Bathymetry Retrieval Research Articles

Pan-European Satellite-Derived Coastal Bathymetry—Review, User Needs and Future Services

Low-lying coastal zones are home to around 10% of the world’s population and to many megacities. Coastal zones are largely vulnerable to the dynamics of natural and human-induced changes. Accurate large-scale measurements of key parameters, such as bathymetry, are needed to understand and predict coastal changes. However, nearly 50% of the world’s coastal waters remain unsurveyed and for a large number of coastal areas of interest, bathymetric information is unavailable or is often decades old. This lack of information is due to the high costs in time, money and safety involved in collecting these data using conventional echo sounder on ships or LiDAR on aircrafts. Europe is no exception, as European seas are not adequately surveyed according to the International Hydrographic Organisation. Bathymetry influences ocean waves and currents, thereby shaping sediment transport which may alter coastal morphology over time. This paper discusses state-of-the-art coastal bathymetry retrieval methods and data, user requirements and key drivers for many maritime sectors in Europe, including advances in Satellite-Derived Bathymetry (SDB). By leveraging satellite constellations, cloud services and by combining complementary methods, SDB appears as an effective emerging tool with the best compromise in time, coverage and investment to map coastal bathymetry and its temporal evolution.

Shallow Water Bathymetry Retrieval Using a Band-Optimization Iterative Approach: Application to New Caledonia Coral Reef Lagoons Using Sentinel-2 Data

To achieve high accuracy bathymetry retrieval using remote sensing images with robust performance in a 0 to 25 m-deep lagoon with sharp bottom depth variations, a new Iterative Multiple Band Ratio (IMBR) algorithm is tested against known Multiple Band Ratio (MBR) and Single Band Ratio (SBR) algorithms. The test was conducted using the five multispectral bands, at 10 to 60 m resolution, of a Sentinel-2 image of the 25 km2 Poe lagoon, a UNESCO World Heritage Area. The IMBR approach requires training datasets for the definitions of depth threshold at which optimal band ratios vary. IMBR achieved accuracy, quantified with an original block cross-validation procedure across the entire depth range reached a mean absolute error of 46.0 cm. It compares very favorably against MBR (78.3 cm) and the various SBR results (188–254 cm). The method is suitable for generalization to other sites pending a minimal ground-truth dataset crossing all the depth range being available. We stress that different users may need different precisions and can use MBR or SBR algorithms for their applications. For the hydrodynamic modelling applications that are developing in New Caledonia, the IMBR solutions applied to Sentinel imagery are optimal.

Hyperspectral Bathymetry Retrieval using a Newly Developed Normalized Algorithm in Shallow Water

Hyperspectral Bathymetry Retrieval using a Newly Developed Normalized Algorithm in Shallow Water

Estimation of Bathymetry and Benthic Habitat Composition from Hyperspectral Remote Sensing Data (BIODIVERSITY) Using a Semi-Analytical Approach

The relevant benefits of hyperspectral sensors for water column determination and seabed features mapping compared to multispectral data, especially in coastal areas, have been demonstrated in recent studies. In this study, we used hyperspectral satellite data in the accurate mapping of the bathymetry and the composition of water habitats for inland water. Particularly, the identification of the bottom diversity for a shallow lagoon (less than 2 m in depth) was examined. Hyperspectral satellite data were simulated based on aerial hyperspectral imagery acquired above a lagoon, namely the Vaccarès lagoon (France), considering the spatial and spectral resolutions, and the signal-to-noise ratio of a satellite sensor, BIODIVERSITY, that is under study by the French space agency (CNES). Various sources of uncertainties such as inter-band calibration errors and atmospheric correction were considered to make the dataset realistic. The results were compared with a recently launched hyperspectral sensor, namely the DESIS sensor (DLR, Germany). The analysis of BIODIVERSITY-like sensor simulated data demonstrated the feasibility to satisfactorily estimate the bathymetry with a root-mean-square error of 0.28 m and a relative error of 14% between 0 and 2 m. In comparison to open coastal waters, the retrieval of bathymetry is a more challenging task for inland waters because the latter usually shows a high abundance of hydrosols (phytoplankton, SPM, and CDOM). The retrieval performance of seabed abundance was estimated through a comparison of the bottom composition with in situ data that were acquired by a recently developed imaging camera (SILIOS Technologies SA., France). Regression coefficients for the retrieval of the fractional species abundances from the theoretical inversion and measurements were obtained to be 0.77 (underwater imaging camera) and 0.80 (in situ macrophytes data), revealing the potential of the sensor characteristics. By contrast, the comparison of the in situ bathymetry and macrophyte data with the DESIS inverted data showed that depth was estimated with an RSME of 0.38 m and a relative error of 17%, and the fractional species abundance was estimated to have a regression coefficient of 0.68.

