长江口及南北海域泥沙遥感分析

长江是我国第一大河流，每年有4.245×10⁸t的泥沙输向大海，影响着长江口南北海域和东海大陆架水域的水体悬沙浓度。遥感是进行悬浮泥沙监测的一种有效手段。在长江口海域，表层悬沙与垂线平均含沙量有着较好的相关性。利用MODIS数据对长江口及南北海域水体表层泥沙浓度进行了反演和分析，较好地了解了长江口的泥沙分布和运移，以及对周边海域的影响。     

基于高光谱数据和MODIS影像的鄱阳湖悬浮泥沙浓度估算

本文旨在寻找悬浮泥沙浓度的MODIS遥感影像估算模型,并利用实测的高光谱数据对其敏感波段和反演模型进行测试和验证。以鄱阳湖为研究区域,利用光谱数据进行分析,为利用遥感影像建模提供依据。进一步利用同步进行的鄱阳湖水质采样分析与MODIS影像中等分辨率各个波段反射率及其组合进行相关分析,寻找反演悬浮泥沙浓度的敏感波段。实验表明,MODIS的第一波段反射率对于悬浮泥沙浓度有很好的匹配(R² = 0.91; n = 25),进而建立了鄱阳湖地区的悬浮泥沙浓度遥感定量估算模型。利用估算模型和鄱阳湖地区历史MODIS影像,得到了鄱阳湖悬浮泥沙浓度分布图。基于对汛期鄱阳湖悬浮泥沙浓度的连续监测,可对长江倒灌入鄱阳湖现象的形态进行观测。     

南海岛礁附近悬浮泥沙时空分布的遥感研究

岛礁附近海域的悬浮泥沙分布时空变化对研究岛礁生态环境有重要影响。基于30 m高分辨率的Landsat 8陆地成像仪影像,利用经验算法反演南海岛礁附近水体悬浮泥沙浓度。研究结果显示,南海永暑礁有3个明显的高悬浮泥沙区,位于西南部、东北部和潟湖,环礁美济礁和渚碧礁的高悬浮泥沙区主要位于潟湖内。在时间序列上,悬浮泥沙增加主要出现在岛礁填岛阶段。在空间上,悬浮泥沙较高值分布最远约离岛10 km,基本分布面积在25 km~2以内。高悬浮泥沙分布区的方位也与同期海域的物理环境,特别是风场和流场的方位相关。岛礁建设活动与海域物理条件在短期内对附近海域悬浮泥沙分布造成明显影响,填岛工程完成约14 d后,悬浮泥沙基本恢复至原有水平。     

基于双角度AATSR遥感数据的组分温度反演

以甘肃省张掖绿洲为研究区域,基于双角度、多光谱AATSR数据,利用土壤-植被线性混合辐射传输模型反演了张掖绿洲整个生长季的植被和土壤组分温度,并对AATSR不同观测角度间配准前后反演的组分温度结果进行了比较。结果表明:在利用双角度数据进行组分温度反演时,不同观测角度间的配准对反演结果的影响不容忽视。 进一步利用机载WIDAS观测数据反演的盈科附近植被与土壤组分温度及盈科站实测的地表辐射温度对AATSR数据反演得到的组分温度进行了验证,结果表明基于AATSR双角度数据和土壤-植被线性混合模型的结合反演得到的组分温度具有合理的时间和空间变化趋势,也能够较好地反映张掖绿洲植被生长以及组分温度的变化趋势。     

河口悬沙纵向分散系数的遥感计算方法

计算河口泥沙扩散范围、扩散速率等都涉及一个重要参数——纵向分散系数。由水流扩散方程可以推得:纵向分散系数ē_x=1/2dσ_ξ~2(t)/dt,即已知两个不同时刻的悬沙浓度分布就能算出E_x。本文以长江口为例,应用连续气象卫星数据配以适当的遥感悬沙定量模式,经计算机图像处理将卫星数据转变为悬浮泥沙浓度分布。最终计算出长江口枯水期落潮纵向分散系数ē_x。     

基于半分析模型的石头口门水库总悬浮物浓度反演研究

总悬浮物（TSM）是水质遥感反演的重要参数之一,以遥感技术为手段监测总悬浮物浓度成为未来发展的趋势。利用2008年6月13日在石头口门水库采集的高光谱数据和实验室分析数据,计算得到水体总悬浮物的后向散射系数,并经相关分析,选取675 nm处的后向散射系数建立总悬浮物浓度的反演模型,决定系数为0.8327。与基于遥感反射率的经验方法比较,半分析方法具有一定的物理意义,反演精度更高,且在悬浮泥沙含量较高的区域效果更佳。     

