中国大陆地表温度年变基准场研究

非构造活动或非地震因素对地表热辐射场（地表温度）的影响,对于利用卫星热红外遥感探索地震前兆抑或断层活动有着重要的现实意义.地表温度中,典型非构造活动或非地震因素成份有：由太阳辐射引起的稳定年周期成份和与地形、纬度及能量平衡等因素有关的长期稳定成份,合称年变基准场.本文根据2000～2008年的MODIS/Terra地表温度产品,利用小波分析提取了中国大陆地表温度的年变基准场.在此基础上,结合热传导方程和数学物理方法,获得了年变基准场的（半）定量化表达式.进一步, 利用地表温度的长期稳定成份,获得了温度与海拔、纬度变化之间的定量关系：海拔每增加100 m,温度降低0.51±0.01 K; 纬度每增加1°,温度降低0.77±0.08 K.总之,年变基准场可为利用热红外辐射提取地壳活动信息提供一种参考背景, 温度与海拔、纬度变化之间的定量关系则可用来校正地形起伏和纬度变化对地表温度的影响.     

基于卫星资料的北京陆表水体的热环境效应分析

应用不同分辨率的卫星资料对北京水体类别、密云水库及城区典型水体的热环境特性及城区水体对其周围热环境影响进行研究分析.利用2006年的MODIS卫星地表温度产品对北京不同类型的地表温度研究显示:就北京四季平均状态来说,水体类别在白天具有降温作用,在秋季和冬季夜晚具有保温作用,在春季和夏季夜晚具有降温作用.利用NOAA/AVHRR卫星资料对密云水库的研究分析显示:密云水库在夏季白天具有"冷湖效应",夜晚具有"暖湖效应";密云水库在冬季未结冰时白天和夜间具有"暖湖效应",在结冰时白天具有"冷湖效应"而晚上无冷暖效应.利用FY-3A/MERSI、NOAA/AVHRR和Landsat-TM卫星资料对北京城区典型水体监测结果显示:城区水体不会有热岛现象出现,大面积的水体易出现"冷岛效应".利用2008年夏季Landsat-TM卫星资料对城区典型水体和天坛公园绿地500 m范围内的建筑地温研究分析显示:城区水体温度明显低于天坛公园绿地.城区各水体周边100 m范围内建筑区地温平均下降1.2℃;100～200 m内下降0.6℃;200～300 m内下降0.4℃,300 m范围外无明显变化.天坛公园绿地周边仅100 m内的建筑区地温下降,下降值为0.4℃.这些研究结果表明:卫星资料能有效监测水体的热环境效应,大面积的水体是降低城市地表热岛效应的重要来源,北京城区水体对周边最大300 m范围内的建筑区地表温度具有降温效应.     

应用静止卫星热红外遥感亮温资料反演地表温度的方法研究

双通道多时相反演方法在提取地表温度场时可把地表温度与地表发射率分离计算,是利用静止卫星热红外遥感资料研究地表温度场变化的一种有效方法。该方法计算简单,易于实现海量资料处理,基本满足红外遥感资料在地震预报中的应用要求。     

CMAQ模式卫星产品源同化模型及其在空气质量预报中的应用研究

对比分析了2006年1月和8月华北地区OMINO2卫星遥感二级产品和NO2实测浓度变化趋势的一致性,重点构建了CMAQ空气质量模式卫星遥感产品源同化模型,采用变分订正方法和CMAQ卫星遥感产品源同化技术方案探讨了华北地区冬夏季OMI高分辨率柱浓度卫星资料在空气质量数值预报中的应用.分析结果表明,无论冬季还是夏季,华北地区NO2实测浓度与OMINO2卫星遥感柱浓度的变化趋势具有较好的一致性,两者的相关性较显著,该卫星资料可适用于华北地区卫星遥感-地面观测综合变分分析;经过线性和变分订正后,北京周边地区OMINO2柱浓度的空间分布特征与实测值分布一致,而且可清晰反映出NO2空间分布特征,即NO2的城市局地污染特征较明显.经变分订正的OMINO2柱浓度的空间分布特征可看出,北京的西南、东南地区污染源对北京地区的NO2浓度的影响较大.采用高分辨率的OMINO2卫星遥感资料同化修正排放源时,WRF-CMAQ模式对于华北地区冬、夏季NO2浓度水平预报和趋势预报可取得较显著的改善效果.采用经变分订正的、高分辨率的OMINO2卫星遥感资料进行源同化时,可模拟得到与实测浓度分布较一致的、高分辨率的NO2浓度信息,弥补了采用地面...     

