Turbid Water Bodies Research Articles

Do interspecific differences in morphology influence foraging efficiency of juvenile crappie?

Sympatric black crappie ( Pomoxis nigromaculatus Lesueur in Cuvier and Valenciennes, 1829) and white crappie ( Pomoxis annularis Rafinesque, 1818) often differ in ecological performance, such that white crappie outnumber black crappie in turbid waterbodies and black crappie predominate in clear waters with abundant cover. Recent work suggests that this performance difference is driven by juvenile white crappie having a limnetic body form and juvenile black crappie having a littoral body form, resulting in differential predation mortality between the species within open-water and vegetated environments. We tested whether these morphologies also lead to interspecific differences in foraging efficiency between the congeners in structure and open water. Juvenile black crappie spent more time pursuing fish prey than white crappie across all trials, suggesting that energetic gains of piscivory may differ between species. However, both species had similar capture efficiencies when attacking fathead minnows ( Pimephales promelas Rafinesque, 1820) within vegetated and open-water habitats. We conclude that variation in morphology between juvenile black crappie and white crappie does not have a large influence on foraging efficiency, at least on fish prey, in dense cover or open water, and suggest their performance in sympatry is likely not driven by predatory capabilities at the onset of piscivory within these different environmental contexts. Instead, the relationship between morphology and predator evasion may drive the distribution and relative abundance of crappie species within sympatric populations.

Underwater light source changes nitrogen and phosphorus removal pathways by Vallisneria spinulosa Yan growth system

Vallisneria spinulosa Yan (V.spinulosa Yan) with good ability of water purification is often used for ecological restoration of polluted water bodies. However, it is difficult to survive in turbid water bodies due to the low lighting condition. This study explored the feasibility of introducing artificial underwater light source into water bodies with high turbidity to strengthen the water restoration by V.spinulosa Yan. Addition of underwater light source promoted the clonal reproduction ability of V.spinulosa Yan, thus enhancing the removal loads of total nitrogen (TN), total phosphorus (TP), and nitrate nitrogen (NO3−-N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathrm{NO}}^{-}_{3}{\\mbox{-}}{\\mathrm{N}}$$\\end{document}) by 1.60–3.43 × 10−2, 1.49–3.49 × 10−3, and 0.80–2.06 × 10−2 g m−2 d−1, respectively. Underwater light source significantly reduced the abundance of microbial community on V.spinulosa Yan leaves, as well as most nitrifying bacteria (Nitrosomonadaceae) and denitrifying bacteria (Nitrospira, Comamonadaceae, and Rhodocyclaceae) in the system. But the attachment of some Cyanophyta (Chloroplast and Cyanobacteria) and Photosynthetic bacteria (Rhodobacter) onto the leaves and the growth of Methyloligellaceae in water and sediments were promoted. Nitrogen and phosphorus removal by the growth system of V.spinulosa Yan without underwater light source mainly depended on the biological processes by functional bacteria, while the absorption and co-assimilation effect of V.spinulosa Yan with underwater light source.

Image enhancement-based detection of concrete cracks under turbid water bodies

ABSTRACT Underwater concrete structure crack detection and structural health condition assessment based on image processing is challenging. The complex underwater environment and severe image degradation seriously affect the accuracy of crack detection. To solve these problems, a monocular vision and image-enhanced fractal-based fractal science based on computer vision and image processing techniques are proposed to conduct a non-contact detection study of underwater concrete cracks. This study established a four-level structural health condition to assist in underwater crack measurement and safety assessment. The box-counting method was used as a practical tool to calculate the fractal dimension. Three distances of 0.5, 0.8, and 1.2 m were set to verify the effective distance of the algorithm. The results show that the method proposed in this study can effectively detect cracks in submerged concrete members within 0.6 m and help managers correctly determine the structure's health using the fractal dimension.

