Estimation and validation of atmospheric water vapor content using a MODIS NIR band ratio technique based on AIRS water vapor products

Atmospheric water vapor validation needs simultaneous, well-defined, and independent information which are not easily available causing limitations in the development of remote sensing water vapor retrieval algorithms. This study is concerned with the retrieval of total atmospheric water vapor content and its validation. A band ratio method has been used to estimate the water vapor content based on Moderate Resolution Imaging Spectroradiometer (MODIS) Near InfraRed (NIR) data. The method uses MODIS bands 17, 18, and 19 as NIR bands and band 2 to remove the land cover reflectance. Furthermore, the Atmospheric Infrared Sounder (AIRS) has been used for both algorithm development and analysis of the results. The method has been modified to take into account the dry condition of the central parts of Iran. Using some various datasets, the method is implemented and evaluated quantitatively. The validation of the water vapor estimates has been undertaken by an analysis of AIRS data. The validation results shows error as low as 9 % for the estimated water vapor using the MODIS NIR band ratio method.     

Research Progress of Quality Control for AIRS Data

Satellite data assimilation can provide accurate initial field for Numerical Weather Prediction (NWP) models. So far, data variational assimilation is based on the theory where error obeys Gaussian distribution, so as to apply the least square method. During classical variational assimilation, if the data contain outliers, the results of optimal parameter estimation is meaningless. Therefore, quality control is quite necessary for Atmospheric Infrared Sounder (AIRS) data before data assimilation. This paper made a comment of the advances in the quality control using AIRS data, which analyzed and discussed the research status from five aspects: channel selection, outliers elimination, bias correction, cloud detection and data sparseness. Three methods for channel selection were summarized, which are stepwise iterative method based on information entropy, the cumulative effect coefficient of principal component and principal components—Stepwise regression, respectively. Comparatively, stepwise iterative method based on information entropy is more widely used, but the selected channels are weak related; Channel combination with large amount of information can be obtained through the method of principal components—stepwise regression, but the implementation process is time-consuming due to the algorithm. Both the lane of law and the double weight method were used in outliers elimination, and the result shows that the latter one is better. Two kinds of bias correction method including off-line and on-line, were introduced, which contain static, adaptive, regression method, variational, method based on the radiative transfer model, bias correction with Kalman filter and dynamic update of bias correction technique. It is found that the timeliness of static method is better; while variational method could solve the problems of data drift and so on. The result is better when using bias correction based on the model and Kalman methods, but it is more time-consuming and not suitable for business application. Generally, the effect and timeliness of dynamic update one is the best among them. In this paper four kinds of cloud detection method are discussed here, including the sky field-of-view, sky channel, cloud radiation correction and different instrument cloud products matching. The first two methods are more feasible from the perspective of timeliness for numerical prediction, but the data quantity using could detection method of sky field-of-view is less than sky channel, leading to discarding of lots of channel data in climate sensitive area such as upper channel, and thus affecting the quality of analysis field. Further on, the methods of hops jumper, box and principal component applied to AIRS data sparseness were analyzed. From assimilation timeliness and operability, box method is feasible; although there is high complexity with algorithm of principal component analysis, which has a certain application prospect. After reviewing the quality control section, some further research directions in these fields were given respectively.     

Retrieval of humidity and temperature profiles over the oceans from INSAT 3D satellite radiances

In this study, retrieval of temperature and humidity profiles of atmosphere from INSAT 3D-observed radiances has been accomplished. As the first step, a fast forward radiative transfer model using an Artificial neural network has been developed and it was proven to be highly effective, giving a correlation coefficient of 0.97. In order to develop this, a diverse set of physics-based clear sky profiles of pressure (P), temperature (T) and specific humidity (q) has been developed. The developed database was further used for geophysical retrieval experiments in two different frameworks, namely, an ANN and Bayesian estimation. The neural network retrievals were performed for three different cases, viz., temperature only retrieval, humidity only retrieval and combined retrieval. The temperature/humidity only ANN retrievals were found superior to combined retrieval using an ANN. Furthermore, Bayesian estimation showed superior results when compared with the combined ANN retrievals.     

2000-2005年青藏高原积雪时空变化分析

利用MODIS卫星反演的积雪资料以及同期气象资料,分析了2000-2005年青藏高原积雪分布特征、年际变化及其与同期气温和降水的关系,结果表明:青藏高原积雪分布极不均匀,四周山区多雪,腹地少雪;高原积雪期主要集中在10月到翌年5月;2000-2005年高原积雪年际变化差异较大,积雪面积总体上呈现冬春季减少、夏秋季增加的趋势;气温和降水是影响高原积雪变化的基本因子.冬季,高原积雪面积变化对降水更为敏感;春季,气温是影响高原积雪面积变化更主要的因素.     

