Investigating the Impact of Spatiotemporal Variations in Water Surface Optical Properties on Satellite-Derived Bathymetry Estimates in the Eastern Mediterranean

Satellite-Derived Bathymetry (SDB) methods have found their way into the hydrographers’ toolbox and are part of integrated survey concepts, nautical charts and support global and European programs such as Seabed2030 or EMODnet Bathymetry. The concept of the ‘physics-based’ SDB describes the calculation of bathymetry by modelling the sunlight path from the sun to the seafloor to the satellite sensor. It is a highly sophisticated model which enables the calculation of shallow water depth in the absence of any other survey or ground-truth data. Thus, bathymetric data can also be retrieved for remote and inaccessible areas - in contrast to empirical SDB approaches. Key questions which arise for SDB results are vertical accuracy, potential and feasibility for different sites and the methods to upscale SDB solutions. These questions are addressed in the current European innovation project 4S. Within the project SDB-Online was developed, a fully physics-based SDB concept which is installed in a cloud and accessible via a web user interface. The backend is parallelised and can be accessed via application programming interface (API) which allows a fully scalable and automatic SDB processing. In this study SDB-Online results are validated at ten sites, ranging from the higher latitudes of Canada to turbid UK waters to the Caribbean. Furthermore, a relationship between the Secchi Disc Depth and the cutoff depth of the SDB results is established and a global map of water-clarity potential of the SDB solution is presented.

Satellite Derived Bathymetry (SDB) method uses satellite or other remote multispectral imagery for depth determination in very shallow coastal areas with clear waters. Commonly, SDB survey method can be used when planning hydrographic surveying of marine areas not surveyed or areas with old bathymetric data. This method has become widely used in the past few years. SDB is a survey method founded on analytical modelling of light penetration through the water column in visible and infrared bands. In this article, SDB method was applied by using free-of-charge Landsat 8 and Sentinel 2 satellite images to get the bathymetric data in the area of Hramina Bay in the Central Adriatic. SDB processing procedures and algorithms were described. Processed satellite data was uploaded on geodetic software and ENC S-57 format. The bathymetric map of Hramina Bay obtained by the SDB method was compared with the approach usage band Electronic Nautical Chart (ENC) HR400512 with satisfying positional and vertical accuracy.

<p>Bathymetric data are a key parameter to assess shallow-water hydrodynamic processes. In-situ surveys provide high data quality; however, surveys are expensive and cover a limited spatial extent. To fill this gap, over recent years, the Satellite Derived Bathymetry (SDB) techniques have been developed. The present work aims to elaborate a technique to estimate bathymetric data from satellite images for intertidal zones. The method applied in this work is composed of 6 steps: (1) image querying and pre-processing is done through Google Earth Engine application (API) using Copernicus Sentinel 2A and B, product type 2A. (2) Identification of the intertidal zone for the study area by temporal variability of the Normalized Difference Water Index (NDWI). (3) Recognition of the waterline in each image by the use of an adaptive threshold technique; and assignment of the elevation for each detected waterline based on local observed tide heights. (4) Validation of the estimated bathymetry by comparison with LiDAR measurements. (5) Implementation of a SDB correction: numerical and/or statistical and, (6) assessment of the validity of SDB for hydrodynamic modelling. The SDB technique was applied to 4 different estuaries in New Zealand: Maketu, Ohiwa, Whitianga and Tauranga Harbour showing similar or better estimations in comparison to previous works using optical or synthetic aperture radar (SAR). For Tauranga Harbour, results from the statistical and dynamical corrections showed that the major error source is due to the image optical properties and environmental conditions when the image was acquired (35%). However, the tidal propagation can significantly decrease the SDB accuracy (13%). Finally, the use of the SDB in numerical simulations does not present huge differences in the predicted waterlevels in comparison to the use of survey bathymetry, showing that SDB could be potentially used for coastal flooding simulations.  </p>