Simulation Study of Polarization Imaging Model for Mie Scattering Turbid Water Bodies

Simulation Study of Polarization Imaging Model for Mie Scattering Turbid Water Bodies

Spatially Intensive Patterns of Water Clarity in Reservoirs Determined Rapidly With Sensor‐Equipped Boats and Satellites

AbstractOptical water quality affects the quantity and spectral composition of underwater light available to photoautotrophs along with light reflected off the water surface. In reservoirs, prominent gradients for optical water quality and clarity measures occur between inlets and outlets, allowing rapid surveys of ecosystem structure across diverse conditions along with tests of relationships between in situ and remotely sensed water indices. We used a sensor‐equipped boat in a clay‐rich subtropical drinking water reservoir to examine continuous spatial patterns of water clarity and the relationship between in‐lake turbidity and normalized difference turbidity index (ndti) from Sentinel 2 imagery acquired on the same date. Over the 62.3 km boat path, surface turbidity varied between 14 and 85 NTU (mean = 30), with highest values in shallower water near the main inlet. Results indicated a strong linear relationship between sensor turbidity and lab‐determined turbidity. A positive relationship between the satellite turbidity index and in‐lake turbidity, combined with a negative exponential relationship between Secchi depth and turbidity, provided a basis for predicting water clarity metrics continuously over the entire lake surface. In this turbid water body, predicted Secchi depth and turbidity over the whole lake had means of 0.22 m and 54 NTU, with 75% of the lake area <0.29 m and >26 NTU. Pursuit of general relationships involving optical properties of water from high‐speed, data‐intensive spatial surveys and remotely sensed surface reflectance facilitates further development of spatially explicit models of aquatic systems.

Recent advances in acoustic technology for aquaculture: A review

AbstractAcoustic technology has great application prospects in aquaculture. In particular, two indispensable, critical technologies for the future aquaculture industry are multi‐sensor acquisition that can achieve multi‐scale information fusion, collection and establishment of a global acoustic fish database and highly developed deep learning intelligent algorithms that can establish a correlation mechanism between fish acoustic and behaviour characteristics. Acoustic technology offers remarkable advantages in large and turbid water bodies for studying spatial and temporal distribution patterns of aquatic organism populations, developing on‐demand feeding systems and estimating biomass. This article reviews the development of acoustic technology and its application in aquaculture over the last 30 years. It further analyses, in detail, the advantages and disadvantages of acoustic technology in evaluating aquatic organism biomass and morphological and physical indicators, aquatic organism behaviour and welfare improvement. Challenges of acoustic technology in acquiring dynamic target data accurately, building a global acoustic fish database and establishing connections between fish behaviour and acoustic characteristics are also discussed. In brief, this article aims to help researchers and practitioners better understand the current state‐of‐the‐art acoustic technologies, which can provide strong support for smart aquaculture applications.

Suitability of Sentinel-3 chlorophyll a products based on optical water types

ABSTRACT Analysis of the suitability of water colour products for the reliability of subsequent applications, or improvements of the algorithm at later stages, is important. We used three types of water bodies: Type 1 (Southern Ocean and Qinghai Lake) clean oceanic water bodies dominated by chlorophyll and clean inland water bodies dominated by chlorophyll and coloured dissolved organic matter; Type 2 (Taihu Lake and Chagan Lake) turbid inland water bodies dominated by suspended matter and chlorophyll and Type 3 (Xiaoxingkai Lake and Daxingkai Lake) turbid inland water bodies dominated by suspended matter. The accuracies of two Chl a concentration products data for synchronous Sentinel-3 OLCI Neural Network (NN) and ocean colour 4 band ratio (OC4Me) algorithms were verified using two metrics combined with in situ data (to compare the applicability of Chl a concentration algorithms for the three types of water bodies). The optimal Chl-a concentration algorithm was selected to analyse the spatial and temporal variation characteristics of monthly concentrations of Chl a. Based on the analysis, (1) the NN algorithm had better applicability to Type 1 water bodies ; the NN algorithm had better applicability to Type 2 water bodies, ; and both algorithms had poor applicability to Type 3 water bodies, (2) Limitations: An overall low derivation value was obtained for the Sentinel-3 OLCI NN algorithm with water concentrations of Chl a, higher than 25 mg m−3. The OC4Me Chl a concentration product only applies to the concentrations of Chl a in Type 2 during the summer. (3) When the Sentinel-3 OLCI Chl a concentration product was compared with the moderate-resolution imaging spectroradiometer (MODIS) and visible infrared imaging radiometer (VIIRS) Chl a concentration products, the distribution trends of the Chl a concentrations were consistent. Furthermore, the Sentinel-3 OLCI Chl a concentration product was more dominant in spatial resolution.

