The purpose of this paper is to derive a reliable theory to predict the performance of a narrow-FOV bathymetric lidar. A fundamental discrepancy between the theoretical estimate and experimental results was the inspiration for the work presented here Meeting oceanographic mapping requirements is a critically important goal for littoral laser bathymetry. In contrast to traditional airborne lidar system which are optimized for recovering signals from the deepest possible waters , the above challenge may be met with a radical narrowing to the lidar transmit beam and receiver field of view (FOV) employed in EAARL (Experimental Advanced Airborne Research Lidar, NASA). In this paper we discuss theoretical analysis carried out on the basis of a sophisticated “multiple-forward scattering and single-backscattering model” for lidar return signals allows a quantitative estimation of the advantages of a narrow-FOV system over traditional bathymetric lidars (SHOALS-400, SHOALS-100, LADS Mk II) when used in clear shallow-water cases. Some of those advantages are: · Increase in bottom definition (or reduced false-alarm probability) due to the enhanced contrast of the bottom return over the background backscatter from the water column, · Enhancement in depth measurement accuracy resulting from narrower bottom return pulse width, · Reduction of post-surface return effects in the lidar photo-multiplier detector due to a more rapid decay of water column backscatter, · Greatly improved rejection of ambient light permitting lidar operations in all zenith sun angles and flight directions. The model computations make it possible to estimate the maximal operational depth for the system under consideration by the implementation of statistical theory of detectability. These computations depend on the prevailing seawater optical properties and lidar parameters. The theoretical predictions are compared with results obtained in the field test of the EAARL system carried out in Florida Keys in 2001.

The shape of signal from sea water column obtained with an oceanographic lidar is not sufficient to pick out a contribution from a separate physical characteristic of the water. A possible approach consists in compensating the deficiency in information obtained from return signal of traditional depth-sounding lidar by using a variable field-of-view (FOV) receiver which register simultaneously (or subsequently) return signals obtained with several different FOVs. The proposed method calls for at least three FOV angles to be involved - a narrow one (approximately equal to lidar transmitter (laser beam) divergence), a wide (FOV plane angle of 50-70 mrad), and an intermediate (the angle of the order of 15-25 mrad). The approach is based on the fact that the effective system attenuation coefficients, Kn, Kw, and Ki, derived from the slopes of backscatter return variations with depth for the narrow-, the wide-, and the intermediate-FOV receiver correspondingly, are different functions of seawater IOPs. The functions are established theoretically using a sophisticated model of lidar return that allows for a wide variety of shapes of the volume scattering function; a characteristic of the actual VSF shape (for forward scattering) is to be determined, in parallel with IOP estimates, from the recorded lidar waveforms. It seems to be of significance that a system to realize the proposed method may be created on the base of an existing hydrographic (bathymetric) lidar with only a simple enough modification of basic optical scheme.

A pump-and-probe (P&P) airborne LIDAR has been recently developed at NASA Goddard Space Flight Center. It provides remote measurement of phytoplankton photosynthetic variables along with pigment and organic matter fluorescence, down-welling and upwelling hyperspectral measurements and sea surface temperature. The utilization of an airborne platform provides for rapid remote characterization of phytoplankton photosynthetic activity, biomass and diversity over large aquatic areas. The P&P LIDAR technique is one of the first practical implementations of 'superactive' remote sensing. This presentation summarizes results of six airborne measurement campaigns conducted in 1999-2002 in the Chesapeake Bay, Delaware Bay, Middle Atlantic Bight, and Gulf of Mexico. The P&P technology has been complemented by a Laser Phytoplankton Analyzer (LPA), a shipboard laser fluorometer dedicated to technological advancement in pigment analysis that will be implemented in future LIDAR systems. It combines high-resolution spectral measurements of phytoplankton pigment fluorescence excited at several selected wavelengths with active assessment of the physiological status of the phytoplankton photosynthetic apparatus. Emission/excitation measurements provide a potential for assessing concentrations of photosynthetic accessory pigments (Chlorophyll a, b, c, photosynthetic carotenoids and phycobilins) and identifying major phytoplankton functional groups. The LPA was extensively tested in laboratory experiments with phytoplankton cultures and their mixtures. In November 2002, the LPA was utilized for pigment fluorescence analysis of natural phytoplankton over a range if environmental conditions on a research cruise in the Middle Atlantic Bight and Delaware Bay.

