This PDF file contains the front matter associated with SPIE Proceedings Volume 7825, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Short-term and seasonal estimates of colored dissolved organic matter (CDOM) absorption at 412 nm and surface salinity were derived from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) satellite data for the Louisiana-Texas coast during 2005 using an empirical CDOM algorithm and a conservative CDOMsalinity relationship. Field measurements obtained during various seasons in 2005 indicated high correlations between field and satellite estimates of CDOM suggesting satellite estimates to be good representation of the surface CDOM and salinity fields. Discharge from the Mississippi and Atchafalaya rivers strongly influenced the seasonal surface CDOM distribution as well as during a frontal passage. Clear satellite imagery obtained before and after the passage of a cold front in March 2005 indicated a general decrease in surface CDOM and an offshore increase of elevated CDOM suggesting that frequent frontal passages contribute to mixing of riverine CDOM and its offshore transport. A comparison of SeaWiFS-derived salinity with the salinity outputs of a three-dimensional Navy Coastal Ocean Model (NCOM) indicated similar salinity trends offshore and a region of freshwater influence along the inner shelf. Following the frontal passage, changes in the surface salinity were observed mainly closer to the coast with a general increase in mid-shelf waters, likely due to mixing of lower salinity surface waters with higher salinity sub-surface waters. Short-term salinity model simulation could be improved with daily assimilation of river discharge data. SeaWiFS derived seasonal salinity estimates provided a synoptic view of the effects of coastal circulation and riverine discharge on shelf variability.

°Empirical orthogonal function (EOF) analysis was used to study spatio-temporal variability of the Moderate Resolution Imaging Spectroradiometer (MODIS) imagery of sea surface temperature (SST (C)) and chlorophyll (mg m^-3) for the eastern Bering Sea for May, June, July, August, September (MJJAS) for a period of 7 years (2003 - 2009). The EOF analysis was conducted on SST and chlorophyll monthly composites that were normalized by subtracting the spatial and temporal means with cloud, ice and land cover masked out. The SST in eastern Bering Sea showed a transition from a warm period (2003-2005) to cooler period (2006-2009). The first 3 EOF modes of SST were retained as they explained 76.7% of the spatio-temporal variability, with the first SST EOF mode explaining 59.5% of the total variation of SST in the study area during the study period. For the chlorophyll dataset, the first 3 EOF modes explained greater than 58.5% of the spatio-temporal variability, with the first chlorophyll EOF explaining 28.14% of total variance in chlorophyll. The decreasing amplitude of first SST EOF and switching from mostly positive to negative amplitude of the third chlorophyll EOF mode in 2006 was consistent with the May SST Index, Ice Cover Index and Bering Sea Pressure Index (BSPI).

The Long Island Sound Coastal Observational platform (LISCO) near Northport, New York, has been recently established to support satellite data validation. LISCO is equipped with both multispectral SeaPRISM and hyperspectral HyperSAS radiometers for ocean color measurements. LISCO substantially expands observational capabilities for the continuous monitoring and assessment of ocean color satellite data quality. This offers the potential for improving the calibration and validation activities of current and future Ocean Color satellite missions, as well as for satellite intercomparisons and spectral characterization of coastal waters. Results of measurements made by both the multi and hyperspectral instruments, in operation since October 2009, are presented, evaluated and compared with ocean color satellite data. The comparisons with the normalized water-leaving radiance derived from SeaPRISM with that from MERIS, MODIS and SeaWiFS showed satisfactory correlations (r > 0.9 at 550nm) and consistencies (APD < 15% at 550nm). Similar and equivalent results are obtained when the hyperspectral HYPERSAS data are compared with the same satellite datasets. The results confirm that the LISCO site is appropriate for use in calibration/validation of the ocean color satellites in coastal waters and as a key element of the AERONET-OC network. This makes it possible to foresee a wider use of the LISCO site to monitor current and future ocean color multispectral (NPOESS, Sentinel) and hyperspectral (HICO) satellite missions.

