Aquatic Environmental Monitoring Research Articles

Pulse Feature-Enhanced Classification of Microalgae and Cyanobacteria Using Polarized Light Scattering and Fluorescence Signals.

Harmful algal blooms (HABs) pose a global threat to the biodiversity and stability of local aquatic ecosystems. Rapid and accurate classification of microalgae and cyanobacteria in water is increasingly desired for monitoring complex water environments. In this paper, we propose a pulse feature-enhanced classification (PFEC) method as a potential solution. Equipped with a rapid measurement prototype that simultaneously detects polarized light scattering and fluorescence signals of individual particles, PFEC allows for the extraction of 38 pulse features to improve the classification accuracy of microalgae, cyanobacteria, and other suspended particulate matter (SPM) to 89.03%. Compared with microscopic observation, PFEC reveals three phyla proportions in aquaculture samples with an average error of less than 14%. In this paper, PFEC is found to be more accurate than the pulse-average classification method, which is interpreted as pulse features carrying more detailed information about particles. The high consistency of the dominant and common species between PFEC and microscopy in all field samples also demonstrates the flexibility and robustness of the former. Moreover, the high Pearson correlation coefficient accounting for 0.958 between the cyanobacterial proportion obtained by PFEC and the cyanobacterial density given by microscopy implies that PFEC serves as a promising early warning tool for cyanobacterial blooms. The results of this work suggest that PFEC holds great potential for the rapid and accurate classification of microalgae and cyanobacteria in aquatic environment monitoring.

Electrochemical biochemical oxygen demand biosensors and their applications in aquatic environmental monitoring

The biochemical oxygen demand (BOD) is critical for assessing water quality, linking the quantity of biodegradable organic carbon with pollution levels. Electrochemical biosensors have shown great improvements over traditional BOD measurement methods by offering the advantages of simplicity, rapid response, high sensitivity, and real-time monitoring. In this review, we discuss different types of BOD electrochemical biosensors, including oxygen electrode-, microbial fuel cell (MFC)-, and microbial electrolysis cell (MEC)-type sensors, focusing on recent advances in microbial community composition, electrode materials, and optimization approaches. Moreover, current challenges in developing effective electrochemical biosensors for BOD detection are presented to meet diverse sample needs.

Comprehensive Analysis of Phylogenetic Relationship and Optimal Codons in Mitochondrial Genomes of the Genus Pseudogastromyzon

As indicator organisms for water pollution detection, Pseudogasteromyzon species play a vital role in aquatic environment monitoring. We have successfully sequenced the mitogenomes of P. fasciatus jiulongjiangensis and P. myersi and downloaded the mitogenomes of nine other Pseudogastromyzon fish on GenBank to conduct a detailed comparative analysis of their phylogenetic relationships and evolutionary history. The findings revealed a conservation in both gene composition and gene order. Except for the trnS1 gene lacking dihydrouracil arms, the other 21 tRNAs showed the typical clover-leaf secondary structure. According to the ΔRSCU method, we identified the seven most abundant optimal codons: CUA, GUA, CCA, CAA, GAA, AGC, and GGC. The construction of maximum parsimony, maximum likelihood, and Bayes trees yielded congruent topologies, and the 11 Pseudogastromyzon species were clustered into two major clusters. Among them, one of which was composed of P. fangi, P. changtingensis changtingensis, and P. changtingensis tungpeiensis, while the remaining eight species formed another cluster, further subdivided into five smaller clusters. Distinct clusters formed between P. fasciatus jiulongjiangensis and P. meihuashanensis, P. cheni and P. peristictus, and P. laticeps and P. lianjiangensis, and the remaining two species were clustered separately, thereby enhancing our understanding of them. Furthermore, our analysis results of divergence times revealed that these 11 Pseudogasteromyzon species underwent rapid differentiation in the Pleistocene epochs. Overall, our study sheds light on the phylogenetic relationship and evolutionary history of Pseudogasteromyzon species, providing a necessary knowledge foundation for further understanding the intricacies of an ecosystem health assessment.

Induction of Micronuclei and Other Nuclear Abnormalities in Channa punctata from Garbage Dumping Area of Deepor Beel- Ramsar Site, North East India

The fish ecology of Deepor Beel is being threatened by the continuous flow of effluents from the solid wastes of Municipal garbage dump. The present study has been aimed to investigate the genotoxic effect of the solid wastes on Channa punctata of Deepor Beel using micronucleus assay. Fishes were collected from garbage dump affected and unaffected area of Deepor Beel. The micronuclei assay demonstrated significantly higher frequency of micronuclei and other nuclear abnormalities in Channa punctata collected from affected site as compared to unaffected reference site. This study depicted the usefulness of micronucleus assay as an adequate approach for ecotoxicological monitoring of aquatic environment and its biodiversity.