基于尺度分析的断层热信息遥感图像增强方法——以江山—绍兴断裂金衢段为例

根据热红外遥感影像上断层热信息具有特征几何尺寸的特点,以断层系统热信息分析为目标,提出了一种基于尺度分析的断层热信息遥感图像增强方法.在江山—绍兴断裂金衢段的实际工作中,通过断层两侧地表高温区域尺度分析、特征尺度网格抽样和样本插值成图等步骤,有效地降低了背景干扰,客观地描述了研究区与断层相关的热信息的空间分布形态及特征.多种尺度分析结果表明,在9 km2特征尺度上,断层热信息特征规律明显：地表高温区域沿北东走向的江山—绍兴断裂带和常山—漓渚断裂带两侧分布,呈线性特征;在淳安—温州断裂带与衢州—天台断裂带交叉位置地表温度较高.研究结果经实测资料验证,基本特征与实测资料相符.     

遥感区域地表植被二氧化碳通量的机理及其应用

提出了一个计算二氧化碳通量的遥感模型和根据遥感数据和辅助的微气象参 数估算区域分布的算法．在中国科学院禹城综合试验站利用光谱仪、红外测温仪、波文 比和涡度相关测试系统,在冬小麦生长期进行了连续的野外观测．二氧化碳通量和地 面模拟遥感数据２种信息流同时获取．从而建立了遥感模型,开展了与卫星的地面同步 观测．利用ＮＯＡＡ－ＡＶＨＲＲ数据和地面辅助数据,作出了华北地区白天的二氧化碳通 量区域分异影像图．根据此图地表植被二氧化碳的汇可以一目了然．     

MODIS热红外温度数据反演的算法实现及其与地温数据的相关性分析

利用分裂窗法实现对MODIS遥感卫星热红外温度的反演。该方法计算简单,易于实现海量资料处理,基本满足红外遥感资料在地震监测预报中的应用要求。经过不同时间频度、不同空间范围的计算,将反演结果与气象台站地表温度进行了时序的相关性分析,结果显示反演温度与日值地温的相关性可达0.7,其与月值地温的相关性高达0.9,以证实了MODIS数据反演温度的可用性。     

卫星遥感微波地表发射率研究综述

在大气参数反演、卫星资料同化及微波降雨算法等研究领域均需要全球尺度上较为精确的微波地表发射率数据的支持,而微波地表发射率目前难以精确的获取.鉴于此,本文介绍了三种主要的微波地表发射率估算方法,并从卫星遥感的角度阐述当前微波地表发射率的估算、应用现状及存在的问题,最后对微波地表发射率的研究做出展望.     

亮温与降水量的关系

卫星红外遥感亮温数据值除与地球的自转和公转存在密切关系外,还与大气环境和地理环境有密切关系。应用青海省黄河门堂观测点的降水量和水位,以及对应点地表亮温资料进行对比分析,其结果为：降水量与亮温存在可比性,在峰值时间区段内,亮温与降水量呈现明显的反向变化关系;降水量的波动变化导致地表温度的波动变化;水位、降水量和亮温属于温度对气候变化较为敏感的物理量。     

GPS-MR技术用于降雪厚度监测研究

降雪是重要的淡水资源,也是全球气候系统中重要的组成部分.目前全球雪深探测主要依靠站点稀疏的人工探测仪或低时空分辨率的遥感卫星.近几年,基于不同地表介质引起GPS多路径效应的GPS-MR技术以其全天候,自动化,时空分辨率高、无需建网等诸多优点成为雪深探测领域的一个新型探测手段,本文在国内首次开展了基于测量型接收机的GPS-MR探测雪深研究.首先详细给出了基于GPS信噪比观测值的GPS-MR技术进行雪深探测的基本原理;其次结合实测GPS观测数据和雪深测量数据,分别利用单颗和多颗GPS卫星对GPS-MR探测雪深的可行性与探测精度进行深入研究与分析.初步实验结果表明基于信噪比观测值的GPS-MR技术可用于雪深探测,与实测雪深数据相比其探测精度约为10cm左右,尤其可用于暴雪监测.GPS-MR技术不仅可充分挖掘全球密集GNSS网络用于地表环境监测(雪深探测、海平面变化、土壤湿度、植被变化、火山活动等)的潜能,而且可进一步精化GNSS多路径模型误差,以期进一步提高GNSS用于精密定位与地表环境监测的精度.     

