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The collection of spectral data with sensors fixed to various platforms (e.g., satellites, occupied aerial vehicles, and small unoccupied aerial systems (sUAS)) has allowed for the estimation of optically active constituents (OACs) common in surface waters. However, in small, complex, and optically shallow waters where multiple OACs (e.g., chlorophyll-a and total suspended solids) impact the spectral signature, these technologies have experienced significant limitations. Altering the scale at which these examinations are performed on surface waters (e.g., ponds and lakes) to mesocosm systems (37 cm in height and 30 cm in diameter) provides information on the interactions between multiple OACs and insight on the impact aquatic optical depth has on remotely sensed spectra. This field study examines optically shallow and optically deep mesocosm systems simulated in five-gallon buckets to determine the role aquatic optical depth has on developing accurate surface-water quality models. Results demonstrated an accurate representation of OACs in optically deep mesocosms compared with optically shallow mesocosms when assessed with sUAS (i.e., relative percent differences in predicted iron concentrations of −86 and 16 for optically shallow and deep waters, respectively). The interferences observed under these conditions were comparable to literature values when studying optically complex water bodies with hyperspectral data. This study provides a basis for understanding the benefits and limitations of monitoring in-situ water quality via sUAS.

Evaluation of water quality is a priority work nowadays. In order to monitor and map, the water quality for a wide range on different scales (spatial, temporal), the geospatial technique has the potential to minimize the field and laboratory work. The review has emphasized the advance of remote sensing for the effectiveness of spectral analysis, bio-optical estimation, empirical method, and application of machine learning for water quality assessment. The water quality parameters (turbidity, suspended particles, chlorophyll, etc.) and their retrieval techniques are described in a scientific manner. Available satellite, bands, resolution, and spectrum ranges for specific parameters are critically described in this review with challenges in remote sensing for water quality analysis, considering non-optical active parameters. The application of statistical programmes like linear (multiple regression analysis) and non-linear approaches is discussed for better assessment of water quality. Emphasis is given on comparison between different models to increase the accuracy level of remote sensing of water quality assessment. A direction is suggested for future development in the field of estimation of water pollution assessment through geospatial techniques. [ABSTRACT FROM AUTHOR]

• A lake TSI inversion method based on optical mechanism was proposed. • a t-w (440) was confirmed to be the optimal optical characterization parameter of TSI. • DNN model was superior to other models in the inversion of both TSI and a t-w (440). • Trophic status of lakes across China was assessed using the proposed approach. • TSI of 961 lakes in China in 2020 ranged from 6 to 96, with a mean value of 48. Widespread eutrophication has been considered as the most serious environment problems in the world. Given the critical roles of lakes in human society and serious negative effects of water eutrophication on lake ecosystems, it is thus fundamentally important to monitor and assess water trophic status of lakes. However, a reliable model for accurately estimating the trophic state index (TSI) of lakes across a large-scale region is still lacking due to their high complexity. Here, we proposed an optical mechanism-based deep learning approach to remotely estimate TSI of lakes based on Landsat images. The approach consists of two steps: (1) determining the optical indicators of TSI and modeling the relationship between them, and (2) developing an approach for remotely deriving the determined optical indicator from Landsat images. With a large number of in situ datasets measured from lakes (2804 samples from 88 lakes) across China with various optical properties, we trained and validated three machine learning methods including deep neural network (DNN), k-nearest neighbors (KNN) and random forest (RF) to model TSI with the optical indicators and TSI and derive the determined optical indicator from Landsat images. The results showed that (1) the total absorption coefficients of optically active constituents at 440 nm (a t-w (440)) performs best in characterizing TSI, and (2) DNN outperforms other models in the inversion of both TSI and a t-w (440). Overall, our proposed optical mechanism-based deep learning approach demonstrated a robust and satisfactory performance in assessing TSI using Landsat images (root mean squared error (RMSE) = 5.95, mean absolute error (MAE) = 4.81). This highlights its merit as a nationally-adopted method in lake water TSI estimation, enabling the convenience of the acquisition of water eutrophic information in large scale, thereby assisting us in managing lake ecology. Therefore, we assessed water TSI of 961 lakes (>10 km2) across China using the proposed approach. The resulting a t-w (440) and TSI ranged from 0.01 m−1 to 31.42 m−1 and from 6 to 96, respectively. Of all these studied lakes, 96 lakes (11.40 %) were oligotrophic, 338 lakes were mesotrophic (40.14 %), 360 lakes were eutrophic (42.76 %), and 48 were hypertrophic (5.70 %) in 2020. [ABSTRACT FROM AUTHOR]

