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Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in‐water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions.

Radiative transfer (RT) simulations have long been used to study the relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean, from which ocean apparent optical properties (AOPs) can be calculated. For example, inverse models used to estimate IOPs from ocean color radiometric measurements have been developed and validated using the results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range of near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development is the generation of a synthetic dataset of seawater IOPs that serves as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by the 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 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 (not considered in previous synthetically generated optical databases) and three simulation scenarios associated with the 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, 30, and 60∘ assuming clear skies, standard atmosphere, and a wind speed of 5 m s−1. Thus, overall 29 880 RT simulations were performed. The output results of these simulations include radiance distributions, plane and scalar irradiances, and a whole set of AOPs, including remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines, where all optical variables are reported in the spectral range of 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 IOPs and AOPs. The database is available at the Dryad open-access repository of research data (https://doi.org/10.6076/D1630T, Loisel et al., 2023).

The primary source of drinking water in Ethiopia's semi-arid mountainous regions is groundwater. The present study aims to assess the hydrogeochemical characteristics of low-grade basement rocks dominated by mountainous catchments. Moreover, it examines the suitability of the groundwater quality for drinking purposes in Irob, Tigray, northern Ethiopia. However, relatively little is known about the water chemistry and groundwater quality of the resources in the area. Fifteen samples of groundwater were collected and examined for ions according to standard procedures. The outcomes were assessed against the World Health Organization (WHO) drinking water quality criteria. To identify the source of dissolved ions and the process involved, graphic interpretations were applied. The results show that Ca–Mg–SO4–HCO3 and Ca–SO4–HCO3 were the dominant water types. Gibbs plots and ionic ratios reveal that silicate weathering, carbonate dissolution and ion exchange control water chemistry. Furthermore, the findings reveal that 60, 80, 46.67, 46.67, 60, 6.67, 60 and 53.33% of samples are above the safe limits of the WHO for hardness, alkalinity, total dissolved solids, electrical conductivity, calcium (Ca2+), potassium (K+), bicarbonate (HCO3-) and sulfate (SO42-), respectively. Consequently, the groundwater quality assessment demonstrates that the water sources in lower parts of the catchment are unsuitable for drinking.

Optical water types (OWTs) can represent diverse ranges of Chlorophyll-a (Chl-a), total suspended matter (TSM), and colored dissolved organic matter (CDOM) concentrations, which make them extremely useful for monitoring water quality, for example, detecting eutrophic conditions or tuning remote sensing algorithms. In this study, the objective is to assess OWTs found in Brazilian waters using in situ remote sensing reflectance (Rrs), acquired for water bodies encompassing a wide range of optical characteristics. Eight OWTs are obtained based on Rrs spectral shape and magnitude, which represent different limnological characteristics of Brazilian waters. The OWT 1 is clear waters with low TSM, Chl-a, and CDOM (median ( x~ ): TSM of 2.64 g m−3, Chl-a of 6.04 mg m−3, and CDOM of 0.6 m−1); OWT 2 represents moderate turbid waters (TSM x~ : 5.14); OWTs 3, 4, and 5 are characterized by waters with high Chl-a concentration ( x~ : 33.1, 39.6, and 180.4 mg m−3, respectively); OWT 6 is characterized with the highest CDOM concentration ( x~ : 4.07 m−1); OWTs 7 and 8 consist of waters with the highest TSM concentrations from terrestrial input ( x~ : 19.55 and 93.25, respectively). Hence, those OWTs could support satellite monitoring by helping to tune algorithms and also providing wide spatial–temporal monitoring.

The optical complexity of shallow Case 2 waters challenges remote sensing accuracy due to the non-linear behaviour of optically active constituents. This study evaluates the spectral divergence between the target-derived effective attenuation (Keff) and the ambient scalar attenuation coefficient (K0) across 12 Baltic Sea locations. Using hyperspectral radiometry and K-Means clustering, three optical water types (OWTs) were identified. We demonstrate that the historical static approximation based on the diffuse attenuation coefficient (Keff ≈ 2Kd) is systematically biased in scattering-dominated environments. Our empirical results yielded a regional relationship of Keff = 2.33K0 (R2 = 0.65); however, residual analysis reveals that linear multipliers fail to capture non-linear light decay. Random Forest regression identified total suspended matter (TSM) as the primary driver of Keff variance (28.0%), confirming that “geometric rejection” of scattered photons artificially inflates signal loss in turbid waters. This divergence is most pronounced in the 500–650 nm range, where low absorption facilitates multiple scattering events. We conclude that active remote sensing requires a sensor-fusion approach, utilising passive OWT classification to dynamically parameterise active attenuation models.

Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-color data. There is a growing demand from the ecosystem modeling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeler these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modelers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size [pico- (<2 μm), nano- (2–20 μm) and micro-phytoplankton (>20 μm)]. The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterize the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups.