Exploiting the Electrical Nature of Biofilms for Long-Term Monitoring of Quiescent Aquatic Environments via Open-Circuit Microbial Potentiometric Sensors: Evidence of Long-Distance Electrical Signaling

This study was based on the hypothesis that spatial–temporal characterization of contaminant-affected redox gradients in a quiescent system could be measured by microbial potentiometric sensor (MPS) arrays incorporated in large, natural biofilm networks. Two experimental chambers, each containing at least 48 equidistantly located MPS electrodes, were fabricated to examine reproducibility of the patterns. The MPS electrodes were exposed to biofilm growth conditions by introducing high dissolved organic carbon (DOC) and dechlorinated tap water at the bottom of the experimental chamber; and the spatial–temporal changes in the MPS array signals were recorded, which showed that signal trends were correlated to the induced changes in DOC. The results indicated that MPS arrays measured the spatial–temporal changes in the aqueous solution caused by an influx of carbon rich water, which could not be detected by conventional oxidation-reduction potential (ORP) electrodes. Interestingly, the experiments conducted over long time periods revealed unusual behaviors like electrical signaling and possible potentiometrically driven communication within the biofilm. These observed behaviors suggest that biofilms may create a large network through which communication signals can be generated and propagated by inducing changes in electric potentials similar to a sophisticated electronic device.

Essential and toxic elements in juvenile migratory commercial fish species in the Paraná River alluvial valley (South America): an approach for aquatic environmental monitoring.

The Paraná River is the sixth largest in the world, and the lower section of the river is one of the largest and most productive floodplain wetlands in South America. The alluvial plain is an important habitat for nursery and feeding areas for commercial fish; however, it has been heavily anthropized due to industries, agricultural activities, and the growth and expansion of metropolitan areas. The aim of this study was to determine element accumulation (As, Cd, Cr, Cu, Fe, Mn, Pb, and Zn) in young-of-the-year fish muscle from a floodplain lagoon of the lower Paraná River (Argentina) during summer and winter seasons, in relation with abiotic matrices (water and sediment). Four commercial fish species were assessed: sábalo (Prochilodus lineatus), boga (Megaleporinus obtusidens), dorado (Salminus brasiliensis), and pirapitá (Brycon orbignyanus). The concentrations of eight elements were detected by quadrupole inductively coupled plasma mass spectrometry. In water samples, Cd, Cr, Mn, Fe, and Zn levels were under the permissible limits for aquatic biota protection in both seasons, except for Pb (> 1 μg L-1) and Cu (> 2 μg L-1). In sediment, the average concentrations of all elements were under the limits set by national and international regulatory authorities. Differences in elemental concentrations between species and seasons were found. In general, the levels of elements in water were higher in summer than in winter, while in sediment, the lowest concentrations of elements were detected during the summer. The order of element concentration in fish muscle was Cd (0.01-0.04 μg g-1) < As (0.02-0.16 μg g-1) < Pb (0.03-0.23 μg g-1) < Mn (0.77-4.32 μg g-1) < Cu (1.01-4.07 μg g-1) < Cr (1.09-4.30 μg g-1) < Zn (15.8-31.7 μg g -1) < Fe (12.6-49.7 μg g-1). The accumulation assessment showed that the four fish species significantly accumulate Cr, Cu and Zn, and As and Zn from water and sediment, respectively. The correlation analysis showed a relationship between fish size and As, Cr, Cu, Fe, Mn, and Zn concentrations. Similarly, the highest values of element concentrations in muscle were detected during the summer when the fishes are early juveniles. The level of As, Cd, and Pb detected in B. orbignyanus, M. obtusidens, and P. lineatus juveniles suggested that these species had the potential to be used as biomarkers for assessing accumulation of toxic elements in the environment. Also, this study reveals that the accumulation patterns differ between size and fish species, which should be a considered insight at the moment of selecting a bioindicator to monitor pollution in the ecosystem.

The Use of Multiple Biomarkers to Assess the Health of Anuran Amphibians in the Brazilian Cerrado Savanna: An Ecotoxicological Approach.