基于MODIS和AMSR-E资料的青海省旬合成雪被图像精度评价

利用2002年10月1日至2008年3月31日青海省MODIS/Terra-Aqua每日雪被产品(MOD10A1、MYD10A1)和AMSR-E/Aqua每日雪水当量产品,研究了MODIS和AMSR-E旬数据的融合算法,合成了AMSR-E旬积雪分类图像(AE_10D)、MODIS旬积雪分类图像(MOYD_10D)及二者...     

Retrieval of hydrometeors from microwave radiances with a polarized radiative transfer model

This paper reports the results of a Bayesian-based algorithm for the retrieval of hydrometeors from microwave satellite radiances. The retrieval technique proposed makes use of an indigenously developed polarized radiative transfer (RT) model that drives a data driven optimization engine (Bayesian) to perform retrievals of rain and other hydrometeors in a multi-layer, plane parallel raining atmosphere. For the sake of completeness and for the purposes of comparison, retrievals with Artificial Neural Networks (ANN) have also been done. Retrievals have been done first with a simplified two-layer atmosphere, where assumed values of hydrometeors are given to the forward model and these are taken as ‘measured radiances’. The efficacy of the two retrieval strategies is then tested for this case in order to establish accuracy and speed. The highlight of the work is however, the case study wherein a tropical storm in the Bay of Bengal is taken up, to critically examine the performance of the retrieval algorithm for an extreme event wherein a 14-layer realistic, raining atmosphere has been considered and retrievals are done against Tropical Rainfall Measuring Mission (TRMM) measured radiances. The key novelties of the work are:

基于MODIS卫星遥感图像数据典型地表发射率光学特性分析

地表发射率是热红外遥感中的重要参量,也是辐射传输中的重要参数。基于MOD11B1卫星遥感图像数据,利用HDF插件获取典型地表温度参数,具体包括沙地、黄土、草坪、江水、冰面和雪地。在ENVI Classic软件环境下,针对2015年12月至2016年8月不同区域、不同季节的典型地表进行6个热红外波段发射率数据获取,研究不同季节典型地表的发射率随波长以及温度的变化规律。研究结果表明：冬季典型地表发射率参数最高且变化范围小在0.02内。沙地的发射率数值平均在0.870~0.990之间;草坪、黄土和江水的发射率数值平均在0.910~0.990之间,冰面和雪地的发射率数值平均在0.965~0.985之间。草坪、沙地、黄土、江水、雪地和冰面地表发射率在波长3~5 μm范围内随温度成波浪型分布;草坪、江水、雪地和冰面地表发射率在波长8~12 μm范围内随温度不变化。     

基于MODIS数据的山东省土壤墒情遥感动态监测与分析

干旱灾害是我国主要的自然灾害之一。近年来,连续性、极端干旱灾害时有发生,对我国粮食安全、饮水安全和生态安全造成严重威胁。土壤墒情是旱情监测的重要指标,遥感技术具有观测范围广、实时性强以及成本低廉等优势,可以广泛应用于土壤墒情监测。本文分析了土壤墒情与地表参数NDVI(归一化植被指数)和LJST(地表温度)的关系,建立了基于NDVI和LST、并考虑土壤类型的土壤墒情遥感监测模型。利用该模型,基于MODIS遥感影像和地面实测墒情,对2010年10月到2011年5月山东省旱情进行了动态监测。监测结果显示:山东省的旱情经历了不断加重,再到逐渐缓解。然后又局部加重。最终全部缓解的过程,干旱核心区为鲁南地区,与实际情况一致。     

基于MODIS资料的2000-2004年江河源区陆地植被净初级生产力分析

基于EOS/MODIS卫星遥感资料的分析表明,2000-2004年江河源地区陆地植被平均年NPP为82.04 gC.m-2,相当于同期全国陆地植被年NPP的23%,其中2001年的年NPP最小,只有78.04gC.m-2,2002年最大,为85.44 gC.m-2.根据年NPP分布显示,黄河源区的植被生长状况要好于长江源区,其中在黄河源东南部陆地植被的年NPP>250 gC.m-2,为江河源区植被年生长最大的区域;该地区的植被年NPP最小值的区域分布在长江源的西北部地区,年NPP大部分<50 gC.m-2.江河源地区植被的年NPP表现为显著的年际变化特征,不同地区年NPP的变化特征各不相同;高寒草甸的年NPP为该地区所有陆地植被年NPP中最大,其5 a平均值为89.38 gC.m-2,其次为高寒草原和灌木及草本植被;由于地处高寒地区,温度成为影响该地区陆地植被净初级生产力的主要因素.     