Quantifying ocean colour properties has evolved over the past two decades from being able to merely detect their biological activity to the ability to estimate chlorophyll concentration using optical satellite sensors like MODIS and MERIS. The production of chlorophyll spatial distribution maps is a good indicator of plankton biomass (primary production) and is useful for the tracing of oceanographic currents, jets and blooms, including harmful algal blooms (HABs). Depending on the type of HABs involved and the environmental conditions, if their concentration rises above a critical threshold, it can impact the flora and fauna of the aquatic habitat through the introduction of the so called "red tide" phenomenon. The estimation of chlorophyll concentration is derived from quantifying the spectral relationship between the blue and the green bands reflected from the water column. This spectral relationship is employed in the standard ocean colour chlorophyll-a (Chlor-a) product, but is incapable of detecting certain macro-algal species that float near to or at the water surface in the form of dense filaments or mats. The ability to accurately identify algal formations that sometimes appear as oil spill look-alikes in satellite imagery, contributes towards the reduction of false-positive incidents arising from oil spill monitoring operations. Such algal formations that occur in relatively high concentrations may experience, as in land vegetation, what is known as the "red-edge" effect. This phenomena occurs at the highest reflectance slope between the maximum absorption in the red due to the surrounding ocean water and the maximum reflectance in the infra-red due to the photosynthetic pigments present in the surface algae. A new algorithm termed the surface algal bloom index (SABI), has been proposed to delineate the spatial distributions of floating micro-algal species like for example cyanobacteria or exposed inter-tidal vegetation like seagrass. This algorithm was specifically modelled to adapt to the marine habitat through its inclusion of ocean-colour sensitive bands in a four-band ratio-based relationship. The algorithm has demonstrated high stability against various environmental conditions like aerosol and sun glint.

In the frame of the Global Monitoring for Environment and Security (GMES) program ESA is currently implementing the Sentinel-3 mission [1], [2]. It is designed as a constellation of two identical polar orbiting satellites, separated by 180 deg, for the provision of longterm operational marine and land monitoring services. As such, the Sentinel-3 satellite carries a topography mission including mainly RF instruments and an ocean and land colour mission composed of optical instruments. The aim of this paper is to first provide a brief overview of the Sentinel-3 mission and then focus on the core instrument of the topography mission [3], the SRAL radar altimeter [4], and its latest development steps.

Research was conducted to monitor arctic river ice conditions in Siberia. The lack of traditional hydrological measurements in those remote inaccessible regions makes the use of satellite data a key technique in obtaining information on their hydrological cycle. The Global Flood Detection System (GFDS) based on microwave satellite data of AMSR-E system was used to observe river ice conditions in the polar region. The orbital gauging observations are serving the basis of artic river monitoring. The ice break-up in spring is a significant change in the river condition well detectable by the GFDS system. Variations in the time-series of several years can be mean to quantify effects of global climate change. Results show more significant change in the spring melting than in the freezing period. The ice-break up is showing more variation in Siberian rivers than in those of North-America. For this reason only the break-up period in the Siberian rivers was investigated. Limitation of the system is the length of the time-series still the trend shows that the period of ice-break up in the rivers are moving towards early spring.

This work describes the potential of oil spill classification from optical satellite images, as investigated by applying different machine learning techniques to a dataset of more than 300 oil spill candidates, which have been detected from multi-spectral satellite sensors during the years 2008 and 2009, over the entire area of the Mediterranean Sea. A set of geometrical and grey level features from Synthetic Aperture Radar (SAR) literature has been extracted from the regions of interest in order to characterize possible oil spills and feed the classification system. Results obtained by applying different machine learning classifiers to the dataset, and the achieved performance are discussed. In particular, as a first approach to oil spill classification, simple statistical classifiers and neural networks were used. Then, a more interpretable fuzzy rule-based classifier was employed, and performance evaluation was refined by exploiting Receiver Operating Characteristic (ROC) analysis. Finally, since oil spill dataset collection happens incrementally, a suitable technique for online classification was proposed, encompassing at the same time cost-oriented classification, in order to allow for a dynamic change of the misclassification costs. This latter goal has been achieved by building an ensemble of cost-oriented, incremental and decremental support vector machines, exploiting the concept of the ROC convex hull.