基于资源一号02D高光谱卫星影像的青海湖悬浮物浓度反演研究

水体悬浮物浓度是描述水体光学特性的一个重要参数。卫星遥感具有大范围、快速、高频次动态监测的优势,有助于加强对青海湖水环境质量的监测,降低监测成本。而资源一号02D(ZY1-02D)卫星高光谱影像作为新的数据源,具有高空间分辨率、高光谱分辨率的优点,为湖泊的水质高精度监测提供了可能性。为了验证ZY1-02D高光谱相机在水质遥感监测应用中的适用性,以ZY1-02D高光谱影像为遥感数据源,同时辅助实测数据,构建青海湖悬浮物浓度反演模型,并进行精度验证,评价模型的准确性,最后将模型应用于青海湖悬浮物浓度反演。研究结果表明：青海湖悬浮浓度反演模型平均相对误差为21.1%,均方根误差为0.296 mg/L,精度较好,青海湖悬浮物浓度反演结果呈现湖心低岸边高的特征,与同期Sentinl-2和同期Landsat 8数据反演结果进行对比,反演结果保持一致,说明ZY1-02D高光谱影像能够作为悬浮物浓度遥感反演的数据源之一。     

矿物高光谱解混进展研究综述

由于高光谱传感器低空间分辨率特征,岩石高光谱一般是矿物组分的综合反映。矿物高光谱解混对矿产勘查、矿物含量定量反演和野外地质填图等提供了可行的鉴定方法。首先介绍了2种主要的光谱混合模型;其次基于矿物混合机理特征,从模型驱动法和数据驱动法2个方面,对近年高光谱数据的端元提取和丰度求解算法进行归纳,分析各解混算法的原理和优缺点;然后从实验室实测数据、模拟数据和高光谱影像数据3个方面,对目前已开展的混合矿物高光谱解混实验进行概括,总结各算法的解混效果和适用性;最后针对各解混算法的特点和研究现状指出未来矿物高光谱解混的研究方向。     

基于Hyperion影像的射阳河口无机氮磷浓度反演研究

利用射阳河口实测光谱及模拟Hyperion光谱数据与表层水体无机氮磷营养盐浓度进行相关性分析,以及氮磷与悬沙浓度的光谱相关性分析,构建定量模型,实现了射阳河口水体无机氮磷浓度的定量反演。结果表明:现场实测光谱反射率与氮、磷浓度以及悬沙浓度间的相关性具有很强的相似性,相关系数曲线的变化趋势几乎完全一致。对Hyperion的各波段与896nm间进行了9种波段组合与氮磷浓度进行相关性分析,相关系数最大的因子为中心波长为428nm波段与896nm波段光谱反射率的归一化差值指数因子(F3(428,896)),分别为0.80与0.79。以F3(428,896)因子构建线性模型,模型检验的相对RMSE分别为36.63%和47.33%,反映了模型具有良好的预测精度。同时,模型充分显示了河口氮磷含量与悬沙浓度的高度相关性以及氮磷含量本身的高相关性。     

基于ASTER数据的混合像元分解技术在水质监测与评价中的应用

以ASTER数据为数据源对湖北省钟祥市胡集虎山尾矿库水质状况进行了监测与评价。文中运用FLAASH模型对ASTER数据进行了大气辐射纠正,并对数据进行了几何纠正。因ASTER数据空间分辨不能很好地满足水质监测的精度要求,故而文中使用波谱沙漏分析对ASTER数据进行混合像元分解,提取到研究区的水体信息,同时在获得基于ASTER数据的纯净像元指数基础上,利用反距离权重算法对野外采样点处混合像元的光谱反射率值进行计算。通过对比试验结果,利用数学统计方法建立了实测污染物数据与水体反射率比值间显著相关的反演模型,并对模型进行显著性检验,最后利用建立的模型对水体反射率进行运算处理,得到研究区水体相应污染物浓度分布,完成了对研究区水体的水质状况的监测与评价。     

A novel technique to detect turbid water and mask clouds in Greenland fjords

The existing National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) MOD35_L2 cloud mask performance was assessed using imagery of Kangerlussuaq Fjord, Greenland. It was found to perform suboptimally, especially near glacially fed river mouths, due to sediment-laden water being highly reflective in near-infrared wavelengths. In situ observations of suspended sediment concentration (SSC) were compared against MODIS band 1 and 2 reflectance and show a reflectance saturation effect past which increasing SSC values fail to increase the reflectance of water. A new mask optimized for turbid waters uses the reflectance saturation effect observed in high-SSC water. This new mask and a custom adaption of individual tests within the existing MOD35_L2 cloud mask were tested. The new mask outperforms the standard MOD35_L2 mask and the combination of new and custom masks was shown to screen out clouds very well in Greenland fjords. It is thought that with local measurements of MODIS band 1 and 2 reflectance values of turbid water to serve as training data, this mask should perform equally well in other turbid coastal waters.     