Changes in the natural landscape and the indiscriminate use of pesticides can have a major impact on aquatic environments and have contributed to the worldwide decline of amphibian populations. In the present study, we sampled tadpoles of three anuran amphibians (Boana albopunctata, Physalaemus cuvieri, and Dendropsophus minutus) from ponds in six different agricultural landscapes of the Brazilian Cerrado savanna and evaluated whether and to what extent genotoxic and mutagenic damage was related to land use (the amount of forest and agricultural remnants, and related physicochemical factors) and the presence of pesticides in the water of the study ponds. We also evaluated the hepatotoxicity in P. cuvieri, which was the most abundant species at five of the six sampling points. Clomazone and atrazine were the most common pesticides found in the ponds. The B. albopunctata and P. cuvieri tadpoles presented similar patterns of DNA damage among the sampling points. The least DNA damage was found in the D. minutus tadpoles, although this species was present in only one of the study ponds. More binucleated and anucleated cells were observed in B. albopunctata, but there was no significant variation among species in terms of the number of micronuclei or other erythrocytic nuclear abnormalities. Land use and physicochemical factors did not explain the variation in the DNA damage observed in the three anurans. The hepatotoxicity analyses of P. cuvieri revealed the presence of a series of alterations, including the enlargement of the sinusoids, vacuolization of the hepatocytes, the infiltration of inflammatory cells, hepatic steatosis, and dilation of the blood vessels. The interaction between physicochemical factors and the biomarkers analyzed in the present study is complex. In particular, it will be important to better elucidate which factors are contributing, either directly or indirectly, to the decline of anuran amphibian populations, especially in threatened biomes, such as the Brazilian Cerrado. In this case, we would encourage further in situ studies that assess the ecotoxicology of the landscape, together with the systematic monitoring of aquatic environments, to guarantee the long-term integrity of amphibian populations, and those of other organisms that play an essential functional role in the ecosystem. Environ Toxicol Chem 2023;00:1-18. © 2023 SETAC.

Performance of bacterial and mitochondrial qPCR source tracking methods: A European multi-center study

With the advent of molecular biology diagnostics, different quantitative PCR assays have been developed for use in Source Tracking (ST), with none of them showing 100% specificity and sensitivity. Most studies have been conducted at a regional level and mainly in fecal slurry rather than in animal wastewater. The use of a single molecular assay has most often proven to fall short in discriminating with precision the sources of fecal contamination. This work is a multicenter European ST study to compare bacterial and mitochondrial molecular assays and was set to evaluate the efficiency of nine previously described qPCR assays targeting human-, cow/ruminant-, pig-, and poultry-associated fecal contamination. The study was conducted in five European countries with seven fecal indicators and nine ST assays being evaluated in a total of 77 samples. Animal fecal slurry samples and human and non-human wastewater samples were analyzed. Fecal indicators measured by culture and qPCR were generally ubiquitous in the samples. The ST qPCR markers performed at high levels in terms of quantitative sensitivity and specificity demonstrating large geographical application. Sensitivity varied between 73% (PLBif) and 100% for the majority of the tested markers. On the other hand, specificity ranged from 53% (CWMit) and 97% (BacR). Animal-associated ST qPCR markers were generally detected in concentrations greater than those found for the respective human-associated qPCR markers, with mean concentration for the Bacteroides qPCR markers varying between 8.74 and 7.22 log10 GC/10 mL for the pig and human markers, respectively. Bacteroides spp. and mitochondrial DNA qPCR markers generally presented higher Spearman's rank coefficient in the pooled fecal samples tested, particularly the human fecal markers with a coefficient of 0.79. The evaluation of the performance of Bacteroides spp., mitochondrial DNA and Bifidobacterium spp. ST qPCR markers support advanced pollution monitoring of impaired aquatic environments, aiming to elaborate strategies for target-oriented water quality management.

Efficient Microalgae Species Identification using Compact Convolutional Neural Network

In this study, we propose a novel approach for microscopic algae species classification by implementing a compact Convolutional Neural Network (CNN) model. Our methodology was tested on a diverse dataset consisting of 18 distinct species of microscopic algae, demonstrating a remarkable classification accuracy exceeding 99%. The outstanding performance of this model is attributed to its compact architecture which maintains high precision while minimizing computational resources, making it a feasible option for real-time applications. Furthermore, we incorporated advanced data augmentation techniques to enhance the generalization capability of our model. By artificially expanding the training dataset, we effectively increased the model's robustness to variance in input data, which significantly contributed to the model's high classification accuracy. The research findings underscore the potential of compact CNN models coupled with data augmentation strategies in high-precision microscopic algae classification tasks, paving the way for future innovations in the field of aquatic microbiology and environmental monitoring.

