基于MODIS和AMSR-E反演京津冀地区大气水汽含量及比较

为了长时间、大范围获取水汽数值,利用2005~2008年光学遥感的MODIS近红外、红外水汽产品,以及微波遥感AMSR-E数据,2种方法反演水汽。微波AMSR-E亮温数据采用Merritt N.Deeter(2007)亮温极化差方法,选取18.7GHz和23.8GHz 2个波段,得到AMSR-E升轨、降轨大气水汽数值。以京津冀地区为研究区域,通过地统计相关性分析、时间序列分析、年际间变化分析,可知2种方法4种资料反演的大气水汽数值的R2都达到0.95,时间分布符合中国雨带移动规律,空间分布不均。MODIS数据反演值比AMSR-E值要低,得到2种方法反演水汽的各自优缺点。     

An AMSR-E data unmixing method for monitoring flood and waterlogging disaster

Spectral remote sensing technique is usually used to monitor flood and waterlogging disaster. Although spectral remote sensing data have many advantages for ground information observation, such as real time and high spatial resolution, they are often interfered by clouds, haze and rain. As a result, it is very difficult to retrieve ground information from spectral remote sensing data under those conditions. Compared with spectral remote sensing technique, passive microwave remote sensing technique has obvious superiority in most weather conditions. However, the main drawback of passive microwave remote sensing is the extreme low spatial resolution. Considering the wide application of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data, an AMSR-E data unmixing method was proposed in this paper based on Bellerby’s algorithm. By utilizing the surface type classification results with high spatial resolution, the proposed unmixing method can obtain the component brightness temperature and corresponding spatial position distribution, which effectively improve the spatial resolution of passive microwave remote sensing data. Through researching the AMSR-E unmixed data of Yongji County, Jilin Provinc, Northeast China after the worst flood and waterlogging disaster occurred on July 28, 2010, the experimental results demonstrated that the AMSR-E unmixed data could effectively evaluate the flood and waterlogging disaster.     

MODIS_LST与AMSR-E_BT的相关性及地表温度反演

本文以2007年和2008年MODIS每日地表温度（LST）数据及AMSR-E地表亮温（BT）数据为研究对象,结合土地覆盖类型数据,统计分析MODIS_LST与AMSR-E_BT在不同土地覆盖类型、频率和极化方式条件下的相关性。结果表明,频率在18.7、23.8和36.5 GHz的AMSR-E-BT与MODIS_LST的相关性较大,且在垂直极化通道上的相关性较在水平极化上大;不同土地覆盖类型,与MODIS_LST相关性较大所对应的AMSR-E微波通道不同。同时,考虑混合像元问题对相关性的影响,对25种不同地物类型组合下MODIS_LST与AMSR-E-BT的相关性进行统计分析,发现混合像元中地物类型越多,则二者相关性越小。最后,采用多元线性回归分析法,根据不同土地覆盖类型建立反演回归模型,对部分研究区域MODIS-LST进行反演,误差平均在±3.15 K以内,与不考虑下垫面覆盖的模型比较,反演MODIS_LST精度平均提高了1.5 K。     

Atmospheric correction of ocean color imagery over turbid coastal waters using active and passive remote sensing

This paper demonstrates an atmospheric correction method to process MODIS/Aqua (Moderate-resolution Imaging Spectroradiometer) ocean color imagery over turbid coastal waters with the aid of concurrent CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization) aerosol data, assuming that there exists "nonturbid" water in the study area where MODIS aerosol optical properties can be retrieved accurately. Aerosol properties from CALIOP measurements were obtained and related to those from MODIS. This relationship, combined with CALIOP aerosol data, was extended to turbid water to derive MODIS aerosol properties, where atmospheric correction using MODIS data alone often fails. By combining MODIS and CALIOP data, aerosol signals were separated from the total signals at the satellite level, and water-leaving radiances in turbid waters were subsequently derived. This method was tested on several MODIS/Aqua ocean color images over South China turbid waters. Comparison with field data shows that this method was effective in reducing the errors in the retrieved water-leaving radiance values to some extent. In the Zhujiang (Pearl) River Estuary, this method did not overestimate the aerosol effects as severely, and provided far fewer negative water-leaving radiance values than the NASA (National Aeronautics and Space Administration) default methods that used MODIS data alone.     

