Chl-a Distribution Research Articles

Spatiotemporal distribution of chlorophyll-a concentration in the south China sea and its possible environmental regulation mechanisms.

In this paper, the spatial and temporal distribution of chlorophyll-a (Chl-a) concentration in the South China Sea (SCS) and its major environmental regulator mechanisms were studied by using satellite remote sensing data sea surface temperature (SST), sea surface wind (SSW), and aerosol optical depth (AOD) spanning from January 2000 to December 2022. The results show that Chl-a in the SCS exhibit notable spatio-temporal variations: they peak in winter (∼0.234mgm-3) and autumn (∼0.156mgm-3), and decline in spring (∼0.144mgm-3) and summer (∼0.136mgm-3). Spatially, Chl-a near the coast and in upwelling areas are generally higher than those in offshore areas. A monthly average time series correlation analysis across the entire SCS shows that Chl-a significantly correlate with SST (R=-0.78, P<0.01) and SSW (R=0.78, P<0.01), and moderately correlate with AOD (R=0.29, P<0.01). The regulator of environmental factors also shows seasonal differences: during the winter monsoon period, Chl-a has the highest partial correlation with SSW (R=0.73, P<0.01), followed by SST (R=-0.55, P<0.01), and no significant partial correlation with AOD (R=0.14, P>0.05); during the summer monsoon period, Chl-a has the highest partial correlation with SST (R=-0.63, P<0.01), followed by AOD (R=0.40, P<0.01), and no significant partial correlation with SSW (R=0.12, P>0.05). A comprehensive analysis indicates that the mixing and upwelling processes regulated by the winter monsoon and SST exert a greater influence on nutrient variations. The enhanced mixing caused by the winter monsoon and the cold environment promote the growth of phytoplankton, leading to higher Chl-a concentrations in winter compared to other seasons. In contrast, the increased temperature in the summer monsoon period significantly weakens the mixing effect of wind speed and nutrients influx from deep layers to surface layers. Consequently, the external nutrient sourced from aerosol becomes crucial in determining Chl-a distribution, especially in oligotrophic regions near the southern SCS and the basin. However, in regions where other nutrient sources significantly contribute, such as the coastal areas influenced by seasonal upwelling, the contribution of aerosols is negligible.

Sayılarla balıkçılık: Muara Kintap balıkçılığını veri odaklı balıkçılık alanı tahmin haritalarıyla güçlendirmek

The paper aims to enhance the fishing efficiency and sustainability of Muara Kintap fishers by using data-driven Fishing Area Forecast Maps (FAFM). This study makes significant contributions to the field of fisheries management by demonstrating the effective use of satellite data for local-scale fisheries management, bridging the gap between scientific research and practical applications, as well as promoting sustainable fishing practices and improving the fishers' livelihoods. The research ingeniously combined the wisdom of local fishers captured through the Fishing Points app with cutting-edge technology. Aqua MODIS satellite imagery captured detailed Muara Kintap waters condition, revealing sea surface temperature (SST) between 28.4 °C and 29.7 °C and chlorophyll-a (Chl-a) concentrations ranging from 0.38 to 6.27 mg/m³. The results strongly indicate that the distribution of Chl-a is a more influential predictor of fish catch than SST. This discovery underscores the intricate relationship between marine parameters and fish distribution. By providing FAFM, informed by Chl-a data, the fishers were able to make data-driven decisions, optimizing catches and promoting the long-term sustainability of their livelihoods. The study's impact transcends data analysis, highlighting the importance of collaboration and knowledge sharing among researchers, fishers, and policymakers in fostering sustainable fishing practices in Muara Kintap and beyond.