The surveillance of ships at sea is a routine application that makes use of space-based Synthetic Aperture Radar (SAR) images. Presently, such applications continue to be improved upon, making use of various techniques to better detect ships in the SAR images. This paper will provide an overview of the recent improvements made to the SAR images that are used by such applications. Leveraging the programmability of the RADARSAT-2 sensor, MDA is in the process of developing and assessing two new ScanSAR beam modes: a 450km wide ship detection optimized beam mode in the HH polarization channel, and a 530km wide multi-purpose beam mode in the HH and HV polarization channels. It will be shown that these new beam modes are significantly better, offering nearly uniform ship detection of much smaller vessels across the full swath width, as compared to the existing RADARSAT-2 ScanSAR beam modes.

The Gavdos calibration facility for satellite radar altimeters has been operational as of 2004. The island is located along repeating ground tracks of Jason-1 and Jason-2 satellites (crossover point for passes No.109 ascending and No.018 descending and adjacent to Envisat), and because of its small size, both altimeter and radiometer measurements are not significantly contaminated by land. This makes Gavdos an ideal place for the calibration of satellite altimeters. In this work, three different techniques have been applied for calibrating the Jason altimeter measurements at Gavdos Cal/Val facility. These are: (i) The conventional: In-situ observations made by tide gauges, GNSS receivers, meteorological and other sensors in conjunction with precise geoid models are applied for determining the altimeter bias; (ii) The MSS: instead of the geoid, the mean sea level, provided by the CLS10_MSS model, is used as a reference surface for estimating the bias; and (iii) Microwave transponder measurements are implemented and examined over the cross over point on land to produce the altimeter bias as well. This paper presents the results regarding these calibration techniques.

SWIM is a satellite-based Ku-band real aperture radar designed for measuring the directional ocean wave spectrum. This radar works at six incidence angles (from 0 degree to 10 degree) with the 360 degree azimuth scanning. The measurement principal has been proposed by Jackson and the simulation technology has been developed by Hauser, which depends on the comparisons between estimated modulation spectrum and reference one. Based on the method after the slightly modification, we consider the SWIM performance for measuring wave properties in cases of no noise, noise and noise with correction. Moreover, we propose the four methods to calculate the coefficient α(θ) , which include integration method, derivative method, wind speed experience method and significant wave height method. We substitute the α(θ) into the simulation, in which the forward simulation only use the integration method and the afterward simulation use the four method alternatively. From the results, it shows that the SWIM has the capacity of measuring the wave spectrum but it requires the proper processing for the elimination and correction of noise. Among the four methods, the wind speed experience method has a big deviation with reference spectrum, the integration and derivative methods have good results of measurements and the significant wave height method has moderate error and shows good in low wind speed.

Light detection and ranging (LIDAR) is currently used for bathymetric measurement or underwater target detection. A new underwater-target detection scheme named modulated lidar was recently proposed. The study reported here deals with optimization of the modulation process to be applied under such detection conditions. A theoretical model was extracted from available experimental results by deconvolution and further used to simulate realistic backscattered signals for the development of a new modulation scheme. Then, an optimum set of amplitude modulation code parameters was obtained by maximizing the target signal-to-noise ratio. This paper will highlight some particularly promising waveform configurations.

Estimating the Stokes vector components of the polarized water radiance from above water measurements is a challenging task, mainly because of their small magnitude and the strong contamination by the sky light reflected on the sea surface. Consequently, in most applications the Stokes vector components are considered equal to zero except of I, the total reflectance. In this study, both below and above water measurements are used to assess the feasibility of such retrievals and their use to determine the water composition. In-water inherent optical properties (IOPs) were measured with commercially available instrumentation. In addition, in-water polarization characteristics were measured by our multi-angular hyperspectral sensor which provided the Stokes components for a scattering angles range of the 0-180° and a full spectral range between 400 and 750 nm. Second, a customized HyperSAS (Satlantic) instrument is used from the coastal platform in Long Island Sound, NY (LISCO) acquiring above water measurements. That instrumentation includes, in the standard configuration, two hyperspectral radiance sensors for measuring upwelling and sky radiances and one irradiance sensor for measuring downwelling irradiance. In our installation, HyperSAS capabilities were augmented by adding two radiance sensors having two polarizers oriented at 0 and 45°, with respect to a reference axis ("HyperSAS-POL"). An ad hoc procedure, which included measurements and radiative transfer computations, has been developed enabling to estimate the contribution of the sky glint and subtract it from the signal directly measured by HyperSAS-POL. As a result, the retrieved spectral shape of the underwater degree of polarization is consistent with what obtained from in situ underwater measurements and depends on the IOPs of the ocean itself. In addition, the demonstrated correctness of this polarized measurements from LISCO site enable us to provide continuous time series from the beginning of June 2010.