顾及地表汇水分析的城市DEM构建

城市暴雨内涝是城市最普遍的自然灾害之一。受城市复杂下垫面环境的影响,城市暴雨积水具有响应时间短、淹没速度快的特点。数字高程模型（Digital Elevation Model, DEM）是城市地表汇水分析的关键,是暴雨积水分析的基础,而传统方法构建的DEM不能满足复杂下垫面环境下城市暴雨积水淹没分析的需求。本文以构建顾及地表汇水分析的城市DEM为目的,提出基于多源数据融合思想和地形修正思想的DEM构建方法,即将数字摄影测量技术构建的大范围DEM与车载激光扫描技术构建的城市道路高精度DEM相融合,再利用影响雨水汇流路径的建筑物、水系等要素对DEM进行修正的构建方法。本文以北京市海淀区公主坟地区为例进行研究,结果表明该方法构建的DEM在道路区域高程精度可达厘米级,实现了对城市道路易积水区域的重点表达,基于该DEM的汇水分析结果基本符合城市下垫面汇水特点,因此,本文构建的DEM适用于城市道路、桥区汇水路径分析和积水淹没分析。     

基于多源卫星遥感的暴雨灾情时空动态信息的提取

强暴雨淹没耕地形成灾害的同时,对耕地作物的生长也产生着极大的影响,而暴雨灾害对耕地作物生长的影响是一个渐变过程,需要由时空动态的观测进行监测。多源卫星遥感观测技术具有捕捉地面瞬间状态和刻画过程的优势。论文利用Terra/MODIS、Landsat和Sentinel卫星观测数据,挖掘多源卫星遥感观测数据,提出了一种利用NDVI变化的特征值进行灾情动态信息提取方法;并以2016年发生暴雨灾害的巢湖地区为实验区进行了方法的应用和讨论。结果表明,基于MODIS多时相NDVI变化结果提取的信息能够获得受灾害影响开始时期和持续时长等丰富的时空动态信息,根据这些信息可以统计得出大范围区域中受灾害影响的面积。另外,结合利用30 m和10 m的Landsat和Sentinel观测数据提取的水淹区,可为在暴雨致灾范围方面提供准确的参考信息。多源遥感作为评估灾情信息的依据之一,其获取的灾情动态信息能够为灾后耕地的恢复情况以及国家灾后损失评估和救助决策提供科学的数据依据。     

基于LTDR AVHRR和MODIS观测的全球长时间序列叶面积指数遥感反演

叶面积指数是描述土壤-植被-大气之间物质和能量交换的关键参数,获取大区域长时间序列叶面积指数有助于研究气候变化条件下植被的响应及反馈。本文利用MODIS观测和经过重新处理的地表长时间数据集（Land Long Term Data Record）LTDR AVHRR数据,生成了全球1981-2012年叶面积指数数据。算法通过建立二者之间像元级关系,利用高质量MODIS观测约束历史AVHRR数据的反演,这有助于减小2种存在显著差别传感器反演结果的不一致性,也有助于提高AVHRR反演质量。首先算法利用高质量MODIS地表反射率反演2000-2012年叶面积指数,然后利用多年每8 d的LTDR AVHRR地表反射率数据计算简单比植被指数（Simple Ratio,SR）,利用SR平均值和MODIS LAI平均值建立像元级AVHRR SR-MODIS LAI关系。在此基础上,实现1981-1999年AVHRR LAI反演,最终得到全球1981-2012年叶面积指数数据。本算法反演的AVHRR和MODIS LAI与全球植被的空间分布吻合,能表征主要生物群系类型的季节变化特征,2个数据集一致性较好,并且与NASA MODIS LAI标准产品（MOD15A2）的空间分布和季节变化曲线吻合较好。     

EOS/MODIS资料在渭河洪涝动态监测中的应用

通过对MODIS资料中植被、水体、土壤等不同地物的光谱特性进行分析,提出了一种用于洪涝灾害监测的遥感资料处理流程和量化判识指标及淹没面积估算方法.并在2003年渭河流域洪涝灾害的监测中进行了实际应用,效果良好.     

Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network

It is more difficult to retrieve land surface temperature(LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2(AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System(AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4° and 1.9° when five frequencies(ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.     

Comparative analysis of microwave brightness temperature data in Northeast China using AMSR-E and MWRI products

With such significant advantages as all-day observation, penetrability and all-weather coverage, passive microwave remote sensing technique has been widely applied in the research of global environmental change. As the satellite-based passive microwave remote sensor, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) loaded on NASA’s (National Aeronautics and Space Administration of USA) Aqua satellite has been popularly used in the field of microwave observation. The Microwave Radiation Imager (MWRI) loaded on the Chinese FengYun-3A (FY-3A) satellite is an AMSR-E-like conical scanning microwave sensor, but there are few reports about MWRI data. This paper firstly proposed an optimal spatial position matching algorithm from rough to exact for the position matching between AMSR-E and MWRI data, then taking Northeast China as an example, comparatively analyzed the microwave brightness temperature data derived from AMSR-E and MWRI. The results show that when the antenna footprints of the two sensors are filled with either full water, or full land, or mixed land and water with approximate proportion, the errors of brightness temperature between AMSR-E and MWRI are usually in the range from −10 K to +10 K. In general, the residual values of brightness temperature between the two microwave sensors with the same spatial resolution are in the range of ±3 K. Because the spatial resolution of AMSR-E is three times as high as that of MWRI, the results indicate that the quality of MWRI data is better. The research can provide useful information for the MWRI data application and microwave unmixing method in the future.     

