Remote sensing of the El Hierro submarine volcanic eruption plume

Submarine volcanic eruptions took place at the island El Hierro (Canary Islands) between October 2011 and March 2012. The event produced plumes of discoloured waters due to the discharge of volcanic matter, magmatic gases, and hydrothermal fluids. The expelled materials, which behaved like oceanic tracers, were detected from the site of the volcano to the open sea by remote-sensing techniques using different level-2 (L2) products of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. In order to assess the effect of the anomalous turbidity, three atmospheric correction schemes were evaluated: SeaWiFS Data Analysis System (SeaDAS) standard, near-infrared–shortwave-infrared (NIR-SWIR), and Management Unit of the North Sea Mathematical Models (MUMM). The comparison between them verified that the SeaDAS standard atmospheric correction was the most suitable. The downwelling diffuse attenuation coefficient at 490 nm (K_d(490)) was used as a measure of plume intensity in comparison with other submarine volcanic eruptions. The MODIS-derived K_d(490) values verified that the intensity of the El Hierro plume was moderate. Only in some specific situations did the values barely exceed 0.4 m^–1. The remote-sensing reflectance (R_rs) was used for the characterization of the affected waters. The R_rs spectra also allowed a comparison with other volcanic and sulphide events reported in previous studies. Similarities were found, both with submarine volcanic eruptions in the southwestern Pacific and with sulphide events at the Namibian coast, in composition and properties of optically active water constituents. A classification schema based on K_d(490) values and R_rs ratios was developed and used in connection with MODIS red–green–blue (RGB) composites as well as surface current velocities from altimeter missions to investigate the spatio-temporal development of the volcanic plume. The spreading and transport of volcanic material observed at the ocean surface was caused by the predominant surface currents coupled with different mesoscale eddies. Discoloured waters were identified more than 200 km away from the eruption site. Field data from oceanographic surveys verified the high concentration of sulphur compounds in affected waters and confirmed the overestimation by the MODIS default algorithm of chlorophyll-a concentrations in the volcanic plume.     

基于ASAR和MODIS图像的藻华范围提取

本文将同期的EnviSat-ASAR图像和MODIS图像进行配准,利用经过大气校正后的MODIS光学图像,根据经验模型反演得到叶绿素a的浓度,借此对太湖的藻华和非藻华区域进行划分,并以此为据在ASAR图像上研究两者的V/V后向散射特性,使用阈值法对藻华范围进行提取,并将结果与MODIS图像上得到的藻华范围进行对比,结果较好的吻合.分析二者结果表明,风速会造成的藻华漂移和沉浮并且影响湖面粗糙度,是决定提取结果好坏的关键因素.另外从成像机理上,太湖粗糙度不规则性和介电特性上的误差使得藻华提取阈值不具有适用性,也是此次提取藻华结果不完全一致的原因,恰恰也成为了利用微波数据监测藻华的局限所在.     

应用MODIS数据监测巢湖蓝藻水华的研究

以巢湖为研究区域,以MODIS 卫星影像为数据源,结合准同步的地面水质监测数据,将MODIS 250 m分辨率的波段反射率与叶绿素a浓度实测值进行相关分析。在此基础上通过回归拟合,构建基于中分辨率成像光谱仪(MODIS) 的叶绿素遥感提取模型。应用模型成功提取出蓝藻爆发水域chl-a的分布。从MODIS遥感图像上可以清晰地反映出巢湖这次蓝藻爆发的强度、地点和分布范围 。研究结果表明:用MODIS影像监测巢湖蓝藻水华是可行的,其中250m分辨率波段1 、2的比值组合r2/r1与叶绿素a浓度实测值高度相关(R=0.909 3),适于反演叶绿素a浓度。     

Remote sensing for the identification of coastal plumes: case studies of Delaware Bay

Two Delaware coastal plume events are studied using various remote sensing data. Satellite images obtained from the Advanced Very High Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) are processed and enhanced to identify coastal plumes from Delaware Bay. Both visible band and infrared sensors give similar results in terms of the plume boundary. The gradient images of both types of sensors show the twin‐front structure near Cape Henlopen, which is also captured in a Synthetic Aperture Radar (SAR) image. Six consecutive SST images are used to track plume variability due to tidal forcing and the features of the plume front are found to be different between ebb and flood tides. Five consecutive SST images taken during a pronounced upwelling‐favourable wind event show the plume widened and separated offshore, while cooler water upwelled onshore of the separated plume.     