Spatiotemporal distribution of sedimentary chloropigments and organic carbon in a shelf sea ecosystem, with a focus on the cold water mass

The Yellow Sea is one of the most thoroughly studied shelf seas in the northwestern Pacific. However, many aspects of the northern Yellow Sea sediments remain poorly understood. Sedimentary chloropigments and total organic carbon (TOC) are crucial components that indicate benthic primary productivity and ecosystem functioning in the marginal sea system. The present study investigated the temporal and spatial variations of sediment chloroplastic pigments (Chl-a and Pha-a) and TOC contents in relation to seasons and the northern Yellow Sea Cold Water Mass (NYSCWM). Decadal trends were also examined using linear regression on time-series data from 12 cruises conducted over a nine-year period (2006–2014). The correlations between sedimentary Chorophy-a (Chl-a), Pheophorbide-a (Pha-a), TOC, and their ratios with other abiotic and biotic factors were analyzed to detect any effects of the NYSCWM on the spatiotemporal distribution of sediment Chl-a, Pha-a, TOC contents, as well as the ecological relevance of their ratios as indicators of food quality (freshness) in the benthic system. The NYSCWM substantially influences the spatial and seasonal distribution of sedimentary Chl-a, Pha-a, and TOC contents. High values of Pha-a, TOC, and the ratios of Pha-a/(Chl-a + Pha-a) and TOC/Chl-a were often observed in the central region of the NYSCWM, where Chl-a showed clear seasonal fluctuations while Pha-a and TOC remained relatively constant. While sedimentary TOC appears to negatively mirror the spatial pattern of primary productivity in the water column, the spatial pattern of sedimentary Chl-a in relation to NYSCWM is less evident. However, the contents of Pha-a and TOC in the central NYSCWM sediments were higher than those at surrounding NYSCWM stations. Surface sediment Pha-a/(Chl-a + Pha-a) ratio was negatively correlated with meiofauna abundance and biomass and positively correlated with nematode/copepod ratio. This finding indicates that the quality of sediment organic matter is important to meiofauna. Conversely, the lower organic degradation rate at the lower bottom temperature of the NYSCWM may explain the observed higher Pha-a and TOC contents in its central area. Time-series data collected over nine years at the NYSCWM stations showed a decreasing trend for Chl-a, while TOC and the ratio of Pha-a/(Chl-a + Pha-a) exhibited increasing tendencies. This trend may imply a reduced quality of food supply to the benthic food web in the shelf sea system, along with degraded ecosystem functioning and service under the impacts of global change and anthropogenic disturbances.

A novel method to estimate the 3D chlorophyll a distribution in the South China Sea surface waters using hydrometeorological parameters

Chlorophyll a (Chl-a) is a key indicator of marine ecosystems, and certain hydro-meteorological parameters (HMPs) are highly correlated with its fluctuations. Here, relevant and accessible HMPs were used as inputs, combined with machine learning (ML) algorithms for estimating 3D Chl-a in the South China Sea (SCS). With the inputs of temperature, salinity, depth, wind speed, wind direction, sea surface pressure, and relative humidity, the LightGBM-based model performed well, achieving high R2 values of 0.985 and 0.789 in validation and testing sets, respectively. Based on a large number of in situ measurements, this model enables the estimation of the 3D distribution of summer Chl-a in the SCS over the past fifteen years using a 3D hydrographic dataset combined with surface meteorological parameters. The results show that the 3D distribution of the model estimated Chl-a is characterized similarly to the previous studies and can capture the effect of hydro-meteorological conditions on Chl-a distribution. The environmental variables affecting Chl-a were considered more comprehensively in this study, and the methodological framework has the potential to be applied to the low-cost monitoring of the remaining water quality parameters.

Vertical Structure and Seasonal Variability of Chlorophyll Concentrations in the Southern Tropical Indian Ocean Revealed by Biogeochemical Argo Data