Assessment of waterlogging risk in Lixiahe region of Jiangsu Province based on AVHRR and MODIS image

Four images of 1991 AVHRR, 2003 and 2007 MODIS were used to extract waterlogging inundated water of three years, and three inundated water maps were overlaid to estimate waterlogging affected frequency. Based on waterlogging affected frequency, waterlogging hazard of pixel scale was assessed. According to the weighed score of area percentage of different waterlogging affected frequency in 13 counties/cities of Lixiahe region, waterlogging hazard rank of every county/city was assessed. Waterlogging affected frequency map and 1 km× 1 km grid landuse map were used to assess waterlogging risk of pixel scale; and then waterlogging risk rank of every county/city was assessed by the similar method by which waterlogging hazard rank of every county/city was assessed. High risk region is located mainly in core zone of Lixiahe hinterland, medium risk region is adjacent to high risk region, and low risk region is located in the most outlying area of risk zone and mainly in south to middle part of Lixiahe region. Xinghua and Gaoyou belong to high risk city, Jiangyan belongs to medium risk city, and the other counties/cities have low or lower waterlogging risk. The method of assessing waterlogging risk in this paper is simple and applicable. This paper can provide guidance for the waterlogging risk analysis in broader area of Huaihe River Basin.     

IMPACT OF FUTURE SEA LEVEL RISE ON FLOOD AND WATER LOGGING DISASTERS IN LIXIAHE REGION

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基于长时间序列MODIS数据的鄱阳湖湖面面积变化分析

湖泊是气候、水和生物地球化学循环的关键组分,遥感观测有助于获得湖泊面积变化及对环境的响应信息。本文基于长时间序列遥感观测提出了一种利用像元水淹没/陆地出露比例的湖面变化分析算法,并应用于鄱阳湖的湖面提取及变化分析。首先,用全年遥感数据合成获得当年最大湖面;然后,在此最大湖面范围内,识别出每景数据的水体和陆地状态;最后,统计一定时期内每个像元的水淹没比例,并据此分析湖泊的年际和季节变化特征。该算法的最大优势是克服了云的干扰,以及湖面快速变化造成的年际变化对比分析困难问题。算法应用于2000-2010年MODIS观测,分析结果表明,本算法检测出的湖面面积与鄱阳湖水文站水位观测资料非常一致;算法可以获得鄱阳湖湖面面积的显著季节变化信息;2000-2010年间,鄱阳湖湖面面积在丰水期未发现有规律的变化;在枯水期,2003年后湖面面积急剧减小。     

Impact of future sea level rise on flood and water logging disasters in lixiahe region

Lixiahe region is one of the susceptible area to flood and waterlogging disasters in China due to its low topographic relief and having difficulty in draining floodwater away. The condition will be more serious if sea level rises in the future. The estimated results by some scientists indicate that the sea level could rise probably 20–100 cm by 2050. However, what the effect will future sea level rise exerts on flood drainage and on flood or waterlogging disasters? A hydrological system model has been developed to study the problem in the lower reaches of the Sheyang River basin. Predicted results from the model show that, if sea level rises, drainage capacity of each drainage river will decrease obviously, and the water level will also rise. From the change of drainage capacity of drainage rivers the trends of flood and waterlogging disasters are analyzed in the paper if the severe flood that happened in the past meets with future sea level rise. Some countermeasures for disaster reduction and prevention against sea-level rise are put forward.     

