MODIS渤海海冰遥感资料反演

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。     

2017年夏季北极中央航道海冰观测特征及海冰密集度遥感产品评估

2017年夏季中国第八次北极科学考察期间，"雪龙"号极地考察船首次成功穿越北极中央航道，期间全程开展了海冰要素的人工观测。中央航道走航期间的平均海冰密集度和平均冰厚分别为0.64和1.5 m，海冰密集度时空变化大且以厚当年冰为主，高纬密集冰区的浮冰大小显著高于海冰边缘区。基于"雪龙"号的船基走航观测海冰密集度评估比较了国际上常用的5种常用的微波遥感反演海冰密集度产品，同走航目测海冰密集度点对点的比较，误差最大的为德国不来梅大学AMSR2基于Bootstrap算法的产品，平均误差和均方根误差分别为0.19和0.28；误差最小的为欧洲气象卫星应用组织基于AMSR2数据和OSHD和TUD两种不同算法的产品，平均误差分别为-0.02和0.01，均方根误差均为0.20。从日平均比较来看，AMSR2基于Bootstrap算法的误差最大，平均误差和均方根误差分别为0.15和0.20；AMSR2/OSI SAF（TUD）的误差最小，平均误差和均方根误差分别为0.0和0.11，OSI SAF产品更接近人工观测结果。     

基于19GHz修正91GHz频段改进的ASI海冰密集度算法

基于数据融合算法思想，利用低频修正高频微波数据提出改进的ASI海冰密集度反演算法，对北极海冰进行反演研究。目前用于整体海冰密集度反演的算法中，使用低频数据的算法受天气影响较弱，但空间分辨率相对较低；而使用高频数据的算法，空间分辨率相对较高，但受天气影响较大，虽然使用天气滤波器处理，能消除那些被误判成海冰的水点，但并没有改变冰点的密集度。改进的ASI算法，利用低频数据（19GHz）修正高频数据（85.5GHz），进而得到修正后的85.5GHz的极化差P''，将P带入ASI算法，最终得到以2008-2016年每年的1月3日SSMIS数据为例的北冰洋整体海冰密集度反演结果。结果表明，改进后的ASI算法得到的总体海冰面积介于ASI与NASA Team两个结果之间；在边缘海冰区，改进后的ASI算法结果与传统的ASI算法结果在海冰面积与平均海冰密集度上都有较大差异，且前者更接近NASA Team算法。因此改进后的ASI算法，在空间分辨率上优于NASA Team算法，在受天气影响程度上更弱于ASI算法，并且有效变了边缘海冰区像元的海冰密集度。     

基于MODIS数据的渤海海冰厚度反演算法优化

海冰厚度是监测与研究渤海海冰的重要参数。为了获取更加可靠的渤海海冰厚度数据,本研究基于MODIS数据对海冰厚度反演中的冰水分离环节和冰厚计算方法都进行了改进。对于冰水分离环节,本文在Canny边缘检测算子提取海冰基础上,加入了二值化处理、阈值判别等步骤,实现了较高精度的渤海海冰范围自动化提取。通过试验确定了海冰厚度与反照率指数关系模型中的参数,包括海冰衰减系数和海水反照率参数,使其更加符合渤海海区的物理特征。将改进后算法的海冰厚度反演结果与渤海海上石油平台实测数据进行比较,并分析了误差来源。结果表明,经过对算法的改进,海冰厚度与反照率指数关系模型的反演结果与实测数据之间的平均绝对误差由7.05 cm缩小到2.74 cm,相关系数由0.434提高到0.485。     

“海洋1号”卫星在海冰监测和预报中的应用

“海洋1号A”卫星是我国发射的第一颗海洋卫星.2002~2003年冬季,该卫星首次应用于我国渤海海冰监测和预报.建立了从卫星1B级数据开始的海冰反演系统,提供海冰遥感图像和海冰密集度、冰厚与冰外缘线数值产品,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.介绍所发展的“海洋1号A”卫星海冰反演系统的流程、算法和反演参数,海冰反演结果,及其在渤海海冰监测和预报中的应用.     

海冰密集度卫星遥感反演研究进展

海冰密集度是海冰的重要参数之一,在冰区导航、海上作业、海冰模式验证和气候模型改进等方面具有重要意义。卫星遥感具有覆盖范围广、重访周期短、成本相对低等优势,已成为获取海冰密集度的主要观测手段。本文从主被动微波遥感和光学遥感的角度,回顾了现阶段海冰密集度卫星遥感反演研究进展情况,包括海冰监测传感器、海冰密集度反演算法和海冰密集度产品等。结果表明,被动微波遥感是目前获取海冰密集度的主要方式,已发展出许多成熟的业务化算法;主动微波遥感数据已成为制作冰情图的主要数据源,海冰密集度反演算法由合成孔径雷达SAR（Synthetic Aperture Radar）图像分类向深度学习算法发展;光学遥感海冰密集度算法较为成熟,但受限于云层和夜晚限制,其反演结果多用于其他海冰密集度产品的验证。受传感器硬件限制,3种观测手段各有其长处与不足。为获得高精度、高时空分辨率的海冰密集度数据,开展多源数据融合研究是解决传感器性能瓶颈的有效手段。大数据时代,基于深度学习的海冰密集度卫星遥感反演技术快速发展,需要深度融入海冰密集度卫星遥感领域知识。海冰密集度卫星遥感反演应着力于海冰预报服务,致力于提高我国的海冰预报能力。     