Improving Statistical Uncertainty Estimate of Satellite-Derived Bathymetry by Accounting for Depth-Dependent Uncertainty

For mapping the near-shore seafloor bathymetry, retrieving depth information using multispectral satellite image is highly cost-effective. To effectively detect and characterize the bathymetry variation, accurate and reliable information about the uncertainty of the derived depths is critical. In estimating the uncertainty of the resulted satellite-derived bathymetry (SDB), the conventional homoscedasticity assumption states that the error variance of the observations is constant across different depth ranges. However, this assumption is violated due to the influence of various environmental factors inherently correlated with depth. In this article, we develop a data-driven approach to extract the depth-dependent pattern of observation error. The residual information of the regression is analyzed to model the influence of the depth on the uncertainty of retrieval bathymetry, while nonlinearity and outliers are also considered. This results in a more realistic estimate of SDB accuracy. Our experimental results reveal that the observation uncertainty is significantly correlated with the depth in the bathymetry retrieval process. It is also shown that the presented algorithm effectively captures the depth-dependent pattern of the observation uncertainty, and further provides a more realistic characterization of the uncertainty information of SDB.

Determination of the Initial Value Ranges of Nonlinear Solutions for a Log Ratio Bathymetric Inversion Model and Bathymetry Retrieval

The log-ratio model (LRM) with three model parameters ( ${\boldsymbol{n}}$ , ${{\boldsymbol{m}}_1},$ and ${{\boldsymbol{m}}_0}$ ) has been widely used in shallow water bathymetry of multispectral images. At present, obtaining LRM model parameters by optimization algorithms depends on the setting of initial values [ ${\boldsymbol{n}}(0)$ , ${{\boldsymbol{m}}_1}(0),$ and ${{\boldsymbol{m}}_0}(0)$ ] or initial value ranges. However, how to set the initial value can obtain model parameters more effectively and credibly, and the significance of each initial value in the optimization process is very important for LRM bathymetric retrieval method. In this article, remote sensing images and bathymetric data were used to determine the initial value ranges of credible model parameters, and then the sensitivity of each parameter was analyzed in the algorithm optimization process from the partial differential perspective, and the bathymetry was retrieved in the sea area near Ganquan Island. The results show that according to the partial differential analysis of model parameters, ${\boldsymbol{n}}$ , with the smallest change rate in three LRM parameters and the rate of ${{\boldsymbol{m}}_1}$ and ${{\boldsymbol{m}}_0}$ is at least five times of it. So ${\boldsymbol{n}}(0)$ needs more iterations to achieve the optimal. Therefore, the initial value ${\boldsymbol{n}}(0)$ determines whether the optimization method can obtain credible LRM model parameters and the algorithm convergence speed. And when ${\boldsymbol{n}}(0) \in ({1/{{\boldsymbol{R}}_{{{\bf min}}}}, 1000} ]$ ( ${{\boldsymbol{R}}_{{{\bf min}}}}$ is the minimum reflectance in blue and green bands), there must be a credible model parameter solution. The bathymetric retrieval results of the sea area with coral and sand substrates show the applicability of our initial value ranges in different substrate area.

