Simulation of shallow water depth data merging for HJ-1A/HSI and EO-1/Hyperion

HyperSpectral Imagery (HSI) of the coastal zone often focuses on the estimation of bathymetry. However, the estimation of bathymetry requires knowledge, or the simultaneous solution, of water column Inherent Optical Properties (IOPs) and bottom reflectance. The numerical solution to the simultaneous set of equations for bathymetry, IOPs, and bottom reflectance places high demands on the spectral quality, calibration, atmospheric correction, and Signal-to-Noise (SNR) of the HSI data stream. In October of 2002, a joint FERI/NRL/NAVO/USACE HSI/LIDAR experiment was conducted off of Looe Key, FL. This experiment yielded high quality HSI data at a 2 m resolution and bathymetric LIDAR data at a 4 m resolution. The joint data set allowed for the advancement and validation of a previously generated Look-Up-Table (LUT) approach to the simultaneous retrieval of bathymetry, IOPs, and bottom type. Bathymetric differences between the two techniques were normally distributed around a 0 mean, with the exception of two peaks. One peak related to a mechanical problem in the LIDAR detector mirrors that causes errors on the edges of the LIDAR flight lines. The other significant difference occurred in a single geographic area (Hawk Channel) suggesting an incomplete IOP or bottom reflectance description in the LUT data base. In addition, benthic habitat data from NOAA’s National Ocean Service (NOS) and the Florida Wildlife Research Institute (FWRI) provided validation data for the estimation of bottom type. Preliminary analyses of the bottom type estimation suggest that the best retrievals are for seagrass bottoms. One source of the potential difficulties may be that the LUT database was generated from a more pristine location (Lee Stocking Island, Bahamas). It is expected that fusing the HSI/LIDAR data streams should reduce the errors in bottom typing and IOP estimation.

Seawater inherent optical properties (IOPs) are key parameters in a wide range of applications in environmental studies and oceanographic research. In particular, the absorption coefficient (a) is the typical IOP used to obtain the concentration of chlorophyll-a in the water-a critical parameter in biological oceanography studies and the backscattering coefficient (b(b)) is used as a measure of turbidity. In this study, we test a novel instrument concept designed to obtain both the absorption and backscattering coefficients. The instrument would emit a collimated monochromatic light beam into the water retrieving the backscattered light intensity as a function of distance from the center of illumination. We use Monte Carlo modeling of light propagation to create an inversion algorithm that translates the signal from such an instrument into values of a and b(b). Our results, based on simulations spanning the bulk of natural values of seawater IOP combinations, indicate that a 6.2 cm diameter instrument with a radial resolution of 1 cm would be capable of predicting b(b) within less than 13.4% relative difference and a within less than 57% relative difference (for 90% of the inverted a values, the relative errors fall below 29.7%). Additionally, these errors could be further reduced by constraining the inversion algorithm with information from concurrent measurements of other IOPs. Such a compact and relatively simple device could have multiple applications for in situ optical measurements, including a and b(b) retrievals from instrumentation mounted on autonomous underwater vehicles. Furthermore, the same methodology could possibly be used for an out-of-water sensor.

Tables S1 to S5 The supporting information provides the best-fit coefficients and their standard deviation of the relationships between PON and IOPs for the open-ocean dataset (Table S1) and the whole dataset (Table S2 andS3).In addition, the supporting information includes the slope (S), intercept (I), and statistical metrics of the relationships between PON derived from IOP-based algorithms and measured PON for the open-ocean dataset (Table S4) and the whole dataset (Table S5).

Plane-to-point transformations are used to develop a version of the Hydrolight computer program with which to compute the spatial dependence of the irradiance and the scalar irradiance of the light field away from an isotropic point source deep within a spatially uniform ocean. The transformations are also used to derive analytic approximations for determining the diffuse attenuation coefficient and the mean cosine of the radiance far from an isotropic point source. Approximations for determining the asymptotic diffuse attenuation coefficient from measurements at only two distances far from the source are derived and numerically tested with the modified version of the Hydrolight computer program. New spatial integrals of the outward irradiance are also derived that provide a different way for correlating the inherent optical properties of seawater.