基于MODIS双卫星积雪遥感数据的积雪日数空间分布研究

结合Terra和Aqua卫星的积雪产品,获取2001-2006年全国新的逐日积雪覆盖数据,并利用此数据通过两种方案获取了全国积雪日数分布,对比发现3大稳定积雪区中,新疆地区积雪稳定性及连续性最好,东北其次,而青藏高原地区最差;通过595个气象台站年积雪日数数据分区分不同植被类型修正MODIS获得的年积雪日数.结果表明:...     

基于地基GPS与MODIS遥感影像的映射函数与大气可降水汽分析

为了提高短期和中小尺度灾害性天气可降水汽预报的准确性和实时性,利用西安地基GPS数据和MODIS遥感影像,采用NMF、GMF和VMF1映射函数设计了4种获取天顶干延迟初始值的方案,探讨了映射函数对计算天顶总延迟的影响。结果表明：在卫星高度角为10°时,VMF1映射函数反演效果和精度最佳,GMF映射函数次之,NMF映射函数最弱,而在卫星高度角为15°时,上述3种映射函数效果相当。最后,结合综合水汽含量与可降水汽的关系,通过计算地基GPS和MODIS遥感影像综合水汽含量,得到了地基GPS和MODIS遥感影像可降水汽（PPWV-GPS,PPWV-MODIS）,并拟合了二者之间的线性关系,结果为PPWV-MODIS=1.421 7PPWV-GPS-2.143,相关系数为0.952 1。     

Evaluating the vegetation destruction and recovery of Wenchuan earthquake using MODIS data

The Ms 8.0 Wenchuan earthquake in 2008 has led to huge damage to land surface vegetation in northwest Sichuan, one of the typical ecological fragile regions in China. In this paper, the vegetation degradation by the earthquake and its recovery after the disaster are evaluated from analysis of MODIS Gross Primary Productivity (GPP) time series products and other ancillary GIS data. The results suggest that local vegetation GPP after the earthquake in the heavy afflicted area has decreased by 22%. The local vegetation productivity in the heavy afflicted area had recovered to 84 and 87% after 1 and 2 months later. Since August 2008, the vegetation productivity has increased to a nearly normal level.     

利用MODIS和AMSR-E进行积雪制图的比较分析

MODIS和被动微波辐射计AMSR-E提供了识别积雪的不同方法.MODIS首先计算反映积雪在1.6μm强吸收特性的归一化差值积雪指数NDSI,在剔除卷云的影响后,得到MODIS积雪分布.AMSR-E则根据积雪在微波波段的差异性散射特性识别积雪.通过案例分析比较了MODIS和AMSR-E积雪分布,发现由于云的遮蔽使MODIS积雪分布面积会比实际小,但由于MODIS的空间分辨率很高,得到的积雪边界线轮廓清晰.而微波由于不受云的影响,得到的AMSR-E积雪分布比较符合实际,但积雪的边界线较粗.     

Analysis of marine aerosol optical depth retrieved from IRS-P4 OCM sensor and comparison with the aerosol derived from SeaWiFS and MODIS sensor

Aerosol optical depth is regularly derived from SeaWiFS and MODIS sensor and used by the scientific community in various climatic studies. In the present study an attempt has been made to retrieve the aerosol optical depth using the IRS-P4 OCM sensor data and a comparison has been carried out using few representative datasets. The results show that the IRS-P4 OCM retrieved aerosol optical depth is in good agreement with the aerosols retrieved from SeaWiFS as well as MODIS. The RMSE are found to be ±0.0522 between OCM and SeaWIFS and ±0.0638 between OCM and MODIS respectively. However, IRS-P4 OCM sensor retrieved aerosol optical depth is closer to SeaWiFS (correlation = 0.88, slope = 0.96 and intercept = ?0.013) compared to MODIS (correlation = 0.75, slope = 0.91 and intercept = 0.0198). The mean percentage difference indicates that OCM retrieved AOD is +12% higher compared to SeaWiFS and +8% higher compared to MODIS. The mean absolute percentage between OCM derived AOD and SeaWiFS is found to be less (16%) compared to OCM and MODIS (20%).     