Mathematical modeling is employed widely to optimize parameters of oceanographic lidar systems under development and to estimate a system efficiency in a given conditions. The so called “multiple-forward scattering and single-backscattering model” based on small-angle scattering approximation for radiation transfer equation (L.S. Dolin et al.) is brought by now to the level where it is used in engineering design of laser-based depth-sounding instruments, underwater location and imaging sensors, etc. Some recent concerns of optical oceanography such as precision depth measurement in littorals and estimation of ocean-water optical properties from remote sensing data, calls for application of airborne lidar systems with relatively narrow field-of-view (FOV). The case is not covered by known analytical models and necessitates a generalization of corresponding theory. A mathematical model presented in this paper is applicable for lidar return signal from seawater column at arbitrary relation between sounding laser beam divergence and receiver FOV angle. In the limiting cases of wide and narrow FOV the proposed model is shown to be consistent with expressions obtained previously. Model computations of the system attenuation coefficient as a function of receiver FOV angle for various seawater optical properties are found to be in good agreement with well-known results of Monte-Carlo simulation by H.R. Gordon.

Science and Technology International (STI) has developed a six-band multispectral imager optimized for surf-zone reconnaissance and mine countermeasures (MCM). Airborne surf-zone MCM requires both accurate spectral imaging and high spatial resolution. Vibration and aircraft motion degrade the image quality. However weight, volume and power constraints preclude stabilized operation of the cameras. Thus, the MTF needs to be measured in flight to insure it meets the resolution requirements. We apply the slanted-edge MTF method to the in-flight characterization of airborne high-resolution cameras, analyzing images of orthogonal slanted edges to estimate the motion and vibration contributions to the MTF, and show that the system exceeds the resolution requirements for surf-zone MCM. We also develop a methodology for scaling to other altitudes and speeds, and show that the system will perform well throughout its operational envelope. The slanted-edge method is more accurate and reproducible than the alternative of placing MTF bar targets under the aircraft flight path. Further, the slanted-edge targets are easier to deploy and recover, and ease the navigation tolerances.

The NASA Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Program had a worldwide, ongoing ocean color data collection program, as well as an operational data processing and analysis capability. SIMBIOS data collection takes place via the SIMBIOS Science Team. In addition, SIMBIOS had a calibration and product validation component (Project Office). The primary purpose of these calibration and product validation activities were to (1) reduce measurement error by identifying and characterizing true error sources, such as real changes in the satellite sensor or problems in the atmospheric correction algorithm, in order to differentiate these errors from natural variability in the marine light field; and (2) evaluate the various bio-optical and atmospheric correction algorithms being used by different ocean color missions. For each sensor, the SIMBIOS Project reviews the sensor design and processing algorithms being used by the particular ocean color project, compares the algorithms with alternate methods when possible, and provides the results to the appropriate project office.

Sun photometers are used to characterize the radiative properties of the atmosphere. They measure both the incident solar irradiance as well as the sky radiance (from scattered incident flux). Global networks of sun photometers provide data products such as aerosol optical thickness derived from these measurements. Instruments are typically calibrated for irradiance responsivity by cross-calibration against a primary reference sun photometer and for radiance responsivity using a lamp-illuminated integrating sphere source. A laser-based facility for Spectral Irradiance and Radiance Responsivity Calibrations using Uniform Sources (SIRCUS) has been developed at the National Institute of Standards and Technology. Sensors can be calibrated in this facility for absolute spectral irradiance and radiance responsivity with combined expanded (k = 2) uncertainties ranging from 0.15% to 0.25%. Two multi-channel filter radiometers used in the Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) program of the National Aeronautics and Space Administration (NASA) at the Goddard Space Flight Center (GSFC) were calibrated for radiance and irradiance responsivity using conventional approaches and using laser-illuminated integrating spheres on SIRCUS. The different calibration methods are compared, the uncertainties are evaluated, and the impact on remote sensing applications is discussed.