This study presents isotopic compositions and their vertical profile of meteoric and surface water samples collected in the Southern Himalaya since 2015, with elevations extending all the way up to Mt. Everest’s summit. The data covering a wide altitudinal ranges and rich water types are presented for the first time. The series of in situ samples up to 8848 m asl lead to the following discoveries: (1) the dominance of rainy-season precipitation to surface-water composition in the Southern Himalaya, (2) the high correlation and high similarity between meteoric and surface-snow isotopes, thus implying the representation of surface-snow isotopes to high-elevation climatology, (3) a significant altitude effect in river and ground water, with the higher altitudinal lapse rate in ground water δ18O highlighting strong local impacts on the vertical profile of surface-water isotopes, (4) different transitions suggested by the vertical profiles of δ18O variation in snow and ice in the Southern Himalaya, with the transition in snow δ18O at a vertical zone between 6030 and 6280 m asl, and that in ice at 5775 m asl, and (5) complex circulation processes on top of the Himalaya, featuring the interaction of large-scale circulation with local mountain valley circulation, katabatic wind, and sublimation in the extremely cold and high environment. They, thus, confirm the correlation between isotopes and altitudes in regions influenced by complex circulation patterns to clarify the altitude effect, and suggest the application of isotopic study/isotopic chemistry in geological study.

Water transparency (or Secchi disk depth: ZSD) is a key parameter of water quality; thus, it is very important to routinely monitor. In this study, we made four efforts to improve a state-of-the-art ZSD estimation algorithm that was developed in 2019 on the basis of a new underwater visibility theory proposed in 2015. The four efforts were: (1) classifying all water into clear (Type I), moderately turbid (Type II), highly turbid (Type III), or extremely turbid (Type IV) water types; (2) selecting different reference wavelengths and corresponding semianalytical models for each water type; (3) employing an estimation model to represent reasonable shapes for particulate backscattering coefficients based on the water type classification; and (4) constraining likely wavelength range at which the minimum diffuse attenuation coefficient (Kd(λ)) will occur for each water type. The performance of the proposed ZSD estimation algorithm was compared to that of the original state-of-the-art algorithm using a simulated dataset (N = 91,287, ZSD values 0.01 to 44.68 m) and an in situ measured dataset (N = 305,  ZSD values 0.3 to 16.4 m). The results showed a significant improvement with a reduced mean absolute percentage error (MAPE) from 116% to 65% for simulated data and from 32% to 27% for in situ data. Outliers in the previous algorithm were well addressed in the new algorithm. We further evaluated the developed  ZSD estimation algorithm using medium resolution imaging spectrometer (MERIS) images acquired from Lake Kasumigaura, Japan. The results obtained from 19 matchups revealed that the estimated  ZSD matched well with the in situ measured  ZSD, with a MAPE of 15%. The developed ZSD estimation algorithm can probably be applied to different optical water types due to its semianalytical features.

Yue, Y.L.; Qing, S.; Diao, R.X., and Hao, Y.L., 2020. Remote sensing of suspended particulate matter in optically complex estuarine and inland waters based on optical classification. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 303-317. Coconut Creek (Florida), ISSN 0749-0208. Accurate suspended particulate matter (SPM) concentration retrieval across complex estuarine to inland waters from ocean color remote sensing reflectance (Rrs(λ)) faces challenges. In this paper, an optical classification-based SPM retrieval algorithm in optically complex estuarine and inland waters was proposed and tested in the Yellow River Estuary and Daihai Lake, China. Firstly, the in situ measured Rrs(λ) (n = 204) were classified into two optical water types with the method defined by Matsushita et al. (2015). Secondly, we designed several mathematical models and selected the optimal algorithm according to the goodness of fit. Optimal algorithms were developed for each water type to achieve accurate SPM retrieval. Through the construction of the optimal retrieval algorithm in each water type, the uncertainty of SPM retrievals has been reduced from 95 % to about 39 % compared with the algorithm without optical classification. The retrieval algorithm based on optical water classification was further applied to the Sentinel-2 MSI L2A data over the study area and produced reliable SPM maps. Independent validation with the in situ-satellite match-ups further demonstrates the algorithm's validity (uncertainty of about 47 %). In contrast, applications of other SPM retrieval algorithms resulted in less reliable SPM results with either unsatisfactory retrieval accuracy in class1 (the lowest value of r can reach 0.02). The optical classification, together with the optimal retrieval algorithm for each optical type, is proved to be a feasible way for SPM retrieval in high accuracy over optically complex waters.

The hydrogeochemical characteristics, water quality and health risk statuses of waters in Umunya district, southeastern Nigeria were studied, in attempt to evaluate their suitability for drinking and domestic purposes. Twelve groundwater and 3 surface water samples were analyzed for 26 physicochemical and hydrogeochemical parameters, using standard techniques. Results show that dominance of cations and anions is in the order Ca2+ > Na+ > K+ > Mg2+ and HCO3– > Cl– > NO3– > SO4–, respectively. Order of dominance of the heavy metals is Pb > Zn > Fe > Ni > Mn > Cr > Ba. Eight water types were identified, with Ca–Na–HCO3 (26.66%) and Na–Cl–HCO3 (20%) dominating the study area. All the water types characterize five major facies. Further, the result revealed that the physical properties and chemical ionic concentrations in the waters are well below standard maximum permissible limits, although majority of the samples have pH values off the allowable limits of 6.5–8.5, classing the waters as slightly acidic. Generally, the water quality in the study area is deteriorated due to the presence of high levels of heavy metals. Water quality index results show that 46.67% of the water samples are in excellent and good categories. 13.33% are in poor water category, whereas 40% are in category unsuitable for drinking purposes. A good percentage of the waters predispose users to health risks. Stoichiometric and statistical analyses revealed that the variations in chemistry and quality of the waters are due to combined influence of human activities and geogenic processes (silicate weathering and ionic exchanges). Treatment of contaminated waters before use is, therefore, recommended.