A Combined Machine Learning and Residual Analysis Approach for Improved Retrieval of Shallow Bathymetry from Hyperspectral Imagery and Sparse Ground Truth Data

Mapping shallow bathymetry by means of optical remote sensing has been a challenging task of growing interest in recent years. Particularly, many studies exploit earlier empirical models together with the latest multispectral satellite imagery (e.g., Sentinel 2, Landsat 8). However, in these studies, the accuracy of resulting bathymetry is (a) limited for deeper waters (>15 m) and/or (b) is being influenced by seafloor type albedo. This study explores further the capabilities of hyperspectral satellite imagery (Hyperion), which provides several spectral bands in the visible spectrum, along with existing reference bathymetry. Bathymetry predictors are created by applying the semi-empirical approach of band ratios on hyperspectral imagery. Then, these predictors are fed to machine learning regression algorithms for predicting bathymetry. Algorithm performance is being further compared to bathymetry predictions from multiple linear regression analysis. Following the initial predictions, the residual bathymetry values are interpolated by applying the Ordinary Kriging method. Then, the predicted bathymetry from all three algorithms along with their associated residual grids is used as predictors at a second processing stage. Validation results show that by using a second stage of processing, the root-mean-square error values of predicted bathymetry is being improved by ≈1 m even for deeper water (up to 25 m). It is suggested that this approach is suitable for (a) contributing wide-scale, high-resolution shallow bathymetry toward the goals of the Seabed 2030 program and (b) as a coarse resolution alternative to effort-consuming single-beam sonar or costly airborne bathymetric laser surveying.

Combined geometric-radiometric and neural network approach to shallow bathymetric mapping with UAS imagery

There is a pressing need for shallow bathymetry in regions around the world, including in fragile marine ecosystems, such as coral reefs. Unfortunately, mapping these shallow-water areas using in situ or boat-based methods is expensive, time consuming, and potentially dangerous, due to the need to put personnel and/or boats in high-energy nearshore areas, which may contain rocks, reefs, and other submerged hazards. For this reason, passive remote sensing methods of shallow bathymetric mapping have been rapidly growing in interest and usage. Two general categories of approaches using passive imaging can be distinguished as: (1) those based on geometry (stereo-photogrammetry); (2) those based on radiometry (attenuation of light in the water column). These two broad categories of approaches have been tested on imagery collected using aircraft and satellites in numerous previous studies. Recent work within the geometric category includes processing unmanned aircraft system (UAS) imagery using Structure from Motion and Multi-View Stereo (SfM-MVS) photogrammetry, while radiometric methods encompass the broad range of spectral bathymetry retrieval algorithms. Each broad approach category has advantages and disadvantages. Fortunately, the geometric and radiometric approaches are highly complementary. The geometric approaches tend to work best in areas of high bottom texture, which facilitates feature matching in the SfM-MVS software. Meanwhile, the radiometric approaches work best in relatively homogeneous bottom types. To leverage the strengths of each type of approach and overcome their respective weaknesses, this work develops and tests a combined geometric-radiometric bathymetric mapping approach designed for shallow-water mapping from UAS imagery. Four radiometric models of varying complexity are tested, ranging from a color-based lookup table approach to neural networks. Two UAS flights on Buck Island, off St Croix in the U.S. Virgin Islands (USVI), are used to assess the accuracy of the methodology when compared to aerial bathymetric lidar data. The results show that the combined geometric-radiometric approach provides an increase in spatial coverage of up to 61% and improvement (i.e. decrease) in 95th percentile error of up to 49% when compared to traditional refraction-corrected SfM-MVS bathymetry.

