2000—2015年北极夏季反照率变化及其与海冰密集度的关系

作为影响极区热平衡的关键因素,北极反照率会对北极气候与环境变化产生重要影响。采用CLARA-A2-SAL(The CM SAF Cloud,Albedo and Radiation)反照率产品分析2000—2015年北极夏季反照率月尺度和年尺度的时空变化,并探索北极反照率与海冰密集度的关系。结果表明,近15年间北极夏季反照率降幅达到12.20%,8月反照率下降趋势最显著,每10年减少0.040;随着海冰的融化,北极夏季反照率发生由高到低的变化;北极中央密集冰区与周边海域的反照率变化呈现出显著的空间差异性;北极反照率与海冰密集度呈稳定的正相关。研究北极反照率的时空变化及其影响因素可为解释海冰变化成因、预测北极海冰变化趋势提供参考。     

格陵兰地区GLASS反照率产品质量评价及反照率变化趋势分析

冰雪反照率是影响和评估全球气候变化的重要因子。北极格陵兰岛拥有世界第二大冰盖,定量获取该地区反照率是研究北半球能量收支变化的关键。全球陆表卫星(global land surface satellite,GLASS) 产品系统生产的反照率产品是目前国际上时间序列最长（1981—2017年）的全球反照率产品。利用格陵兰气候观测网络(Greenland climate network, GC-Net)与格陵兰冰架监测计划(programme for monitoring of the Greenland ice sheet, PROMICE)网络观测的反照率数据,评估了格陵兰地区GLASS地表反照率产品的精度;并基于2000—2017年的GLASS地表反照率产品,分析了格陵兰地区7月份反照率的年际变化趋势与空间分布特征。结果表明:GLASS与GC-Net反照率的均方根误差(root mean squared error, RMSE)为0.077 8(决定系数R2=0.490 7),与PROMICE反照率差异的RMSE为0.078 6(R2=0.899 9),GLASS产品的反照率数值呈现一定的低估现象,但已满足格陵兰地区冰雪反照率研究的需要。基于2000—2017年7月份格陵兰地区的GLASS反照率变化分析可以看出,格陵兰地区的反照率在此期间整体呈现变小的趋势,平均速率约为0.000 6 /a,变小的地区约占格陵兰总面积的64%;其中,位于格陵兰西部海拔750~1 500 m之间的区域对气候变化最为敏感,反照率变小速率也最大,达到了0.026 /a。     

3种反演算法的地表反照率遥感产品对比分析

地表反照率对于地表能量平衡和全球气候变化具有重要的影响。近年来,地表反照率产品已经取得了较为广泛的应用,但是仍存在时间分辨率低和数据缺失等问题。本文基于均匀站点观测数据与MODIS的MCD43B3产品验证了GLASS(Global LAnd Surface Satellite)地表反照率产品的3种算法及其对应的反照率产品:AB1(ABD01)、AB2(ABD03)和STF(ABD06)。并分析比较了GLASS反照率初级产品到最终产品的质量提升。结果表明:最终产品ABD06与地面观测数据符合最好,RMSE为0.050,R2为0.788;同时,ABD06与MCD43B3反照率产品一致性最好,RMSE优于0.03,R2达到0.9,且ABD06有效数据最多(ABD01为4290,ABD03为3296,ABD06为5507),比较适合生产长时间序列的反照率产品。大量数据的统计结果还表明:3种GLASS产品80%以上的数据与地面观测数据的偏差均能满足气候模式对反照率的精度要求(0.02—0.05)。     

利用Landsat TM数据和地面观测数据验证GLASS反照率产品

Global LAnd Surface Satellite Products System(GLASS)反照率产品基于Angular Bin(AB)算法,仅使用单一观测角度的地表或大气层顶反射率数据就能较为准确地反演地表宽波段反照率,具有较高的时间分辨率,可以反映降雪、融雪、收割等状况下地表反照率的快速变化。遵循"一检两恰"的验证流程对这一反照率产品进行验证,首先使用FLUXNET站点验证数据对AB算法反演的Landsat Thematic Mapper(TM)高分辨地表反照率数据进行验证,再将TM高分辨反照率聚合到GLASS像元尺度对GLASS反照率产品进行验证。挑选FLUXNET的5个站点,筛选无云条件下的TM高分辨率影像,共获得103组有效验证数据。验证结果表明,GLASS反照率产品具有较高的精度,总体误差约为0.0163,可以满足大多数应用的精度需求。     