MODIS imagery of turbid plumes in San Diego coastal waters during rainstorm events

Data of normalized water-leaving radiance, nLw, obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite at spatial resolution of 250 m (band 1 centered at 645 nm) and 500 m (band 4 at 555 nm) are used to study turbid plumes in coastal waters of southern California during rainstorm events in winter of 2004-2005. Our study area includes San Diego coastal waters, which extend approximately 25 km offshore between Point Loma and 10 km south of the US-Mexican border. These waters are influenced by terrigenous input of particulate and dissolved materials from San Diego and Tijuana watersheds and non-point sources along the shore. Optimum threshold values of satellite-derived normalized water-leaving radiances at both wavebands were established for distinguishing the plume from ambient ocean waters. These threshold values were determined by searching for a maximum correlation between the estimates of satellite-derived plume area calculated using a broad range of nLw values and the environmental variables characterizing rainfall, river discharge, wind, and tides. A correlation analysis involving the amount of precipitated water accumulated during a storm event over the San Diego and Tijuana watersheds was selected as the basis for final determinations of the optimum threshold nLw_thr and subsequent calculations of the plume area. By applying this method to a sequence of MODIS imagery, we demonstrate the spatial extent and evolution of the plume during rainstorm events under various conditions of precipitation, river discharge, wind forcing, and coastal currents.     

Observed chlorophyll-a bloom in the southern Bay of Bengal during winter 2006–2007

The analysis of 6-year chlorophyll-a data provided by the Moderate Resolution Imaging Spectral (MODIS) radiometer revealed anomalous chlorophyll-a bloom in the southern Bay of Bengal during the winter 2006–2007. The plausible causative mechanisms for such a large chlorophyll-a during the winter 2006–2007 are analysed through surface wind field, surface net heat flux and sea surface height anomaly (SSHA) data. The chlorophyll-a bloom developed in November 2006 near the northern tip of Sumatra. It was intensified and propagated slowly westward during December 2006 to January 2007 and then weakened by February 2007. The combined effect of shallowing of thermocline and weakening of barrier layer due to anomalous westward propagating upwelling Rossby waves associated with the Indian Ocean Dipole (IOD) event and relatively strong wind field causing entrainment of subsurface nutrient-rich water to euphotic zone leads to the bloom in the southern Bay of Bengal.     

Detection of turbid waters and absorbing aerosols for the MODIS ocean color data processing

Atmospheric correction for the ocean color products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) uses two near-infrared (NIR) bands centered at 748 and 869 nm for identifying aerosol type and correcting aerosol contributions at the MODIS visible wavelengths. The ocean is usually assumed to be black for open oceans at these two NIR bands with modifications for the productive waters and aerosols are assumed to be non- or weakly absorbing. For cases with strongly absorbing aerosols and cases with the significant NIR ocean contributions, the derived ocean color products will have significant errors, e.g., the derived MODIS normalized water-leaving radiances are biased low considerably. Both cases lead to a significant drop of the sensor-measured radiance at the short visible wavelengths, and they both have similar and indistinguishable radiance characteristics at the short visible wavelengths. To properly handle such cases, the strongly absorbing aerosols and turbid waters need to be identified. Therefore, an appropriate approach (different from the standard procedure) may be carried out. In this paper, we demonstrate methods to identify the turbid waters and strongly absorbing aerosols using combinations of MODIS-measured radiances at the short visible, NIR, and short wave infrared (SWIR) bands. The algorithms are based on the fact that for the turbid waters the ocean has significantly large contributions at the NIR bands, whereas at the SWIR bands the ocean is still black due to much stronger water absorption. With detection of the turbid waters, the strongly absorbing aerosols can then be identified using the MODIS measurements at the short visible and NIR bands. We provide results and discussions for test and evaluation of the algorithm performance with various examples in the coastal regions for the turbid waters and for various absorbing aerosols (e.g., volcano ash plumes, dust, smoke). The proposed algorithms are efficient in the data processing, and can be carried out prior to the atmospheric correction procedure.     