Estimates of light availability in the water column require accurate quantification at different depths of the diffuse attenuation coefficient ( K ). This study examines spatial and seasonal variability of K derived variables [euphotic zone depth ( Z ), attenuation depth ( Z )], their relationship with optically active constituents (OACs), and their impact on the underwater light field at Ibitinga Reservoir (São Paulo, Brazil). Radiometric data were acquired using profiling spectroradiometers operating from 320 to 950 nm in four campaigns (February-July 2014) concurrently with OAC water samplings. Minimum and maximum values of K PAR were, respectively, 0.99 and 3.45 m for average ranges of OACs varying from 14.4 to 16.2 mg/l for dissolved total carbon, 11.5-100.5 μg/l for chlorophyll- a, 1.8-14.5 mg/l for total suspended solid, and from 0.73 to 1.71 m for absorption coefficient of colored dissolved organic matter (CDOM) in 440 nm ( a ). CDOM removed blue wavelengths in the first meter of the water column throughout seasons, while green wavelengths were predominant with increasing depth. Furthermore, this study demonstrated that K variability is mainly influenced by the presence of phytoplankton and CDOM in the reservoir, and reveals that the seasonal variability of K was much larger than spatial variability. [ABSTRACT FROM AUTHOR]

AIM: This paper describes an experiment carried out to evaluate in vivo fluorescence (IVF) as an alternative method for chlorophyll-a estimation in optically complex aquatic environment (Amazon floodplain lakes) METHODS: The experiment consisted of collecting in situ measurements at 26 sampling stations distributed throughout Curuai floodplain lakes. For each sampling station the following parameters were measured: temperature, turbidity, depth, Secchi depth, chlorophyll-a (Chl-a) concentration, total suspended solids (TSS) and dissolved organic carbon (DOC), concurrently with several transects of IVF. Two methods were tested for quantifying the fluorescence measurement to be used as input for the chlorophyll-a estimates: instantaneous IFV and average IVF. Global and regional models were tested and assessed by analyzing optically active components (Chl-a, DOC and TSS) of the water. RESULTS: Regardless of fluorescence estimating method, the results indicate that it was not possible to fit a global model for estimating Chl-a from IVF for all the lakes in the Curuai floodplain. Regional models provided contrasting results according to the concentration of optically active components. The best results were observed for aquatic systems with a single dominant component homogenously distributed throughout the lake. The results highlight the influence of the ratios Chl-a/TSS, Chl-a/DOC and Phaephytin/Chl-a in the relationship between IVF and chlorophyll concentration. CONCLUSIONS: It was not possible to develop a global model to account for the entire region of Curuai floodplain. The search for regional models provided insights on the main factors affecting the relationship between IVF and Chl-a concentration. Nevertheless this work reinforces the great potential of fluorometry technique, since even with a small number of samples it was possible to set a good model in the main lake of the Curuai floodplain. In spite the fact that this is not an accurate method, it is very useful for assessing the chlorophyll spatial distribution with relatively low cost. These possibilities are very interesting in the execution of field missions in the Amazon region.OBJETIVO: Este trabalho descreve os experimentos realizados para avaliar as medidas de fluorescência in vivo (IVF) em um ambiente aquático opticamente complexo (planície de inundação amazônica), como método alternativo para a quantificação da clorofila-a na água. MÉTODOS: Foram coletados parâmetros de qualidade da água em 26 estações amostrais distribuídas em diversos lagos da planície de inundação de Curuai: temperatura, pH, turbidez, condutividade, profundidade, transparência ao disco de Secchi, concentração de clorofila-a (Chl-a), total de sólidos em suspensão (TSS) e carbono orgânico dissolvido (DOC) simultaneamente a quilômetros de transectos de IVF. Dois métodos de determinação do valor de fluorescência foram testados para o ajuste de modelos de estimativa da concentração de clorofila: fluorescência instantânea e fluorescência média. Também foram testados um modelo global e modelos regionais, os quais foram analisados em termos dos componentes opticamente ativos na água (Chl-a, DOC e TSS). RESULTADOS: Os resultados indicam que independentemente do método de estimativa de fluorescência empregado, não foi possível ajustar um modelo global para a planície de Curuai. Os modelos regionais apresentaram resultados contrastantes, de acordo com a concentração dos componentes opticamente ativos. Melhores resultados foram observados nos sistemas aquáticos com um único componente dominante, distribuído homogeneamente pelo respectivo lago. Os resultados destacam a influência das razões Chl-a/TSS, Chl-a/DOC e Feofitina/Chl-a no ajuste entre a IVF e a Chl-a. CONCLUSÕES: Não foi possível ajustar um modelo global para a planície de Curuai através de medidas de IVF. Entretanto, a busca por modelos regionais forneceu informações sobre os principais fatores que afetam a relação entre a IVF e a Chl-a. Dessa forma, este trabalho reforça o grande potencial da fluorometria, pois mesmo com um baixo número de amostras foi possível estabelecer um bom modelo no principal lago da planície. Ainda que a técnica não possa ser considerada extremamente precisa, ela é útil para avaliar o padrão de variação espacial da clorofila, com baixo custo. Estas possibilidades são muito interessantes na realização de missões de campo na bacia amazônica.