The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on 14 December 2002, and its functions were checked until 2003 spring. The Global Imager (GLI) on board ADEOS-II has 36 channels (thirty 1-km resolution, six 250-m resolution) from ultraviolet to thermal infrared to facilitate understanding the global environmental changes in oceans, land and clouds with high accuracy. Ocean algorithms (e.g., ocean atmospheric correction and sea-surface temperature) need highly accurate sensor characterization coefficients because they retrieve sea-surface upward radiance precisely from the top of the atmosphere. The NASDA GLI calibration team includes members of sensor development, ground system integration, and science application groups. The team started investigating GLI characteristics and radio- and geo-correction processes in the initial verification period. In this paper, we will describe the initial results, radiometric accuracy, 12- or 48-detector dependency, scan-mirror surface, incident-angle dependency, and dynamic range related to oceanographic applications.

The GLI was launched on board the ADEOS-II on December 14, 2002. For the early phase evaluations of the observation radiances, the GLI calibration team carried out vicarious calibrations by using MOBY measurements. To achieve the calibrations, we used two methods, which utilize two near-infrared channels and the measurement of the aerosol optical thickness, to predict the aerosol optical properties. Applying these methods, we derived early GLI vicarious calibration factors for ocean-color channels.

The paper presents initial results of atmospherically corrected ocean color data from the Global Imager (GLI), a moderate resolution spectrometer launched in December 2002 aboard ADEOS-II satellite. The standard GLI atmospheric correction algorithm, which includes an iterative procedure based on in-water optical modeling is first described, followed by brief description of standard in-water algorithms for output geophysical parameters. Ship/buoy-observed and satellite-derived marine reflectances, or normalized water-leaving radiance, are then compared, under vicarious calibration correction factors based on global GLI-SeaWiFS data comparison. The results, over 15 water-leaving radiance match-up data collected mostly off California and off Baja California, show standard errors in GLI estimate of 0.1 to 0.36 μW/cm²/nm/sr for 412, 443, 490, and 565 nm bands, with improved standard errors of 0.09 to 0.14 μW/cm²/nm/sr if in situ data set is limited to those obtained by in-water radiance measurement. Under provisional de-striping procedure, satellite-derived chlorophyll a estimates compares well with 35 ship-measured data collected off California within one day difference from the satellite observation, showing standard error factor of 1.73 (+73% or -43% error).

Atmospheric correction of ocean colour is routinely achieved by fitting radiometric observations at near near-infrared wavelengths to radiances predicted for a range of aerosol types. The best-fitting candidate aerosol model can then be used to compute radiances in the visible part of the spectrum, enabling an atmospheric correction to be applied there. The Navy Aerosol Model (NAM) is a multi-component aerosol model which may be suitable for this purpose. The components of NAM are closely tied to the physical processes which generate them and this allows for some expectation on the spatial homogeneity of the component optical depths. Presented is an atmospheric correction scheme based upon NAM and implemented for SeaWiFS. Some conclusions are drawn about the efficacy of extrapolating to visible wavelengths those estimates of aerosol type and amount made at near-infrared wavelengths.