A 2-layer non-hydrostatic model with improved dispersive behaviour is presented. Due to the assumption of a constant non-hydrostatic pressure distribution in the lower layer, the dispersive behaviour is improved without much additional computational time. A comparison with linear wave theory showed that this 2-layer model gives a better result for the dispersion relation and shoaling of waves in intermediate water. This means that the 2-layer model is applicable in shallow and intermediate water depths (up to relative depths kh equals 4), whereas the 1-layer model is only applicable in shallow water depths (kh smaller than 1). Three laboratory experiments, including a fringing reef and a barred beach, were used to validate the presented mode for different hydrodynamic conditions. Based on these results, it can be concluded that the 2-layer model can be applied to accurately simulate the bulk wave height and spectral properties. The low frequency wave height, the setup and in particular the second order statistics contain more scatter, but the model accurately captured the general trend. Furthermore, the model showed good results for complex bathymetries in shallow to intermediate water.

We studied the composition and variation of nematode body size as a function of nematode depth in the sediment and water depth at 18 stations, ranging from 137 to 4951 m depth, along the Galician margin (NE Atlantic). The analysis is based on more than 10000 individuals and demonstrates that nematodes have adapted mainly in response to the vertical changing conditions in the sediment. Nematode length and width are similar in the upper 0.5 cm of sediment at all stations, but differ greatly as their depth in the sediment increases. The observed trends are consistent with an adaptation to changing oxygen concentrations in the sediments. In lower slope and abyssal plain stations, oxygen decreases gradually until depletion at about 5 cm depth, and nematode body width decreases concurrently, resulting in higher oxygen absorption efficiency per unit of volume. On the shelf and in the canyons, sediments become anoxic at short distances below the sediment-water interface, and more than 50% of the nematodes are present in the anoxic part of the sediment. Here, mean nematode length increases as their depth in the sediment increases. This may result in increased mobility, which enables the organisms to migrate between anoxic patches of food and parts of the sediment where oxygen is available and/or the ability to bridge the gap between oxic and anoxic patches with their body. Order of magnitude calculation shows that in shelf sediments, the chemosynthetic bacterial production, which accompanies the oxidation of reduced substances, can not fulfill the feeding requirements of nematodes in the anoxic zone. The different size adaptations with sediment depth in shallow as compared to deep stations combined with deeper penetration of nematodes into the sediment at shallow water depth cause the observed pattern of miniaturization with increasing water depth. We conclude that nematode size distribution is determined by food availability. However, organic matter, as a food source, is not directly responsible, as a limiting resource, but indirectly because its respiration generates reduced conditions in the sediment. Within the smaller size range (

Hydroelastic analysis of floating elastic thick plate in shallow water depth

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

Also, as electric spark and electrodynamic sources, used mainly in works of the upper part of the section, non-explosive sources, which have become widespread, include pneumatic sources. This type of sources meets all the requirements for geophysical equipment. The main problem of such kind of sources is the presence of intense pulsations, long duration of the emitted signals, as well as not very reliability of some clusters. Especially topical is the problem of pulsations during the shallow water acquisitions. The shallow water depth does not allow to extract the signature acceptable for the construction of the effective operator from the data due to the interference of pulsations from the direct wave with the bottom, and the suppression operator which extracted from the theoretical signature, often gives an unacceptable result. To solve the bubble oscillation problems in shallow-water acquisition, it is proposed to use combined information of bubble oscillation extracted from a direct wave, extracted from bottom reflection, waveform predicting deconvolution and adaptive subtraction algorithms.

