基于AMSR-E数据的多年冰密集度反演算法研究

In recent years, the rapid decline of Arctic sea ice area(SIA) and sea ice extent(SIE), especially for the multiyear(MY) ice, has led to significant effect on climate change. The accurate retrieval of MY ice concentration retrieval is very important and challenging to understand the ongoing changes. Three MY ice concentration retrieval algorithms were systematically evaluated. A similar total ice concentration was yielded by these algorithms, while the retrieved MY sea ice concentrations differs from each other. The MY SIA derived from NASA TEAM algorithm is relatively stable. Other two algorithms created seasonal fluctuations of MY SIA, particularly in autumn and winter. In this paper, we proposed an ice concentration retrieval algorithm, which developed the NASA TEAM algorithm by adding to use AMSR-E 6.9 GHz brightness temperature data and sea ice concentration using 89.0GHz data. Comparison with the reference MY SIA from reference MY ice, indicates that the mean difference and root mean square(rms) difference of MY SIA derived from the algorithm of this study are 0.65×106 km2 and0.69×106 km2 during January to March, –0.06×106 km2 and 0.14×106 km2 during September to December respectively. Comparison with MY SIE obtained from weekly ice age data provided by University of Colorado show that, the mean difference and rms difference are 0.69×106 km2 and 0.84×106 km2, respectively. The developed algorithm proposed in this study has smaller difference compared with the reference MY ice and MY SIE from ice age data than the Wang's, Lomax' and NASA TEAM algorithms.     

利用遗传算法反演实时近海面气温和比湿

使用Advanced Microwave Scanning Radiometer for EOS(AMSR-E)的海洋产品数据海表温度、风速,大气水蒸气、云液态水,通过遗传算法建立其与近海面气温和比湿之间的经验关系,进行近海面气温和比湿的实时反演.反演结果与The Tropical Ocean-Atmosphere(TAO)和The National Data Buoy Center(NDBC)的浮标实测资料进行比较,实时近海面气温和比湿的均方根误差分别为1.18℃和1.36 g/kg.分析结果表明,利用遗传算法采用AMSR-E海洋产品数据可以较好地反演近海面气温和比湿.     

利用南大洋漂流浮标数据评估AMSR-E SST

利用AOML(Atlantic Oceanographical and Meteorological Laboratory)SVP漂流浮标的海表面温度数据,针对30°S以南的南大洋海域,对目前主要使用的微波遥感产品(AMSR-E,Ad-vanced Microwave Scanning Radiometer for the Earth Observing System)反演的SST进行了较为系统的评估。结果表明,AMSR-E SST比浮标数据偏冷,偏差为-0.01℃,标准差为0.70℃。夏季的偏差为0.004℃,标准差为0.64℃;冬季的偏差为-0.06℃,标准差为0.75℃,冬季的偏差和标准差较大。温差ΔT受流速影响,随着流速的增大而减小,且这种趋势在夏季更为显著。具备托伞结构的浮标与总体情况基本一致,而无托伞结构的浮标受流速的影响要大一些。同时,温差ΔT受水汽的影响,随着水汽的增加而减小,且这种影响在冬季更大一些。进一步对4个穿极和绕极浮标的追踪分析表明,温差ΔT受大洋海流系统的影响显著。在海流大的大西洋边界流和南极绕极流中,温差ΔT的不确定性要明显大于总体情况。     

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。     

近1000年来西格陵兰荷尔斯泰因斯堡港生物硅指示的海洋生产力变化

We analyzed the biogenic silica(BSi) content and produced a diatom-based summer sea-surface temperature(SST) reconstruction for sediment core GC4 from the Holsteinsborg Dyb, West Greenland. Our aim was to reconstruct marine productivity and climatic fluctuations during the last millennium. Increased BSi content and diatom abundance suggest relatively high marine productively during the interval of AD 1000–1400, corresponding in time to the Medieval Warm Period(MWP). The summer SST reconstruction indicates relatively warm conditions during AD 900–1100, followed by cooling after AD 1100. An extended cooling period during AD 1400–1900 is characterized by prolonged low in reconstructed SST and high sea-ice concentration. The BSi values fluctuated during this period, suggesting varying marine productivity during the Little Ice Age(LIA).There is no significant correlation between the BSi content and SST during the last millennium, suggesting that the summer SST has little influence on marine productively in the Holsteinsborg Dyb. A good correspondence between the BSi content and the element Ti counts in core GC4 suggests that silicate-rich meltwater from the Greenland ice sheet was likely responsible for changes in marine productively in the Holsteinsborg Dyb.     