A methodology is proposed to retrieve marine reflectance and chlorophyll-a concentration from space by decomposing the satellite reflectance into principal components. The components sensitive to the ocean signal are combined to retrieve the principal components of marine reflectance, allowing reconstruction of marine reflectance and estimation of chlorophyll-a concentration. Multi-layered perceptrons are used to approximate the functions relating the useful principal components of satellite reflectance to the principal components of marine reflectance. The algorithm is developed and evaluated using non-noisy and noisy synthetic data sets created for a wide range of angular and geophysical conditions. In the absence of noise on satellite reflectance, the relative error on marine reflectance does not exceed 2%. Accurate retrieval of the first principal component of marine reflectance allows a global relative error of 5.4% on chlorophyll-a concentration. In the presence of 1% non-correlated and 5% spectrally correlated noise on satellite reflectance, the relative error is increased to 6% and 21%, respectively. Application to SeaWiFS imagery yields marine reflectance and chlorophyll-a concentration fields that resemble those obtained from the standard SeaWiFS processing, but are generally less contrasted. Accuracy can be improved by including bio-optical variability in the simulated marine reflectance ensembles.

Asian dust aerosol layer of 4-6 km altitude accompanied by low clouds was observed with a LIDAR in Tokyo urban area on April 10 2001. In addition, column-integrated size distribution of aerosol was measured with a SkyRadiometer. To synthesize the top of atmosphere (TOA) reflectance, radiative transfer simulation was conducted assuming aerosol/cloud vertical structure and aerosol size distribution that were estimated after the ground observations. The refractive index of Asian dust was derived from a laboratory measurement of sampled Chinese soil particles. The synthesized TOA reflectance was compared to the SeaWiFS-derived one sampled at the low cloud pixels whose airmass is the same as the one passed at the observation site. TOA reflectance of the one of Asian dust models compare generally well with few percent difference in reflectance. We estimated an affect of Asian dust aerosol to ocean color remote sensing. Simulated TOA radiance absorbed by Asian dust was 20.0 W/m²/μm/sr in 443 nm. It is suggest that the existence of Asian dust occurs to dervie negative water-leaving radiance.

Real-time communication has been playing an increasingly important role in our work, life and ocean monitor. With the rapid progress of computer and communication technique as well as the miniaturization of communication system, it is needed to develop the adaptable and reliable real-time communication software in the ocean monitor system. This paper involves the real-time communication software research based on the point-to-point satellite intercommunication system. The object-oriented design method is adopted, which can transmit and receive video data and audio data as well as engineering data by satellite channel. In the real-time communication software, some software modules are developed, which can realize the point-to-point satellite intercommunication in the ocean monitor system. There are three advantages for the real-time communication software. One is that the real-time communication software increases the reliability of the point-to-point satellite intercommunication system working. Second is that some optional parameters are intercalated, which greatly increases the flexibility of the system working. Third is that some hardware is substituted by the real-time communication software, which not only decrease the expense of the system and promotes the miniaturization of communication system, but also aggrandizes the agility of the system.

Light emerging from the sea surface carries information of the water constituents. General empirical methods to derive in-water ocean color algorithms use measurements near the sea surface to relate the emerging radiative signals to the water contents. Problems with existing algorithms are frequently reported and there is no single algorithm adopted for Case 2 waters. There is a general trend in investigators moving from pure empirical methods to model-based techniques to solve the inverse problem. Among these techniques, the non-linear optimization approach (NOA) offers the highest accuracy without any dependency on the simulated or training data, but generally requires substantial computation time. Our research presents an approach to substantially decrease the computation time of the NOA by using a look-up-table (LUT) technique to correct the effects of inelastic scattering. A series of sensitivity tests was made to determine the critical factors required to accurately simulate the remote sensing reflectance. Results show that the inherent optical properties (IOPs) and inelastic scattering play a significant role, while variations of the ambient optical environment and surface wind speed are negligible. A LUT was then derived from numerous forward simulations using the Hydrolight radiative transfer model. All processes of inelastic scattering were considered and a set of three-variable (chlorophyll concentration, CDOM ratio and backscattering fraction) biooptical models, was used to yield a flexible parameterization of IOPs. This new approach was validated against in situ measurements. To examine its application to a large variety of water types, an extensive model-to-model comparison was made for a wide range of combinations of IOPs. Results show that our model provides both fast and accurate retrievals of chlorophyll concentration, CDOM ratio and backscattering fraction for an optically homogeneous water body. This new inversion approach may accelerate the use of ocean color remote sensing.