Remote sensing of suspended sediment concentrations of large rivers using multi-temporal MODIS images: an example in the Middle and Lower Yangtze River,China

Conventional measurements of suspended sediment concentration (SSC) are expensive, especially for large river systems. This study aims to examine the potential of estimating SSC of large rivers using high temporal resolution Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data. In contrast to a small number of the samples used by previous studies on remote sensing of SSC, a large number of samples (n?=?153) obtained on 63 dates at five sites across the entire Middle and Lower Yangtze River were employed to investigate the relationship between SSC and the water reflectance of MODIS images. The water reflectance difference between Bands 2 and 5 provided a relatively accurate SSC estimate after atmospheric correction, with 25% mean absolute relative error and 29.7% relative root mean square error. The errors were lower for larger SSC values. Thus, there is a potential that the Terra MODIS could be employed to estimate SSC frequently for large turbid river systems.     

A practical approach to retrieving the finer areas of algal bloom in inland lakes from coarse spatial resolution satellite data

Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) images have been used to monitor algal blooms in the open ocean, coastal waters, and inland lakes. However, it is difficult to obtain an accurate definition of algal bloom areas in inland lakes due to the spatial resolution of the generated images. This study developed a practical approach that uses a linear spectral mixing model with a moving window (LSMM), to obtain a finer algal bloom area. The approach analyses the differences in areas of algal bloom retrieved from MODIS images with 250 and 500 m spatial resolutions from 2012 to 2015 and synchronous VIIRS images with 750 m spatial resolution. Forty-two data sets with 126 satellite images were selected. The results showed that the average relative area difference (RAD) of algal bloom in the MODIS 500 m image was approximately 21.31% compared with the MODIS 250 m image and approximately 33.77% compared with the VIIRS image. A 5 × 5 window size was selected for the MODIS 500 m and VIIRS images. The results demonstrated that the approach can be successfully applied to MODIS 500 m and VIIRS images because the RAD significantly improved. The average RAD decreased to 9.39% in the MODIS 500 m image and to 12.84% in the VIIRS image. The relationship between the landscape of the algal bloom patch and the RAD showed that the performance of the LSMM method improved as the patch density (PD) increased from 0 to 2. When the perimeter-area ratio (PARA) is greater than 2 and the mean patch size (MPS) is less than approximately 5 km², the LSMM method significantly improved the RAD. An independent validation demonstrated that the LSMM method developed for MODIS and VIIRS images can be successfully applied to other coarser-resolution spatial imageries such as Geostationary Ocean Color Imager (GOCI) images. The LSMM method is more effective than the other methods for determining the fragmented landscape of algal blooms.     

Downscaling MODIS-derived water maps with high-precision topographic data in a shallow lake

ABSTRACT

Remotely sensed imagery is the most efficient and widely used data source to monitor the water area changes. However, a trade-off always exists between temporal resolution and spatial resolution for satellite images. Taking the southern Dongting Lake as an example, this study was conducted to develop a method of downscaling the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived coarse spatial resolution water maps in shallow lakes with high-precision digital elevation model. The main principle of the method is to identify and adjust the horizontal location errors of the waterlines extracted from coarse-resolution data by analysing and modifying the elevation leaps using finer-scale topography information. Moving average filter was used to smooth the errors of waterlines caused by the geometric inaccuracies and classification uncertainties of the coarse data. The optimal local window size of the moving average filter was selected automatically using an exponential decay function model and a curvature algorithm for each pixel in the waterlines. In reference to Landsat Thematic Mapper data, the accuracy of the downscaling result is distinctly higher than that of the original MODIS normalized difference water index-derived water maps. The presented method is proved to be an effective tool for acquiring water maps of shallow lake with high spatio-temporal resolution using coarse- or moderate-resolution satellite imagery and high-precision topographic data.     