Remote sensing and spectral analysis of plumes from ocean dumping in the new york bight apex

Experiments conducted in the Atlantic Coastal Zone indicate that plumes resulting from ocean dumping of acid wastes and sewage sludge have distinguishable spectral characteristics. Remotely sensed wide-area synoptic coverage provides information on these pollution features that is not readily available from other sources. Photographic and multispectral scanner data remotely sensed from aircraft were interpreted by two methods. First, qualitative analyses in which pollution features are located, mapped, and identified without concurrent sea truth and, second, quantitative analyses in which concurrently collected sea truth is used to calibrate the remotely sensed data and to determine quantitative distributions of one or more parameters in a plume. For the qualitative analyses an in-scene calibration technique was developed that “normalizes” environmental effects, thereby potentially providing a means of plume identification that is independent of the specific scene and the multispectral scanner used. Application of this technique to data from several experiments indicates that plumes resulting from acid wastes and sewage sludge have distinctive spectral characteristics over a range of environmental conditions and for two multispectral scanners flown at altitudes of 3.0 and 19.7 kilometers. In addition to qualitative analyses that used the in-scene calibration, quantitative analysis techniques were applied to sewage sludge dump plumes. A calibrated regression equation that related remotely sensed radiances to suspended solids concentrations was developed in order to map synoptic suspended solids distributions in plumes.     

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。     

Dust source identification using MODIS: A comparison of techniques applied to the Lake Eyre Basin, Australia

The impact of mineral aerosol (dust) in the Earth's system depends on particle characteristics which are initially determined by the terrestrial sources from which the sediments are entrained. Remote sensing is an established method for the detection and mapping of dust events, and has recently been used to identify dust source locations with varying degrees of success. This paper compares and evaluates five principal methods, using MODIS Level 1B and MODIS Level 2 aerosol data, to: (a) differentiate dust (mineral aerosol) from non-dust, and (2) determine the extent to which they enable the source of the dust to be discerned. The five MODIS L1B methods used here are: (1) un-processed false colour composite (FCC), (2) brightness temperature difference, (3) Ackerman's (1997: J.Geophys. Res., 102, 17069-17080) procedure, (4) Miller's (2003:Geophys. Res. Lett. 30, 20, art.no.2071) dust enhancement algorithm and (5) Roskovensky and Liou's (2005: Geophys. Res. Lett. 32, L12809) dust differentiation algorithm; the aerosol product is MODIS Deep Blue (Hsu et al., 2004: IEEE Trans. Geosci. Rem. Sensing, 42, 557-569), which is optimised for use over bright surfaces (i.e. deserts). These are applied to four significant dust events from the Lake Eyre Basin, Australia. OMI AI was also examined for each event to provide an independent assessment of dust presence and plume location. All of the techniques were successful in detecting dust when compared to FCCs, but the most effective technique for source determination varied from event to event depending on factors such as cloud cover, dust plume mineralogy and surface reflectance. Significantly, to optimise dust detection using the MODIS L1B approaches, the recommended dust/non-dust thresholds had to be considerably adjusted on an event by event basis. MODIS L2 aerosol data retrievals were also found to vary in quality significantly between events; being affected in particular by cloud masking difficulties. In general, we find that OMI AI and MODIS AQUA L1B and L2 data are complementary; the former are ideal for initial dust detection, the latter can be used to both identify plumes and sources at high spatial resolution. Overall, approaches using brightness temperature difference (BT10-11) are the most consistently reliable technique for dust source identification in the Lake Eyre Basin. One reason for this is that this enclosed basin contains multiple dust sources with contrasting geochemical signatures. In this instance, BTD data are not affected significantly by perturbations in dust mineralogy. However, the other algorithms tested (including MODIS Deep Blue) were all influenced by ground surface reflectance or dust mineralogy; making it impossible to use one single MODIS L1B or L2 data type for all events (or even for a single multiple-plume event). There is, however, considerable potential to exploit this anomaly, and to use dust detection algorithms to obtain information about dust mineralogy.     

Remote detection of Trichodesmium blooms in optically complex coastal waters: Examples with MODIS full-spectral data

Remote detection of the Trichodesmium spp. cyanobacteria blooms on the west Florida shelf (WFS) has been problematic due to optical complexity caused by sediment resuspension, coastal runoff, and bottom interference. By combining MODIS data measured by the ocean bands and land bands, an approach was developed to identify surface mats of Trichodesmium on the WFS. The approach first identifies possible bloom patches in MODIS FAI (floating algae index) 250 m resolution imagery derived from the Rayleigh-corrected reflectance at 667, 859, and 1240 nm. Then, spectral analysis examines the unique reflectance characteristics of Trichodesmium at 469, 488, 531, 551, and 555 nm due to specific optical properties (absorption, backscattering, and fluorescence) of the unusual pigments in Trichodesmium. These spectral characteristics (i.e., high-low-high-low-high reflectance at 469-488-531-551-555 nm, respectively) differentiate Trichodesmium mats unambiguously from other features observed in the FAI imagery, such as Sargassum spp. Tests in other coastal locations show that the approach is robust and applicable to other optically complex waters. Results shown here can help study Trichodesmium bloom dynamics (e.g., initiation and bloom formation) and may also help design future sensors to better detect and quantify Trichodesmium, an important N₂ fixer in the global oceans.     