The paper is devoted to the development of optical methods for investigations of surface waves. The no coherent optical multichannel spectrum analyzer for space - time (3D) spectral measurements of sea surface waves in real time is developed. The model of space - time (3D) spectra of sea surface short scale wave's optical image taking into account elevation of the long energy surface waves and comparative measurements of optical image spectra and Doppler spectra of X-band scatterometer are presented. A method for retrieval of absolute value of long wave elevations from optical image spectra is proposed. The high-speed optical system based on linear array of CCD photodiodes for registration space - time images of capillary waves is created. The data on free and bounded capillary waves derived with this system in laboratory tank with artificial diffuse illumination is presented.

Carefully collected airborne imagery demonstrates the ability to see water surface features as well as shallow bottom features such as submerged vegetation and manmade targets. Traditional photogrammetric imagery and airborne digital imagery both suffer from a loss in image clarity due to a number of factors, including capillary and small gravity waves, the water column or in-situ constituents. The use of submerged as well as surface man-made calibration targets deployed during airborne or in-situ subsurface image acquisitions forms a preliminary basis for correcting imagery in order to improve subsurface and surface features and their detection. Methods presented as well as imagery at 490 nm, 532 nm and 698-700 nm clearly show subsurface features in shallow waters. The techniques utilized include the use of large frame cameras with photogrammetric films in combination of special filters, such as a Wratten # 70, in order to provide narrower spectral features near the vegetative "red edge" to be used to improve interpretation of hyperspectral push broom imagery. Combined imagery from several sensors and platforms, including autonomous underwater vehicles, form the basis of data fusion for surface and subsurface automatic feature extraction. Data presented from a new hyperspectral imaging system demonstrates the utility of sub-meter hyperspectral imagery for use in subsurface feature detection.

In recent years, the ocean oil spill pollution has already become one of terrible disasters on earth. Every year, thousands of tons of crude oil spill enter global oceans. It heavily pollutes seas as the results of ecological disasters, such as oil tanks, tank washings from oil tankers, discharges of machinery wastes. It's very important to monitor the ocean oil spill pollution. In this context, a novel means of the UV push-broom imaging for airborne remote sensing was described and validated. Firstly, a new-style UV linear array detector was designed, based on the GaN material sensitive to UV radiation from 300nm-370nm, 512-pixel, in possession of the domestic intellectual property in China, and this UV detector was the first device using the technology to manufacture GaN-base-512-pixel linear array detector successfully. It had virtues such as the UV radiation band for detection can be controlled by different ingredients of the GaN-base material, so it wasn't necessary to achieve the aim using special UV optic film filters, and this new-type linear array detector was flexible and high efficient to image actual objects for UV remote sensing. Secondly, an UV prototype camera (includes two visible channels) was fulfilled, using the GaN-base-512-pixel UV and visible linear array detectors to implement push-broom imaging, IFOV (500μrad), in nadir and limb view angle (15°), SNR prior to approximately 3000 under the condition of a standard solar constant. Thirdly, airborne validation of a new-style ultraviolet push-broom camera for ocean oil spill pollution surveillance had been achieved for the first time in Yellow Sea area of China in Sept, 2009. Not only the quality of UV push-broom images was good, but also all parameters of the camera were well fulfilled. The new-type UV imaging technology using GaN-based linear array detector for push-broom was successfully validated. The result shows that using ultraviolet push-broom imaging remote sensing method has the great potential to monitor the real oil spill pollution on the sea. Meanwhile, it also shows that UV band must have a high detecting sensitivity to be useful and benefited for detecting the marine oil spill than other visible bands. In future, this technology can be applied for the ocean oil spill pollution surveillance, preparing for UV imaging remote sensing under the airborne or the space platform, and it can be carried out from the medium to high spatial resolution. In conclusion, it is significant to the UV remote sensing development.