A técnica de análise derivativa de dados espectrais foi usada para estudar a variação dos constituintes opticamente ativos (COAs) na água, por meio de dados de campo e de imagens do sensor orbital Hyperion/EO-1. A imagem Hyperion usada neste estudo foi adquirida no dia 23 de junho de 2005, no final do período de cheia. Uma campanha de campo foi realizada entre 23 e 29 de junho de 2005, para coletar dados espectrais e limnológicos in situ. A imagem foi pré-processada visando eliminar faixas de pixels anômalos e convertida de valores de radiância para reflectância de superfície, portanto, corrigidos dos efeitos de absorção e espalhamento atmosféricos. Uma análise da correlação foi realizada para examinar a associação da reflectância e de sua primeira derivada espectral com as concentrações dos COAs. Melhores resultados foram obtidos após a diferenciação dos espectros, o que ajudou a reduzir a influência de efeitos indesejáveis, provindos de diferentes fontes de radiância, sobre as medidas de reflectância da superfície da água realizadas em ambos os níveis de aquisição de dados. Por meio de ajustes de regressões empíricas, considerando o conjunto de dados Hyperion, a primeira derivada espectral em 711 nm explicou 86% da variação da concentração de sedimentos inorgânicos em suspensão (µg.l-1) e a primeira derivada espectral em 691 nm explicou 73% da variação na concentração da clorofila-alfa (µg.l-1). As relações de regressão foram não-lineares, pois, em geral, as águas que se misturam na planície de inundação Amazônica se tornam opticamente complexas. A técnica de análise derivativa hiperespectral demonstrou potenciais para mapear a composição dessas águas.Derivative analysis of spectral data was used as a technique to study the variation of optically active constituents (OACs) of water, using field data and hyperspectral imagery of EO-1 Hyperion orbital sensor. The Hyperion image used in this study was acquired on June 23, 2005, at the end of the high water period for the Amazon River. A field campaign was carried out between June 23 and 29, 2005 to collect spectral and limnological in situ data. The image was pre-processed to remove stripes of abnormal pixels and converted from radiance to surface reflectance values, thus, correcting the effects of atmospheric absorption and scattering. A correlation analysis was carried out to examine the association of the spectral reflectance and its first derivative to the concentrations of OACs. Better results were obtained after spectra differentiation, which helped to reduce the influence of undesirable effects, coming from different sources of radiance, on the measurements of water surface reflectance taken at both data acquisition levels. Through empirical regression fits, considering the Hyperion dataset, the first spectral derivative at 711 nm explained 86% of the variation of suspended inorganic sediment concentration (µg.l-1), and the first derivative at 691 nm explained 73% of the variation of chlorophyll-a concentration (µg.l-1). The regression relations were nonlinear because, generally, the water masses that mix in the Amazon floodplain become optically complex. The hyperspectral derivative analysis demonstrated potential for mapping the composition of these waters.