The amount of Earth observation images available to the public has been the main source of information, helping governments and decision-makers tackling the current world’s most pressing global challenge. However, a number of highly skilled and qualified personnel are still needed to fill the gap and help turn these data into intelligence. In addition, the accuracy of this intelligence relies on the quality of these images in times of temporal, spatial, and spectral resolution. For the purpose of contributing to the global effort aiming at monitoring natural and anthropic processes affecting coastal areas, we proposed a framework for image processing to extract the shoreline and the shallow water depth on GeoEye-1 satellite image and orthomosaic image acquired by an unmanned aerial vehicle (UAV) on the coast of San Vito Lo Capo, with image preprocessing steps involving orthorectification, atmospheric correction, pan sharpening, and binary imaging for water and non-water pixels analysis. Binary imaging analysis step was followed by automatic instantaneous shoreline extraction on a digital image and satellite-derived bathymetry (SDB) mapping on GeoEye-1 water pixels. The extraction of instantaneous shoreline was conducted automatically in ENVI software using a raster to vector (R2V) algorithm, whereas the SDB was computed in ArcGIS software using a log-band ratio method applied on the satellite image and available field data for calibration and vertical referencing. The results obtained from these very high spatial resolution images demonstrated the ability of remote sensing techniques in providing information where techniques using traditional methods present some limitations, especially due to their inability to map hard-to-reach areas and very dynamic near shoreline waters. We noticed that for the period of 5 years, the shoreline of San Vito Lo Capo sand beach migrated about 15 m inland, indicating the high dynamism of this coastal area. The bathymetric information obtained on the GeoEye-1 satellite image provided water depth until 10 m deep with R2 = 0.753. In this paper, we presented cost-effective and practical methods for automatic shoreline extraction and bathymetric mapping of shallow water, which can be adopted for the management and the monitoring of coastal areas.

Abstract. Topographic and bathymetric data are essential for accurate predictions of flooding in estuaries because water depth and elevation data are fundamental components of the shallow-water hydrodynamic equations used in models for storm surges and tides. Where lidar or in situ acoustic surveys are unavailable, recent efforts have centred on using satellite-derived bathymetry (SDB) and satellite-derived topography (SDT). This work is aimed at (1) determining the accuracy of SDT and (2) assessing the suitability of the SDT and SDB for extreme water level modelling of estuaries. The SDT was created by extracting the waterline as it tracks over the topography with changing tides. The method was applied to four different estuaries in Aotearoa / New Zealand: Whitianga, Maketū, Ōhiwa and Tauranga harbours. Results show that the waterline method provides similar topography to the lidar with a root-mean-square error equal to 0.2 m, and it is slightly improved when two correction methods are applied to the topography derivations: the removal of statistical bias (0.02 m improvement) and hydrodynamic modelling correction of waterline elevation (0.01 m improvement). The use of SDT in numerical simulations of surge levels was assessed for Tauranga Harbour in eight different simulation scenarios. Each scenario explored different ways of incorporating the SDT to replace the topographic data collected using non-satellite survey methods. In addition, one of these scenarios combined SDT (for intertidal zones) and SDB (for subtidal bathymetry), so only satellite information is used in surge modelling. The latter SDB is derived using the well-known ratio–log method. For Tauranga Harbour, using SDT and SDB in hydrodynamic models does not result in significant differences in predicting high water levels when compared with the scenario modelled using surveyed bathymetry.

Selecting a representative optical deep-water area is crucial for accurate satellite-derived bathymetry (SDB) based on semi-theoretical and semi-empirical models. This study proposed a deep-water area selection method where potential areas were identified by integrating remote sensing imagery with existing global bathymetric data. Specifically, the effects of sun glint correction for deep-water areas on SDB estimation were investigated. The results indicated that the computed SDB had significant instabilities when different optical deep-water areas without sun glint correction were used for model training. In comparison, when sun glint correction was applied, the SDB results from different deep-water areas had greater consistency. We generated bathymetric maps for the Langhua Reef in the South China Sea and Buck Island near the U.S. Virgin Islands using Sentinel-2 multispectral images and 70% of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) bathymetry data. Additionally, 30% of the ICESat-2 bathymetry data and NOAA NGS Topo-bathy Lidar data served as the validation data to evaluate the qualities of the computed SDB, respectively. The results showed that the average quality of the SDB significantly improved with sun glint correction application by a magnitude of 0.60 m in terms of the root mean square error (RMSE) for two study areas. Moreover, an evaluation of the SDB data computed from different deep-water areas showed more consistent results, with RMSEs of approximately 0.4 and 1.4 m over the Langhua Reef and Buck Island, respectively. These values were consistently below 9% of the maximum depth. In addition, the effects of the optical image selection on SDB inversion were investigated, and the SDB calculated from the images over different time periods demonstrated similar results after applying sun glint correction. The results showed that this approach for optical deep-water area selection and correction could be used for improving the SDB, particularly in challenging scenarios, thereby enhancing the accuracy and robustness of SDB.

