Physical, Bio-Optical State and Correlations in North–Western European Shelf Seas

Forel-Ule index extraction and spatiotemporal variation from MODIS imagery in the Bohai Sea of China

In large-scale water quality evaluation, traditional field-measured data lack spatial-temporal representativeness, and the role of conventional remote sensing parameters (SST, Chla, TSM, etc.) is controversial. By calculating and grading the hue angle of a water body, a Forel-Ule index (FUI) can be obtained, which provides a comprehensive statement of water condition. Using MODIS imagery, hue angles are extracted with better accuracy than the literature's method. It is found that FUI changes in the Bohai Sea have correlated consistently with water quality. The decreasing trend of non-excellent water quality areas in the Bohai Sea was highly correlated with FUI (R² = 0.701) during the government-dominated land-based pollution reduction program (2012-2021). FUI can monitor and evaluate seawater quality.

Can Forel–Ule Index Act as a Proxy of Water Quality in Temperate Waters? Application of Plume Mapping in Liverpool Bay, UK

The use of ocean colour classification algorithms, linked to water quality gradients, can be a useful tool for mapping river plumes in both tropical and temperate systems. This approach has been applied in operational water quality programs in the Great Barrier Reef to map river plumes and assess trends in marine water composition and ecosystem health during flood periods. In this study, we used the Forel–Ule colour classification algorithm for Sentinel-3 OLCI imagery in an automated process to map monthly, annual and long-term plume movement in the temperate coastal system of Liverpool Bay (UK). We compared monthly river plume extent to the river flow and in situ water quality data between 2017–2020. The results showed a strong positive correlation (Spearman’s rho = 0.68) between the river plume extent and the river flow and a strong link between the FUI defined waterbodies and nutrients, SPM, turbidity and salinity, hence the potential of the Forel–Ule index to act as a proxy for water quality in the temperate Liverpool Bay water. The paper discusses how the Forel–Ule index could be used in operational water quality programs to better understand river plumes and the land-based inputs to the coastal zones in UK waters, drawing parallels with methods that have been developed in the GBR and Citclops project. Overall, this paper provides the first insight into the systematic long-term river plume mapping in UK coastal waters using a fast, cost-effective, and reproducible workflow. The study created a novel water assessment typology based on the common physical, chemical and biological ocean colour properties captured in the Forel–Ule index, which could replace the more traditional eutrophication assessment regions centred around strict geographic and political boundaries. Additionally, the Forel–Ule assessment typology is particularly important since it identifies areas of the greatest impact from the land-based loads into the marine environment, and thus potential risks to vulnerable ecosystems.

Review of the Forel-Ule Index based on in situ and remote sensing methods and application in water quality assessment

Water pollution is considered an acute worldwide environmental issue. At present, the commonly adopted method of water quality characterisation involves the retrieval of optically active water quality parameters based on remote sensing reflectance (R_rs), but this method is subject to the limitation that understanding local scatter and absorption characteristics of light is essential to precisely derive these parameters. Water colour primarily depends on water constituents and is traditionally gauged with the Forel-Ule (FU) scale. In recent years, R_rs within the visible region has been considered to determine the Forel-Ule Index (FUI) for water colour measurement. The FUI exhibits the advantages of remote sensing and does not rely on local retrieval algorithms. Therefore, this index can characterise natural waters in a simple and globally effective manner. As there exists a lack of review articles on the FUI, we present a comprehensive review of this index that may help researchers progress. First, we introduce the most recent techniques for FUI measurement, especially remote sensing-deriving methods. Then, we summarise FUI applications in water quality assessment of oceans and inland waters. Finally, FUI development trends, challenges and application perspectives are examined.