基于SMAP卫星雷达资料的海冰密集度反演技术研究

SMAP是美国于2015年初发射的一颗卫星，搭载了L波段的雷达。它采用圆锥扫描方式，具有固定的入射角、较大的幅宽和千米级的分辨率，在海冰监测方面具有独特的优势。本文利用SMAP卫星雷达资料分别与德国Bremen大学海冰密集度产品和美国国家冰雪数据中心（NSIDC）海冰密集度产品建立3.125 km和25 km匹配数据集，分析了L波段雷达后向散射系数、极化比和归一化极化差与海冰密集度之间相关性，建立基于人工神经网络的海冰密集度反演算法。为了验证SMAP卫星雷达资料反演海冰密集度的精度，本文选择德国Bremen大学和美国冰雪数据中心发布的海冰密集度产品分别与SMAP海冰密集度产品进行对比分析，SMAP海冰密集度与Bremen海冰密集度的偏差为0.07、均方根误差为0.14；与NSIDC海冰密集度的偏差为0.04、均方根误差为0.18，这表明SMAP海冰密集度产品与现有业务化海冰密集度产品具有很好的一致性。     

基于GOCI数据渤海海冰厚度算法研究

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）。     

基于HY-2A/SCAT数据极地海冰检测方法研究

本文基于HY-2A/SCAT数据,采用贝叶斯算法、线性判别算法、支持向量机算法、基于主成分分析(Principal component Analysis,PCA)的BP神经网络算法对极地地区的海冰进行检测,并将检测结果与SSMIS海冰密集度数据进行比较。结果表明:四种检测算法得到的海冰边界介于SSMIS 0%～30%海冰密集度边界之间。在高风速条件下,海冰和海水的后向散射特征区分不明显可能造成冰水误判,以2013年9月16日北极海冰检测为例,贝叶斯算法检测结果误判最少,其次为基于PCA的BP神经网络算法,线性判别和支持向量机两种算法误判率较高。考虑到检测算法的运行效率和冰水误判率,选择贝叶斯算法和支持向量机算法进行海冰面积的季节趋势分析,两种算法得到的海冰面积变化趋势都能反映季节性变化,且在海冰生长季支持向量机算法探测的海冰面积与SSMIS 15%密集度海冰范围保持较好的一致性。     

基于MODIS数据的渤海海冰遥感探测系统的设计

为了满足渤海海冰预报业务化的需求,针对渤海海冰和MODIS数据的特点,结合MO-DIS-TERRA数据,编程实现了基于MODIS数据的渤海海冰遥感探测系统.系统以IDL和VisualC++作为开发语言,为海冰信息提取提供了一个统一的平台,实现了海冰面积、海冰外缘线、海冰厚度和海冰密集度信息提取,并以专题图和数值的形式输...     

渤海冬季海冰反照率变化

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%）。     

基于高光谱遥感的渤海海冰厚度半经验模型

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.     

Application of the HY-1 satellite to sea ice monitoring and forecasting

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.     

Application of the HY-1 satellite to sea ice monitoring and forecasting

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.     

The estimate of sea ice resources quantity in the Bohai Sea based on NOAA/AVHRR data

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).     

渤海冰漂移对海面风场、潮流场的响应

在对海冰漂移动力学分析基础上,利用MODIS资料,采用MCC方法获取渤海大范围冰覆盖的海域冰速场,并利用NCEP风速资料和潮流资料进行回归分析,得到渤海冰漂移速度与风速和流速的关系.利用MODIS和NOAA/AVHRR资料获取的渤海冰速资料的综合分析显示:渤海海冰运动,除受盛行风控制外,还受到复杂的海岸地形、流和冰内应力的共同作用,所得到的大范围海冰运动规律和多年历史观测资料分析结果基本一致,并清楚地显示了冰边缘带海冰运动的特征,弥补了局地、单站海冰观测的局限性.     

Arctic sea ice concentration retrieval using the DT-ASI algorithm based on FY-3B/MWRI data

Sea ice concentration (SIC) is one of the most important indicators when monitoring climate changes in the polar region. With the development of the Chinese satellite technology, the FengYun (FY) series has been applied to retrieve the sea ice parameters in the polar region. In this paper, to improve the SIC retrieval accuracy from the passive microwave (PM) data of the Microwave Radiation Imager (MWRI) aboard on the FengYun-3B (FY-3B) Satellite, the dynamic tie-point (DT) Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) (DT-ASI) SIC retrieval algorithm is applied and obtained Arctic SIC data for nearly 10 a (from November 18, 2010 to August 19, 2019). Also, by applying a land spillover correction scheme, the erroneous sea ice along coastlines in melt season is removed. The results of FY-3B/DT-ASI are obviously improved compared to that of FY-3B/NT2 (NASA-Team2) in both SIC and sea ice extent (SIE), and are highly consistent with the results of similar products of AMSR2 (Advanced Microwave Scanning Radiometer 2)/ASI and AMSR2/DT-ASI. Compared with the annual average SIC of FY-3B/NT2, our result is reduced by 2.31％. The annual average SIE difference between the two FY- 3Bs is 1.65×106 km2, of which the DT-ASI algorithm contributes 87.9％ and the land spillover method contributes 12.1％. We further select 58 MODIS (Moderate-resolution Imaging Spectroradiometer) cloud-free samples in the Arctic region and use the tie-point method to retrieve SIC to verify the accuracy of these SIC products. The root mean square difference (RMSD) and mean absolute difference (MAD) of the FY-3B/DT-ASI and MODIS results are 17.2％ and 12.7％, which is close to those of two AMSR2 products with 6.25 km resolution and decreased 8％ and 7.2％ compared with FY-3B/NT2. Further, FY-3B/DT-ASI has the most significant improvement where the SIC is lower than 60％. A high-quality SIC product can be obtained by using the DT-ASI algorithm and our work will be beneficial to promote the application of FengYun Satellite.