Environmental DNA as a detection tool for small-bodied, cryptic, threatened fish in a highly turbid freshwater lake system

Context Where conservation efforts are undertaken to decrease downward trends in fish populations, comparatively few resources are directed to small-bodied cryptic species. The true extent of the decline of many of these species is therefore often unknown. Where surveys have occurred, they are frequently limited by budget and hence effort. Consequently, there is a risk that rare species may not be physically captured despite their presence at a site. Such an outcome has dire consequences for the conservation of remnant populations of threatened fish. To counter possible false negative detections, environmental DNA is increasingly being used in conjunction with, or as a precursor to, physical surveys. The Southern Purple-spotted Gudgeon (Mogurnda adspersa) is a small, threatened freshwater fish native to Australia. Recent surveys captured M. adspersa in two highly turbid waterbodies in north-central Victoria. This capture represented the first detection of the species in the state in more than 20 years. Because these waterbodies are part of a network of hydrologically connected systems, it was suspected that the species likely had a broader distribution in the region. Aims To develop a probe-based eDNA assay for M. adspersa and compare its sensitivity against a physical sampling program. Methods Detection (presence/absence) between eDNA and traditional surveys was compared across multiple sites. Key results eDNA presents an effective tool for determining the presence of M. adspersa. The eDNA survey demonstrated significant clustering of eDNA detections towards the outlets of lakes sampled, suggesting concentrated eDNA at this point, or that, due to the channels being shallower, the eDNA of resident individuals may be less diluted. Conclusions Based on these results, future survey of rare, cryptic species in highly turbid lake systems should in the first instance include a broad scale eDNA survey, with sampling concentrated at outlet channels. Implications The likely most cost-effective approach to determining the presence/absence of rare species in lake systems is the collection of eDNA samples at outlet channels.

Phytoplankton Community Structure and Its Relationship with Environmental Factors in Nanhai Lake

In order to determine the characteristics of phytoplankton community structure in Nanhai Lake in Baotou City and its relationship with environmental factors, water and phytoplankton samples were collected and composition and biomass were investigated at six sites in the spring, summer, and autumn of 2021. This article used correlation analysis and redundancy analysis (RDA) combined with the community turnover index (BC) to analyze the phytoplankton functional groups. The results showed that 7 phyla and 68 genera of phytoplankton were identified in the water body, of which Chlorophyta, Bacillariophyta, Cyanobacteria, Chrysophyta, Euglenophyta, Cryptophyta, and Pyrrophyta accounted for 34%, 32%, 16%, 6%, 4%, 4%, and 3%, respectively. The phytoplankton in the water body was classified into 23 functional groups, of which MP and D functional groups were the long−term dominant functional groups, indicating that the habitat is a turbid water body. The ecological state index (Q) value ranged from 1.94 to 3.13, with an average value of 2.74. The comprehensive nutritional index (TSIM(∑)) was between 49.32 and 52.11, with an average value of 51.72, indicating that Nanhai Lake was in a mesotrophic state. Correlation analysis and redundancy analysis (RDA) showed that multiple nutrients, transparency (SD), chemical oxygen demand (COD), water temperature (WT), and Chlorophyll a (Chl−a) were the main environmental factors affecting the biomass of dominant functional groups in the water body. The study showed the characteristics of the functional groups of algae in a precious urban lake in arid and semi−arid areas of China and their relationship with environmental factors (physical and chemical indicators, anions and cation ions, and heavy metal ions), and provided a scientific basis for its water quality evaluation.

Distinguishing Algal Blooms from Aquatic Vegetation in Chinese Lakes Using Sentinel 2 Image

Algal blooms frequently occur in numerous lakes in China, risking human health and the environment. In contrast, aquatic vegetation contributes to water purification. Due to the similar spectral characteristics shared by algal and aquatic vegetation, both are hardly distinguishable in remote sensing imaging, especially in turbid water bodies. To address this challenge, this study constructed a method to effectively extract algal blooms and aquatic vegetation from the turbid water bodies using Sentinel 2 images with high spatial resolution. Our results showed that the accuracy of the extraction of vegetation information could reach 96.1%. Since this method combined the vegetation extraction results from multiple indices, it effectively tackled the mis-extraction when only the Floating Algae Index (FAI) or the Normalized Difference Vegetation Index (NDVI) is used in water with high turbidity. By combining the image time series information with the natural phenological characteristics of the aquatic vegetation and algal blooms, an improved Vegetation Presence Frequency (VPF) was developed. It effectively distinguished algal blooms and aquatic vegetation without actual measurement data. Based on the above method and process, the information of algal blooms and aquatic vegetation was sufficiently distinguished in five typical lakes in China (Lake Hulun, Lake Hongze, Lake Chaohu, Lake Taihu, and Lake Dianchi), and the spatial distribution was reasonably mapped. The overall identification accuracy of aquatic vegetation and algal blooms using the improved VPF ranged 71.8–84.3%. The spatial transferability test of the method in the independent lakes with the various optical properties indicated the prospects of its application in other turbid water bodies. This study should provide strong methodological and theoretical support for future monitoring of algal blooms in turbid water bodies with vigorous aquatic vegetation, especially in the absence of actual measurement data. This should have practical relevance for water environment management and governance departments.