Automatic detection of coastal plumes using Landsat TM/ETM+ images

Riverine fresh water outflows create coastal plumes that are distinguished from surrounding sea water by their specific spectral signature. Coastal waters are unique ecosystems, and they are very important in terms of living resources and oceanographic processes. River plumes and coastal turbid waters have important effects on coastal marine ecosystems, and they also influence marine life cycles, sediment distribution, and pollution. Remote sensing and digital image-processing techniques provide an effective tool to detect and monitor these plume zones over large areas. The primary goal of this study was automatic detection and monitoring of coastal plume zones using multispectral Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) imagery. For that purpose, the proposed algorithm exploits spectral features of the multispectral images by using feature extraction and decision-making steps. The procedure has two main stages: (1) some pre-processing operations were applied to the images in order to extract the plume core reflectance values with maximum turbidity and offshore water mass reflectance values; (2) a k-means algorithm was applied with initial seed values of reflectance computed from the pre-processing stage to classify coastal plume zones. Spatial pattern and variability of optical characteristics of coastal plume zones were then defined following the results of the classification process. The algorithm was automatically applied in three different regions with three multispectral Landsat images acquired on different dates, and yielded a very high classification accuracy in detecting coastal plume zones.     

Detection of monsoonal phytoplankton blooms in Luzon Strait with MODIS data

The Luzon Strait experiences a seasonal surge of phytoplankton based on ocean color data. To examine the timing and position of the bloom, daily MODIS chlorophyll data (2002-2005) were acquired and analyzed. Supporting data such as MODIS SST and nLw bands, QuikSCAT wind data, river discharge, and bathymetry were also used and processed in order to investigate the possible driving forces behind the bloom occurrence. Results show that chlorophyll concentration in the Luzon Strait dramatically increases during NE monsoon reaching to approximately 2.0 mg/m³. Highest chlorophyll concentration is observed near the western slope of the Luzon Strait. The monsoonal bloom in the Luzon Strait is likely due to upwelling and can be possibly associated with the interaction of the northward flowing Luzon coastal current with the westward intrusion of Kuroshio during NE monsoon. The positive wind stress curl also contributes to this occurrence. The influence of Cagayan River is only limited within few kilometers from the river mouth.     

Classification of MODIS images combining surface temperature and texture features using the Support Vector Machine method for estimation of the extent of sea ice in the frozen Bohai Bay,China

Image classification of frozen areas and adjacent sea ice is important for monitoring the evolution of ocean freezing. This paper proposes a novel approach to the Moderate Resolution Imaging Spectroradiometer (MODIS) image classification and estimation of the extent of sea ice in frozen areas during recent global surface warming hiatus. We derived the texture feature (TF) and surface temperature (ST) from the MODIS image for classification and sea ice detection. We extracted MODIS TF by a grey-level co-occurrence matrix (GLCM), and retrieved the MODIS ST using a split-window method, and finally classified the image using a Support Vector Machine (SVM) convoluting the ST and TF methods. Results were compared and validated with those of conventional spectral-based supervised classification approaches. Results show that the overall accuracy and kappa coefficient (κ) using the proposed method was much higher in comparison with those of the spectral-based maximum likelihood and SVM methods. The SVM fusion ST and TF method was effective and useful for MODIS 500 m image classification and sea ice mapping in frozen area. Combining ST and TF can improve sea ice extent estimation accuracy in the frozen Bohai Bay.     