Satellite-derived bathymetry using Sentinel-2 in mesotidal coasts

Coastal regions are areas of great socioeconomic and ecological value. Within them, deltas are particularly vulnerable, as they experience intense anthropogenic pressures and the effects of climate change, which are even more pronounced in the Mediterranean Sea. These environments face an increasing risk of coastal flooding, wetland loss, shoreline retreat, and infrastructure deterioration.Therefore, it is essential to have high spatial and temporal resolution data for monitoring these areas. In this context, bathymetric information is crucial for marine environmental planning, navigation, fisheries management, and many other applications. However, both large- and small-scale bathymetric data are limited and expensive. In response to this limitation,cost-effective alternatives for bathymetric monitoring have been explored, with Satellite-Derived Bathymetry (SDB) emerging as a viable option to complement conventional techniques.This research used a Do It Yourself (DIY) bathymetric sensor, specifically designed to obtain in situ data and extract Satellite-Derived Bathymetry (SDB) at the river mouth of the Ebro Delta (NW Mediterranean Sea), in order to analyze changes in the bathymetry between 2023 and 2025. For this purpose, four sampling campaigns were carried out at the mouth of the delta, combining the obtained data with images from the Sentinel-2A/B mission of the Copernicus Program. The Sentinel-2 images were processed using the ACOLITE processor to perform atmospheric and sunglint corrections. The objective of this research is to demonstrate the feasibility of using DIY technologies to obtain in situ bathymetric data in coastal areas, which can support the extraction of SDB. These technologies are especially useful for countries in the process of development and initiatives with open and collaborative science platforms (i.e. citizen science). Validating this methodology will contribute to providing access to bathymetric data essential for coastal zone management and mitigation of the future impacts of climate changes. 

The acquisition of bathymetric data in shallower waters is difficult to attain using traditional survey methods because the areas to investigate may not be accessible to hydrographic vessels, due to the risk of grounding. For this reason, the use of satellite detection of depth data (satellite-derived bathymetry, SDB) constitutes a particularly useful and also economically advantageous alternative. In fact, this approach based on analytical modelling of light penetration through the water column in different multispectral bands allows to cover a big area against relatively low investment in time and resources. Particularly, the empirical method named band ratio method (BRM) is based on the degrees of absorption at different bands. The accuracy of the SDB is not comparable with that of traditional surveys, but we can certainly improve it by choosing satellite images with high geometric resolution. This article aims to investigate BRM applied to high geometric resolution images, IKONOS-2, concerning the Bay of Pozzuoli (Italy), and improve the accuracy of results performing the determination of the relation between band ratio and depth. Two non-linear functions such as the exponential function and the 3rd degree polynomial (3DP) are proposed, instead of regression line, to approximate the relationship between the values of the reflectance ratios and the true depth values collected in measured points. Those are derived from an Electronic Navigational Chart produced by the Italian Hydrographic Office. The results demonstrate that the adopted approach allows to enhance the accuracy of the SDB, specifically, 3DP supplies the most performing bathymetric model derived by multispectral IKONOS-2 images.

Abstract. In recent decades, research has been developed to estimate near-shore bathymetry depth values using satellite imagery. Visible and infrared bands are used to derive elevation profile estimates, so to obtain bathymetric in rapid way without mobilisation of persons or equipment and saving the costs. For consequence, Satellite Derived Bathymetry (SDB) is seen as a valid approach for shallow waters survey: strongly supported by the activity of scholars and researchers, multiple methods are available in the literature. This article aims to investigate and compare different SDB methods for sea depth extraction from Sentinel-2 satellite multispectral images, with particular attention to the accuracy of the results. The experiments are conducted on imagery including Blue, Green, Red and Near Infrared bands, with 10 m resolution, concerning the Bay of Pozzuoli (Italy). After removing the glint, the effects caused by the reflection of sunlight through single scattering from sea surface, three methods are applied: Band Ratio method (BRM), 3rd-degree polynomial regression line method (3DPM), and principal component analysis method (PCAM). 3DPM can be seen as a variant of the BRM where the linear law that interprets the correlation between the band ratio values and the depth values is replaced by the third order function. Models are trained using depth data extracted from an Electronic Navigational Chart (ENC) at 1:7,500 scale, which is also used to verify result accuracy. The experiments demonstrate that the 3DPM is better able to obtain a more precise bathymetric model, confirming the greater adaptability of the 3rd order function to interpretate the variability of the interaction of light with water along the water column.