AbstractThe variability of chlorophyll (Chla) in the Southern Tropical Indian Ocean (STIO) is not fully understood. This study utilized biogeochemical Argo (BGC‐Argo) and satellite observations to investigate the seasonal Chla variations in the upper layer (above 200 m) and their relationships to physical dynamics. The results indicate the existence of a well‐developed deep Chla maximum (DCM) layer situated between depths of 50 and 150 m. The shallowest DCM was at the Seychelles‐Chagos thermocline ridge because of permanent upwelling. Both the northern (4°S–12°S, 52°E−92°E) and southern (12°S–25°S, 52°E−92°E) regions experience surface blooms during July–August. However, they exhibit distinct Chla changes in response to different physical processes and nitrate concentrations below the mixed layer. In the northern region, the thermocline plays a critical role in regulating DCM depth and intensity. From April to June, subsurface upwelling and near‐surface stratification processes promote nutrient and Chla accumulation in the subsurface layer, resulting in elevated surface Chla levels in the subsequent months. In contrast, the southern region is characterized by oligotrophic conditions, where light availability primarily governs Chla variability below the mixed layer. Specifically, from November to January, when light intensity intensifies, Chla increases below the mixed layer. Furthermore, BGC‐Argo data revealed a long‐lived cyclonic eddy that facilitated the westward transport of Chla, significantly contributing to surface Chla blooms through eddy‐pumping and eddy‐trapping mechanisms. This research elucidates the fundamental characteristics of Chla distribution from a three‐dimensional perspective and furthers our understanding of the complex biophysical interactions within the STIO.

Multiple remotely sensed datasets and machine learning models to predict chlorophyll-a concentration in the Nakdong River, South Korea.

The Nakdong River is a crucial water resource in South Korea, supplying water for various purposes such as potable water, irrigation, and recreation. However, the river is vulnerable to algal blooms due to the inflow of pollutants from multiple points and non-point sources. Monitoring chlorophyll-a (Chl-a) concentrations, a proxy for algal biomass is essential for assessing the trophic status of the river and managing its ecological health. This study aimed to improve the accuracy and reliability of Chl-a estimation in the Nakdong River using machine learning models (MLMs) and simultaneous use of multiple remotely sensed datasets. This study compared the performances of four MLMs: multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), and eXetreme Gradient Boosting (XGB) using three different input datasets: (1) two remotely sensed datasets (Sentinel-2 and Landsat-8), (2) standalone Sentinel-2, and (3) standalone Landsat-8. The results showed that the MLP model with multiple remotely sensed datasets outperformed other MLMs with 0.43 - 0.86 greater in R2 and 0.36 - 5.88 lower in RMSE. The MLP model demonstrated the highest performance across the range of Chl-a concentrations and predicted peaks above 20 mg/m3 relatively well compared to other models. This was likely due to the capacity of MLP to handle imbalanced datasets. The predictive map of the spatial distribution of Chl-a generated by MLP well captured the areas with high and low Chl-a concentrations. This study pointed out the impacts of imbalanced Chl-a concentration observations (dominated by low Chl-a concentrations) on the performance of MLMs. The data imbalance likely led to MLMs poorly trained for high Chl-a values, producing low prediction accuracy. In conclusion, this study demonstrated the value of multiple remotely sensed datasets in enhancing the accuracy and reliability of Chl-a estimation, mainly when using the MLP model. These findings would provide valuable insights into utilizing MLMs effectively for Chl-a monitoring.

Temporal and spatial variation patterns of chlorophyll a in marine ranching under global interannual events

Marine ecosystems are facing numerous environmental challenges due to global climate change. In response to these challenges, the establishment and growth of marine ranching has emerged as a pivotal solution. Chlorophyll a concentration (Chla) is recognized as a valuable indicator for the ecological assessment of marine ranching. This study focused on the spatiotemporal distribution of Chla and its response to environmental factors according to the dataset in the marine ranching area of Haizhou Bay (Lianyungang, Jiangsu, China) from 2003 to 2022. The results showed that Chla had a significant cycle of summer > spring > autumn and was distributed evenly in the central area of the marine ranching. During interannual changes, Chla patches were centered in the central region during 2014, 2015, and 2016. The Chla patches predominantly focused on the eastern area in 2018–2019, shifting to the western area in 2020–2021. The generalized additive model (GAM) indicated that salinity, depth, temperature, biological oxygen demand (BOD5) and SiO3−–Si were the main environmental factors affecting Chla during spring, summer and autumn. However, during El Niño events, salinity, depth, temperature, BOD5 and transparency became the main environmental factors. We concluded that salinity, depth and temperature consistently played a crucial role in determining Chla under various climate conditions, and SiO3−–Si and transparency will no longer be an environmental factor limiting Chla. In addition, The effect of interannual variability on upwelling and vertical mixing of water layers may potentially alter the spatial distribution pattern of Chla. These findings can offer ideas into predicting the variation of Chla in marine ranching under global interannual events in the future. Furthermore, this can contribute to the comprehensive assessment of ecological benefits and the in-depth construction of marine ranching. Ultimately, it can provide essential data and scientific references for offshore ecological environment assessment and ecosystem restoration.