多尺度综合观测中国区域气溶胶时空分布

正大气气溶胶是悬浮于空气中的微小固体或液体颗粒状物质,其来源多种多样,包括自然源与人工源。气溶胶通过散射与吸收短波和长波辐射改变大气和地表的辐射能量收支,这称为直接气候效应。它还作为水云或冰晶凝结核影响云的形成与发展,带来间接气候效应。此外,落在冰雪地表上的黑碳气溶胶,可以改变地表反照率,并由此改变地表能量收支以致影响气候。因此,摸清气溶胶的时空分布特征并把握气溶胶对气候的影响在气候变化研究中非常重要。     

中国陆地区域陆表温度业务化遥感反演算法及产品运行系统

地表温度反演的裂窗算法已成功应用于NOAA系列卫星热红外遥感数据。目前，裂窗算法中应用较为广泛的一种是Becker等人于1990年提出的局地裂窗算法，主要是通过辐射传输模型模拟不同地表条件和大气状况下，地表温度和发射率对红外辐射亮温的影响，从而发展出一个利用AVHRR4，5通道亮温数据反演地表温度的线性模型。在晴空无云和地表比辐射率能精确估算的情况下，Becker算法反演地表温度的精度在1K以内。Becker算法用Lowtran程序模拟计算地表辐射量，且模型中参数主要针对NOAA-9传感器特性得到。本文在Becker算法的基础上，针对NOAA-16／17传感器热红外通道光谱响应函数特性，利用最新的、计算光谱分辨率更高的MODTRAN程序模拟不同大气状况下，不同地表温度和发射率对NOAAAVHRR4，5通道辐射亮温响应特性的影响，改进Becker算法中模型参数，使之能适用于NOAA-16／17热红外数据。同时，本文利用植被指数NDVI，在中国陆地区域lkm分辨率最新地表分类数据的基础上，得到模型中需要的地表比辐射率参数，将改进的模型应用于1km分辨率NOAA17数据，得到了旬合成中国陆地区域范围地表温度，通过地面气象台站实测数据对比验证．取得了较好的结果。     

基于多源遥感数据的北极新冰提取及范围时序变化分析

本文利用多源遥感数据从不同空间尺度对2004年至2012年间北极地区新冰进行了提取并对其范围变化进行了分析。统计分析结果表明,北极海冰总体覆盖面积与北极近地表气温的变化呈反比;新冰覆盖面积的变化与海冰总面积的变化相比较缓慢;北极各个区域新冰覆盖范围的变化与各区域近地表温度的变化有较大的关联。     

用NOAA气象卫星AVHRR的定量资料计算冬小麦种植面积的两种方法

本文提出了用NOAA气象卫星AVHRR的定量资料计算冬小麦种植面积的方法,即绿度指数法和单通道法。绿度指数法是根据冬小麦在特定生育期内绿度值基本上保持为常数的特点,用几块巳知样地的种植面积,推算整体种植面积;单通道法是只使用AVHRR第二通道的反照率来计算冬小麦种植面积。这两种方法的优点在于不需考虑大气削弱的订正处理问题,从而使计算得以简化。     

利用BRDF原型和单方向反射率数据估算地表反照率

地表反照率是影响地表能量收支平衡的决定性参数之一,精确反演地表反照率需要考虑地表各向异性反射特征。本文尝试以双向反射分布函数BRDF原型为地表各向异性反射的先验知识,通过单方向反射率反演地表反照率。首先根据地面实测及MODIS多角度反射率数据对反演方法进行分析和精度评价,然后借助MODIS BRDF产品统计出研究区的主导BRDF原型,并联合环境一号卫星(HJ-1)单方向反射率数据反演30 m地表反照率,最终将结果与地表实测数据进行比较。结果表明:BRDF原型对BRDF的变化进行了约束,且能够适用于几十米尺度的遥感数据反照率的反演;不同级别的各向异性反射特征的分布是不均一的,借助于主导BRDF原型能够使大部分样本的地表反照率满足精度要求;利用研究区MODIS BRDF产品统计得到的主导BRDF原型为先验知识,通过HJ-1数据反演得到的地表反照率与地表实测反照率有较高的一致性,而朗伯假定条件下的反照率高于实测结果。本文算法简单高效,可为产生全国范围的中高分辨卫星反照率产品提供有价值的算法参考。     