In an earlier ocean-color algorithm, water’s optical properties are classified into two categories. The major properties, such as the absorption and backscattering properties, vary widely and have significant influence on ocean color. The minor properties, such as the spectral slope of the gelbstoff absorption and the spectral power of particle backscattering, affect the ocean color modestly. The main objective of ocean-color remote sensing is to derive the major properties from water color. In model-based inversion algorithms, it is required to know the values of the minor properties. In this study, neural networks (NN) are used to estimate the minor properties. The NN-estimated minor properties are further used in a quasi-analytical algorithm to analytically derive the major properties. Significant improvements are found in the derivation of absorption and backscattering coefficients of coastal waters. The results here indicate an advantage of the neural network approach in inexplicitly linking a water property with water color, especially when there is no apparent relationship that can be explicitly expressed. The results further demonstrate the capability of the quasi-analytical algorithm to analytically derive major water properties from water color.

An important step to determine whether certain coastal environment parameters can be estimated from remote sensing measurements is to establish their identifiability from the water leaving remote sensing reflectance. This work addresses the sensitivity analysis of water leaving remote sensing reflectance to water constituents. The model used in the sensitivity analysis is Hydrolight, a radiative transfer code for ocean waters. We use the Morris factor screening method to determine which parameters have a substantial influence on the remote sensing reflectance. From Morris results, we realize a more precise sensitivity analysis focusing on the most influential parameters, using variance decomposition (Sobol method). As an important example of application, we perform a sensitivity analysis of coral reefs in coastal shallow waters, where the concentrations of Chlorophyll, yellow substance and suspended sediments were limited to a feasible range of variability. The results of the sensitivity analysis lead us to the formulation of band relationships for the estimation of water depth and seabed reflectance in coral reefs.

Remote sensing algorithms for retrieving estimates of oceanic constituent concentrations, such as chlorophyll concentration, require as input measurements of spectral water-leaving radiance. This measurement is typically obtained from space-based sensors such as SeaWiFS or MODIS. Some polar orbiting sensors have tilt and scan capabilities such that sensor view angles can at times exceed 70 degrees from the zenith. Inherent in algorithms applied to such remotely sensed measurements are assumptions on how the off-nadir radiance varies in intensity compared to the total upwelling irradiance. Typical ocean reflectance equations incorporate two factors, f (in R = f b_b/a) and Q (where Q = E_u/L_u), which relate the water-leaving radiance in 3 dimensions to the incident (sky and sun) irradiance. A Monte Carlo ocean optical model was developed and used to investigate the three dimensional nature of water leaving radiance for typical open ocean optical conditions. Equations for predicting f were developed as well as a data base of Q factors for different solar and viewing geometries as functions of b, b_b and a. Results derived from the Monte Carlo model were then used here to develop a more in-depth study of f based on HYDROLIGHT. Improved equations for predicting f have been developed and are compared to predictions of f from HYDROLIGHT. Examples of how these improved estimates of f and Q may be applied to a chlorophyll concentration algorithm for open ocean waters will be presented.

The optical properties of sea water, including ocean color (chlorophyll concentration), diffuse attenuation coefficient, and surface/subsurface reflectance, may be readily estimated from space-borne sensors (eg. SeaWiFS, MODIS) in the open ocean (Case 1 waters). However, in near-shore and shallow waters (Case 2), the presence of other organic materials and suspended sediment, as well as bottom reflection, may affect the spectrum of water leaving radiance making esrimation of optical properties based upon mutlispectral measurements more complex. In this work, we investigate the impact of these additional components on the water-leaving radiance and associated optical properties of the ocean using; i) in-situ measurements of the optical properties of the water column, and; ii) modeling of the radiance field within marine environments typical of Case 2 waters off the coast of Western Australia. We conclude by suggesting improvements to the accuracy of remotely sensed ocean color in near-shore and shallow waters.