基于MODIS影像的植被覆盖度提取研究

利用线性光谱混合模型对河南省三门峡地区MODIS1B影像进行植被覆盖度（fv）信息提取,将结果与反映植被覆盖度的NDVI比较,并提出在实测资料缺乏的情况下利用同期高分辨率ETM＋图像对选取样本区域进行定量验证的方法。结果表明,对于MODIS数据,线性光谱混合（LSMM）分解方法能有效提取大区域范围的植被覆盖度信息,比NDVI-fv经验统计方法更具有理论意义,为快速、准确、高效的植被监测提供了新思路。     

Become a Member of The Remote Sensing and Photogrammetry Society (RSPSoc)

An inversion algorithm based on the Classification and Regression Tree (CART) has been developed to retrieve sea ice from Moderate Resolution Imaging Spectroradiometer (MODIS) images in the Bohai Sea where the sea water is characterized by a high concentration of suspended sediment in coastal areas. The inversion algorithm has been successfully applied to the sea-ice extraction from 2009 to 2012. The estimated sea ice is compared with previous studies and the comparison shows reasonable agreement. The model is further examined using sea-ice data from higher-spatial-resolution satellites, and the result indicates that the CART method is able to successfully retrieve sea ice in high sediment environments in the Bohai Sea. To comprehensively understand the working principles of the CART, a series of sensitivity studies to model input parameters such as sampling locations, the number of bands, and the effect of the thermal infrared band (TIB), was conducted. The sensitivity studies show that the CART method is easy to set up and the results are realistic. The TIB may play an important role in sea-ice inversion in turbid waters. The algorithm is also compared with a ratio-threshold segmentation (RTS) method, a common way to retrieve sea ice from satellite images in open oceans, and the comparison indicates that the algorithm developed in the present article is superior to the RTS method in high sediment environments.     

Spectral mixture analysis for bi-sensor wetland mapping using Landsat TM and Terra MODIS data

Spatial and temporal resolution is essential for understanding the spatial and temporal characteristics and dynamics of wetland ecosystems. However, single satellite imagery with both high spatial resolution and high temporal frequency is currently unavailable. Instead, the development of a bi-sensor monitoring technique utilizing spatial details of middle-to-high resolution data and temporal details of coarse spatial resolution data is highly desirable. For the initial work on our time-series bi-sensor wetland mapping, the applicability of multiple endmember spectral mixture analysis (MESMA) using single-date bi-sensor imagery with different orbiting periods was investigated. Landsat-5 Thematic Mapper (TM) and Terra Moderate Resolution Image Spectrometer (MODIS) data were utilized in the Poyang Lake area in China and the Great Salt Lake area in the USA to examine three decisive elements in utilizing MESMA: (1) the method of optimal endmember selection; (2) the threshold between two- and three-endmember models; and (3) the treatment of shade fractions. As a result, we found that (1) the number of spectra for an endmember spectrum similar to other endmember spectra meeting the modelling restrictions of maximum and minimum land-cover fractions and root mean square error (RMSE) within a class (In_CoB), the number of spectra for an endmember spectrum similar to other endmember spectra meeting the modelling restrictions outside of a class (Out_CoB), the ratio of In_CoB to Out_CoB multiplied by the inverse number of spectra within the class (CoBI) and the endmember average RMSE (EAR) were optimal endmember selection methods for the TM maps, whereas CoBI, EAR and minimum average spectral angle (MASA) were optimal endmember selection methods for the MODIS maps; (2) the MODIS maps were more sensitive to change in the two- and three-endmember modelling thresholds than the TM maps; and (3) the addition of shade fractions to dark water fractions were an appropriate shade treatment. This research demonstrated how MESMA can be applied for multi-scale mapping of wetland ecosystems, how the difference in observation dates between the TM and MODIS data affects the agreement in land-cover fractions and how spectral similarity between dark water and shade affects the agreement in land-cover fractions.     

Sensitivity of near infrared total water vapour estimate to calibration errors

Analysis of satellite data to estimate the precipitable water (also called the columnar water vapour) amount often leads to systematic errors in deduced precipitable water (PW). The causes of systematic errors are likely to be instrumental calibration errors rather than variability of atmospheric parameters. We use the MODTRAN 4.0 radiative transfer code to model effects of various calibration errors on the Multi-spectral Thermal Imager (MTI) daytime total water vapour estimate. From the considered sources of calibration errors (spectral band centre error, spectral bandwidth error and radiometric calibration error) the radiometric calibration error has the largest influence on the accuracy of total water vapour estimate. When the radiometric calibration error between 1% and 5% is combined with the estimated spectral band centre error of 1 nm and the bandwidth error of 0.5 nm, the total systematic error of the columnar water vapour estimate is expected to be between 8% and 26%. The accuracy of the retrieved PW using the MTI imagery over the NASA Stennis site and Oklahoma DOE (Department of Energy) ARM (Atmospheric Radiation Measurement program) site is about 17%, well within the estimated range due to calibration errors. A similarity between the MTI and the MODIS (Moderate Resolution Imaging Spectral-Radiometer) bands used for water vapour estimate suggests that a similar error analysis may be valid for the MODIS sensor. However, the narrow band instruments (with bandwidth around 10 nm) are much more sensitive to the band centre calibration error.