基于RTTOV模式的大气二氧化碳反演参数敏感性分析

大气二氧化碳是开展全球气候变化和碳循环研究的关键数据。卫星遥感技术与模式模拟相结合的反演方法已成为获取该数据的重要手段,但模式输入参数本身的误差会对大气二氧化碳反演精度产生影响,须在反演算法设计中加以关注。本文利用RTTOV10快速辐射传输模式模拟Aqua/AIRS红外探测仪17个大气二氧化碳反演通道,计算了这些通道上大气顶出射辐射对温度廓线、臭氧廓线、水汽廓线、地表温度和地表发射率的参数误差的不确定性,并与二氧化碳增加0.5%时造成的不确定性进行对比,分析二氧化碳对上述参数误差的敏感性。结果表明,温度廓线误差是干扰AIRS大气二氧化碳反演的主要因素,其次是臭氧廓线误差,而水汽廓线、地表温度和地表发射率的误差对二氧化碳反演的影响在除去个别通道后可忽略不计。最后,本文以通道为单位,确定了各通道上的高敏感参数、敏感参数和不敏感参数,为二氧化碳反演通道的选择和反演算法的设计提供了参考。     

林下植被遥感反演研究进展

林下植被在森林生态系统碳、水和营养元素的累积和循环方面有着重要作用与科学意义。多角度、高光谱和激光雷达遥感系统凭借对森林分层结构的敏感性,成为量化林下植被的重要手段。本文综述了森林林下植被的遥感反演研究进展：首先讨论了林下植被的定义,其次对当前林下植被的遥感反演现状作了深入分析,总结了多角度、高光谱和激光雷达遥感观测的森林背景的反射率、林下植被的叶面积指数、高度和覆盖度的遥感反演原理和方法。基于不同卫星观测角度下森林冠层和森林背景对总反射的贡献差异,林下反射率可通过多个角度的观测数据进行反演。此外,借助激光雷达穿透冠层直接观测林下植被的优势,总结了激光点云数据和回波波形信息反演林下植被的覆盖度和高度的方法,以及今后使用遥感技术反演的难点和获取林下植被信息的主要发展方向。     

A model for retrieval of dual linear polarization radar fields from model simulation outputs

An algorithm for retrieving polarimetric variables from numerical model fields is developed. By using this technique, radar reflectivity at horizontal polarization~ differential reflectivity, specific differential phase shift and correlation coefficients between the horizontal and vertical polarization signals at zero lag can be derived from rain, snow and hail contents of numerical model outputs. Effects of environmental temperature and the melting process on polarimetric variables are considered in the algorithm. The algorithm is applied to the Advanced Regional Prediction System （ARPS） model simulation results for a hail storm. The spatial distributions of the derived parameters are reasonable when compared with observational knowledge. This work provides a forward model for assimilation of dual linear polarization radar data into a mesoscale model.     

Atmospheric profile retrieval with AIRS data and validation at the ARM CART site

The physical retrieval algorithm of atmospheric temperature and moisture distribution from the Atmospheric InfraRed Sounder （AIRS） radiances is presented. The retrieval algorithm is applied to AIRS clear-sky radiance measurements. The algorithm employs a statistical retrieval followed by a subsequent nonlinear physical retrieval. The regression coefficients for the statistical retrieval are derived from a dataset of global radiosonde observations （RAOBs） comprising atmospheric temperature, moisture, and ozone profiles. Evaluation of the retrieved profiles is performed by a comparison with RAOBs from the Atmospheric Radiation Measurement （ARM） Program Cloud And Radiation Testbed （CART） in Oklahoma, U. S. A.. Comparisons show that the physically-based AIRS retrievals agree with the RAOBs from the ARM CART site with a Root Mean Square Error （RMSE） of 1K on average for temperature profiles above 850 hPa, and approximately 10% on average for relative humidity profiles. With its improved spectral resolution, AIRS depicts more detailed structure than the current Geostationary Operational Environmental Satellite （GOES） sounder when comparing AIRS sounding retrievals with the operational GOES sounding products.     

基于AMSR-E的北疆地区积雪深度反演

利用北疆地区2007/2008-2009/2010年度积雪季(12月至次年2月)的AMSR-E降轨19 GHz与37 GHz波段的水平极化亮温数据, 结合北疆地区45个气象台站的实测雪深数据, 建立了北疆地区基于AMSR-E亮度温度数据的雪深反演模型, 并对模型的精度进行评价. 结果显示: 雪深在3~10 cm时, 模型反演的雪深值负向平均误差为-5.1 cm, RMSE值为6.1 cm; 雪深在11~30 cm时, 模型反演雪深值的平均误差仅为2.6 cm, RMSE、 正向平均误差、 绝对平均误差均较小; 雪深大于30 cm时, 模型反演的各项误差较大. 用合成方法反演北疆地区2006/2007-2010/2011年度5个积雪季的平均雪深分布和最大雪深分布, 结果显示北疆地区积雪主要分布于北部阿尔泰山和南部天山一带, 其中阿勒泰地区所占比重最大, 中部的准噶尔盆地腹地、 克拉玛依地区雪层较浅.     

