮体浓度与风浪的月际变化趋势基本一致;沿岸流携带高浓度悬浮体沿山东半岛输运形成沿岸浑浊带,沿岸流的强度变化直接控制浑浊带的季节变化。
文摘海洋水色遥感技术是新兴的探测技术,能够通过遥感平台上搭载的探测器对海表水色信息进行探测进而获取海洋信息。随着极地轨道卫星发展逐渐成熟,地球静止卫星也进入了发展阶段,开启了水色遥感技术的新时期。本文基于地球同步卫星的优势特点,着重总结了第一颗地球静止水色卫星GOCI(geostationary ocean color imager)的主要参数信息及标准水色算法,同时对各算法的应用范围、计算公式及各方法的联系进行了简要的介绍,并针对其实际应用进行了讨论,验证了GOCI数据的实用性,最后指出了GOCI在目前研究过程中存在的问题,并对第二代地球同步遥感器进行简要论述,以期为海洋探测研究提供更多的技术支持。
文摘结合GOCI(Geostationary Ocean Color Imager)传感器的波段特征,基于缨帽变换方法设计了一种简单有效的绿潮指数(TCT-GTI)算法。结合目视判断的绿潮识别结果,将TCT-GTI算法与两种已有的遥感算法(AFAI和IGAG算法)监测结果进行对比发现,TCT-GTI算法的绿潮遥感监测结果精度较高,具有较高的可信度。将TCT-GTI算法应用到2017年多景GOCI影像,对中国黄海海域绿潮信息进行提取,同时分析绿潮覆盖面积的日变化特征,研究2017年绿潮暴发事件的漂移轨迹。研究结果表明,绿潮覆盖面积在中午12:00达到最大,这可能是光合作用等因素的影响。2017年,绿潮暴发事件经历了西北至东北的漂移路径,由江苏盐城外海海域向西北方向漂移至南黄海东部,然后继续向东北方向移动,抵达山东半岛南岸,逐渐消亡。