北极海冰密集度动态系点值ASI反演算法研究

海冰密集度是极区海冰监测的重要因素,使用AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) 89GHz数据ASI反演算法得到的海冰密集度是目前能够获得的分辨率最高的微波数据.在以前的算法中往往使用固定的系点值,本研究实现了动态系点值ASI (the Arctic Radiation And Turbulence Interaction Study (ARTIST) Sea Ice)算法,更重要的是在统计开阔水系点值的时候消除了云对系点值的影响,使得纯水系点值更接近真实状况.得到2010年平均的开阔水和海冰的系点值分别为50.8K和7.8K,通过每天的系点值得到的反演方程在低密集度区增大了海冰密集度,在高密集冰区减小了海冰密集度,从而在一定程度上改善了微波数据的反演准确度.通过和北极区域选取40幅不受云影响的MODIS 500m分辨率宽频大气层顶反照率(broadband TOA albedo)计算的海冰密集度进行了比较验证.结果显示,40个个例中,95%本文的平均差异比使用固定系点值算法产品的小,而且75%的均方根差异比使用固定系点值算法产品的小.     

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。     

Cross-calibration of brightness temperature obtained by FY-3B/MWRI using Aqua/AMSR-E data for snow depth retrieval in the Arctic

This study cross-calibrated the brightness temperatures observed in the Arctic by using the FY-3B/MWRI L1 and the Aqua/AMSR-E L2A. The monthly parameters of the cross-calibration were determined and evaluated using robust linear regression. The snow depth in case of seasonal ice was calculated by using parameters of the crosscalibration of data from the MWRI T_b. The correlation coefficients of the H/V polarization among all channels T_b of the two sensors were higher than 0.97. The parameters of the monthly cross-calibration were useful for the snow depth retrieval using the MWRI. Data from the MWRI T_b were cross-calibrated to the AMSR-E baseline.Biases in the data of the two sensors were optimized to approximately 0 K through the cross-calibration, the standard deviations decreased significantly in the range of 1.32 K to 2.57 K, and the correlation coefficients were as high as 99%. An analysis of the statistical distributions of the histograms before and after cross-calibration indicated that the FY-3B/MWRI T_b data had been well calibrated. Furthermore, the results of the cross-calibration were evaluated by data on the daily average T_b at 18.7 GHz, 23.8 GHz, and 36.5 GHz(V polarization), and at 89 GHz(H/V polarization), and were applied to the snow depths retrieval in the Arctic. The parameters of monthly cross-calibration were found to be effective in terms of correcting the daily average T_b. The results of the snow depths were compared with those of the calibrated MWRI and AMSR-E products. Biases of 0.18 cm to 0.38 cm were observed in the monthly snow depths, with the standard deviations ranging from 4.19 cm to 4.80 cm.     

Modoki对南半球中高纬度气候及海冰异常的影响

利用诊断分析方法分析研究了热带太平洋ENSO Modoki现象对南半球中高纬度气候和海冰的影响。分析了ENSO Modoki现象的季节变化特征,并定义了ENSO Modoki的活跃年和活跃季节。利用偏相关分析、合成分析和超前滞后相关分析等诊断分析方法,分析了1979年1月到2010年12月间ENSO Modoki影响南半球中高纬气候及海冰异常的遥相关模态及可能的遥相关动力机制。研究结果显示费雷尔环流的异常变化是两者之间遥相关的可能方式。     

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.     

基于FY-3C微波辐射计数据的极区海冰密集度反演方法研究

极区海冰影响大气和海洋环流,对全球气候变化起着重要的作用。海冰密集度是表征海冰时空变化特征的重要参数之一。本文研究了利用FY-3C微波扫描辐射计亮温数据反演极区海冰密集度的方法。经过时空匹配、线性回归,修正了FY-3C微波辐射计亮温数据。使用两种天气滤波器和海冰掩模滤除了大气影响所造成的开阔海域虚假海冰;使用最小密集度模板去除陆地污染效应。通过计算2016年、2017年极区海冰面积及范围两个参数,对得到的海冰密集度产品进行了验证,两年的海冰范围和面积趋势基本与NSIDC产品一致,平均差异小于3%。本研究结果为发布我国自主卫星的极区海冰密集度业务化产品奠定了基础,制作的产品可保障面临中断的40多年极区海冰记录的连续性。     

基于遥感数据的渤海海冰面积提取订正模型研究

为了获取准确的海冰信息,利用高空间分辨率和高时间分辨率数据相结合的方法来测算海冰面积,在假设TM海冰面积是比较准确的前提下,用TM海冰面积来订正MODIS和NOAA／AVHRR海冰面积,以提高这两种高时间分辨率遥感资料对海冰面积的判别精度。选取2003年2月5日的TM、MODIS和NOAA／AVHRR数据,在这3个图像上分别选择20个样本,然后进行了海冰面积的线性回归分析,得到的海冰面积提取订正模型。并分别以此两种模型为基础,对2002年12月8日至2003年2月26日和2005年2月1日至2月11日冬季渤海海冰面积进行修正,并将由MODIS和NOAA／AVHRR提取的海冰面积、用模型订正后和国家海洋环境预报中心给出的海冰面积做了详细的比较。结果表明：线性回归可以提高海冰信息的提取精度。此研究为海冰防灾减灾和海冰资源量估算提供依据。     