Analysis of multi-dimensional SAR for determining the thickness of thin sea ice in the Bohai Sea

Flat thin ice (<30 cm thick) is a common ice type in the Bohai Sea, China. Ice thickness detection is important to offshore exploration and marine transport in winter. Synthetic aperture radar (SAR) can be used to acquire sea ice data in all weather conditions, and it is a useful tool for monitoring sea ice conditions. In this paper, we combine a multi-layered sea ice electromagnetic (EM) scattering model with a sea ice thermodynamic model to assess the determination of the thickness of flat thin ice in the Bohai Sea using SAR at different frequencies, polarization, and incidence angles. Our modeling studies suggest that co-polarization backscattering coefficients and the co-polarized ratio can be used to retrieve the thickness of flat thin ice from C- and X-band SAR, while the co-polarized correlation coefficient can be used to retrieve flat thin ice thickness from L-, C-, and X-band SAR. Importantly, small or moderate incidence angles should be chosen to avoid the effect of speckle noise.     

论地理知识图谱

网络文本蕴含大量隐式地理空间信息,为地理知识获取与知识服务提供了巨大潜能。地理知识图谱是将传统地理信息服务拓展到地理知识服务的关键,也是网络文本蕴含地理信息采集与处理的终极目标。本文系统评述了开放地理语义网、开放地理实体及关系抽取、地理语义网对齐、知识图谱存储方法等地理知识图谱相关主题的研究进展,从网络文本蕴含地理空间信息量与质量评价、地理信息语义理解、空间语义计算模型和异构地理语义网对齐等方面剖析了目前亟需解决的关键科学问题。     

基于迭代法的同轴法X射线相衬成像的相位复原

相位复原问题是指在只有强度可以测量的情况下,从强度数据中提取出物体的相位信息。相位复原是同轴法X射线相衬成像的关键问题之一。本文采用误差递减法,通过限制物体的空间大小,结合测量到的强度数据,在物体的空域和频域都进行约束。通过反复迭代,进行带宽外推,最终实现物面和像面的相位复原。仿真实验证明了此迭代算法的收敛性,并且以塑料管为样品,在上海光源采集到同轴法X射线相衬成像的实验数据,对上述算法进行了验证。     

基于高斯曲线拟合-FFT方法的南海风场反演

海面风场是海洋学的基本参量,获取海面风场对了解海洋的物理过程以及海洋与大气之间的相互作用至关重要。宽阔的海域面积及复杂的海面状况通常使南海海面上的风场信息很难被及时获取。ENVISAT ASAR是一种全天候全天时监测海面的微波雷达传感器,可实时获取海面风场数据。本文基于已有ASAR数据对南海海面风场进行反演实验,首先将结合高斯曲线拟合的FFT风向反演方法应用于南海风向反演,并参考Cross-Calibrated Multi-Platform （CCMP）风场数据去除180o方向模糊获得海面风向。然后,将高斯曲线拟合-FFT风向与传统的峰值-FFT风向进行对比,最后将准确率较高的高斯曲线拟合-FFT风向分别输入CMOD4模型和CMOD5模型获得海面风速大小。实验结果与CCMP参考数据的比较结果表明,在风条纹不明显的情况下,利用结合高斯曲线的FFT风向反演方法和CMOD4模型风速反演方法可有效地进行南海海面风场反演。该成果对利用SAR数据实时获取南海大面积海面风场信息,尤其是观测点缺乏海域的风场信息,具有重要的指导意义。     

Comparative assessment of semantic-sensitive satellite image retrieval: simple and context-sensitive Bayesian networks

In recent years, Bayesian networks using unsupervised extracted image features have been applied in many remote sensing information mining systems to enable semantic-sensitive image retrieval. However, a simple Bayesian network insufficiently accounts for the spatial information, that is, the relations among image regions, for the semantic inference process. This drawback significantly impacts the retrieval performance, especially if the utilised features contain no or little spatial information. Therefore, this article proposes a context-sensitive Bayesian network, which infers semantic concepts of image regions based on the spectral and textural characteristics of the regions themselves as well as their contexts, that is, the adjacent regions. In order to compare the context-sensitive Bayesian network with the simple Bayesian network, comprehensive experiments were conducted based on high-resolution multispectral IKONOS imagery. The results show that the incorporation of the image regions' spatial relations not only significantly improves the accuracy of the semantic concepts inference, but also allows more flexibility in choosing the type of low-level features.