基于夜间多时相遥感数据的热异常与地震的相关性

本文使用第二代欧洲气象卫星MSG搭载的SEVIRI传感器数据,基于卫星数据稳健分析技术提取了意大利地区2015—2017年间的三次热异常,并在此基础上增加热异常的判定条件,利用静止气象卫星的高时间分辨率特性,基于夜间多时相遥感数据探究热异常与地震的相关性。结果显示:通过夜间多时相遥感数据均值可更清晰地展示出研究区热异常的时空变化,基于改进后的判定条件可以有效地判断热异常是否可能与地震有关。此外,夜间多时相热异常的时空分布研究表明,云层的扩散与消失可能对热异常提取结果有重要影响。      

Physical statistical algorithm for precipitable water vapor inversion on land surface based on multi-source remotely sensed data

Water vapor plays a crucial role in atmospheric processes that act over a wide range of temporal and spatial scales, from global climate to micrometeorology. The determination of water vapor distribution in the atmosphere and its changing pattern is very important. Although atmospheric scientists have developed a variety of means to measure precipitable water vapor(PWV) using remote sensing data that have been widely used, there are some limitations in using one kind satellite measurements for PWV retrieval over land. In this paper, a new algorithm is proposed for retrieving PWV over land by combining different kinds of remote sensing data and it would work well under the cloud weather conditions. The PWV retrieval algorithm based on near infrared data is more suitable to clear sky conditions with high precision. The 23.5 GHz microwave remote sensing data is sensitive to water vapor and powerful in cloud-covered areas because of its longer wavelengths that permit viewing into and through the atmosphere. Therefore, the PWV retrieval results from near infrared data and the indices combined by microwave bands remote sensing data which are sensitive to water vapor will be regressed to generate the equation for PWV retrieval under cloud covered areas. The algorithm developed in this paper has the potential to detect PWV under all weather conditions and makes an excellent complement to PWV retrieved by near infrared data. Different types of surface exert different depolarization effects on surface emissions, which would increase the complexity of the algorithm. In this paper, MODIS surface classification data was used to consider this influence. Compared with the GPS results, the root mean square error of our algorithm is 8 mm for cloud covered area. Regional consistency was found between the results from MODIS and our algorithm. Our algorithm can yield reasonable results on the surfaces covered by cloud where MODIS cannot be used to retrieve PWV.     

Land cover mapping using time series HJ-1/CCD data

It is very difficult to have remote sensing data with both high spatial resolution and high temporal frequency; thus, two categories of land-use mapping methodology have been developed separately for coarser resolution and finer resolution data. The first category uses time series of data to retrieve the variation of land surface for classification, which are usually used for coarser resolution data with high temporal frequency. The second category uses fine spatial resolution data to classify different land surface. With the launch of Chinese satellite constellation HJ-1in 2008, four 30 m spatial resolution CCDs with about 360 km coverage for each one onboard two satellites made a revisit period of two days, which brought a new type of data with both high spatial resolution and high temporal frequency. Therefore, by taking the spatiotemporal advantage of HJ-1/CCD data we propose a new method for finer resolution land cover mapping using the time series HJ-1/CCD data, which can greatly improve the land cover mapping accuracy. In our two study areas, the very high resolution remote sensing data within Google Earth are used to validate the land cover mapping results, which shows a very high mapping accuracy of 95.76% and 83.78% and a high Kappa coefficient of 0.9423 and 0.8165 in the Dahuofang area of Liaoning Province and the Heiquan area of Gansu Province respectively.     

Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.     

Predicting global landslide spatiotemporal distribution： Integrating landslide susceptibility zoning techniques and real-time satellite rainfall estimates

Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of sufficient ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of high-resolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing a preliminary real-time prediction system to identify where rainfall-triggered landslides will occur is proposed by combining two necessary components： surface landslide susceptibility and a real-time space-based rainfall analysis system （http：//trmm.gsfc.nasa.gov）. First, a global landslide susceptibility map is derived from a combination of semi-static global surface characteristics （digital elevation topography, slope, soil types, soil texture, land cover classification, etc.） using a GIS weighted linear combination approach. Second, an adjusted empirical relationship between rainfall intensity-duration and landslide occurrence is used to assess landslide hazards at areas with high susceptibility. A major outcome of this work is the availability for the first time of a global assessment of landslide hazards, which is only possible because of the utilization of global satellite remote sensing products. This preliminary system can be updated continuously using the new satellite remote sensing products. This proposed system, if pursued through wide interdisciplinary efforts as recommended herein, bears the promise to grow many local landslide hazard analyses into a global decision-making support system for landslide disaster preparedness and mitigation activities across the world.     