SMART-SDB: Sample-specific multiple band ratio technique for satellite-derived bathymetry

Spectrally-based retrieval of bathymetry is challenging in inland/coastal waters due to variations in factors other than water depth across a water body, including bottom type, optically significant constituents, water surface roughness, and others. Optimal band ratio analysis (OBRA) is the most widely used method to deal with these confounding effects to retrieve water depth. OBRA identifies the pair of bands that provides the highest accuracy among all possible pairs when using a log-transformed band ratio model. However, this approach fits a single ratio model to all training samples without fully accounting for the heterogeneity of the aforementioned complicating factors. To deal with this problem, we introduce a novel method called Sample-specific Multiple bAnd Ratio Technique for Satellite-Derived Bathymetry (SMART-SDB). The proposed SMART-SDB technique partitions the feature space of the spectral data and creates for every subspace a different band ratio model that performs better than the other models within that subspace. Bathymetry is then predicted based on the closest model/s in the feature space by performing a sample-specific k-nearest neighbor (K−NN) analysis. The depth estimates provided by the neighboring models are averaged with weights proportional to their inverse distance in the feature space. The proposed method can be used in both inland and coastal waters. Here, we thoroughly examine its effectiveness in the challenging and heterogenous environments of fluvial systems using a wide range of spectral data including radiative transfer simulations, an airborne hyperspectral image of the Snake River (USA), and a WorldView-3 satellite image of the Sarca River (Italy). The results of this study indicate a significant improvement in depth retrieval for estimations based on the SMART-SDB method relative to standard OBRA, either when a single model or multiple models are employed.

An Approach to Minimize Atmospheric Correction Error and Improve Physics-Based Satellite-Derived Bathymetry in a Coastal Environment

Physics-based radiative transfer model (RTM) inversion methods have been developed and implemented for satellite-derived bathymetry (SDB); however, precise atmospheric correction (AC) is required for robust bathymetry retrieval. In a previous study, we revealed that biases from AC may be related to imaging and environmental factors that are not considered sufficiently in all AC algorithms. Thus, the main aim of this study is to demonstrate how AC biases related to environmental factors can be minimized to improve SDB results. To achieve this, we first tested a physics-based inversion method to estimate bathymetry for a nearshore area in the Florida Keys, USA. Using a freely available water-based AC algorithm (ACOLITE), we used Landsat 8 (L8) images to derive per-pixel remote sensing reflectances, from which bathymetry was subsequently estimated. Then, we quantified known biases in the AC using a linear regression that estimated bias as a function of imaging and environmental factors and applied a correction to produce a new set of remote sensing reflectances. This correction improved bathymetry estimates for eight of the nine scenes we tested, with the resulting changes in bathymetry RMSE ranging from +0.09 m (worse) to −0.48 m (better) for a 1 to 25 m depth range, and from +0.07 m (worse) to −0.46 m (better) for an approximately 1 to 16 m depth range. In addition, we showed that an ensemble approach based on multiple images, with acquisitions ranging from optimal to sub-optimal conditions, can be used to estimate bathymetry with a result that is similar to what can be obtained from the best individual scene. This approach can reduce time spent on the pre-screening and filtering of scenes. The correction method implemented in this study is not a complete solution to the challenge of AC for satellite-derived bathymetry, but it can eliminate the effects of biases inherent to individual AC algorithms and thus improve bathymetry retrieval. It may also be beneficial for use with other AC algorithms and for the estimation of seafloor habitat and water quality products, although further validation in different nearshore waters is required.

Shallow water bathymetry with multi-spectral satellite ocean color sensors: Leveraging temporal variation in image data