Bathymetric information has become essential to help maintain and operate coastal zones. Traditional in situ bathymetry mapping using echo sounders is inefficient in shallow waters and operates at a high logistical cost. On the other hand, lidar mapping provides an efficient means of mapping coastal areas. However, this comes at a high acquisition cost as well. In comparison, satellite-derived bathymetry (SDB) provides a more cost-effective way of mapping coastal regions, albeit at a lower resolution. This work utilises all three of these methods collectively, to obtain accurate bathymetric depth data of two pocket beaches, Golden Bay and Għajn Tuffieħa, located in the northwestern region of Malta. Using the Google Earth Engine platform, together with Sentinel-2 data and collected in situ measurements, an empirical pre-processing workflow for estimating SDB was developed. Four different machine learning algorithms which produced differing depth accuracies by calibrating SDBs with those derived from alternative techniques were tested. Thus, this study provides an insight into the depth accuracy that can be achieved for shallow coastal regions using SDB techniques.

Achieving coastal and shallow-water bathymetry is essential for understanding the marine environment and for coastal management. Bathymetric data in shallow sea areas can currently be obtained using SDB (satellite-derived bathymetry) with multispectral satellites based on depth inversion models. In situ bathymetric data are crucial for validating empirical models but are currently limited in remote and unapproachable areas. In this paper, instead of using the measured water depth data, ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) ATL03 bathymetric points at different acquisition dates and multispectral imagery from Sentinel-2/GeoEye-1 were used to train and evaluate water depth inversion empirical models in two study regions: Shanhu Island in the South China Sea, and Heron Island in the Great Barrier Reef (GBR) in Australia. However, different sediment types also influenced the SDB results. Therefore, three types of sediments (sand, reef, and coral/algae) were analyzed for Heron Island, and four types of sediments (sand, reef, rubble and coral/algae) were analyzed for Shanhu Island. The results show that accuracy generally improved when sediment classification information was considered in both study areas. For Heron Island, the sand sediments showed the best performance in both models compared to the other sediments, with mean R2 and RMSE values of 0.90 and 1.52 m, respectively, representing a 5.6% improvement of the latter metric. For Shanhu Island, the rubble sediments showed the best accuracy in both models, and the average R2 and RMSE values were 0.97 and 0.65 m, respectively, indicating an RMSE improvement of 15.5%. Finally, bathymetric maps were generated in two regions based on the sediment classification results.

Bathymetric data could be extracted by means of remote sensing techniques from satellite sensors known as satellite derived bathymetry (SDB). This study applies the use of remote sensing data for extraction of bathymetry within a specified time range. Logarithmic and linear analytical methods are applied to Sentinel 2A and Landsat 8 imagery to retrieve bathymetry data. We intend to analyse the pattern of error in regards to depth and epoch. The collection of satellite image, the corresponding storage, and processing makes use of Google Earth Engine (GEE). The result shows that the root mean square error (RMSE) of depth is ranging from 1.6 m to 5.4 m from both sources of imageries. Better accuracy is obtained by applying logarithmic method to Landsat imagery.

In this review paper the focus is to summarize the study of variation in optical, electrical, structural and morphology properties of swift heavy ions of polymer composite. Polyaniline is mainly used in the electrical and electrochemical properties. The synthesis techniques such as chemical oxidative method, electrochemical method, sol-gel method, doping method and many other techniques were used to prepare polyaniline composites. The variation in optical properties can be analysed with the help of UV-Vis and photoluminescence spectroscopy. The structural and morphology can be analysed with the help of FTIR and SEM (scanning electron microscope). The crystal structure, lattice constant and d-spacing can be analysed with the help of XRD.

Thermochromic glazing performance: From component experimental characterisation to whole building performance evaluation