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AMSR-E

AMSR-E的相关文献在2003年到2021年内共计107篇,主要集中在自动化技术、计算机技术、大气科学(气象学)、农业基础科学 等领域,其中期刊论文100篇、会议论文7篇、相关期刊54种,包括遥感信息、遥感学报、高技术通讯等; 相关会议6种,包括全国农业遥感与信息技术应用研讨会、第一届研究生IT创新学术论坛、第一届青藏高原能量和水分循环国际研讨会等;AMSR-E的相关文献由290位作者贡献,包括施建成、张立新、李震等。

AMSR-E—发文量

期刊论文>

论文:100 占比:93.46%

会议论文>

论文:7 占比:6.54%

总计:107篇

AMSR-E—发文趋势图

AMSR-E

-研究学者

  • 施建成
  • 张立新
  • 李震
  • 蒋玲梅
  • 王磊
  • 周清波
  • 杨虎
  • 毛克彪
  • 冯琦胜
  • 唐华俊
  • 期刊论文
  • 会议论文

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    • 朱琳; 白雲
    • 摘要: 针对数值气象预报及陆面模拟分析系统中的同化模块,本文以ERA-Interim再分析资料中的地表状态变量数据为依据,采用社区微波发射模型(community microwave emission model,CMEM)为观测算子,模拟水平和垂直极化方向的大气层顶亮温,并采用高级微波扫描辐射计-地球观测系统(advanced microwave scanning radiometer-earth observing system,AMSR-E)亮温观测,对模拟结果进行验证和精度评定,分析亮温模拟与亮温观测存在偏差的原因.研究结果表明,CMEM模拟垂直极化方向的微波亮温比水平极化模拟效果好,更接近AMSR-E观测数据;CMEM模拟的微波亮温在中国西北和西南地区的模拟效果较好,模拟与观测的相关系数达0.8左右,而中国南方地区亮温模拟效果较差,说明CMEM观测算子对裸地或者植被稀疏的地区模拟效果好,对有植被覆盖密集的地区亮温模拟效果较差.该研究为CMEM在中国地区的应用提供了理论依据.
    • 官莉; 张渝晨
    • 摘要: 为缓解星载微波辐射计探测资料在低频6.9 GHz波段的无线电频率干扰(radio frequency in-terference,RFI),第二代先进的微波扫描福射计(advanced microwave scanning radiometer-2,AMSR-2)在AMSR-E通道设置的基础上增加了中心频率为7.3 GHz的两个极化通道.除此之外,AMSR-2在硬件方面将天线反射器的直径由1.6 m扩大为2.0 m,并减小了各通道的波束宽度.本文基于AMSR-E 2010-10-2011-09(AMSR-E 工作最后一年)和 AMSR-2 2012-08-2013-07(AMSR-2工作第一年)、2016-08-2017-07(AMSR-2工作四年后)长时间序列的观测资料通过平均值标准差法识别全球陆地6.9 GHz和7.3 GHz通道的无线电频率污染.研究结果表明,AMSR-2 7.3 GHz观测资料中识别出的RFI污染像元数量上远小于AMSR-E 6.9 GHz观测资料中受污染像元个数,尤其是美国和日本地区,体现了新增通道对RFI的有效缓解.AMSR-2在硬件方面的改进增大了主波束效率,提高了空间分辨率;与AMSR-E共有的6.9 GHz通道相比,全球RFI从出现范围和强度上都有所减小.
    • 张宾; 秦凯; 吴涛; 石铁伟; 樊文智
    • 摘要: 岩石加载实验证实受载岩体能够产生特定频段的微波辐射能量增强,卫星遥感数据分析也表明,个别震例强震前可能存在微波辐射异常,这一重要发现亟待普遍性检验与统计特征研究.本文收集了俄罗斯堪察加半岛2003-2011年Aqua AMSR-E的微波辐射亮温数据,并对地震和火山活动信息进行了统计,进而利用改进后的异常识别方法及构建的背景场对9年内多次强震的震前短临异常的位置和时间信息进行了检测与统计.结果表明:①勘察加半岛平均亮温变化存在明显的季节特征,1-4月和11-12月为平稳期、5-10月为剧变期,平稳期的亮温曲线基本保持平稳,而剧变期呈抛物线型,自4月起持续增温至7月,随后持续降温至11月;②受地形地貌影响,半岛南部和东南部亮温较高,中西部亮温较低;③火山点亮温小于地震点和非地震点,火山活动对亮温影响有限;④使用四分位数法判断辐射异常检测指数,发现17次地震中有13次可以识别到震前异常,其中MS≥6.0地震均可检测到异常,且异常发生在地震前1个月以内.%It was confirmed that microwave energy is emitted during the compression of rock and the energy can be detected by some special frequency bands.The analysis of satellite remote sensing data found that there may be abnormal microwave radiation before several earthquakes.This important discovery is in urgent need of universal inspection and statistical analysis.In this study,the data of Aqua AMSR-E microwave radiation brightness temperature of nine years (2003-2011) in Kamchatka Peninsula were collected,and the information of earthquake and volcanic activity in this period was counted.The time and location information of microwave radiation anomalies before all the strong earthquakes over the past nine years were studied by using the improved method of anomaly identification and the construction of the background