İskenderun Körfezi kıyı alanlarında sıcaklık ve klorofil-a için uydu ve model temelli veri setlerinin temsil yeteneği üzerine bir değerlendirme

This study investigates the monitoring of sea surface temperature (SST) and chlorophyll-a (chl-a) levels in Iskenderun Bay using satellite and modeling data and evaluates the possible use of these datasets for monitoring marine ecosystems. Datasets derived from MODIS-Aqua satellite imagery and modeling data obtained from the Copernicus MyOcean and in-situ measurements were used in the study. According to the analysis on paried data sets of the distribution of SST and chl-a, sattelite and model datasets showed statistically significant correlations with in-situ measurements for SST. However, only satellite dataset showed significant correlations for Chl-a. Evaluations on uncertainty of the data sets revealed that the satellite dataset had a narrower range and less outlier distribution for SST. For chlorophyll-a, both datasets had wide uncertainty ranges and required further improvement. This study highlights the potential of satellite and model datasets for monitoring SST and chl-a variations in Iskenderun Bay.

Tiber River-Driven Chlorophyll-a and Total Suspended Matter Dynamics and Their Impacts along the Central Tyrrhenian Sea Coast: A Sentinel-2 Approach

Chlorophyll-a (Chl-a) and Total Suspended Matter (TSM) are key health indicators of the coastal ocean and seas. The former is linked to primary productivity, while the latter is associated with water quality; both are influenced by change in climate. Recent studies have highlighted a declining trend in Chl-a levels along the Mediterranean coastal region. River discharge plays an important role in regulating the coastal Chl-a concentration levels. The present research primarily focuses on understanding the significance of Tiber River −driven spatial dynamics of Chl-a and TSM along the central Tyrrhenian Sea coasts. The research also focuses on evaluating the applicability of Sentinel-2 and identifying a suitable method for estimating Chl-a and TSM from Sentinel-2. Neural networks and dark spectrum fitting techniques were applied using multiple algorithms to estimate the dynamic distribution of Chl-a and TSM driven by the Tiber River in the study area. Multiple statistical analyses were performed, and statistically significant relationships were observed. The Case-2 Regional Coast Colour Neural Network (C2RCC-Net) outperformed all other algorithms, with an R2 value of 0.903 for Chl-a and an R2 value of 0.966 for TSM. Furthermore, the present research also identified a positive pixel to pixel spatial correlation between Chl-a and TSM in all four seasons, highlighting the positive impact of Tiber River on maintaining Chl-a levels along the coasts of Tyrrhenian Sea. This stands in contrast with the negative trend seen in the Mediterranean scale.

From Do-It-Yourself Design to Discovery: A Comprehensive Approach to Hyperspectral Imaging from Drones

This paper presents an innovative, holistic, and comprehensive approach to drone-based imaging spectroscopy based on a small, cost-effective, and lightweight Unmanned Aerial Vehicle (UAV) payload intended for remote sensing applications. The payload comprises a push-broom imaging spectrometer built in-house with readily available Commercial Off-The-Shelf (COTS) components. This approach encompasses the entire process related to drone-based imaging spectroscopy, ranging from payload design, field operation, and data processing to the extraction of scientific data products from the collected data. This work focuses on generating directly georeferenced imaging spectroscopy datacubes using a Do-It-Yourself (DIY) imaging spectrometer, which is based on COTS components and freely available software and methods. The goal is to generate a remote sensing reflectance datacube that is suitable for retrieving chlorophyll-A (Chl-A) distributions as well as other properties of the ocean spectra. Direct georeferencing accuracy is determined by comparing landmarks in the directly georeferenced datacube to their true location. The quality of the remote sensing reflectance datacube is investigated by comparing the Chl-A distribution on various days with in situ measurements and satellite data products.