Long-Range Self-Powered IoT Devices for Agriculture Aquaponics

The aim of this project is to establish a controlled environment in the aquaculture industry by minimizing human errors and reducing the need for manpower. Additionally, the project seeks to enable remote monitoring of aquatic environments from any location in the world. By utilising mobile phone applications, the physical presence of humans can be significantly reduced. The device is capable of collecting and transmitting data from a variety of sensors, including those that measure temperature, humidity, and pH levels. This real-time monitoring and analysis of the aquaponics systems allows farmers and growers to make informed decisions about water usage, fish health, and other productivity-related factors. Ultimately, this system can improve productivity and efficiency in the aquaculture industry. Aquaculture is a growing industry worldwide, and the need for sustainable and efficient practices is increasing. The use of technology and automation has become more common in recent years as a means of improving productivity and reducing human error. The proposed system for aquaculture monitoring aims to address these issues by utilizing sensors and microcontrollers to create a controlled environment for aquatic organisms. The system employs various sensors to measure critical parameters such as water temperature, pH, and turbidity, and sends this data to a central monitoring system. Farmers and growers can then access this data remotely and make informed decisions about the health and productivity of their aquatic systems. This can lead to better control over water quality and temperature, improved feeding practices, and ultimately higher yields.

Polarization fingerprint for microalgae classification

Microalgae classification is important for aquatic environment monitoring, but it is difficult in some in-situ cases. The Mueller matrix is known to encode the physical properties including the sophisticated microstructure of microalgae cells. In this paper, the concept of polarization fingerprint based on the Mueller matrix is presented to extract the parameters to classify microalgae. An experimental setup based on the particulate Mueller matrix polarimetry (PMP) is used to measure 25 kinds of microalgae covering seven phyla to form a Mueller matrix dataset. The proposed sixteen polarization parameters with explicit physical meanings and associations with the structural properties are selected to compose the polarization fingerprint. As a result, different microalgae can be effectively classified by the polarization fingerprint and machine learning algorithms. The contribution of the parameters in the polarization fingerprint is analyzed to provide the specific features for microalgae classification, which gives insights into microalgae's exclusive physical properties. The extensibility and flexibility of the polarization fingerprint are discussed. This work shows the polarization fingerprint's power to classify microalgae, which can help retrieve in-situ information on the community structures of microalgae, and further monitor the aquatic environment.

Deep focus-extended darkfield imaging for in situ observation of marine plankton

Darkfield imaging can achieve in situ observation of marine plankton with unique advantages of high-resolution, high-contrast and colorful imaging for plankton species identification, size measurement and abundance estimation. However, existing underwater darkfield imagers have very shallow depth-of-field, leading to inefficient seawater sampling for plankton observation. We develop a data-driven method that can algorithmically refocus planktonic objects in their defocused darkfield images, equivalently achieving focus-extension for their acquisition imagers. We devise a set of dual-channel imaging apparatus to quickly capture paired images of live plankton with different defocus degrees in seawater samples, simulating the settings as in in situ darkfield plankton imaging. Through a series of registration and preprocessing operations on the raw image pairs, a dataset consisting of 55 000 pairs of defocused-focused plankter images have been constructed with an accurate defocus distance label for each defocused image. We use the dataset to train an end-to-end deep convolution neural network named IsPlanktonFE, and testify its focus-extension performance through extensive experiments. The experimental results show that IsPlanktonFE has extended the depth-of-field of a 0.5× darkfield imaging system to ~7 times of its original value. Moreover, the model has exhibited good content and instrument generalizability, and considerable accuracy improvement for a pre-trained ResNet-18 network to classify defocused plankton images. This focus-extension technology is expected to greatly enhance the sampling throughput and efficiency for the future in situ marine plankton observation systems, and promote the wide applications of darkfield plankton imaging instruments in marine ecology research and aquatic environment monitoring programs.

Legionnaires' Disease in China Caused by Legionella pneumophila Corby.