基于自适应加权平均的水色遥感数据融合

水色遥感数据在海洋生产力评估、海洋污染监测等领域中应用广泛,但是受云雨天气、太阳耀斑、纬度位置、轨道间隙的影响,单一的水色遥感数据日均覆盖率只能达到10%～15%甚至更少,导致水色遥感技术在海洋领域的应用受到限制。水色遥感数据融合通过对不同水色传感器数据进行整合,使融合数据比单一传感器数据具有更好的空间覆盖和时间覆盖。选择中国东海为研究区域,首先对不同水色传感器的叶绿素浓度数据进行比较,证明同一天不同传感器数据一致性良好,在空间覆盖上既互相重叠又形成互补关系,保证了数据融合的可行性。在业务化运行的加权平均算法的基础上,提出了利用窗口中各数据源有效像元所占的比例来确定其对应权值的自适应加权平均融合算法。目视判别和统计分析结果表明,该算法能够较好地保持源数据细节特征,保证不同数据覆盖区域间的数据连续性,降低数据源噪声,提高数据空间覆盖率和置信度。     

Multi‐scale thermal pattern monitoring of a large lake (Lake Geneva) using a multi‐sensor approach

The applicability of satellite imagery products from different sensors (AVHRR‐derived multi‐channel sea surface temperature (MCSST), MODIS sea surface temperature (SST) products 5‐Min L2 Swath 1 km and Landsat TM band 6 thermal signature) for the comprehensive monitoring of temperature and its temporal patterns over a large lake is tested in this study. The coverage of cloud‐free satellite data for Lake Geneva is reported throughout a year and, more specifically, during a 13 day period in summer 2003. In a second step, we demonstrate the feasibility of the AVHRR/MODIS imagery to discern day and night temperature patterns, by generating day and night climatologies and various spatial statistics over the 13 day period. The different day and night surface thermal patterns observed by satellite imagery could be linked to the thermal structure existing in deeper water using the concept of the diurnal decoupled layer. The forcing of the persistent patterns, two warm cores divided by a saddle‐shaped cold anomaly, is explained by wind periodicity and insolation conditions. The patterns can be matched to features postulated by findings of different limnologists in the past. Other surface temperature related phenomena such as water upwelling and downwelling and the occurrence of plumes are related to meteorological and hydrological events. The lakewide average lake surface water temperature (LSWT) trends for day and night during the study period are roughly parallel. A sudden loss of stored heat can be explained by episodes of long fetch, synoptic wind (bise) that interrupted the predominant breeze regime.     

Satellite monitoring of phytoplankton in the East Mediterranean Sea after the 2006 Lebanon oil spill

Multi-source remote-sensing data were used to study the aftermath of the 2006 Lebanon oil spill: Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data and advanced synthetic aperture radar (SAR) images were used to delineate the extent of the oil spill; QuikScat data were used to derive the wind field; and MODIS Ocean Colour data were used to demonstrate the variability of chlorophyll-a concentration (Chl-a) and sea surface temperature (SST). No significant changes were found in marine phytoplankton in the first few months after the oil spill. However, a big phytoplankton bloom was observed after 10 months, when Chl-a increased from 0.1 to 1.0 mg m^–3. After considering the time-series variability of Chl-a and SST and the spatial variability of Chl-a and bathymetry, we concluded that this phytoplankton bloom was probably related to the Lebanon oil spill.     

Bright spot analysis of ocean-dump plumes using LANDSAT MSS

Identifying ocean-dumped materials by analysing the upwelled solar energy from the plume is complicated by the dispersion of the plume and the spectral absorption of the water. It is shown that the spectral analysis of ocean-dump plumes, using LANDSAT multispectral scanner (MSS) data, should be confined to the brightest area within the plume, the region where the waste material is least dispersed and nearest the surface. The decay of the upwelled radiance with time of the brightest pixel within the plume, at least for iron acid waste, is predictable. An accurate age determination of an acid plume is limited by striping within the MSS data     

Variations of chlorophyll-a in the northeastern Indian Ocean after the 2004 South Asian tsunami

Analysis of satellite remote sensing data has revealed changes in distribution of chlorophyll-a (Chl-a) and sea surface temperature (SST) in the Indian Ocean during the South Asian tsunami in December 2004. Chl-a data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) images were examined for the period from 1998 to 2005. Around the epicentre of the Sumatra earthquake, the Chl-a concentration was found to increase prior to the main event on 26 December 2004 and then decrease during the tsunami event, while a high SST (～30–31°C) was observed in and around the epicentral region. Chl-a concentrations in the coastal waters of the Southeast Asian countries were remarkably low during and after the tsunami. Similar but relatively small variations in Chl-a and SST were observed during the second earthquake on 28 March 2005. Analysis of Chl-a, SST, wind and upwelling water has provided information for understanding the changes in Chl-a concentration during the tsunami. A very large offshore phytoplankton bloom (～300 km²) appeared to the southeast of Sri Lanka about 3 weeks after the tsunami; this might have been caused by a tropical storm that could be responsible for the enhancement of nutrients.