The underwater visibility is very important in underwater vision research and target detection. However, most underwater vision systems cannot guarantee to possess the performance under complex water conditions. This is because underwater images are usually degraded by light-water interactions of absorption and scattering. The ambient light is scattered into the cameras line of sight by water molecules and suspended particles in the water medium, which adds a layer of haze to the image and reduces the contrast of the image. This part of scattered light is usually called background light, which is the main reason for underwater image degradation. In this paper, the formations of background light in underwater imaging under two different lighting conditions: natural illumination and artificial lighting, are analyzed by setting up physical models. The models developed include the parameters such as camera parameters, light source parameters, inherent optical properties, and camera-source-object geometry. Based on the models, the relationship between the background light and the above parameters is studied. Computer analysis shows that the global background light under two illumination conditions has a close relationship between the inherent optical properties of water medium and camera parameters. The global background light under natural illumination is proportional to the scattering coefficient and inversely proportional to the attenuation coefficient. The background light under the two illumination conditions both can be described in simple exponential falloff expressions of the global background light. The simple expression greatly reduces the computational complexity of simulations. The intensity of background light mainly depends on the inherent optical properties, camera-scene distance, camera-source distance and cameras imaging angle. The relationship between the global background light and the inherent optical properties can be used to estimate the attenuation coefficient, scattering coefficient and scene depth information. The result of this paper can be very useful for designing and improving the underwater imaging systems.

<strong class="journal-contentHeaderColor">Abstract.</strong> Radiative transfer (RT) simulations have long been used to study relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean from which the ocean apparent optical properties (AOPs) can be calculated. For example, inverse models to estimate IOPs from ocean color radiometric measurements have been developed or validated using results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range from the near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development was the generation of the synthetic dataset of seawater IOPs which served as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by probability distributions of IOPs that are consistent with global distributions representative of vast areas of open ocean pelagic environments and coastal regions covering a broad range of optical water types. The generation of the synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column and three simulation scenarios associated with sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll-a pigment. Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0&deg;, 30, and 60&deg; assuming clear skies, standard atmosphere, and wind speed of 5 m s^&minus;1. Thus, overall 29880 RT simulations were performed. The output results of these simulations include the radiance distributions, plane and scalar irradiances, and the whole set of AOPs including the remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines where all optical variables are reported in the spectral range from 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving the IOPs and AOPs.

<strong class="journal-contentHeaderColor">Abstract.</strong> Radiative transfer (RT) simulations have long been used to study relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean from which the ocean apparent optical properties (AOPs) can be calculated. For example, inverse models to estimate IOPs from ocean color radiometric measurements have been developed or validated using results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range from the near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development was the generation of the synthetic dataset of seawater IOPs which served as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by probability distributions of IOPs that are consistent with global distributions representative of vast areas of open ocean pelagic environments and coastal regions covering a broad range of optical water types. The generation of the synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column and three simulation scenarios associated with sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll-a pigment. Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0&deg;, 30, and 60&deg; assuming clear skies, standard atmosphere, and wind speed of 5 m s^&minus;1. Thus, overall 29880 RT simulations were performed. The output results of these simulations include the radiance distributions, plane and scalar irradiances, and the whole set of AOPs including the remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines where all optical variables are reported in the spectral range from 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving the IOPs and AOPs.

<strong class="journal-contentHeaderColor">Abstract.</strong> Radiative transfer (RT) simulations have long been used to study relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean from which the ocean apparent optical properties (AOPs) can be calculated. For example, inverse models to estimate IOPs from ocean color radiometric measurements have been developed or validated using results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range from the near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development was the generation of the synthetic dataset of seawater IOPs which served as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by probability distributions of IOPs that are consistent with global distributions representative of vast areas of open ocean pelagic environments and coastal regions covering a broad range of optical water types. The generation of the synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column and three simulation scenarios associated with sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll-a pigment. Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0&deg;, 30, and 60&deg; assuming clear skies, standard atmosphere, and wind speed of 5 m s^&minus;1. Thus, overall 29880 RT simulations were performed. The output results of these simulations include the radiance distributions, plane and scalar irradiances, and the whole set of AOPs including the remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines where all optical variables are reported in the spectral range from 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving the IOPs and AOPs.

