Optical Properties Of Water Body Research Articles

Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

Bathymetry data is indispensable for a variety of aquatic field studies and benthic resource inventories. Determining water depth can be accomplished through an echo sounding system or remote estimation utilizing space-borne and air-borne data across diverse environments, such as lakes, rivers, seas, or lagoons. Despite being a common option for bathymetry mapping, the use of satellite imagery faces challenges due to the complex inherent optical properties of water bodies (e.g., turbid water), satellite spatial resolution limitations, and constraints in the performance of retrieval models. This study focuses on advancing the remote sensing based method by harnessing the non-linear learning capabilities of the machine learning (ML) model, employing advanced feature selection through a meta-heuristic algorithm, and using image extraction techniques (i.e., band ratio, gray scale morphological operation, and morphological multi-scale decomposition). Herein, we validate the predictive capabilities of six ML models: Random Forest (RF), Support Vector Machine (SVM), CatBoost (CB), Extreme Gradient Boost (XGB), Light Gradient Boosting Machine (LGBM), and KTBoost (KTB) models, both with and without the application of meta-heuristic optimization (i.e., Dragon Fly, Particle Swarm Optimization, and Grey Wolf Optimization), to accurately ascertain water depth. This is achieved using a diverse input dataset derived from multi-spectral Landsat 9 imagery captured on a cloud-free day (19 September 2023) in a shallow, turbid lagoon. Our findings indicate the superior performance of LGBM coupled with Particle Swamp Optimization (R2 = 0.908, RMSE = 0.31 m), affirming the consistency and reliability of the feature extraction and selection-based framework, while offering novel insights into the expansion of bathymetric mapping in complex aquatic environments.

A semianalytical algorithm for estimating particulate composition in inland waters based on Sentinel-3 OLCI images

The composition of suspended particles is a key factor in determining the underwater light field, which is of great significance for understanding the variability in the optical properties of water bodies. In this study, the ratio of the phytoplankton absorption coefficient to the backscattering coefficient at wavelength of 681 nm (aph(681)/bb(681)) was found to be an optimal optical indicator of the ratio of chlorophyll-a to the total suspended matter concentration (Chla/TSM), a parameter indicating the particulate composition. Therefore, a semianalytical algorithm was proposed to estimate Chla/TSM from remote sensing reflectance (Rrs(λ)) at 681 nm and 754 nm on Sentinel-3 Ocean and Land Color Instrument (OLCI) images. The validation dataset collected from 11 inland lakes and 3 reservoirs in China and 2 inland lakes in America was used to evaluate the algorithm’s performance. The evaluation results demonstrated that the proposed algorithm could have favorable performance in inland waters. Furthermore, comparison with two other state-of-the-art algorithms (Sun_13 and NTD675) showed that this proposed algorithm had higher estimation accuracy, with an overall winning rate (OWR) of 60%, an unbiased mean absolute percentage error (UMAPE) reduction from 72.95% to 46.44%, a root mean square error (RMSE) decline from 1.42 µg/mg to 0.83 µg/mg and a normalized root mean square error (NRMSE) decline from 11.36% to 6.62%. This algorithm was successfully applied to acquire the Chla/TSM tempo-spatial variation using the OLCI images of Lake Taihu from 2016 to 2019. It was found that the algorithm developed based on OLCI images can be applied to satellite sensors with similar bands, such as Medium-Resolution Imaging Spectrometer (MERIS), and Sentinel-2 multispectral instrument (MSI), etc. As a simple and effective algorithm, the proposed algorithm has the potential to monitor changes in Chla/TSM in inland waters on a global scale.

An Improved Adaptive Subsurface Phytoplankton Layer Detection Method for Ocean Lidar Data