Light availability in lakes or reservoirs is affected by optically active components (OACs) in the water. Light plays a key role in the distribution of phytoplankton and hydrophytes, thus, is a good indicator of the trophic state of an aquatic system. Diffuse attenuation of photosynthetic active radiation (PAR) (Kd(PAR)) is commonly used to quantitatively assess the light availability. The PAR and the concentration of OACs were measured at 206 sites, which covered 26 lakes and reservoirs in Northeast China. The spatial distribution of Kd(PAR) was depicted and its association with the OACs was assessed by grey incidences(GIs) and linear regression analysis. Kd(PAR) varied from 0.45 to 15.04 m-1. This investigation revealed that reservoirs in the east part of Northeast China were clear with small Kd(PAR) values, while lakes located in plain areas, where the source of total suspended matter (TSM) varied, displayed high Kd(PAR) values. The GIs and linear regression analysis indicated that the TSM was the dominant factor in determining Kd(PAR) values and best correlated with Kd(PAR) (R2 = 0.906, RMSE = 0.709). Most importantly, we have demonstrated that the TSM concentration is a reliable measurement for the estimation of the Kd(PAR) as 74% of the data produced a relative error (RE) of less than 0.4 in a leave-one-out cross validation (LOO-CV) analysis. Spatial transferability assessment of the model also revealed that TSM performed well as a determining factor of the Kd(PAR) for the majority of the lakes. However, a few exceptions were identified where the optically regulating dominant factors were chlorophyll-a (Chl-a) and/or the chromophroic dissolved organic matter (CDOM). These extreme cases represent lakes with exceptionally clear waters. [ABSTRACT FROM AUTHOR]

The light-absorbing attributes of minerogenic particle populations are presented for 32 sites from five areas of the Laurentian Great Lakes based on laboratory measurements of non-algal particulate absorption along with a particle size distribution (PSD) metric and total projected area of minerogenic particles per unit volume of water (PAVm) determined with an individual particle analysis (1PA) technique. IPA established clay mineral and calcite dominance as the primary and secondary composition cases, respectively, for the sampled populations. Estimates of spectral absorption properties of these populations are presented, including the mean absorption efficiency (< Q*a,m > ), the imaginary part of the refractive index (n'), and the mean package effect index (a ,m >)- Substantial differences in these attributes among the samples are documented; those for a m > and a a a a a a a a m > were explained well by the effects of optical thickness, which represent the multiplicative effects of n' and a PSD metric. Greater package effect is attributed to increases in n'. Mie theory calculations, based on the n' estimates and the IPA characterizations, depict substantial decreases in the spectral backscattering coefficient (b b,m) for the minerogenic particle populations from the red to blue spectral regions, relative to the case of nonabsorption. Related recommendations for mechanistic remote sensing initiatives to represent the effects of minerogenic particles are presented, including (1) use of PAVm as the optically active constituent metric, (2) use of a ,m > as the corresponding cross-section (interim value of 0.33 or 0.39), and (3) representation of the spectral character for bb,m reported here. [ABSTRACT FROM AUTHOR]

Optical remote sensing in complex waters is challenging because the optically active constituents may vary independently and have a combined and interacting influence on the remote sensing signal. Additionally, the remote sensing signal is influenced by noise and spectral contamination by confounding factors, resulting in ill-posedness and ill-conditionedness in the inversion of the model. There is a need for inversion methods that are less sensitive to these changing or shifting spectral features. We propose WaveIN, a wavelet-enhanced inversion method, specifically designed for complex waters. It integrates wavelet-transformed high-spectral resolution reflectance spectra in a multiscale analysis tool. Wavelets are less sensitive to a bias in the spectra and can avoid the changing or shifting spectral features by selecting specific wavelet scales. This paper applied WaveIN to simulated reflectance spectra for the Scheldt River. We tested different scenarios, where we added specific noise or confounding factors, specifically uncorrelated noise, contamination due to spectral mixing, a different sun zenith angle, and specific inherent optical property (SIOP) variation. WaveIN improved the constituent estimation in case of the reference scenario, contamination due to spectral mixing, and a different sun zenith angle. WaveIN could reduce, but not overcome, the influence of variation in SIOPs. Furthermore, it is sensitive to wavelet edge effects. In addition, it still requires in situ data for the wavelet scale selection. Future research should therefore improve the wavelet scale selection. [ABSTRACT FROM PUBLISHER]

