Retrieving the water depth by satellite is a rapid and effective method for obtaining underwater terrain. In the optical shallow waters, the bottom signal has a great impact on the radiation from the water which related to water depth. In the optical shallow waters, the spatial distribution characteristic of water quality parameters derived by the updated quasi analysis algorithm (UQAA) is highly correlated with the bottom brightness. Because the bottom reflection signal is strongly correlated with the spatial distribution of water depth, the derived water quality parameters may helpful and applicable for optical remote sensing based satellite derived bathymetry. Therefore, the influence on bathymetry retrieval of the UQAA IOPs is worth discussing. In this article, different machine learning algorithms using a UQAA were tested and remote sensing reflectance at water depth in situ points and their detection accuracy were evaluated by using Worldwiew-2 multispectral remote sensing images and laser measurement data. A backpropagation (BP) neural network, extreme value learning machine (ELM), random forest (RF), Adaboost, and support vector regression (SVR) machine models were utilized to compute the water depth retrieval of Ganquan Island in the South China Sea. According to the obtained results, bathymetry using the UQAA and remote sensing reflectance is better than that computed using only remote sensing reflectance, in which the overall improvements in the root mean square error (RMSE) were 1 cm to 5 cm and the overall improvement in the mean relative error (MRE) was 1% to 5%. The results showed that the results of the UQAA could be used as a main water depth estimation eigenvalue to increase water depth estimation accuracy.

In a seaport, accurate bathymetric maps are valuable for both environmental and economic reasons. One of the main complementary methods for measuring shallow water depth is the retrieval of the water depth by satellite. The results of the water depth inversion are greatly influenced by factors related to water quality. The proposed Updated Quasi Analysis Algorithm (UQAA) allows for the calculation of water quality factors, and their spatial distribution characteristic strongly correlates with the trend in water depth distribution. By using satellite-derived bathymetry, these parameters can be used in the model training to extract the underwater terrain. This paper proposes the idea of combining the UQAA with a Convolutional Neural Network (CNN)-based deep learning framework to retrieve the depth of the water and automatically extract the underwater terrain. We compare four different existing machine learning algorithms as baselines, using GF-6 multispectral remote sensing images and in-situ depth data in Nanshan Port as a priori validation set. We find that the result of the CNN model using the UQAA is better than other baselines, where the root mean square error (RMSE) was down to 0.55 m, the mean relative error (MRE) was 6.63%, and the R2 was 0.92. The developed method which introduces the water quality factors containing geographic information as feature quantities, provides a new direction for further improvement. (we4237/Batymetry_UQAA_CNN (github.com))

Obtaining accurate bathymetric maps is very valuable for marine environment monitoring, port planning, and so on. Accurately estimating water depth in turbid coastal waters using satellite remote sensing encounters challenges originating from low water transparency, but it is limited by the quantity, quality, and water quality of samples. This study introduces a fast feature cascade learning model (FFCLM) to enhance the accuracy of bathymetric inversion from multispectral satellite images, particularly when limited field samples are available. FFCLM leverages spectral bands and in situ data to derive effective inversion weights through feature concatenation and cascade fitting. Field experiments conducted at Nanshan Port and Rushikonda Beach gathered water depth, satellite, and in situ data. Comparative analysis with conventional machine learning algorithms, including support vector machine, random forest, and gradient boosting trees, indicates that FFCLM achieves lower errors and demonstrates more robust performance across study areas. This is especially more pronounced when using small training samples ( n < 100). Examination of key parameters and water depth profiles highlights FFCLM’s advantages in generalization and deep-water inversion. This study presents an efficient solution for small-sample bathymetric mapping in turbid coastal waters, utilizing spectral and physical information to overcome sample size limitations and enhancing satellite remote sensing capabilities for shallow water monitoring.

In recent years, remote sensing has become an indispensable supplementary method for determining water depth in the seaports. At present, many scholars use multi-spectral satellite data to invert the water depth of the seaports, but how to select the appropriate satellite data in the seaports area is worth exploring. In this article, the differences in the retrieving ability between domestic and foreign multispectral images are compared, through building the random forest model and the band ratio model, which use different multispectral images to conduct retrieving water depth in Nanshan Port in conjunction with the WBMS multi-beam sounding system. The band ratio model and random forest model are chosen for water depth exploration, remote sensing images use GF-6, GF-2, Sentinel-2B, and Landsat 8 OLI data, which are all popular and easily accessible. The final experiment results from the constant adjustment of the model parameter show that the domestic series of GF-6 images performed the best in this experiment. The Root Mean Square Error (RMSE) and Mean Relative Error (MRE) of the random forest model are only 1.202 and 0.187, respectively. Simultaneously, it is discovered that the ‘Red Edge’ band of GF-6 is also very helpful in improving the accuracy of water depth inversion, which is rarely mentioned in previous studies. To some extent, the preceding studies demonstrate that it is possible to investigate water depth using common multispectral remote sensing images. In the case of some bathymetry inversion models or in some waters, the aforementioned study demonstrates that it is possible to examine the water depth using domestic remote sensing images that are superior to foreign multispectral images in terms of bathymetry inversion ability.

Bathymetry estimation is essential for various applications in port management, navigation safety, marine engineering, and environmental monitoring. Satellite remote sensing data can rapidly acquire the bathymetry of the target shallow waters, and researchers have developed various models to invert the water depth from the satellite data. Geographically weighted regression (GWR) is a common method for satellite-based bathymetry estimation. However, in sediment-laden water environments, especially ports, the suspended materials significantly affect the performance of GWR for depth inversion. This study proposes a novel approach that integrates GWR with Random Forest (RF) techniques, using longitude, latitude, and multispectral remote sensing reflectance as input variables. This approach effectively addresses the challenge of estimating bathymetry in turbid waters by considering the strong correlation between water depth and geographical location. The proposed method not only overcomes the limitations of turbid waters but also improves the accuracy of depth inversion results in such complex aquatic settings. This breakthrough in modeling has significant implications for turbid waters, enhancing port management, navigational safety, and environmental monitoring in sediment-laden maritime zones.

Current efforts for improving the hyperspectral optimization processing exemplar (HOPE) model include further testing of remote-sensing reflectance (Rrs) features containing useful information for bathymetry retrieval via the minimization of the interference stemming from the variability in inherent optical properties and benthic reflectance. In this paper, we found a novel feature originating from the pure water absorption within the narrow spectral region of 570–600 nm. In most coastal regions of clear water, for example, in a coral reefs environment, pure water accounts for the majority of the total absorption in this spectral range. In addition to the depth variation, the spectral behavior of Rrs(570–600) is primarily dominated by a steep increase in pure water absorption with wavelength, whereas the influence of other optical properties such as phytoplankton/CDOM absorption, particle backscattering, and benthic reflectance can be simplified using the spectrally constant shape model. A HOPE pure water (HOPE-PW) algorithm using this feature was developed based on Rrs measurements with a spectral resolution of near 3.5 nm, wherein only four unknown parameters must be resolved. The validation from LiDAR data and comparison with HOPE-BRUCE using PRISM data at 15 sites located in five distinct regions of Palau, Guam, Great Barrier Reef, Hawaiian Islands and Florida Key, confirmed that the HOPW-PW yielded a considerable performance and provided adequate transferability to other sites with varying bottom and water environments. The sensitivity analysis based on Hydrolight-simulated datasets showed that HOPE-PW was less effected by bottom type variations but still had limitations in retrieving water optical properties.