Polar-orbiting ocean color satellites such as Landsat-8, Suomi National Polar-orbiting Partnership (SNPP), and Sentinel-3 offer valuable image data for the derivation of water bathymetry in optically shallow environments. Because of the multi-spectral limitation, however, it is challenging to derive bathymetry over global shallow waters without reliable mechanistic algorithms. In this contribution, we present and test a physics-based algorithm for improved retrieval of bathymetry with multi-spectral sensors. The algorithm leverages the temporal variation of water-column optical properties in two satellite measurements. By incorporating two remote sensing reflectance spectra in an optimization procedure, it enhances the spectral constraining condition for the optimization, thus leading to improved retrieval accuracy. This scheme is evaluated using synthetic multi-spectral data. It is shown that the new approach can provide accurate estimation of water depths over 0–30 m range with three types of benthic substrates (corals, seagrass, and sand) and for a wide range of water column optical properties. Based on the degree of improvement, Landsat-8 appears to be benefited the most, followed by SNPP, and then Sentinel-3. The application of the new approach is demonstrated with satellite images over shallow waters (0–30 m) dominated with coral reefs, seagrass, and sand, respectively. This proof-of-concept study confirms the promise of multi-spectral satellite sensors for accurate water depth retrieval by accounting for the temporal characteristics in multiple measurements, suggesting a path forward for the derivation of bathymetry from the existing satellites over global shallow waters.

Rapid wavelet-based bathymetry inversion method for nearshore X-band radars

A wavelet based method for bathymetry retrieval from X-band radar images is proposed. The method combines traditional Fast Fourier Transform techniques for retrieving peak frequency maps by evaluating the spectral peaks in the time domain, and a localized 2D Continuous Wavelet Transform for retrieving the corresponding peak wavenumbers. The main improvements of the new method compared to conventional FFT-based methods are as follows: a) the wavelet-based approach is localized - naturally fitting the inhomogeneous conditions typically found in the nearshore environment; b) it is continuous and uses wave phase information providing smooth bathymetry maps with good accuracy (RMSE in the range 2.6–6.6% of the maximum water depth); c) it requires relatively small number of successive images without the limitation of requiring a uniform time step.In order to verify the proposed method, 2D simulations of wave shoaling and refraction were performed for different sea conditions and over several bottom topographies. A radar image model including tilt and shadowing modulations together with speckle noise was applied to the simulated surface elevations to provide synthetic radar images. The method's ability to reconstruct the original bathymetry is shown to be robust in intermediate to shallow water depths (kph < 1) for all cases. The method was also applied to real data acquired in Sylt island (Germany). A comparison between a bathymetric survey held prior to a storm and a radar reconstructed one during the storm shows very good agreement. These results reassure the high capability of this new method to be used in operational settings.

MAPPING BATHYMETRY BY LANDSAT DATA IN SHALLOW COASTAL ENVIRONMENT (CASE STUDY: THE SOUTHEAST OF THE CASPIAN SEA)

Abstract. Compared to traditional methods, remote sensing (RS) technique is an efficient, fast, low-priced and effective solution in bathymetry. In this study, a linear equation was applied between field and reflectance data to achieve bathymetry map. Landsat-8 is characterized by additional visible band compared with previous Landsat bands on coastal zone application, therefore, we apply two regions of spectrum to compare the accuracy of bathymetry, Bands 1, 3, 4 and bands 2, 3 and 4 respectively. Furthermore, Logarithmic transformation also applied on both spectral regions to have bathymetry map. The coefficient obtained from reflectance and filed data was applied on Tm bands to retrieve the bathymetry of Gomishan lagoon in 2000. The results show that the second spectral region, Bands 2, 3 and 4, is highly correlated with filed data and more accurate (R2 = 0.94, RMSE = 0.35). Moreover, there is a relatively high accuracy between the retrieval of Gomishan bathymetry and the report of recent studies.

Bathymetry Retrieval Method of LiDAR Waveform Based on Multi-Gaussian Functions

Ma, Y.; Zhang, J.; Zhang, Z., and Zhang, J.Y., 2019. Bathymetry retrieval method of LiDAR waveform based on multi-Gaussian functions. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 324-331. Coconut Creek (Florida), ISSN 0749-0208.This paper provides a water depth inversion approach to laser radar waveform data based on multi-Gauss function in order to address the following two problems: the impact of noise on the traditional bathymetry and the poor accuracy of the water depth inversion in deep water. This approach employs L-M optimization algorithm to make multiple Gauss functions iterate and fit the LiDAR waveform data, and then uses peak detection method to extract the echo signal from the surface and the bottom of the water. In this paper, the water depth inversion is carried out using the simulation data of LiDAR water echo and the Aquarius LiDAR waveform data around the water area of the Ganquan Island respectively. Taking the mean relative error (MRE) and the mean absolute error (MAE) as the evaluation index of accuracy, the result shows that the detection model of LiDAR bathymetry has a higher ability of water depth inversion in the range of detectable water depth. For simulation data, the MAE is 20 cm and the MRE is below 7 % in the water depth range of 2 m to 10 m. While for real LiDAR waveform data, the MAE of water depth inversion is between 30 cm and 75 cm, and the MRE is below 12 %.