The airborne FLS-Lidars are based on the method of Laser Induced Fluorescence (LIF) and aimed at the analytical remote sensing of water objects. Scanning the laser beam across the flight trajectory and recording the comprehensive LIF spectrum with hyperspectral detector per every laser pulse provide detail maps of spectral properties of the water basins. A multi-tier model for integrated environmental assessment is applied for further analysis of this information to combine the benefits of "big-picture" capability of remote sensing techniques and GIS solutions with localized on-theground environmental data gathering. In this concept far looking satellite and airborne systems provide the highest tier information. The airborne data acquisition with FLS-Lidar is considered as the middle tier characterized by vast amount of LIF data with high spatial (less than 10 m) and spectral (less than 5 nm in UV/VIS spectral ranges) resolution. The lower tier is anchored with the geographical locations of important findings detected at the middle tier. Taken water samples are analyzed with fastscreening technology of Spectral Fluorescence Signatures (SFS) giving more analytical qualitative and quantitative results. And the base tier includes detail laboratory analysis of characteristic samples selected at the lower tier. Precisely geo-referenced LIF data of hyperspectral FLS-Lidar anchored to and calibrated by the ground SFS data allows detection of pollution incidents and mapping of environmental trends over vast water systems like coastal zone, lakes and rivers.r

As an effective survey tool for oil spill detection, the airborne hyper-spectral sensor affords the potentiality for retrieving the quantitative information of oil slick which is useful for the cleanup of spilled oil. But many airborne hyper-spectral images are affected by sun glitter which distorts radiance values and spectral ratios used for oil slick detection. In 2005, there's an oil spill event leaking at oil drilling platform in The South China Sea, and an AISA+ airborne hyper-spectral image recorded this event will be selected for studying in this paper, which is affected by sun glitter terribly. Through a spectrum analysis of the oil and water samples, two features -- "spectral rotation" and "a pair of fixed points" can be found in spectral curves between crude oil film and water. Base on these features, an oil film information retrieval method which can overcome the influence of sun glitter is presented. Firstly, the radiance of the image is converted to normal apparent reflectance (NormAR). Then, based on the features of "spectral rotation" (used for distinguishing oil film and water) and "a pair of fixed points" (used for overcoming the effect of sun glitter), NormAR₈₉₄/NormAR₅₁₆ is selected as an indicator of oil film. Finally, by using a threshold combined with the technologies of image filter and mathematic morphology, the distribution and relative thickness of oil film are retrieved.

Imagery is presented along with calibration and testing procedures of several airborne imaging systems. The low altitude airborne systems include a cooled hyperspectral imaging system with 1024 spectral channels and 1375 spatial pixels. The hyperspectral imaging system is collocated with a full resolution high definition video recorder for simultaneous HD image acquisition, 12.3 megapixel digital images for multispectral "sharpening" the hyperspectral imagery, or large frame 9 inch film cameras yielding scanned aerial imagery with approximately 2200 by 2200 pixel multispectral imagery. Two high spectral (252 channels) and radiometric solid state spectrographs are used for collecting upwelling radiance (sub-meter pixels) and a downwelling irradiance using a fiber optic irradiance sensor. These sensors are utilized for cross calibration and independent acquisition of ground or water reflectance signatures. The upwelling spectrograph is integrated to collect signatures collocated with a 12.3 megapixel Nikon D2Xs with 5 HZ WAAS GPS input for mixed pixel analysis in conjunction with the other sensing systems. In addition to the airborne hyperspectral sensors, traditional photogrammetric imagery can be collected from dual 9 inch frame cameras capable of using a combination of panchromatic, color or color infrared film types. The panchromatic film can be collected with special filters designed to create spectral windows, such as the "red edge" which is useful for land or shallow submerged vegetation and live coral detection. The 9 inch film negatives are scanned to produce over 250 megapixel scanned multispectral imagery for sub pixel assessments us and image fusion sharpening of the hyperspectral imagery. The scanned imagery covers larger spatial regions and is thus useful for geospatial registration, rectification, and spatial sharpening of the hyperspectral imagery along flight lines. All of the airborne sensor systems allow for modern research in the use of sun and sky glint regions in imagery to identify water surface wave field characteristics as well as oil slicks. The systems described provide unique data sets of for modern airborne or satellite remote sensing algorithm development and testing of radiative transfer models.