Phytoplankton, as the foundation of primary production, is of great significant for the marine ecosystem. The vertical distribution of phytoplankton contains key information about marine ecology and the optical properties of water bodies related to remote sensing.The common methods to detect subsurface phytoplankton biomass are often in situ measurements and passive remote sensing; however, the bio-argo measurement is discrete and costly, and the passive remote sensing measurement is limited to obtain the vertical information. As a component of active remote sensing, lidar technology has been proved as an effective method for mapping the vertical distribution of phytoplankton. In the past years, there have been few studies on the phytoplankton layer extraction method for lidar data. The existing subsurface layer extraction algorithms are often non-automatic, which need manual intervention or empirical parameters to set the layer extraction threshold. Hence, an improved adaptive subsurface phytoplankton layer detection method was proposed, which incorporates a curve fitting method and a robust estimation method to determine the depth and thickness of subsurface phytoplankton scattering layer. The combination of robust estimation method can realize automatic calculation of layer detection threshold according to the characteristic of each lidar signal, instead of an empirical fixed value used in previous works. In addition, the noise jamming signal can also be effectively detected and removed. Lidar data and in situ spatio-temporal matching Chlorophyll-a profile data obtained in Sanya Bay in 2018 was used for algorithm verification. The example result of step-by-step process illustrates that the improved method is available for adaptive threshold determination for layer detection and redundant noise signals elimination. Correlation analysis and statistical hypothesis testing shows the retrieved subsurface phytoplankton maximum depth by the improved method and in situ measurement is highly relevant. The absolute difference of layer maximum depth between lidar data and in situ data for all stations is less than 0.75 m, and mean absolute difference of layer thickness difference is about 1.74 m. At last, the improved method was also applied to the lidar data obtained near Wuzhizhou Island seawater, which proves that the method is feasiable and robust for various sea areas.

Estimating Organic and Inorganic Part of Suspended Solids from Sentinel 2 in Different Inland Waters

Inland waters are very sensitive ecosystems that are mainly affected by pressures and impacts within their watersheds. One of water’s dominant constituents is the suspended particulate matter that affects the optical properties of water bodies and can be detected from remote sensors. It is important to know their composition since the ecological role they play in water bodies depends on whether they are mostly organic compounds (phytoplankton, decomposition of plant matter, etc.) or inorganic compounds (silt, clay, etc.). Nowadays, the European Space Agency Sentinel-2 mission has outstanding characteristics for measuring inland waters’ biophysical variables. This work developed algorithms that can estimate the total concentration of suspended matter (TSM), differentiating organic from inorganic fractions, through the combined use of Sentinel-2 images with an extensive database obtained from reservoirs, lakes and marshes within eastern zones of the Iberian Peninsula. For this, information from 121 georeferenced samples collected throughout 40 field campaigns over a 4-year period was used. All possible two-band combinations were obtained and correlated with the biophysical variables by fitting linear regression between the field data and bands combination. The results determined that only using bands 705 or 783 lead to the obtaining the amount of total suspended matter and their organic and inorganic fractions, with errors of 10.3%, 14.8% and 12.2%, respectively. Therefore, remote sensing provides information about total suspended matter dynamics and characteristics as well as its spatial and temporal variation, which would help to study its causes.

Overview of Underwater Transmission Characteristics of Oceanic LiDAR

Oceanic LiDAR (hereafter referred to as O-LiDAR) is an important remote sensing device for measuring the near-coastal water depth and for studying the optical properties of water bodies. With the commercialization of LiDAR, the theoretical research on the underwater transmission characteristics of LiDAR has been intensified worldwide. Primary research interests include the simulation and modeling of LiDAR underwater echo signals and the inversion of optical parameters using LiDAR water echo signals. This article provides an overview of the principle of LiDAR echo signal formation, and comprehensively summarizes the LiDAR echo signal simulation modeling methods and the corresponding factors that affect modeling accuracy by focusing on the characteristics of different methods. We found that the current simulation methods of LiDAR underwater transmission echo signals primarily include an analytical method based on the radiation transfer equation and a statistical method based on the Monte Carlo (MC) model. The radiation transport equation needs to be appropriately simplified using the analytical method, usually using the quasi-single-small-angle approximation principle. The analytical method has high calculation efficiency but its accuracy is dependent to the quasi-single small-angle approximation. The statistical method can analyze the influence of various factors on echo signals by controlling the variables, but it has poor calculation efficiency. Finally, the semianalytical MC model was used to quantitatively analyze the three main factors (LiDAR system parameters, water body optical parameters, and environmental parameters) affecting underwater LiDAR transmission characteristics, and summarizes the mechanism and results of different factors.