Thermal plasticity and evolution shape predator-prey interactions differently in clear and turbid water bodies.

Warming and eutrophication negatively affect freshwater ecosystems by modifying trophic interactions and increasing water turbidity. We need to consider their joint effects on predator-prey interactions and how these depend on the thermal evolution of both predator and prey. We quantified how 4°C warming and algae-induced turbidity (that integrates turbidity per se and increased food for zooplankton prey) affect functional response parameters and prey population parameters in a common-garden experiment. We did so for all combinations of high- and low-latitude predator (damselfly larvae) and prey (water fleas) populations to assess the potential impact of thermal evolution of predators and/or prey at a high latitude under warming using a space-for-time substitution. We then modelled effects on the system stability (i.e. tendency to oscillate) under different warming, turbidity and evolutionary scenarios. Warming and turbidity had little effect on the functional response parameters of high-latitude predators. In contrast, warming and turbidity reduced the handling times of low-latitude predators. Moreover, warming increased the search rates of low-latitude predators in clear water but instead decreased these in turbid water. Warming increased stability (i.e. prevented oscillations) in turbid water (except for the 'high-latitude predator and high-latitude prey' system), mainly by decreasing the prey's carrying capacity and partly also by decreasing search rates, while it did not affect stability in clear water. Algae-induced turbidity generally decreased stability, mainly by increasing the prey's carrying capacity and partly also by increasing search rates. This resembles findings that nutrient enrichment can reduce the stability of trophic systems. The expected stability of the high-latitude trophic system under warming was dependent on the turbidity level: our results suggest that thermal plasticity tends to destabilize the high-latitude trophic system under warming in clear water but not in turbid water, and that thermal evolution of the predator will stabilize the high-latitude system under warming in turbid water but less so in clear water. The extent to which thermal plasticity and evolution shape trophic system stability under warming may strongly differ between clear and turbid water bodies, with their contributions having a more stabilizing role in turbid water.

Remote sensing quantitative monitoring of chlorophyll a concentration in Taihu Lake based on measured spectrum of water surface

In order to solve the regional and seasonal limitations of the algorithm, the database is introduced into the chlorophyll a concentration inversion. Usually, the inherent optical quantity of different water bodies in different seasons is obtained through a large number of field experiments and laboratory analysis, and stored in the database, and the index is established according to water body, season and inherent optical quantity. The main content of this paper is to quantitatively monitor the concentration, distribution and evolution of chlorophyll a in turbid and eutrophic water bodies such as Taihu Lake by remote sensing. The research of inversion method and strategy based on measured spectrum of water surface provides theoretical and methodological support for its application in remote sensing images. The data obtained in this paper mainly include: water quality parameters, apparent optical quantity and inherent optical quantity of Taihu Lake. The results show that the correlation coefficient R2=0.936, which is linear and sensitive to high-value areas. This result not only reflects the feasibility of the ratio index model for chlorophyll content inversion, but also shows that the absolute error is basically below 23 mg/m3, and the inversion result is relatively stable.

Land Surface Temperature and Its Impact on Land Use and Land Cover: Long-Term Rainfall Analysis Using CHIRPS PENTAD Data in Telangana's Mahabubnagar District

Drastic changes at a global level in land use and land cover are having a negative impact on the environment, both directly and indirectly. One of the most important factors contributing to global warming is rapid urbanization, which is affected by surface temperature and water resources. One of the primary contributors to climate change and global warming is temperature. Rapid urbanization, which results in polluted water and causes other human activities in nearly every section of the country, has a significant impact on surface waterbodies. Land surface temperature can be determined using the Landsat series' thermal band. Temperatures have risen from 2005 to 2020, according to image analysis. Land surface temperature is directly affected by changes in land use and land cover. According to the report, urban areas and built-up regions should be developed in tandem with additional plantings and vegetation; this will result in keeping the temperature under control. The purpose of this research is to identify how land surface temperature changes over time, how it influences land use and land cover changes, and how it affects surface waterbodies, as turbid waterbodies have higher temperatures than clear waterbodies. The time-series change in water spread area over key waterbodies from 2005 to 2020 is also discussed in this study. Long-term rainfall analysis (1984-2014) has also been carried out to determine how drought affected the study area during the year 2015.