Interannual variability of the early summer (May-June) coccolithophore blooms within surface waters of the Black Sea was studied by means of satellite-based bio-optical observations. The performance of two coccolithophore detection algorithms were tested for Black Sea conditions, and were found to provide comparable spatial patterns consistent with the corresponding true color images. An analysis of six, year-long OCTS and SeaWiFS imagery from 1997 onwards points to the presence of a major phytoplankton bloom in every early summer season. Blooms are dominated by densely populated coccolithophore algae within the entire basin, except during 2001. In the early summer of 2001, the coccolithophore activity was limited to the northeastern coastal zone, and the bloom in the rest of the basin was formed by non-coccolithophore groups, as suggested by their relatively strong chlorophyll signature. More coccolithophore over, limited coccolithophore abundance noted in the historical CZCS data suggests substantial differences in terms of spatial coverage and total biomass from the early 1980s to the late 90s. The increasing contribution of coccolithophores to the early summer phytoplankton community structure during the last decade is also consistent with the current view of dramatic shifts in taxonomic composition from diatoms to coccolithophores and flagellates, as a part of transformations that took place in the Black Sea biogeochemistry and ecosystem structure under changing anthropogenic and climate forcing during the 1980s and 1990s, respectively.

Hyperspectral remote sensing provides a particularly useful means of determining inherent optical properties of coastal waters where constituents other than phytoplankton add to the optical complexity of the water column. The substantial number of channels, about 200 for most hyperspectral sensors, enables many of the constituents within the water column to be identified spectrally. Additionally, in shallow water the water leaving radiance may include a signal reflected from the sea bottom. A hyperspectral radiometer was deployed on monthly oceanographic cruises off the coast near Perth, Western Australia, to make observations. Field measured reflectance spectra were used as input into a slightly modified version of a reflectance model developed by Lee et al, 1999. Products, including the concentrations of chlorophyll-a (Chl-a), coloured dissolved organic matter (CDOM), and suspended sediments (SS), as well as the water column depth (H), were extracted from the reflectance model by incorporating an optimisation technique. A Levenberg-Marquardt retrieval scheme was utilised in the optimisation. This scheme involved minimizing the difference between the modelled and measured spectral reflectance curves. Water samples were also collected on the monthly oceanographic cruises and used to determine the concentrations of chlorophyll-a, CDOM and SS. Water depth was measured using the boat's echo sounder. The model-derived products were compared to in situ measurements. The mean difference between model retrieved depth and in situ depth was 12.5 % or 1.4 m (R² = 0.98, N=11). Excluding two field measurements taken in the Marmion Marine Park, the mean RMS difference in depth was 7.6 % or 0.9 m (R² = 0.99, N=9). The mean RMS difference between retrieved Chl-a concentration and in situ measured Chl-a was 11.1 % or 0.044 mgm^-3 (R² = 0.91, N=8). These preliminary results suggest that the reflectance model works well for depth and Chl-a retrieval for Western Australian coastal waters and their sandy substrate.

The SeaWinds scatterometer (like NSCAT and ERS) is able to detect unequivocal signatures of meteorological features including cyclones, fronts, anticyclones, easterly waves and other precursors of hurricanes and typhoons. Through collaborative efforts between NASA and NOAA, National Weather Service marine forecasters are using SeaWinds data to improve analyses, forecasts and significant weather warnings for maritime interests. This results in substantial economic savings as well as the reduction of weather related loss of life at sea. The impact of SeaWinds on Numerical Weather Prediction models is on average modest but occasionally results in significant forecast improvements.