基于OC-CCI数据的南海高叶绿素a浓度水域面积的时空变化研究

浮游植物是海洋生态系统食物链的基础组成, 并通过光合作用影响着海表二氧化碳通量变化。文章基于高叶绿素a浓度水域面积指标构建南海浮游植物生物量的估算体系。利用遥感数据, 采用经验正交函数分解插值方法, 重构长时间序列的南海叶绿素a浓度场, 并研究了南海高叶绿素a浓度水域面积特征的时空分布。结果发现: 高叶绿素a浓度水域面积变化有着显著季节特征, 在冬季面积达到最大值, 在夏季达到最小值, 但是该水域对应的叶绿素a浓度却在冬季达到最小值, 在夏季达到最大值, 这一特征可能是由于风驱动的海表动力过程使得海表叶绿素重新分布; 空间分布上, 高叶绿素a浓度水域常年存在于海岸附近, 特别是在中国沿海、越南沿岸、泰国湾以及婆罗洲岛附近。在巽他陆架与湄公河口东部中央海盆, 高叶绿素a浓度区域面积呈年际变化。受厄尔尼诺调控的南海季风, 导致不同年份湄公河口东南沿海存在不同程度的北部冷水侵入, 北部冷水入侵可能是引起局地浮游植物生物量增减的原因。     

Estimation of chlorophyll-a concentration in the Zhujiang Estuary from SeaWiFS data

The chlorophyll-a concentration is generally overestimated for the southern China coastalwaters if the default algorithm of the SeaDAS is employed. An algorithm is developed for retrieval of chlorophyll-a concentration in the Zhujiang Estuary, Guangdong Province, China, by using simulated reflectance data. The simulated reflectance is calculated corresponding to the SeaWiFS wavelength bands, via a general model by inputting measured water components, i.e. , the suspended sediment, chlorophyll-a, and yellow substance (DOC) concentration data of 130 samples. Empirical relationships of the chlorophyll-a concentration to 240 different band combinations are investigated based on the simulated reflectance data, and the band combination, R_5R_6/R_3R_4, is found to be the optimum one for the development of an algorithm valid for the Zhujiang Estuary. This algorithm is then employed to determine the chlorophyll-a concentration from SeaWiFS data. The estimated concentrations have a better accuracy than those obtai     

基于遥感反演的珠江河口表层悬沙浓度分位数趋势分析 *

利用中分辨率成像光谱仪(moderate resolution imaging spectroradiometer, MODIS)一级产品并结合航次数据, 反演2003—2015年间珠江河口表层悬浮泥沙浓度(suspended sediment concentration, SSC), 分析其分位数长期变化趋势并探讨其影响因素。结果表明, 珠江河口SSC呈总体下降趋势, 平均下降速率约为1.03mg·L ^-1·yr ^-1。口门外以及河口东北部区域平均SSC下降更快, 最高可达约4.0mg·L ^-1·yr ^-1。总体上, 珠江河口SSC高值下降速率大于低值, 且存在空间差异性。低值下降趋势显著地区主要分布在淇澳岛北部的口门外区域, 其SSC大小值之间的差异逐年增加; 而高值下降速率快的地区主要分布在虎门口、龙穴岛东南岸以及东航道附近水域, SSC每年大小值差异呈减小趋势。珠江河口SSC分位数趋势受径流输沙量、河口地形变化以及风的影响。由水库修建所致的上游输沙量减少导致淇澳岛北部口门外区域SSC的低值部分显著下降。虎门口、龙穴岛东南岸以及东航道附近水域的加深使得这些区域SSC高值部分下降显著, 而西滩区域的变浅使得冬季潮汐混合减弱, 导致SSC高值部分下降趋势显著。此外, 由风速下降引起的表层风混合减弱也是导致西滩南部SSC高值下降趋势显著的原因之一。     