Novel methods for monitoring low chlorophyll-a concentrations in the large, oligotrophic Lake Malawi/Nyasa/Niassa

The use of remote sensing for monitoring chlorophyll-a (chla) and modelling eutrophication has advanced over the last decades. Although the application of the technology has proven successful in ocean ecosystems, there is a need to monitor chla concentrations in large, nutrient-poor inland water bodies. The main objective of this study was to explore the utility of publicly available remotely sensed Sentinel-2 (S2) imagery to quantify chla concentrations in the nutrient-deficient Lake Malawi/Niassa/Nyasa (LMNN). A secondary objective was to compare the S2 derived chla with the Global Change Observation Mission–Climate (GCOM-C) chla product that provides uninterrupted data throughout the year. In situ chla data (n = 76) from upper, middle and lower sections of LMNN served as a reference to produce remote sensing-based quantification. The line-height approach method built on color index, was applied for chla concentrations below 0.25 mg/m3. Moderate Resolution Imaging Spectroradiometer 3-band Ocean Color (MODIS-OC3) – was adopted when chla concentration exceeded 0.35 mg/m3. The MODIS-OC3 algorithm had generic model coefficients that were calibrated for each in situ sample by using GCOM-C Level 3 chla product. A weighted sum of the two algorithms was applied for chla concentrations that fell between 0.25 and 0.35 mg/m3. The above methods were then applied to the S2 data to estimate chla at each pixel. S2 showed a promising accuracy in distinguishing chla levels (MSE = 0.18) although the chla range in the lake was relatively narrow, particularly using the locally calibrated coefficients of the OC3 algorithm. Chla distribution maps produced from the S2 data revealed limited spatial variation across the LMNN with higher concentrations identified in the coastal areas. S2-derived chla and GCOM-C chla comparison showed fairly good similarity between the two datasets (MSE = 0.205). Accepting this similarity, monthly chla dynamics of the lake was profiled using the temporally reliable GCOM-C data that showed oligotrophic conditions (1.7 mg/m3 to 3.2 mg/m3) in most parts of the lake throughout the year. The study's findings advance the potential for both remote sensing approaches to provide vital information at the required spatial and temporal resolution for evidence-based policymaking and proactive environmental management in an otherwise very data deficient region.

Comparative analysis of machine learning methods for prediction of chlorophyll-a in a river with different hydrology characteristics: A case study in Fuchun River, China

Eutrophication is a serious threat to water quality and human health, and chlorophyll-a (Chla) is a key indicator to represent eutrophication in rivers or lakes. Understanding the spatial-temporal distribution of Chla and its accurate prediction are significant for water system management. In this study, spatial-temporal analysis and correlation analysis were applied to reveal Chla concentration pattern in the Fuchun River, China. Then four exogenous variables (wind speed, water temperature, dissolved oxygen and turbidity) were used for predicting Chla concentrations by six models (3 traditional machine learning models and 3 deep learning models) and compare the performance in a river with different hydrology characteristics. Statistical analysis shown that the Chla concentration in the reservoir river segment was higher than in the natural river segment during August and September, while the dominant algae gradually changed from Cyanophyta to Cryptophyta. Moreover, air temperature, water temperature and dissolved oxygen had high correlations with Chla concentrations among environment factors. The results of the prediction models demonstrate that extreme gradient boosting (XGBoost) and long short-term memory neural network (LSTM) were the best performance model in the reservoir river segment (NSE = 0.93; RMSE = 4.67) and natural river segment (NSE = 0.94; RMSE = 1.84), respectively. This study provides a reference for further understanding eutrophication and early warning of algal blooms in different type of rivers.