东北地区MODIS和AMSR-E积雪产品验证及对比

通过2002-2008年6个积雪季节的Terra-Aqua/MODIS积雪产品(MOD10A2、MOD10C2)和Aqua/AMSR-E雪水当量产品,分析了东北地区积雪覆盖面积的变化特征,以研究区气象站点观测的积雪数据为真实值来验证两种产品积雪信息的精度,探讨了云覆盖、土地利用类型和雪深对积雪覆盖精度的影响.结果表明:云的存在对微波数据积雪识别的影响较小,在积雪量较多的12月至次年的2月随云量百分比的变化,MOD10A2积雪覆盖面积比例大体出现负变化.因此,在有云情况下AMSR-E数据反演积雪精度最好.对比草地、耕地、林地和居民地4种土地覆盖类型对监测积雪覆盖精度的影响,发现林地对其影响最大,在林区3种积雪产品的积雪识别精度分别为55.8%、81.2%、85.4%;雪深对AMSR-E积雪产品识别精度影响较小,总体精度为97.8%;积雪深度对MOD10A2积雪产品识别精度影响较大,总体精度为57.3%.MOD10A2、MOD10C2和AMSR-E 3种积雪产品的总体反演精度分别为69.3%、76.6%、76.3%.有必要开发适用于东北地区的积雪覆盖算法,提高估算精度,为能量平衡估算、气候模型、农业生产、土壤墒情监测服务.     

氧协同菱铁矿降解微囊藻毒素机理研究

已知在无氧条件下天然矿物菱铁矿能成功水解微囊藻毒素-LR(MC-LR),然而实际应用过程中难以避免水中溶解氧(O₂)的存在,因此有必要深入研究不同溶氧量(DOC)条件下菱铁矿对MC-LR降解的影响。本文通过在无氧和有氧条件下菱铁矿对MC-LR的降解实验,发现在有氧条件下(溶氧量1.62～21.87 mg/L), MC-LR的降解速率均比无氧条件(k₀=0.030 16 mg·L^-1·h^-1)高且其降解速率随O₂含量增加而依次增加,当O₂加入量为1.0 mL(溶氧量21.87 mg/L)时,菱铁矿对MC-LR降解速率最大(0.083 34 mg·L^-1·h^-1),为无氧条件下的2.76倍。通过外加腐殖酸(humic acid, HA)和自由基进行捕获实验,发现FeCO₃能直接活化O₂产生超氧自由基(·O~-₂),以实现对MC-LR的氧化,但外加HA抑...     

青藏高原Soumi-NPP和MODIS积雪范围产品的对比分析

Soumi-NPP（Soumi Polar-orbiting Partnership）卫星作为接替服役超期的Terra、Aqua卫星,其积雪范围产品在青藏高原的精度尚未被评价。以Soumi-NPP积雪范围产品为研究对象,利用气象台站点数据并结合更高分辨率的Landsat-8 OLI数据,评价该产品的精度,并与MODIS（Moderate Resolution Imaging Spectroradiometer）积雪范围产品进行对比分析。结果表明：使用气象台站进行数据验证时,NPP、MOD与MYD三种积雪范围产品的总精度均较高,但三者积雪漏分误差都较大,其中MYD的漏分误差最大,为64.2%;当雪深小于5 cm时,三种积雪范围产品的积雪分类精度都较低,雪深大于等于5 cm时,NPP积雪范围产品的积雪分类精度最高,为82.3%,MOD与MYD的精度分别为77.1%和69.4%;利用Landsat-8 OLI数据验证时,Soumi-NPP积雪范围产品的Kappa系数最高,其均值为0.707,为高度一致性。而MOD10A1与MYD10A1的Kappa系数较低,分别为0.476与0.557,为中等一致性;Soumi-NPP积雪范围产品的Kappa系数大多在0.6以上,精度比较稳定,而MODIS积雪范围产品的Kappa系数波动较大,精度稳定性较差。Soumi-NPP积雪范围产品相较于MODIS积雪范围产品,其精度有了较大的提升,为准确监测青藏高原积雪范围提供了一个更优的选择。     

MODIS在水文水资源中的应用与展望

MODIS是新一代遥感技术,其遥测数据与其他单独的遥感平台(如NOAA和Land Sat)所获得的数据相比,具有免费、较高时间分辨率(0.5d)、空间分辨率(250m)和光谱分辨率(波谱范围0.4～14μm,36个光谱通道)等优势和特点.详细介绍了国内外的研究现状,着重对MODIS在洪水过程和洪灾范围实时动态监测、冰川和积雪、降水、植被、土壤水分、蒸发、水文模型、水质等方面的应用和研究进展进行了评述,指出MODIS在水文水资源中具有广阔的应用前景.