Retrieval of nearshore bathymetry from Sentinel-2A and 2B satellites in South Florida coastal waters

This study examines the relatively high-resolution MultiSpectral Instrument (MSI) onboard Sentinel-2A and 2B for generating bathymetric maps through a ratio transform model in South Florida (United States). Atmospheric correction of imagery is implemented though ACOLITE software, providing accurate performance and consistency over different Sentinel-2A/B scenes and three different study sites. Vertical calibration uses 5–10 points collected from digital charts, independent of lidar surveys, which are used for validation and error analysis. Satellite-Derived Bathymetry (SDB) has Median Absolute Error (MedAE) of 0.5 m in West Palm Beach (at depths ranging between 0 and 18 m, limit of lidar survey for validation), 0.4 m in Key West (0–5 m), and 0.22 m in Dry Tortugas (0–6 m), in conditions with low turbidity. Accurate bathymetry mapping can be accomplished with both sensors over environments with varying water transparency conditions, with the advantage of a fast, flexible, and economical solution. The 10-m MSI can capture small-scale features, such as tidal channels, straits relevant to navigation or steep slopes. While the least error is achieved by calibrating each image separately, a generic calibration produces only a moderately greater error with MedAE still ∼1m, indicating the robustness of the approach. The research highlights the great potential of the 5-day revisit, suggesting that the twin Sentinel-2 mission of the Copernicus programme may enhance SDB to leverage its use for several operational purposes, particularly in remote and inaccessible regions of the world.

Retrieval of Near-Shore Bathymetry From Multispectral Satellite Images Using Generalized Additive Models

Deriving shallow-water depth from multispectral satellite imageries appears very promising in near-shore bathymetric mapping for its high efficiency and low costs. Lyzenga’s linear band method, which currently is the most widely used algorithm in the field, performs a linear regression between some known water depths and image-derived input variables. In real-world situations, the linear assumption in Lyzenga’s method cannot be exactly satisfied because of complex environmental factors, and its performance is, therefore, often limited. In this letter, we improve Lyzenga’s method by introducing generalized additive models to describe the nonlinear relationship between water depths and image-derived regressors. The performance of the new method was evaluated by using Landsat-8 multispectral data over the coastal areas of Oahu Island, Hawaii. The experimental results of two independent validation regions show that the depth-retrieval accuracy of the proposed algorithm was about 20% better than that of Lyzenga’s method, and the performance improvement was most pronounced in the depth interval of 5–10 m.

Bathymetry retrieval from optical images with spatially distributed support vector machines

This paper presents a spatially distributed support vector machine (SVM) system for estimating shallow water bathymetry from optical satellite images. Unlike the traditional global models that make predictions from a unified global model for the entire study area, our system uses locally trained SVMs and spatially weighted votes to make predictions. By using IKONOS-2 multi-spectral image and airborne bathymetric LiDAR water depth samples, we developed a spatially distributed SVM system for bathymetry estimates. The distributed model outperformed the global SVM model in predicting bathymetry from optical satellite images, and it worked well at the scenarios with a low number of training data samples. The experiments showed the localized model reduced the bathymetry estimation error by 60% from RMSE of 1.23 m to 0.48 m. Different from the traditional global model that underestimates water depth near shore and overestimates water depth offshore, the spatially distributed SVM system did not produce regional prediction bias and its prediction residual exhibited a random pattern. Our model worked well even if the sample density was much lower: The model trained with 10% of the samples was still able to obtain similar prediction accuracy as the global SVM model with the full training set.