Abstract: Observations of remote sensing reflectance (Rrs), the signal available to support remote sensing of optically active constituents (OACs) of water quality interest, are presented for multiple sites within each of the Laurentian Great Lakes based on in situ measurements made with a hyperspectral radiometer. Rrs(λ) spectra are contrasted among these lakes and in time and space within selected systems. Qualitative analyses of spectra are provided that identify the inherent optical property (IOP) and coupled OAC conditions responsible for the differences in Rrs(λ). The much higher Rrs peaks observed in the green wavelengths for the lower Great Lakes (Erie and Ontario) are attributed to elevated backscattering levels caused by higher concentrations of minerogenic particles. The credibility of the Rrs(λ) spectra is established through successful closure analyses that demonstrate good matches with IOP-based predictions and consistency of coefficient values for radiative transfer expressions with related literature and theory. A mechanistic forward model of Rrs(λ) is developed that accommodates the effects of three OACs, including metrics of phytoplankton biomass, minerogenic particles and colored dissolved organic material. This includes the development of the critical cross-section relationships that quantify the couplings between the OACs and IOPs, and in turn the IOPs and the Rrs(λ) signal. The model is demonstrated to perform well in matching observations in Lake Erie, and to be sensitive to the representation of the spectral dependency of backscattering and likely variations in the dependence of phytoplankton absorption on chlorophyll. The modeled predicted responses of Lake Erie to different OAC levels are presented. [Copyright &y& Elsevier]

Sun induced chlorophyll fluorescence (SICF) emitted by phytoplankton provides considerable insights into the vital role of the carbon productivity of the earth's aquatic ecosystems. However, the SICF signal leaving a water body is highly affected by the high spectral variability of its optically active constituents. To disentangle the SICF emission from the water-leaving radiance, a new high spectral resolution retrieval algorithm is presented, which significantly improves the fluorescence line height (FLH) method commonly used so far. The proposed algorithm retrieves the reflectance without SICF contribution by the extrapolation of the reflectance from the adjacent regions. Then, the SICF emission curve is obtained as the difference of the reflectance with SICF, the one actually obtained by any remote sensor (apparent reflectance), and the reflectance without SICF, the one estimated by the algorithm (true reflectance). The algorithm first normalizes the reflectance spectrum at 780 nm, following the similarity index approximation, to minimize the variability due to other optically active constituents different from chlorophyll. Then, the true reflectance is estimated empirically from the normalized reflectance at three wavelengths using a machine learning regression algorithm (MLRA) and a cubic spline fitting adjustment. Two large reflectance databases, representing a wide range of coastal and ocean water components and scattering conditions, were independently simulated with the radiative transfer model HydroLight and used for training and validation of the MLRA fitting strategy. The best results for the high spectral resolution SICF retrieval were obtained using support vector regression, with relative errors lower than 2% for the SICF peak value in 81% of the samples. This represents a significant improvement with respect to the classic FLH algorithm, applied for OLCI bands, for which the relative errors were higher than 40% in 59% of the samples. [ABSTRACT FROM AUTHOR]

The trophic state index (TSI) is a vital parameter for aquatic ecosystem assessment. Thus, information on the spatial and temporal distribution of TSI is critical for supporting scientifically sound water resource management decisions. We proposed a semi-analytical approach to remotely estimate TSI based on Landsat 8 OLI data for inland waters. The approach has two major steps: deriving the total absorption coefficient of optically active constituents (OACs) and building the relationship between the total absorption coefficient and TSI. First, version 6.0 of the Quasi-Analytical Algorithm (QAA_V6, developed by Zhongping Lee) was implemented with Landsat 8 OLI data to derive the total absorption coefficients of the OACs. Second, we modeled TSI using the total absorption coefficients of OACs at 440 nm based on a large in situ measurement dataset. The total absorption coefficient of OACs at 440 nm gave satisfactory validation results for modeling TSI with a mean absolute percent error of 6% and a root-mean-square error of 5.77. Then, we performed this approach in three inland waters with various eutrophic statuses to validate its results, and the approach demonstrated a robust and satisfactory performance. Finally, an application of the approach was demonstrated in Lake Qiandaohu. Our semi-analytical approach has a sound optical mechanism and extensive application for different trophic inland waters. • The relationships between TSI and absorption of OACs were built. • A semi-analytical approach for remotely estimating TSI was established. • Trophic status in Lake Qiandaohu was assessed by the approach with Landsat 8 data. [ABSTRACT FROM AUTHOR]