Legionella pneumophila is an intracellular pathogen causing pneumonia in humans. In February 2022, Legionnaires' disease caused by L. pneumophila strain Corby in a patient with lung adenocarcinoma was identified for the first time in China. This paper includes the case report and phenotypic and genomic analysis of the Corby (ICDC) strain. Its biological characteristics were evaluated by antibiotic sensitivity testing and cytology experiments, and genomic analysis was performed to understand its genetic evolution. The patient's clinical manifestations included cough, fever, pulmonary infiltration, and significantly decreased activity endurance. After empirical antimicrobial therapy, infection indicators decreased. The Corby (ICDC) strain was susceptible to nine antibiotics and exhibited strong intracellular proliferation ability. A phylogenetic tree showed that the Corby (ICDC) strain was closely related to the Corby strain, but under the pressure of a complex environment, its genome had undergone more rearrangement and inversion. The type IF CRISPR-Cas system was identified in its genome, and spacer analysis indicated that it had been invaded by several foreign plasmids, bacteria, and viruses during evolution. Legionnaires' disease caused by L. pneumophila strain Corby may be ignored in China, and it is urgent to improve long-term monitoring and investigation of aquatic environments and patients with respiratory infections to prevent a large-scale outbreak of Legionnaires' disease.

Embedded fog models for remote aquatic environmental monitoring

Fog computing is used in Internet of Things (IoT) applications to allow end node processing and storage to facilitate device autonomy and other benefits in light of unstable/unreliable cloud network connections. This paper investigates how a cloud-based IoT remote aquatic environmental monitoring platform can be adapted using a fog architectural approach to provide embedded condition assessment and forecasting functionality. An equivalent IoT hydroponic garden water quality monitoring test platform is developed which is capable of accommodating embedded fog models using a node-level microcontroller (i.e., ESP8266). The test platform is deployed in a hydroponics setting to gather environmental data and evaluate the feasibility of fog intelligence functionality on an embedded system. The first model involves a node-level Fuzzy Logic water quality condition assessment using the current sensor readings. The second model is a Support Vector Machine that makes short-term predictions of water quality parameters based on a history of locally stored sensor readings. The third model contrasts Long Short-Term Memory for water quality parameter prediction. The fog test platform and embedded models are evaluated according to performance, functionality and ease-of-implementation on a microcontroller. Finally, we propose a framework detailing the future modifications for the remote aquatic environmental monitoring platform to achieve embedded fog capabilities based on the experience with the hydroponics test platform.

Jelly-Z: Twisted and coiled polymer muscle actuated jellyfish robot for environmental monitoring

Silent underwater actuation and object detection are desired for certain applications in environmental monitoring. However, several challenges need to be faced when addressing simultaneously the issues of actuation and object detection using vision system. This paper presents a swimming underwater soft robot inspired by the moon jellyfish (Aurelia aurita) species and other similar robots; however, this robot uniquely utilizes novel artificial muscles and incorporates camera for visual information processing. The actuation characteristics of the novel artificial muscles in water are presented which can be used for any other applications. The bio-inspired robot, Jelly-Z, has the following characteristics: (1) The integration of three 60 mm-long twisted, and coiled polymer fishing line (TCPFL) muscles in a silicone bell to achieve contraction and expansion motions for swimming; (2) A Jevois camera is mounted on Jelly-Z to perform object detection while swimming using a pre-trained neural network; (3) Jelly-Z weighs a total of 215 g with all its components and is capable of swimming 360 mm in 63 seconds. The present work shows, for the first time, the integration of camera detection and TCPFL actuators in an underwater soft jellyfish robot, and the associated performance characteristics. This kind of robot can be a good platform for monitoring of aquatic environment either for detection of objects by estimating the percentage of similarity to pre-trained network or by mounting sensors to monitor water quality when fully developed.

Measurement and automatic monitoring insulation resistance of AC/DC mixed unearthed networks

Marine industry is one of the strong sectors bringing great economic benefits in the world and especially in Vietnam. However, the control of marine aquatic environment is always a complicated problem in both solutions and implementation. Currently, monitoring systems are capable of measuring most of the indicators but still face many difficulties with special indicators such as NH3, H2S, which need to integrate many specialized equipment applications, complex measurement procedures, or waiting for results from a laboratory. Therefore, the implementation is hindered and increases the investment cost. In this article, the authors apply an indirect measurement method to determine the rate of ammonia (NH3) for an automatic monitoring system of the aquatic environment. The monitoring system will monitor the basic water indicators in the National technical regulation on aquaculture water including temperature, salinity, pH, and based on that to calculate and determine the distribution rate of NH3 – factor major cause of losses in the marine products industry. The application of this indirect measurement method aims to directly assess the fluctuation of the Ammonia NH3 index and contribute to the reduction of aquatic environmental monitoring models.