数字遥感图像的多源数据融合方法在地质学中的一些应用

简单介绍了几种在地质应用中常用的卫星遥感数据及特点,进一步论述了不同类型的遥感数据及其它地学数据在不同层次上的融合方法;探讨了数据融合技术在突出地质特征信息方面的优势,并给出了几个地质应用的实例     

On Creating Global Gridded Terrestrial Water Budget Estimates from Satellite Remote Sensing

The increasing availability and reliability of satellite remote sensing products [e.g., precipitation (P), evapotranspiration (ET), and the total water storage change (TWSC)] make it feasible to estimate the global terrestrial water budget at fine spatial resolution. In this study, we start from a reference water budget dataset that combines all available data sources, including satellite remote sensing, land surface model (LSM) and reanalysis, and investigate the roles of different non-satellite remote sensing products in closing the terrestrial water budget through a sensitivity analysis by removing/replacing one or more categories of products during the budget estimation. We also study the differences made by various satellite products for the same budget variable. We find that the gradual removal of non-satellite data sources will generally worsen the closure errors in the budget estimates, and remote sensing retrievals of P, ET, and TWSC together with runoff (R) from LSM give the worst closure errors. The gauge-corrected satellite precipitation helps to improve the budget closure (4.2–9 % non-closure errors of annual mean precipitation) against using the non-gauge-corrected precipitation (7.6–10.4 % non-closure errors). At last, a data assimilation technique, the constrained Kalman filter, is applied to enforce the water balance, and it is found that the satellite remote sensing products, though with worst closure, yield comparable budget estimates in the constrained system to the reference data. Overall, this study provides a first comparison between the water budget closure using the satellite remote sensing products and a full combination of remote sensing, LSM, and reanalysis products on a quasi-global basis. This study showcases the capability and potential of the satellite remote sensing in closing the terrestrial water budget at fine spatial resolution if properly constrained.     

Modelling the water balance of a mesoscale catchment basin using remotely sensed land cover data

Hydrological modelling of mesoscale catchments is often adversely affected by a lack of adequate information about specific site conditions. In particular, digital land cover data are available from data sets which were acquired on a European or a national scale. These data sets do not only exhibit a restricted spatial resolution but also a differentiation of crops and impervious areas which is not appropriate to the needs of mesoscale hydrological models. In this paper, the impact of remote sensing data on the reliability of a water balance model is investigated and compared to model results determined on the basis of CORINE (Coordination of Information on the Environment) Land Cover as a reference. The aim is to quantify the improved model performance achieved by an enhanced land cover representation and corresponding model modifications. Making use of medium resolution satellite imagery from SPOT, LANDSAT ETM+ and ASTER, detailed information on land cover, especially agricultural crops and impervious surfaces, was extracted over a 5-year period (2000–2004). Crop-specific evapotranspiration coefficients were derived by using remote sensing data to replace grass reference evapotranspiration necessitated by the use of CORINE land cover for rural areas. For regions classified as settlement or industrial areas, degrees of imperviousness were derived. The data were incorporated into the hydrological model GROWA (large-scale water balance model), which uses an empirical approach combining distributed meteorological data with distributed site parameters to calculate the annual runoff components. Using satellite imagery in combination with runoff data from gauging stations for the years 2000–2004, the actual evapotranspiration calculation in GROWA was methodologically extended by including empirical crop coefficients for actual evapotranspiration calculations. While GROWA originally treated agricultural areas as homogeneous, now a consideration and differentiation of the main crops is possible. The accuracy was determined by runoff measurements from gauging stations. Differences in water balances resulting from the use of remote sensing data as opposed to CORINE were analysed in this study using a representative subcatchment. Resulting Nash–Sutcliff model efficiencies improved from 0.372 to 0.775 and indicate that the enhanced model can produce thematically more accurate and spatially more detailed local water balances. However, the proposed model enhancements by satellite imagery have not exhausted the full potential of water balance modelling, for which a higher temporal resolution is required.     

A review of low‐cost space‐borne data for flood modelling: topography,flood extent and water level

During the last two decades, remote sensing data have led to tremendous progress in advancing flood inundation modelling. In particular, low‐cost space‐borne data can be invaluable for large‐scale flood studies in data‐scarce areas. Various satellite products yield valuable information such as land surface elevation, flood extent and water level, which could potentially contribute to various flood studies. An increasing number of research studies have been dedicated to exploring those low‐cost data towards building, calibration and evaluation, and remote‐sensed information assimilation into hydraulic models. This paper aims at reviewing these recent scientific efforts on the integration of low‐cost space‐borne remote sensing data with flood modelling. Potentials and limitations of those data in flood modelling are discussed. This paper also introduces the future satellite missions and anticipates their likely impacts in flood modelling. Copyright © 2015 John Wiley & Sons, Ltd.