Retrieval of Snow Depth on Sea Ice in the Arctic Using the FengYun-3B Microwave Radiation Imager

Snow on sea ice is a sensitive indicator of climate change because it plays an important role regulating surface and near surface air temperatures. Given its high albedo and low thermal conductivity, snow cover is considered a key reason for amplified warming in polar regions. This study focuses on retrieving snow depth on sea ice from brightness temperatures recorded by the Microwave Radiation Imager(MWRI) on board the FengYun(FY)-3 B satellite. After cross calibration with the Advanced Microwave Scanning Radiometer-EOS(AMSR-E) Level 2 A data from January 1 to May 31, 2011, MWRI brightness temperatures were used to calculate sea ice concentrations based on the Arctic Radiation and Turbulence Interaction Study Sea Ice(ASI) algorithm. Snow depths were derived according to the proportional relationship between snow depth and surface scattering at 18.7 and 36.5 GHz. To eliminate the influence of uncertainties in snow grain sizes and sporadic weather effects, seven-day averaged snow depths were calculated. These results were compared with snow depths from two external data sets, the IceBridge ICDIS4 and AMSR-E Level 3 Sea Ice products. The bias and standard deviation of the differences between the MWRI snow depth and IceBridge data were respectively 1.6 and 3.2 cm for a total of 52 comparisons. Differences between MWRI snow depths and AMSR-E Level 3 products showed biases ranging between-1.01 and-0.58 cm, standard deviations from 3.63 to 4.23 cm, and correlation coefficients from 0.61 to 0.79 for the different months.     

High-resolution Surface Relative Humidity Computation Using MODIS Image in Peninsular Malaysia

1 Introduction The Canadian Fire Weather Index (FWI) System can be used in applied forestry as a tool to investigate and manage all types of fire management (Chrosciewicz, 1978; McRae, 1980; Fyles et al., 1991; McRae et al., 1994). The development of the FWI system (Van Wag- ner, 1987; 1990) over the last two decades allows the routine prediction of fire behavior from weather data (Fyles et al., 1991). The FWI is calculated from point measurements of air temperature, relative humidity…     

QUANTITATIVE MODELING OF SUSPENDED SEDIMENT IN MIDDLE CHANGJIANG RIVER FROM MODIS

1INTRODUCTIONThe quality of water is traditionally determined by col-lectingwatersamplesandanalyzingthesamplesin a lab-oratory. However, sampling is very much hard sledding,time-consuming, slow and expensive, and easily in thecontrol of weather conditions, and does not include allwater areas. Satellite estimates of water quality havefound widespread application. Remote sensing instru-ments have been applied in water quality monitoringwith varying success (DANA and RICHARD, 1999;RUDD…     

THE SIMULATED STORAGE CAPACITY OF FLOOD AND WATERLOGGING IN THE TYPICAL AGRICULTURAL REGION IN JIANGHAN PLAIN

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2000-2013年青藏高原湖泊面积MODIS遥感监测分析

青藏高原上分布着大量的高原内陆湖泊群,该区域湖泊面积与区域及全球气候变化之间存在较强的耦合关系,遥感监测湖泊的分布和面积变化趋势,对分析区域自然生态环境具有重要意义。本研究将MOD09A1（地表反射率8天合成数据）进行逐月合成,提出了一种综合多种水体指数的青藏高原地区湖泊提取方法,并通过活动窗口、DEM和时间序列去噪等方法,消除山体阴影、冰雪等因素的干扰。最后,提取和合成了2000-2013年青藏高原逐年和逐月的湖泊范围,并选取色林错和卓乃湖2个典型湖泊与人工解译Landsat系列影像进行验证分析,其线性拟合度分别为0.99和0.97,从时空变化趋势上分析了青藏高原湖泊面积动态变化。结果表明：（1）2000-2013年,青藏高原地区湖泊范围整体上呈较显著的扩张趋势,湖泊总面积增加速率约为490.98 km² a^-1（R²约为0.96）;（2）1-12月份湖泊面积逐月变化率均大于0,表明青藏高原湖泊面积呈整体扩张,而非季节性扩张。除2-4月份外,其他月份增加速率均在400 km² a^-1以上（R²>0.79）,表现为稳定且持续扩张趋势。     

利用MODIS影像提取火烧迹地方法的研究

火烧迹地监测不仅可以反映火灾对生态系统的影响情况及损失信息,还能为全球碳循环研究提供重要的数据支持。本文利用MODIS地表反射率产品（MOD09A1）的近红外和短波红外波段构建的归一化燃烧率指数（NBR）,计算前后2期影像的NBR差值,并在光谱指数差分法的基础上,结合MODIS植被数据产品（MOD44B）提供的植被覆盖度信息,设置规则提取火烧迹地。本文选择西伯利亚地区东南部的林地、草地、农田等不同生态系统的交界地带作为实验区,利用本文算法提取该区域的火烧迹地。实验结果表明：（1）本文算法的火烧迹地提取效果较好,优于MODIS火烧迹地产品（MCD45A1）,kappa系数由0.70提高到0.75;（2）利用林木覆盖度、草本覆盖度数据,可以减少误判,提高火烧迹地提取的精度,kappa系数分别由0.69、0.73都提高到0.75。