The SeaWinds instrument on the QuikSCAT satellite was designed to measure near surface winds over the ocean at a nominal resolution of 25 km with daily global coverage. Recently, reconstruction and resolution enhancement algorithms have been applied to the QuikSCAT measurements to generate high resolution backscatter fields. These high resolution fields make it possible to retrieve the wind at higher spatial resolution. Substantially finer wind and rain features are evident in the dense wind fields. The tradeoff is a higher noise level in the estimated winds. This paper describes the high resolution wind retrieval approach and the accuracy of the resulting high resolution winds. The limitations of the high resolution winds are considered. Methods for improving the accuracy of the data are presented.

A maximum likelihood estimation (MLE) method for simultaneously retrieving wind and rain from SeaWinds scatterometer data is introduced and evaluated. The new method incorporates rain backscatter and attenuation into the retrieval process via a simple wind/rain backscatter model. Two retrieval methods are examined: First, when no estimate of the rain rate is available, the new MLE method simultaneously estimates wind speed, wind direction and rain rate. Second, when an estimate of the rain is available, the wind is retrieved by directly correcting the geophysical model function using the rain/wind backscatter model. From simulation, the simultaneous wind/rain retrieval approach demonstrates improved wind vector estimates where the rain is significant. The improvement in retrieval is more pronounced in the “sweet spot” of SeaWinds’ cross track. The rain-corrected wind retrieval approach gives somewhat improved wind speed estimates for rain-contaminated wind vectors over the simultaneous wind/rain retrieval method, especially when the effect of rain is small. Validation of the SeaWinds rain data with co-located Tropical Rainfall Measuring Mission precipitation radar rain rates shows that with some limitations the SeaWinds scatterometer can measure rain.

QuikSCAT backscatter, AMSR-E and DMSP SSM/I radiance data have been used to derive sea ice motion for both the Arctic and Antarctic region using the wavelet analysis tracking method. All results from QuikSCAT, AMSR-E and SSM/I in the Arctic for fall/winter period are compatible with buoys and can then be merged by some data fusion methods to generate composite sea ice motion maps for more complete coverage. Furthermore, based on this merged data set, daily sea ice deformation (divergence and maximum shear) maps have been produced and show consistent spatial and temporal patterns. For summer ice, in order to focus the tracking templates in time, descending data are separated from ascending data during pre-processing. Due to the high resolution of AMSR-E 89 GHz data, sea ice motion maps from AMSR-E 89 GHz descending and ascending data are complementary each other, and sea ice drift in summer has been derived by merging results from descending with ascending data for composite daily sea ice motion maps. The general circulation pattern of the derived summer sea ice drift agrees with buoy data. In this study, principal component analysis of both the merged ice tracking result from satellite data and pressure field from buoy have also been examined for the relationship between the principal components and eigenvectors from these two data sets. While the result shows that principal components of modes 1 and 2 from two data sets are highly correlated, which confirms that wind forcing is a major factor driving the ice drift, it also reveals that other high energy modes are not highly correlated, which may be caused by coastal effects. Principal component analysis of Arctic sea ice motion during fall/winter period in different years shows that the reverse of dominant modes or patterns is related to the Arctic Oscillation.

Methods from computer vision and scale-space theory are applied to the study of sea-ice motion in Antarctica. The input data is a sequence of daily images of the continent, obtained from scatterometer data and processed with a resolution enhancing algorithm. The information contained in these images can be studied at different scales when the appropriate filters are applied. Large scales omit detail and smoothen local variations of intensity while smaller scales show much detail and local variation. When motion is studied through different scales, different patterns might be observed. We assume that all the information coded in these images is the radar backscatter, and that it is closely coupled with advection. The Optical Flow method is used to obtain a dense vector field representing sea-ice motion, the method’s limitations are overcome by adding second order constraints to the main equation and through the use of large neighborhoods to normalize the direction of flow. Validation of results has been done to the extent possible, taking into account that there is practically no ground-truth data available for Antarctica in the form of buoy-data. Sea-ice motion results are displayed along available ocean surface wind data, observing a clear consistency along the ocean-ice border. The results are compared to existing studies applying wavelets and it is shown that differences can be explained by the fact that each method is observing motion at a different scale.