台风对海洋叶绿素a浓度影响的定量遥感初探

研究台风对海洋水色环境的影响,是目前海洋遥感技术又一应用领域。由于台风期间天气恶劣,遥感资料较少,国际上主要针对个别典型台风,研究其对海表温度、海洋叶绿素a浓度及初级生产力的影响,很少通过遥感资料系统地对这一影响进行定量分析和建模。自2000到2007年,过境中国近海以及西北太平洋海域台风近百次,作者通过系统地分析这期间MODIS,SeaWiFS的3A级叶绿素a浓度数据,结果发现:(1)台风促进了相应海域叶绿素a浓度的大幅增长,总体上平均增长约1.426倍,个别区域在5倍以上,同时,该增长一般延后3~6d,在7~10d后恢复到原来的水平;(2)进一步对这些数据进行一元统计线性回归,发现叶绿素a浓度增长比(R_chl-a)与台风影响因子(Tsub>w)满足如下关系:R_chl-a=0.0012Tsub>w+1.017,其相关系数达0.8;(3)台风期间叶绿素a浓度与无台风时叶绿素a浓度之间有很强的线性关系,其关系满足:C_chl-a=1.2367C_0chl-a+0.0636,且相关系数高达0.98。这一初步研究结果对进一步通过遥感手段深入研究台风对海洋水色环境的影响有借鉴意义。     

海浪成长状态对TOPEX/Poseidon高度计风速反演的影响

以墨西哥湾同步高度计、浮标资料为例,研究了海浪成长状态对高度计风速反演的影响。同步的高度计风速和浮标风速比较显示,在墨西哥湾地区,海浪成长状态对高度计风速反演有较大影响。在考虑海浪成长状态影响的条件下,利用谱模型反演高度计风速,取得了较好的效果。与目前TOPEX/Poseidon高度计风速反演业务化算法相比,在海浪未充分成长条件下,考虑海浪成长状态影响后,根据谱模型反演获得的风速与浮标风速之间的均方根误差减小了30%,平均误差减小了83%。在利用谱模型算法反演高度计风速时,谱模型中的波龄因子(表示海浪成长状态)可以根据高度计测得的有效波高和风速获得,因此该方法具有广泛的适用性。     

基于OMI的船舶排放控制区SO2减排效益分析

船舶尾气排放是造成海洋污染的主要排放源之一。自 2016 年设立船舶大气污染物排放控制区以来,船舶硫氧化物、氮氧化物和颗粒物排放显著减少。为了对环渤海湾水域排放控制区的大气 SO2 减排效果进行分析,选取了 Aura 卫星搭载的 OMI 传感器痕量数据产品 OMSO2 进行大气 SO2数据反演。选择 2017 年各月的 OMI Level-2 痕量数据,对大气边界层 SO2 柱量值进行提取,采用空间分析方法,对其时空变化进行分析,以监测排放控制区的减排效果。结果表明：春季和冬季 SO2 量值明显高于其他季节,年际变化比较明显,自 2016 年以来,SO2 柱总量峰值逐年降低,并且高值区域逐渐减少,证明排放控制区的设立对大气质量保护具有积极作用。     

1983–2022年南极夏季海冰区反照率的反转变化特征分析

海冰区反照率会影响辐射收支平衡,对全球气候变化有着重要影响。利用遥感反演的反照率数据产品分析了1983–2022年南极及其6个海域夏季海冰区反照率的时空变化,探讨了海冰密集度、气温和大气环流与海冰区反照率的关系。结果表明,卫星反演的南极夏季海冰区反照率与实测反照率结果一致。西威德尔海海冰区多年平均反照率最高（0.61）,罗斯海最低（0.45）。南极夏季海冰区反照率经历了先上升（1983–2015年）后快速下降（2015–2022年）的变化。除了别林斯高晋海–阿蒙森海海冰区反照率前后两个时段均下降外,其余5个海域和南极一样出现了变化趋势的反转。南极海冰区反照率与海冰密集度显著正相关,而与气温显著负相关。1983–2015年夏季气温降低,海冰消融减弱,海冰密集度微弱上升（0.03%/a）,海冰面积平均每10年增加2.07 × 10⁵ km²,导致反射的太阳辐射增多,反照率也微弱上升。2015–2022年夏季气温升高,海冰消融加剧,冰间水道和开阔水域增多,海冰密集度下降,海冰区吸收更多的太阳辐射,造成反射辐射减小,因而海冰区反照率快速下降。此外,南极环状模也是影响罗斯海和威德尔海海冰区反照率变化的因素之一。     

基于GOCI影像的半月潮周期内渤海悬浮泥沙浓度变化研究

悬浮泥沙浓度(SSC)的变化对渤海海域的生态等有着重要的影响.本文基于已有表层水体SSC遥感反演算法进行了优化,利用较高时空分辨率的GOCI影像,基于优化后的模型对渤海表层水体的SSC浓度在半月潮周期中的时空分布与变化特征进行了研究.结果 表明:(1)渤海各海湾表层水体SSC在半月潮周期内的变化规律存在差异.从小潮到大...