A multivariate linear regression model for estimating chlorophyll-a concentration in Quan Son Reservoir (Hanoi, Vietnam) using Sentinel-2B Imagery

Monitoring chlorophyll-a concentration (Chla) in inland waters is vital for environmental assessment. This study develops an empirical multivariate linear regression (MLR) model to directly estimate Chla in Quan Son Reservoir using Sentinel-2B (S2B) Level 2A images. Regression analysis of a 68-point in-situ Chla dataset measured in Quan Son Reservoir between 2021 and 2023, in conjunction with the corresponding S2B reflectance data, reveals a significant correlation between Chla and a combination of the blue (B2), green (B3), and red (B4) bands (coefficient of determination, R² = 0.95). The Chla estimation model is validated using a 30-point in-situ dataset collected on various dates (R² = 0.87; the root-mean-squared error RMSE &lt; 5%). Subsequently, the model is applied to ten S2B images acquired from 2021 to 2023, revealing Chla's spatio-temporal distribution across the reservoir. Two key trends emerge: (1) Chla is lower during winter (November and December) than in summer and early autumn (July and September), and (2) The distribution of Chla undergoes noticeable spatial changes, particularly in July, with elevated levels observed in areas characterized by tourist hotspots. This approach shows promise for monitoring Chla in similar inland waters.

Comparative Evaluation of Semi-Empirical Approaches to Retrieve Satellite-Derived Chlorophyll-a Concentrations from Nearshore and Offshore Waters of a Large Lake (Lake Ontario)

Chlorophyll-a concentration (Chl-a) is commonly used as a proxy for phytoplankton abundance in surface waters of large lakes. Mapping spatial and temporal Chl-a distributions derived from multispectral satellite data is therefore increasingly popular for monitoring trends in trophic state of these important ecosystems. We evaluated products of eleven atmospheric correction processors (LEDAPS, LaSRC, Sen2Cor, ACOLITE, ATCOR, C2RCC, DOS 1, FLAASH, iCOR, Polymer, and QUAC) and 27 reflectance indexes (including band-ratio, three-band, and four-band algorithms) recommended for Chl-a concentration retrieval. These were applied to the western basin of Lake Ontario by pairing 236 satellite scenes from Landsat 5, 7, 8, and Sentinel-2 acquired between 2000 and 2022 to 600 near-synchronous and co-located in situ-measured Chl-a concentrations. The in situ data were categorized based on location, seasonality, and Carlson’s Trophic State Index (TSI). Linear regression Chl-a models were calibrated for each processing scheme plus data category. The models were compared using a range of performance metrics. Categorization of data based on trophic state yielded improved outcomes. Furthermore, Sentinel-2 and Landsat 8 data provided the best results, while Landsat 5 and 7 underperformed. A total of 28 Chl-a models were developed across the different data categorization schemes, with RMSEs ranging from 1.1 to 14.1 μg/L. ACOLITE-corrected images paired with the blue-to-green band ratio emerged as the generally best performing scheme. However, model performance was dependent on the data filtration practices and varied between satellites.

Using Ensemble Learning for Remote Sensing Inversion of Water Quality Parameters in Poyang Lake