Abstract. Accurate shallow-water depth information for island areas is crucial for maritime safety, resource exploration, ecological conservation, and offshore economic activity. Traditional approaches such as shipborne sounding and airborne bathymetric light detection and ranging (lidar) surveys are expensive, time-consuming, and constrained in politically sensitive regions. Moreover, satellite-altimetry-predicted depths exhibit large errors over shallow waters. In contrast, satellite-derived bathymetry (SDB), estimated from multispectral imagery, provides a rapid, open-source, and cost-effective technique for comprehensively characterizing the bathymetry of a region. Given the scarcity of in situ water depth data for the South China Sea (SCS), a shallow-water depth model, HHU24SWDSCS (Hohai University 2024 Shallow-Water Depth Model of South China Sea), was developed using a linear band model by integrating 1298 Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) tracks with 70 Sentinel-2 multispectral images. The model covers over 120 islands and reefs in the SCS region at a resolution of 10 m. Validation against independent ICESat-2 depth data yielded a root mean square error for the model of 0.53–1.24 m (&lt; 5 % of the maximum depth). Further validation using independent airborne lidar bathymetry data in the Lingyang Reef demonstrated an accuracy of 1.01 m. Comparisons with existing bathymetry models revealed the superior performance of the developed model. While traditional bathymetry models exhibit errors of up to tens of meters or larger over island regions and should therefore be used with caution, HHU24SWDSCS demonstrated good accuracy in shallow waters across the SCS. This model thus provides a reference for mapping shallow-water depth close to islands and provides fundamental support for research in oceanography, geodesy, and other disciplines. The HHU24SWDSCS data are freely available at https://doi.org/10.5281/zenodo.13852568 (Wu et al., 2024a).

Inherent optical properties (IOPs) of the coastal oceans are modulated independently by the in-water optical constituents, which cause variations in the water leaving radiances or re-mote sensing reflectances. Accurate determination of IOPs and the optical constituents from water-leaving radiances or reflectances using conventional empirical ratio approaches fail in the coastal oceans. Alternate non-parametric approaches such as neural network (NN) based approaches can be developed to derive the parameters of interest using training datasets. Further NN based approaches can deal with the non-linearity of functional dependence between optical constituents and the IOPs.To retrieve the IOPs, earlier NN models used Levenberg-Marquardt with Bayesian Regularization as an optimizer for learning the weights of the model, which has a slow learning rate. Moreover, with low-resource datasets while retrieving IOPs till the third level, the probability of error propagation becomes high. To overcome these two problems, we present a Modified Neural Network (MNN) algorithm (modification of NN model [3] to retrieve Inherent Optical Properties (IOPs) of ocean waters, in which three Neural Networks (NN) were developed in parallel. Our method is based on the approach where we use the Adam optimizer, instead of the Levenberg-Marquardt since it has a faster training time. Also, the error propagation is observed to be very less even with low-resource data while retrieving IOPs at the third level, with a decent R² score.Results of the MNN algorithm indicate that MNN retrieves IOPs with an R² = 0.99 between measured and predicted values for b<inf>bp</inf>(443) and R² = 0.99 for a<inf>pg</inf>(443) at Level 1. Level-2 products give R² = 0.98 and R² = 0.99 between measured and predicted values for a<inf>pg</inf>(443) and a<inf>dg</inf>(443) respectively. Similarly Level-3 products give R² = 0.97 and R² = 0.51 between measured and predicted values for a<inf>g</inf>(443) and a<inf>d</inf>(443) respectively. The algorithm retrieves better R² score for all parameters except a<inf>d</inf>(443) compared to Semi Analytical Algorithm, Quasi Analytical Algorithm and NN algorithm by [3]. The new technique has the advantage of faster convergence and better generalization capacity for deriving IOPs from complex waters. The new algorithm is also able to separate gelbstoff and detrital absorption.