Sea surface temperature (SST) is one of the most important variables related to the global ocean-atmosphere system, which play an important role in studies of air-sea heat exchange, upper ocean processes, and weather forecast. SST data are routinely measured from ships, buoys and offshore platforms. In this paper, the weekly 4 km resolution AVHRR SST data (1985-2006), the weekly 4 km resolution MODIS SST data (2002-2007) and the daily 25 km resolution AMSR-E SST data (2002-2007) are chosen for merging. These SST data are derived from different Remote Sensors with different spatial and temporal resolution. By merging these SST data, we can get a new SST product and obtain more information. The bayesian hierarchical model using Markov Chain Monte Carlo (MCMC) simulation methods was used to merging the thermal infrared MODIS SST data and passive microwave AMSR-E SST data. The results show that merged SST data have a better completeness than MODIS SST and AMSR-E SST products. Comparing merged SST data with drift buoy SST, the validation result shows that the bias is 0.32118K and RMSE is 0.8026K.

A method is developed for estimating the influence of surface waves and multiple scattering in water on delay and broadening of light pulse signal propagating along the path: atmosphere - rough surface - sea water. The parameters of signal entering an underwater receiver with isotropic directional diagram are calculated. It is shown that surface waves strongly influence the temporal characteristics of signal at small depths of receiver location and in clear water. The influence of geometrical factors, including the size of receiving aperture and the angular characteristics of emitter, is studied. In the calculations, it is assumed that surface waves are isotropic, the elevation spectrum is determined by the Pierson-Moskowitz formula, and the surface slope variance corresponds to the Cox and Munk data. As the Green function of the radiative transfer equation we used the solution of this equation in the refined self-similar approximation taking into account diffusion of photons along multipaths. This method is generalized in the same approximations to evaluate the temporal characteristics of lidar signals reflected from the bottom or water. Delay and broadening of pulse echo signal for two types of lidars, i.e., with isotropic and extremely narrow receiving diagrams, are calculated. The contribution of waves to delay and broadening of echo signal in lidars is, as a rule, several times smaller than the contribution of multiple scattering effects in water; nevertheless it should be taken into account for correct estimation of the limiting depth resolution of lidar. It is shown that lidar with a narrow directional diagram has a better depth resolution than lidar with a wide diagram. However for some sets of parameters in these lidars, the wave contribution to signal delay can significantly exceed the contribution of water. The fulfilled estimates are greatly important for bathymetric lidars.

Remote sensing technology can be used to timely monitor the distribution and growth status of algae on a large scale, but the suspended matter in water bodies may affect the spectral characters of algae, resulting in an inaccuracy of the interpretation of related remote sensing images. It is very important to improve remote sensing reverse precision and to understand the influence of suspended matter in water to algae spectrum's characteristic. In this paper, the spectral reflectance of algae in water with different suspended matter concentration was measured using a Spectroradiometer with spectral range of 350-1050 nm and resolution of 3 nm. The results showed that the spectral reflectivity of the algae increased with increasing concentration of suspended matter at the band of 400-900 nm, and a clear linear relationship between spectral reflectance and suspended matter were found at the wavelength bands. The results obtained in this study could be used to analyze the effects of suspended matter in water body on the spectral reflectivity of algae, and provide a helpful way to rapidly and accurately monitor the distribution and dynamics of algae on a large scale using remote sensing.