Multiple Optimal Depth Predictors Analysis (MODPA) for river bathymetry: Findings from spectroradiometry, simulations, and satellite imagery

Remote mapping of bathymetry can play a key role in gaining spatial and temporal insight into fluvial processes, ranging from hydraulics and morphodynamics to habitat conditions. This research introduces Multiple Optimal Depth Predictors Analysis (MODPA), which combines previously developed depth predictors along with additional predictors derived from the intensity component of the HSI color space transformation. MODPA empirically selects a set of optimal predictors among all candidates utilizing partial least squares (PLS), stepwise, or principal component (PC) regression models. The primary focus of this study was on shallow (<1 m deep) and clearly flowing streams where substrate variability could have a pronounced effect on depth retrieval. Spectroscopic experiments were performed under controlled conditions in a hydraulic laboratory to examine the robustness of bathymetry models with respect to changes in bottom type. Further, simulations from radiative transfer modeling were used to extend the analysis by isolating the effect of inherent optical properties (IOPs) and by investigating the performance of bathymetry models in optically complex and deeper streams. The bathymetry of the Sarca River, a shallow river in the Italian Alps, was mapped using a WorldView-2 (WV-2) image, for which we evaluated the atmospheric compensation (AComp) product. Results indicated the greater robustness of multiple-predictor models particularly MODPA rather than single-predictor models, such as Optimal Band Ratio Analysis (OBRA), with respect to heterogeneity of bottom types, IOPs, and atmospheric effects. The HSI intensity component enhanced the accuracy of depth retrieval, particularly in optically-complex waters and also for low spectral resolution imagery (e.g., GeoEye). Further, the enhanced spectral resolution of WV-2 imagery improved bathymetry retrieval compared to 4-band GeoEye data.

Bathymetry and Water-Level Estimation Using X-Band Radar at a Tidal Inlet

ABSTRACT Zuckerman, S. and Anderson, S., 2018. Bathymetry and water-level estimation using X-band radar at a tidal inlet. Knowledge of bathymetry in coastal areas is a key factor for maintaining shipping channels, identifying hazards to navigation, and measuring sediment transport processes. Remote-sensing techniques are crucial for this purpose, because in situ methods are often costly, time-consuming, and less frequent in occurrence. This paper presents a derived bathymetry and water-level retrieval algorithm that operates on time-series X-band marine radar data. The algorithm is designed to operate on a long-term continuous data stream, generating updated water-level measurements and bathymetry maps as new data are collected. In step 1, individual data sets are first processed to retrieve dominant wave numbers over a pyramid of overlapping tiles. In step 2, depths are estimated at each pixel location in the domain using the shallow-water wave dispersion relation. Last, results from step 2 are used to e...

Benthic coverage and bottom topography of coral reef environment over Pirotan reef, Gulf of Kachchh region, India

Remote sensing measurements in coral reef environments commonly confront the problem of overlying atmosphere and modification of spectral signal due to water column over the bottom substrates. In order to correct these problems, hyperspectral observations offer an advantage over multispectral observations. Airborne hyperspectral remote sensing data from Airborne Visible Infrared Imaging Spectrometer- Next Generation (AVIRIS-NG) sensor was acquired during low tidal condition on 14 February 2016 at Pirotan reef, Gulf of Kachchh region, India. The objective of this study is to map benthic coverage and bottom topography over Pirotan reef. The methodology involved atmospheric correction, simultaneous retrieval of water parameters, bathymetry, water column correction and mapping. Atmospheric correction was performed by removing path radiance and aerosol contribution and dividing by atmospheric transmittance and incoming solar irradiance to obtain remote sensing reflectance. Model derived error minimization technique was used for simultaneous retrieval of water parameters and bathymetry. Derived water parameters were used to account for water column attenuation and retrieve concomitant true bottom signature.