field.The results show that:① The average brightness temperature change of Kamchatka Peninsula has obvious seasonal characteristics,which can be divided into two periods,from January to April and from November to December are stationary periods and from May to October is fluctuation period.The brightness temperature curves of the stationary periods are basically steady and that of the fluctuation period is parabolic,for it shows continual warming from April to July and then continual cooling until November.② Due to the effect of topography,the brightness temperature is higher in the southern and southeastern peninsula,while that of the central and western part is low.③ The brightness temperature of volcanoes is less than that of the seismic and non-seismic points,which indicates that the volcanic activity has a limited influence on the brightness temperature.④ Using the four percentile method to determine the radiation anomaly detection index,it was found that there are 13 times of 17 earthquakes can be identified anomalies before earthquakes,and all MS≥6.0 earthquakes can be detected anomalies,which occurred in one month before the earthquake.
    • 刘源; 秦军; 阳坤; 韩孟磊; 拉珠; 赵龙
    • 摘要: 本文比较了基于AMSR-E被动微波数据的3种土壤冻融判别算法在青藏高原相关地区的分类精度.3种算法分别是:双指标算法、决策树算法、判别函数算法.本文选取了来自青藏高原那曲、玛曲、阿里3个地区土壤温湿度观测网的地表温度数据,并结合AMSR-E被动亮温数据,对上述算法在以上地区的分类精度分别进行了比较评价.结果表明:不论是白天还是夜间,相较于干旱区微波信号来自深层土壤的难以准确探测,在青藏高原半湿润半干旱区算法可取得相对较好的判别准确率;双指标算法相较于其他2种算法,在观测区具有较高的分类精度,且夜间分类精度高于白天;实测数据存在资料代表性不普遍即网格所包含站点信息量不够的问题,这也是后续工作中提高分类精度值得关注的着手点.
    • 刘紫媛; 庞小平; 赵羲; 程子桉
    • 摘要: 利用AMSR-E 36GHz、89 GHz亮度温度计算出的极化比(PR)与热力学冰厚比较,建立了一种针对南极罗斯冰架冰间湖的AMSR-E薄冰反演算法.热力学冰厚由用MODIS晴空下的表面温度数据、ERA-Interim气象数据,根据表面热通量平衡公式反演得到.在排除混合像元以及水汽对89 GHz数据的干扰后,分别对36、89 GHz的AMSR-E亮温极化比拟合指数模型,反演罗斯冰架冰间湖中的0~0.2 m的薄冰冰厚,均方根误差分别为0.0035 m和0.0053 m.
    • 胡旭冉; 韩震; 李静; 丁如一
    • 摘要: 利用集合卡尔曼滤波法对2011-05-14太平洋区域(140°~150°E,15°~25°N)AMSRE SST和AVHRR SST数据与混合坐标海洋模型(HYCOM) SST融合,然后根据均值、方差、信息熵、梯度对融合结果图像质量进行分析,并用XBT现场SST数据对融合结果进行精度验证.结果显示,融合后SST图像未改变原始数据总体分布,均值接近原始数据,而且携带更丰富的信息,方差、信息熵分别为0.788 3°C2和4.787 8,在细节表现上也更突出,其梯度为0.136 8 K/0.1°,远高于原始数据;融合精度也得到了提升,最大绝对误差、最小绝对误差和平均绝对误差分别为0.714 4,0.006 9和0.244 7°C,均方根误差为0.310 6°C,平均相对误差为0.88%.在与最优插值融合结果对比后发现,两者在分布上大致相同,且在细节和精度方面,集合卡尔曼滤波的结果优于最优插值法的结果.
    • 刘源12; 秦军1; 阳坤134; 韩孟磊14; 拉珠1; 赵龙15
    • 摘要: 本文比较了基于AMSR-E被动微波数据的3种土壤冻融判别算法在青藏高原相关地区的分类精度。3种算法分别是:双指标算法、决策树算法、判别函数算法。本文选取了来自青藏高原那曲、玛曲、阿里3个地区土壤温湿度观测网的地表温度数据,并结合AMSR-E被动亮温数据,对上述算法在以上地区的分类精度分别进行了比较评价。结果表明:不论是白天还是夜间,相较于干旱区微波信号来自深层土壤的难以准确探测,在青藏高原半湿润半干旱区算法可取得相对较好的判别准确率;双指标算法相较于其他2种算法,在观测区具有较高的分类精度,且夜间分类精度高于白天;实测数据存在资料代表性不普遍即网格所包含站点信息量不够的问题,这也是后续工作中提高分类精度值得关注的着手点。
    • ZHAO Juan; YU Xiao-Ding