Human impacted shallow lakes in the Pampean plain are ideal hosts for cyanobacterial harmful blooms

The ecological status of Pampean shallow lakes is evidenced by Cyanobacteria Harmful Blooms impairing these nutrient enriched, turbid and polymictic water bodies spread along the Central Plains of Argentina. Under the premise that shallow lakes are sentinels of global climate and eutrophication, a 3-year research in ten lakes located across a climatic gradient explored which factors drove the dynamics of cyanobacterial assemblages frequently driving to bloom prevalence. Contrarily to what is expected, the effect of seasonal temperature on cyanobacteria was subordinated to both the light environment of the water column, which was on turn highly affected by water level conditions, and to nutrient concentrations. Monthly samplings evidenced that cyanobacterial assemblages presented a broad-scale temporal dynamics mostly reflecting inter-annual growth patterns driven by water level fluctuations. Both species composition and biovolume gradually changed across a gradient of resources and conditions and hence, the scenario in each individual lake was unique with patterns at different temporal and spatial scales. More than 35 filamentous and colonial morphospecies constituted the assemblages of Pampean lakes: nostocaleans and chroococcaleans were inversely correlated in the prevailing interannual 3-cycled patterns.

Estimation of suspended matter concentration in manwan reservoir, lancang river using remotely sensed small satellite constellation for environment and disaster monitoring and forecasting (HJ-1A/1B), charge coupled device (CCD) data

ABSTRACT The estimation of total suspended matter (TSM) concentration is crucial in monitoring, evaluating, and protecting water quality. Many empirical and semi-analytical models have been established for clear or extremely turbid water bodies; however, only a few are applicable to inland, extremely turbulent deep rivers. Using in situ data from the water of the Manwan reservoir, we developed a robust algorithm to estimate the TSM concentration in the Manwan reservoir, with a root mean square error (RMSE) ≤ 4.43 mg L−1 and a mean absolute percentage error (MAPE) of 23.2%, indicating the feasibility of the empirical model for estimating TSM. The empirical model was then applied to 251 Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting loaded with the Charge Coupled Device (HJ-CCD) images to derive TSM distribution maps from 2009 to 2018. The estimated TSM concentrations exhibited significant spatial and seasonal changes, revealing relationships among TSM, wind speed, and precipitation. The spatial heterogeneity was significantly higher downstream than upstream in the reservoir due to watershed inputs and anthropogenic dredging activity. The temporal heterogeneity of TSM, significantly higher in summer and autumn than in winter and spring, was mainly caused by seasonal rainfall. Our study shows that the empirical model for HJ-CCD images can be used to quantitatively monitor the TSM in inland rivers.

How Accurate Is an Unmanned Aerial Vehicle Data-Based Model Applied on Satellite Imagery for Chlorophyll-a Estimation in Freshwater Bodies?

Optical sensors are increasingly sought to estimate the amount of chlorophyll a (chl_a) in freshwater bodies. Most, whether empirical or semi-empirical, are data-oriented. Two main limitations are often encountered in the development of such models. The availability of data needed for model calibration, validation, and testing and the locality of the model developed—the majority need a re-parameterization from lake to lake. An Unmanned aerial vehicle (UAV) data-based model for chl_a estimation is developed in this work and tested on Sentinel-2 imagery without any re-parametrization. The Ensemble-based system (EBS) algorithm was used to train the model. The leave-one-out cross validation technique was applied to evaluate the EBS, at a local scale, where results were satisfactory (R2 = Nash = 0.94 and RMSE = 5.6 µg chl_a L−1). A blind database (collected over 89 lakes) was used to challenge the EBS’ Sentine-2-derived chl_a estimates at a regional scale. Results were relatively less good, yet satisfactory (R2 = 0.85, RMSE= 2.4 µg chl_a L−1, and Nash = 0.79). However, the EBS has shown some failure to correctly retrieve chl_a concentration in highly turbid waterbodies. This particularity nonetheless does not affect EBS performance, since turbid waters can easily be pre-recognized and masked before the chl_a modeling.