Determination of the Connectedness of Land Use, Land Cover Change to Water Quality Status of a Shallow Lake: A Case of Lake Kyoga Basin, Uganda

Catchments for aquatic ecosystems connect to the water quality of those waterbodies. Land use land cover change activities in the catchments, therefore, play a significant role in determining the water quality of the waterbodies. Research on the relationship between land use and land cover changes and water quality has gained global prominence. Therefore, this study aimed at determining land use, land cover changes in the catchments of L. Kyoga basin, and assessing their connectedness to the lake’s water quality. The GIS software was used to determine eight major land use and land cover changes for 2000, 2010, and 2020. Meanwhile, water quality data was obtained through both secondary and primary sources. Spearman correlation statistical tool in SPSS was used to correlate the land use, land cover changes, and water quality changes over the two-decade study period. The results showed that different land use and land cover activities strongly correlated with particular water quality parameters. For example, agriculture correlated strongly with nutrients like TP, TN, and nitrates and turbidity, TSS, BOD, and temp. The correlation with nitrates was statistically significant at 0.01 confidence limit. The findings of this study agreed with what other authors had found in different parts of the world. The results show that to manage the water quality of L. Kyoga, management of land use, land cover activities in the catchment should be prioritized. Therefore, the results are helpful to decision and policy makers and relevant stakeholders responsible for water management.

Empirical Formulas for Estimating Backscattering and Absorption Coefficients in Complex Waters from Remote-Sensing Reflectance Spectra and Examples of Their Application

Many standard methods used for the remote sensing of ocean colour have been developed, though mainly for clean, open ocean waters. This means that they may not always be effective in complex waters potentially containing high concentrations of optically significant constituents. This paper presents new empirical formulas for estimating selected inherent optical properties of water from remote-sensing reflectance spectra R_rs(λ), derived, among other things, for waters with high concentrations of dissolved and suspended substances. These formulas include one for estimating the backscattering coefficient b_b(620) directly from the magnitude of R_rs in the red part of the spectrum, and another for estimating the absorption coefficient a(440) from the hue angle α. The latter quantity represents the water's colour as it might be perceived by the human eye (trichromatic colour vision); it is easily calculated from the shape of the R_rs spectrum. These new formulas are based on a combined dataset. Most of the data were obtained in the specific, optically complex environment of the Baltic Sea. Additional data, taken from the NASA bio-Optical Marine Algorithm Dataset (NOMAD) and representing various regions of the global oceans, were used to widen the potential applicability of the new formulas. We indicate the reasons why these simple empirical relationships can be derived and compare them with the results of straightforward modelling; possible applications are also described. We present, among other things, an example of a simple semi-analytical algorithm using both new empirical formulas. This algorithm is a modified version of the well-known quasi-analytical algorithm (QAA), and it can improve the results obtained in optically complex waters. This algorithm allows one to estimate the full spectra of the backscattering and absorption coefficients, without the need for any additional a priori assumptions regarding the spectral shape of absorption by dissolved and suspended seawater constituents.

Classifying Natural Waters with the Forel-Ule Colour Index System: Results, Applications, Correlations and Crowdsourcing

Societal awareness of changes in the environment and climate has grown rapidly, and there is a need to engage citizens in gathering relevant scientific information to monitor environmental changes due to recognition that citizens are a potential source of critical information. The apparent colour of natural waters is one aspect of our aquatic environment that is easy to detect and an essential complementary optical water quality indicator. Here we present the results and explore the utility of the Forel-Ule colour index (FUI) scale as a proxy for different properties of natural waters. A FUI scale is used to distinguish the apparent colours of different natural surface water masses. Correlation analysis was completed in an effort to determine the constituents of natural waters related to FUI. Strong correlations with turbidity, Secchi-disk depth, and coloured dissolved organic material suggest the FUI is a good indicator of changes related to other constituents of water. The increase in the number of tools capable of determining the FUI colours, (i) ocean colour remote sensing products; (ii) a handheld scale; and (iii) a mobile device app, make it a versatile relative measure of water quality. It has the potential to provide higher spatial and temporal resolution of data for a modernized classification of optical water quality. This FUI colour system has been favoured by several scientists in the last century because it is affordable and easy to use and provides indicative information about the colour of water and the water constituents producing that colour. It is therefore within the scope of a growing interest in the application and usefulness of basic measurement methodologies with the potential to provide timely benchmark information about the environment to the public, scientists and policymakers.