    • 摘要: 卫星微波低频通道的亮温资料广泛存在无线电频率干扰信号,在微波观测应用于反演地球物理参数和资料同化之前,应准确识别出受到频率干扰污染的资料.常用的干扰识别方法包括谱差法、平均值和标准差法以及主分量分析法等,但没有可靠的频率干扰源分布数据集用于评估不同识别方法的准确性.本文提出利用两种独立的干扰识别方法进行交叉验证,获得AMSR-E资料中频率干扰信号的识别阈值.结果表明,该方案能有效识别出AMSR-E资料C波段和X波段通道在陆地上的无线电频率干扰信号.AMSR-E C波段资料在美国人口密集的大城市有较强的干扰信号存在,X波段的干扰信号主要分布在欧洲大陆和日本.%Radio-frequency interference (RFI) detection for low-frequency microwave measurements is an important step before these data are applied to geophysical parameter retrieval or data assimilation.There are several robust techniques to identify the RFI signals,such as the mean/standard deviation method and the normalized principal component analysis method.However,verification of these existing detection methods remains an open issue in the absence of a reliable validation data-set of the ‘true’ RFI signals.In this paper,a cross-validation scheme using two independent RFI detection methods is proposed to derive the thresholds for identifying the RFI-contaminated data for the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E).It is shown that the new scheme is effective in the quantitative classification of the RFI signals in the AMSR-E C-and X-band channels over the continents.Strong RFI signals are found to be populated over cities of the United States at AMSR-E C-band,while RFIs at X-band are mainly observed over Europe and Japan.
    • 沈校熠; 柯长青; 张杰
    • 摘要: 基于2002—2011年南极地区AMSR-E逐日海冰密集度数据,计算相应时间段内的海冰外缘线和海冰面积,分析了南极地区这10年来各时间尺度上的海冰变化,揭示了海冰变化的时空特征.结果表明:2002—2011年南极海冰外缘线、海冰面积分别增加了3.64%、3.8%,总体上呈现增加的趋势,其中2008年海冰面积最大.罗斯海、西太平洋和威德尔海的海冰面积呈现增加趋势,而印度洋和别林斯高晋海/阿蒙森海的海冰面积则趋于减小.南极海冰面积一般夏季最小、冬季最大,相同季节海冰面积变化波动较小,不同海区只是变化范围不同.南极一年冰增长速度较低,平均每年增加约0.1×106 km2,且大范围地分布在南极大陆(除威德尔海外)周围.多年冰平均每年减少0.05×106 km2,且多处于威德尔海.海冰面积变化与气温有负相关关系.%Based on daily Antarctic sea ice concentration data acquired by AMSR-E during 2002—2011, this study calculated the extent and area of sea ice and analyzed the spatiotemporal characteristics of its variability over different timescales. The results revealed increases of 3.64% decade–1 in sea ice extent and 3.8% dec-ade–1 in sea ice area. The derived 10-year trend of sea ice for the entire Antarctic sea ice area was signifi-cantly positive, and the year of 2008 experienced the maximum sea ice area. Regionally, increases were found in the Ross Sea, Weddell Sea, and Western Pacific sectors, and small decreases were detected in the Indian Ocean and the Bellingshausen Sea and Amundsen Sea sectors. Seasonally, both sea ice extent and sea ice area in the Antarctic are at a minimum in summer and a maximum in winter. In the same season, the various regions have different ranges of variation in sea ice area. First-year ice is mainly distributed around the Antarctic mainland, except for the Weddell Sea sector, and the trend of sea ice area is generally positive (0.1×106km2·a–1), which is in marked contrast to the decrease in the multiyear ice area of –0.05×106km2·a–1; most multiyear ice is distributed within the Weddell Sea sector. The change of sea ice extent is correlated negatively with temperature.
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