A portable UAV-based laser-induced fluorescence lidar system for oil pollution and aquatic environment monitoring

A portable laser-induced fluorescence (LIF) lidar detection system for the unmanned aerial vehicle (UAV) was developed. The system consists of a transmitting and receiving module, a data processing module and a wireless transmission module. The total weight of the LIF lidar system is 5 kg. The system emits 405 nm laser to irradiate the water body, which can realize the monitoring of oil spills and chlorophyll content in the seawater by receiving the fluorescence signal with a spectral range of 400–750 nm. The fluorescence detection data of the LIF system on the UAV are synchronized to the ground terminal through a wireless transmission module, and the acquired fluorescence data are processed by a ground computer in real-time. The performance of the system was tested at a flight altitude of 30 m, and the spectral data of the seawater and oil slicks seawater were studied. The obtained results demonstrate the ability of the LIF lidar system to realize seawater fluorescence remote sensing measurement. The system is convenient and inexpensive for routinely sea monitoring and oil spill monitoring on a regional scale.

Convergence of illuminating beams suffering from scattering during the individual measurement of suspended particles

The convergent illuminating beam is the key factor during the individual measurement of suspended particles in aquatic suspensions. When the illuminating beam propagates in the suspension, the particle scattering in the optical path may destroy the convergence of the illuminating beam, especially in suspensions with a high particle concentration. In this paper, using the Monte Carlo simulation, the convergence of the illuminating beam is investigated by changing the physical properties of particles, such as size and concentration, and the optical path length of the illuminating beam. A dimensionless quantity, as the product of the scattering coefficient of suspension and the optical path length, is found to determine the achievement of the convergent beam. Moreover, an individual measurement setup based on the convergence of the illuminating beam is used to measure polystyrene microspheres with different concentrations. The experiment results are consistent with those of the simulations. Furthermore, improvement strategies are proposed and proved to effectively keep the convergence of the illuminating beam in turbid water. The results in this work can provide clues for designing a similar optical apparatus used in aquatic environment monitoring.

Implementation and Calibration of an IoT Light Attenuation Turbidity Sensor

Turbidity is an important characteristic of water quality that can indicate the presence of undesirable suspended particulate matter. Having access to an inexpensive and effective turbidity sensor unlocks numerous Internet of Things (IoT) possibilities for remote environmental monitoring. Optical light attenuation turbidity sensors operate on the premise of detecting signal degradation from a light source due to scattering from particles in a solution. This approach is technologically unpretentious and only requires a handful of inexpensive electronic components to construct. However, while this method is touted as “simple”, a significant challenge lies in sensor calibration. That is, converting an analogue signal into a meaningful and accurate digital reading in a known turbidity measurement standard (e.g., Nephelometric Turbidity Units (NTU)). This paper presents an IoT light attenuation turbidity sensor design and explores the calibration process to determine the sensor's range and accuracy. Sensor calibration is undertaken using Formazin turbidity standards and is cross-checked against a commercial turbidimeter. We provide a step-by-step procedure for determining the correct signal strength to use and a functional form for the sensor response to the Formazin standard. Finally, we specify an estimate of the accuracy of the sensor and suggest the next steps in the proposed turbidity sensor's development. Results indicate that the sensor achieves within 2%-10% of accuracy at higher ranges (100–4000 NTU), but its performance becomes significantly less reliable in low NTU ranges (< 100 NTU) where the error rate increases to 26%. The turbidity sensor is used as part of an IoT remote aquatic environmental monitoring platform.

Comprehensive comparison performances of Landsat-8 atmospheric correction methods for inland and coastal waters

Atmospheric correction (AC) is a crucial step in the quantitative analysis of inland and coastal waters. In this study, the performances of six water-based AC methods (SeaDAS, ACOLITE-DSF/EXP, C2RCC, iCOR) were evaluated by using multiple global datasets (N = 139). Four evaluation strategies were applied including spectral similarity and Chlorophyll-a retrieval. The results showed that SeaDAS and ACOLITE-DSF performed the best in terms of analytical match, band ratios, Chlorophyll-a retrieval, and spectral similarity. SeaDAS had the lowest RMSE in the blue-green bands and showed good consistency across the spectral with the lowest median spectral angle of 7°. It should be noted that ACOLITE-DSF outperforms SeaDAS in high turbidity waters. SeaDAS coupled with Chlorophyll-a retrieval algorithms of OC3 and Clark had the lowest RMSE, which showed the advantage of SeaDAS in Chlorophyll-a retrieval. This study provides scientific basis for choosing AC methods of Landsat-8 data for aquatic environment monitoring.