先验知识估算HJ-1 CCD数据地表反照率

以黑河实验区为例, 基于HJ-1 CCD数据和中分辨率成像光谱仪(MODIS) 二向性反射(BRDF)和反照率产品的备用算法(算法Ⅰ), 贝叶斯原理的反照率反演算法(算法Ⅱ)和地表朗伯假设的算法(算法Ⅲ)成功估算了地表短波反照率。对反演结果进行分析并与地表观测数据进行比较, 结果表明: (1) 高分辨率的HJ-1卫星地表反照率不仅能够提供下垫面主要的空间分布特征, 还能提供地表细部结构;(2) 两种基于先验知识的地表反照率和MODIS产品接近, 绝对误差为0.01, 相对误差为4%, 而算法Ⅲ的绝对误差为0.03, 相对误差为13%; 算法Ⅰ和Ⅱ对反照率的改善对地表类型的依赖不明显, 相对误差集中在2%-8%, 反演结果对时相变化依赖较为显著, 在植被最茂盛的季节反照率改善作用明显优于凋谢季;(3) 算法Ⅰ和算法Ⅱ的结果与地表观测结果更为吻合, 均方根误差在0.05以内, 相对误差小于23%, 而算法Ⅲ的均方根误差为0.069, 相对误差为36.3%;(4) 先验知识的作用可能依赖于太阳和观测几何的相对位置, 因此可能依赖于季节和纬度的变化, 以及传感器的成像方式。该研究对缺乏多角度观测的星载传感器的反照率反演有重要的参考价值。     

Surface roughness signatures of summer arctic snow-covered sea ice in X-band dual-polarimetric SAR

ABSTRACT

Surface roughness of sea ice is primary information for understanding sea ice dynamics and air–ice–ocean interactions. Synthetic aperture radar (SAR) is a powerful tool for investigating sea ice surface roughness owing to the high sensitivity of its signal to surface structures. In this study, we explored the surface roughness signatures of the summer Arctic snow-covered first-year sea ice in X-band dual-polarimetric SAR in terms of the root mean square (RMS) height. Two ice campaigns were conducted for the first-year sea ice with dry snow cover in the marginal ice zone of the Chukchi Sea in August 2017 and August 2018, from which high-resolution (4 cm) digital surface models (DSMs) of the sea ice were derived with the help of a terrestrial laser scanner to obtain the in situ RMS height. X-band dual-polarimetric (HH and VV) SAR data (3 m spatial resolution) were obtained for the 2017 campaign, at a high incidence angle (49.5°) of TerraSAR-X, and for the 2018 campaign, at a mid-incidence angle (36.1°) of TanDEM-X 1–2 days after the acquisition of the DSMs. The sea ice drifted during the time between the SAR and DSM acquisitions. As it is difficult to directly co-register the DSM to SAR owing to the difference in spatial resolution, the two datasets were geometrically matched using unmanned aerial vehicle (4 cm resolution) and helicopter-borne (30 cm resolution) photographs acquired as part of the ice campaigns. A total of five dual-polarimetric SAR features―backscattering coefficients at HH and VV polarizations, co-polarization ratio, co-polarization phase difference, and co-polarization correlation coefficient ―were computed from the dual-polarimetric SAR data and compared to the RMS height of the sea ice, which showed macroscale surface roughness. All the SAR features obtained at the high incidence angle were statistically weakly correlated with the RMS height of the sea ice, possibly influenced by the low backscattering close to the noise level that is attributed to the high incidence angle. The SAR features at the mid-incidence angle showed a statistically significant correlation with the RMS height of the sea ice, with Spearman’s correlation coefficient being higher than 0.7, except for the co-polarization ratio. Among the intensity-based and polarimetry-based SAR features, HH-polarized backscattering and co-polarization phase difference were analyzed to be the most sensitive to the macroscale RMS height of the sea ice. Our results show that the X-band dual-polarimetric SAR at mid-incidence angle exhibits potential for estimation of the macroscale surface roughness of the first-year sea ice with dry snow cover in summer.     

Albedo-rainfall Feedback Over Indian Monsoon Region Using Long Term Observations Between 1981 to 2000

Albedo determines radiation balance of land (soil-canopy complex) surface and influence boundary layer structure of the atmosphere. Accurate surface albedo determination is important for weather forecasting, climate projection and ecosystem modelling. Albedo-rainfall feedback relationship has not been studied so far using observations on spatial scale over Indian monsoon region due to lack of consistent, systematic and simultaneous long-term measurements of both. The present study used dekadal (ten-day) composite of satellite (e.g. NOAA) based Pathfinder AVHRR Land (PAL) datasets between 1981 and 2000 over India (68–100°E, 5–40°N) at 8 km spatial resolution. Land surface albedo was computed using linear transformation of red and near infrared (NIR) surface reflectances. The cloud effects were removed using a smoothening filter with harmonic analysis applied to time series data in each year. The monthly, annual and long term means were computed from dekadal reconstructed albedo. The mean per year and coefficient of variation (CV) of surface albedo over seventeen years, averaged over Indian land region, were found to show a significantly decreasing (0.15 to 0.14 and 60 to 40%, respectively) trend between 1981 and 2000. Among all the land use patterns, the inter-annual variation of albedo of Himalayan snow cover showed a significant and the steepest reducing trend (0.42 – 0.35) followed by open shurbland, grassland and cropland. No significant change was noticed over different forest types.. This could be due to increase in snow melting period and snow melt area. A strong inverse exponential relation (correlation coefficient r = 0.95, n = 100) was found between annual rainfall and annual albedo over seven rainfall zones. The decreasing trend in snow-albedo of accumulation period (September to March) follows the declining trend in measured south-west monsoon rainfall between 1988 (980 mm) to 1998 (880 mm) over India. This finding perhaps suggests the possible reversal of reported coupling of increased snowfall followed by lower monsoon rainfall.     