MODIS-Based Research on Secchi Disk Depth Using an Improved Semianalytical Algorithm in the Yellow Sea

Secchi disk depth is a commonly measured parameter representing the optical properties of water bodies. Assessment of water transparency in seas is highly significant to marine-environment monitoring. In this study, an improved Secchi disk depth (Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SD</sub> ) inversion algorithm was proposed based on the Poole-Atkins model by determining the parameter A in the original model. The Forel-Ule index (FUI) is a water color parameter that can be obtained from remote sensing data. Through the analysis of the International Ocean Color Coordinating Group dataset, it was found that there are strong logarithmic and quadratic correlations between the FUI and parameter A, whose R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values are 0.929 and 0.925, respectively. Comparing the results derived from MODIS product data with the in situ measured data in the Yellow Sea showed that the RMSE and MRE of the quadratic formula are 1.83 m and 43.74%, respectively, which reflect better performance than the other empirical formulas. Thus, parameter A can be expressed in quadratic form with FUI as a variable. Finally, we mapped the Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SD</sub> inversion results for the Yellow Sea and analyzed the spatial changes. This study provides new insight for inverting Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SD</sub> transparency algorithms and highlights the value of marine transparency monitoring.

MODIS-Based Radiometric Color Extraction and Classification of Inland Water With the Forel-Ule Scale: A Case Study of Lake Taihu

Serious difficulties are present in the application of remote sensing techniques for optically complex waters, as retrieval of water quality parameters is generally based on detailed local knowledge of optical properties of water bodies for specific areas and at specific times. Water color is measured in traditional water quality observations and characterized by the Forel-Ule scale, as it is intimately related to water compositions. In this paper, a Moderate Resolution Imaging Spectroradiometer (MODIS) based water color extraction and classification approach is developed and applied to Lake Taihu. By using MODIS data together with field data, we attempted to 1) retrieve the dominant wavelength of water color and classify water color into FU-classes; 2) analyze the relationship between water color dominant wavelength and the abundance of optically active component (OACs) in water; and 3) discover seasonal variations of water color based on Lake Taihu. Our results show that the dominant wavelength exhibits some relationship with the three types of OAC concentrations under certain conditions, particularly TSM and Chl-a; inorganic suspended matter (ISM) can be retrieved by using MODIS derived dominant wavelength in appropriate water body. Moreover, differences in water quality for different seasons can be detected by dominant wavelength and FU-class with some prior knowledge of the studied water. Therefore, dominant wavelength may be used as a comprehensive and promising indicator of water quality situation even though much work has to be done in the future to optimize the analyses and verify it on diverse sites.

Optical Properties of Water Body in Xiaojiang River Backwater Area, Three Gorges Reservoir, 2008

To discuss optical properties of water body in Xiaojiang river backwater area after the early impounding stage of the Three Gorges Reservoir (TGR), the correlations between visible light attenuation coefficients (Kd(PAR)) and Secchi Disc transparency (SD), total particulate matters (TPM), dissolved organic carbon (DOC) and chlorophyll-a (Chla) were investigated, and spatiotemporal variations of Kd(PAR) and its influencing factors were analyzed. In the study area, there are no significant spatial variations of Kd(PAR), but the seasonal changes are obvious, with the highest value in winter and the lowest value in summer. Statistics analysis suggests that there are strong correlations between Kd(PAR) and SD, TPM, TIM, the value of R2 are 0.7791, 0.7285, and 0.7637, respectively. The relationship between Kd(PAR) and DOC is less significant(R2=0.1289). However, the correlation between Kd(PAR) and Chla was weak (R2=0.006). Further study shows that, the impact of total particulate matters on the optical properties of water body is as a result of both seasonal impoundment of the TGR and particular hydrological condition of Xiaojiang river basin.

Artificial neural networks and remote sensing in the analysis of the highly variable Pampean shallow lakes

Suspended organic and inorganic particles, resulting from the interactions among biological, physical, and chemical variables, modify the optical properties of water bodies and condition the trophic chain. The analysis of their optic properties through the spectral signatures obtained from satellite images allows us to infer the trophic state of the shallow lakes and generate a real time tool for studying the dynamics of shallow lakes. Field data (chlorophyll-a, total solids, and Secchi disk depth) allow us to define levels of turbidity and to characterize the shallow lakes under study. Using bands 2 and 4 of LandSat 5 TM and LandSat 7 ETM+ images and constructing adequate artificial neural network models (ANN), a classification of shallow lakes according to their turbidity is obtained. ANN models are also used to determine chlorophyll-a and total suspended solids concentrations from satellite image data. The results are statistically significant. The integration of field and remote sensors data makes it possible to retrieve information on shallow lake systems at broad spatial and temporal scales. This is necessary to understanding the mechanisms that affect the trophic structure of these ecosystems.