Retrieval of Secchi Disk Depth in Turbid Lakes from GOCI Based on a New Semi-Analytical Algorithm

The accurate remote estimation of the Secchi disk depth (ZSD) in turbid waters is essential in the monitoring the ecological environment of lakes. Using the field measured ZSD and the remote sensing reflectance (Rrs(λ)) data, a new semi-analytical algorithm (denoted as ZSDZ) for retrieving ZSD was developed from Rrs(λ), and it was applied to Geostationary Ocean Color Imager (GOCI) images in extremely turbid waters. Our results are as follows: (1) the ZSDZ performs well in estimating ZSD in turbid water bodies (0.15 m < ZSD < 2.5 m). By validating with the field measured data that were collected in four turbid inland lakes, the determination coefficient (R2) is determined to be 0.89, with a mean absolute square percentage error (MAPE) of 22.39%, and root mean square error (RMSE) of 0.24 m. (2) The ZSDZ improved the retrieval accuracy of ZSD in turbid waters and outperformed the existing semi-analytical schemes. (3) The developed algorithm and GOCI data are in order to map the hourly variation of ZSD in turbid inland waters, the GOCI-derived results reveal a significant spatiotemporal variation in our study region, which are significantly driven by wind forcing. This study can provide a new approach for estimating water transparency in turbid waters, offering important support for the management of inland waters.

An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel

Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs.

Coastal Oyster Aquaculture Area Extraction and Nutrient Loading Estimation Using a GF-2 Satellite Image

The accurate extraction of an aquaculture area is significant in aquaculture management, post-disaster evaluation, and aquatic environment protection. However, little attention has been paid to the aquaculture area extraction in coastal water with high turbidity. In this study, based on the spectral and geospatial features of aquaculture cages in complex coastal water with varying turbidity, we proposed a new aquaculture area extraction method using a Gaofen-2 (GF-2) satellite image with 0.8-m spatial resolution. The water was classified into clear, medium, and high turbidity categories according to the suspended sediment concentration derived from the inversion of the GF-2 image. Different rules of extraction were developed with respect to those three categories of water body: First, the normalized difference water index threshold was set for the clear water, second, a ratio index ( R = Green/NIR) was established for the medium turbid water body, and third, for the turbid water body, feature analysis with a specified classification rule was established. The experimental results demonstrated that our proposed method worked well, with the high accuracies of 87.3300% for the overall accuracy, even for the high turbidity water. The kappa coefficient was 0.7375, which was much better than the kappa coefficient values of the three conventional classification methods represented in this article. This study provides effective information support and auxiliary decision analysis for management departments to scientifically plan and environmentally manage coastal aquaculture areas.

Atmospheric correction algorithm over coastal and inland waters based on the red and NIR bands: application to Landsat-8/OLI and VNREDSat-1/NAOMI observations.

Water pixel extraction and correction of the atmospheric signal represent prerequisite steps prior to applying algorithms dedicated to the assessment of water quality of natural surface water bodies. The recent multiplication of medium spatial resolution sensors (10-60 m) provides the required constellation to monitoring bio-optical and biogeochemical parameters of surface waters at the relevant spatial-temporal scales. Here we present a new approach to identify water pixels and to extract the atmospheric contribution to the top of atmosphere signal measured by the NAOMI sensor on board the first Vietnamese satellite, VNREDSat-1. After verifying the TOA calibration of NAOMI through a vicarious calibration exercise, we adapt a recent water pixel extraction algorithm (WiPE) to NAOMI, and develop a new atmospheric correction algorithm (referred to as red-NIR) based on the use of the red and NIR bands (the only bands available for that purpose on NAOMI) and spectral relationships. The evaluation of red-NIR with a match-up data set gathering remote sensing reflectance, Rrs, measurements performed at the AERONET-OC stations in moderately turbid waters indicates excellent performance in the blue and green part of the spectrum (similar to the performances reached by the SeaDAS NIR-SWIR algorithms) and lower accuracy in the red. Intercomparison of simultaneous images collected by NAOMI and OLI over a more turbid water body shows an excellent agreement between the NAOMI-Rrs estimated by the present processing, and the OLI-Rrs estimated from the ACOLITE algorithm. This approach will allow sensors that do not have SWIR bands, such as SPOT-6 and -7, to be processed, making their data exploitation available for long-term temporal analyses.