业务化MODIS BRDF模型对冰雪BRDF/反照率的反演能力评估

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。     

Simulation of the Ku-band Radar altimeter sea ice effective scattering surface

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.     

北极一年海冰表面积雪深度遥感反演与时序分析

北极海冰表面的积雪深度是重要的地球物理变量, 是研究物质与能量平衡、计算海冰厚度的重要参数。为减小不同被动微波传感器观测数据的系统误差, 对国防气象卫星计划(Defense Meteorological Satellite Program, DMSP)F17-SSMIS与F13-SSM/I重叠期亮度温度数据进行交叉定标, 建立4个频段48个月尺度定标模型, 并与传统年尺度定标模型进行比较和优选, 在此基础上估算并分析2003-2014年北极一年海冰表面积雪深度变化。结果表明:19H、19V、22V、37V频段1~5月的月尺度模型决定系数高于传统年尺度拟合模型; 2003-2014年, 北极一年海冰表面积雪深度总体呈现下降趋势, 同时积雪深度存在明显的周期性变化, 每年7月积雪深度最小, 9月最大; 东西伯利亚海、拉普捷夫海和巴伦支海海冰表面积雪深度呈现减少的趋势。     

SSM/I极地亮温资料中的4个月振荡现象

SSM/I仪器采用圆锥扫描方式,避免了AMSU-A等跨轨迹横扫仪器观测资料中存在的临边效应,其观测资料更加适用于空间特征分析。因此,利用1998年—2008年SSM/I仪器极地观测亮温资料,经谱分析、小波分析以及经验正交函数(EOF)分析,验证了南、北极地区的4个月振荡(后简称FMO)现象,揭示了南、北极地区FMO现象的时空分布特征。结果显示:(1)SSM/I 19V/H,37V/H通道在南、北极的亮温观测中有显著的FMO现象,北极亮温值FMO峰值出现在3月、7月和11月上旬,南极FMO峰值出现在4月、8月和12月中旬;(2)北极的FMO增强年为1999年、2002年和2006年;南极为1998年、2001年、2005年和2008年;(3)从12月到次年5月,当海冰面积基本不变时,亮温值随表面温度变化而变化;6到10月,表面温度基本不变时,亮温值随海冰面积变化而变化。当海冰FMO和表面温度FMO均位于相对较大值时,亮温的FMO达到峰值;当海冰FMO和温度FMO都相对较小时,则亮温的FMO到达谷值,其中海冰的影响较大;(4)北极大部分地区呈现同位相的FMO现象;南极不同区域间有显著差异:威德尔海(Weddell Sea,20°W—60°W,60°S—75°S)与拉扎列夫海域(Lazarev sea,20°W—30°E,60°S—70°S)FMO位相相反;90°W到180°W的绕极海域内60°S到70°S纬度带与70°S到80°S纬度带的FMO位相相反。综上,SSM/I对极地的观测亮温中有显著的FMO现象,其振荡强度存在年际变化。地表亮温的FMO主要受到表面温度变化和海冰凝结融化过程的共同作用。在空间特征上,北极大部分地区呈现同位相的FMO现象;南极不同区域间有显著差异。     

View on Bay of Bengal upwelling area on the basis of 19-years of satellite sea surface temperature

Abstract

The seasonal and interannual variation of upwelling along the east coast of India between 6°N and 22°N was studied for the period 1985–2003 using NOAA–AVHRR sea surface temperature data. The seasonal migration of pronounced upwelling, which follows the seasonal shift of the winds in transition period and northeast monsoon, was confined. The temporal mean sea surface temperature images clearly show the upwelling season, as does the seasonal sea surface temperature anomaly. These dominant features of the upwelling system are also the most variable, with most of the variance being explained by the seasonal cycle. Quasi-cyclic behaviour of sea surface temperature on interannual scales has also been observed.