Inland bodies of water, such as lakes, play a crucial role in sustaining life and supporting ecosystems. However, with the rapid development of socio-economics, water resources are facing serious pollution problems, such as the eutrophication of water bodies and degradation of wetlands. Therefore, the monitoring, management, and protection of inland water resources are particularly important. In past research, empirical models and machine learning models have been widely used for the water quality assessment of inland lakes. Due to the complexity of the optical properties of inland lake water bodies, the performance of these models is often limited. To overcome the limitations of these models, this study uses in situ water quality data from 2017 to 2018 and multispectral (MS) remote sensing data from Sentinel-2 to construct experimental samples of Poyang Lake. Based on these experimental samples, we constructed a spatio-temporal ensemble model (STE) to evaluate four common water quality parameters: chlorophyll-a (Chl-a), total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD). The model adopts an ensemble learning strategy, improving the model’s performance by merging multiple advanced machine learning algorithms. We introduced several indices related to water quality parameters as auxiliary variables, such as NDCI and Enhanced Three, and used band data and these auxiliary variables as predictive variables, thereby greatly enhancing the predictive potential of the model.The results show that the inversion accuracy of these four inversion models is high (R2 of 0.94, 0.88, 0.92, and 0.93; RMSE of 1.15, 0.01, 0.02, and 0.02; MAE of 0.81, 0.01, 0.09, and 0.10), indicating that the STE model has good evaluation accuracy. Meanwhile, we used the STE model to reveal the spatio-temporal distribution of Chl-a, TP, TN, and COD from 2017 to 2018, and analyzed their seasonal and spatial variation rules. The results of this study not only provide an effective and practical method for monitoring and managing water quality parameters in inland lakes, but also provide water security for socio-economic and ecological environmental safety.

An Improved Data Interpolating Empirical Orthogonal Function Method for Data Reconstruction: A Case Study of the Chlorophyll-a Concentration in the Bohai Sea, China

Chlorophyll-a (chl-a) serves as a key indicator in water quality and harmful algal blooms (HABs) research. While satellite ocean color data have greatly advanced chl-a research and HABs monitoring, missing data caused by cloud cover and other factors limit the spatiotemporal continuity and the utility of remote sensing data products. The Data Interpolating Empirical Orthogonal Function (DINEOF) method, widely used to reconstruct missing values in remote sensing datasets, is open to improvement in terms of computational accuracy and efficiency. We propose an improved method called Concentration-Stratified DINEOF (CS-DINEOF), which uses a coordinate–value correlative data division strategy to stratify the study area into several subregions based on annual average chl-a concentration. The proposed method clusters data points with similar spatiotemporal patterns, allowing for more targeted and effective reconstruction in each sub-dataset. The feasibility and advantage of the proposed method are tested and evaluated in the experiments of chl-a data reconstruction in the water of the Bohai Sea. Compared with the ordinary DINEOF method, the CS-DINEOF method improves the reconstruction accuracy, with an average Root Mean Square Error (RMSE) reduction of 0.0281 mg/m3, and saves computational time by 228.9%. Furthermore, the gap-free images generated from CS-DINEOF are able to illustrate small variations and details of the chl-a distribution in local areas. We can conclude that the proposed CS-DINEOF method is superior in providing significant insights for water quality and HABs studies in the Bohai Sea region.

Variability in the relationship between light scattering and chlorophyll a concentration in oligotrophic tropical regions of the Western Pacific Ocean.

It is important to determine the relationship between the concentration of chlorophyll a (Chla) and the inherent optical properties (IOPs) of ocean water to develop optical models and algorithms that characterize the biogeochemical properties and estimate biological pumping and carbon flux in this environment. However, previous studies reported relatively large variations in the particulate backscattering coefficient (bbp(λ)) and Chla from more eutrophic high-latitude waters to clear oligotrophic waters, especially in oligotrophic oceanic areas where these two variables have little covariation. In this study, we examined the variability of bbp(λ) and Chla in the euphotic layer in oligotrophic areas of the tropical Western Pacific Ocean and determined the sources of these variations by reassessment of in-situ measurements and the biogeochemical-argo (BGC-Argo) database. Our findings identified covariation of bbp(λ) and Chla in the water column below the deep Chla maximum (DCM) layer, and indicated that there was no significant correlation relationship between bbp(λ) and Chla in the upper layer of the DCM. Particles smaller than 3.2 µm that were in the water column above the DCM layer had a large effect on the bbp(λ) in the vertical profile, but particles larger than 3.2 µm and smaller than 10 µm had the largest effect on the bbp(λ) in the water column below the DCM layer. The contribution of non-algal particles (NAPs) to backscattering is up to 50%, which occurs in the water depth of 50 m and not consistent with the distribution of Chla. Phytoplankton and NAPs were modeled as coated spheres and homogeneous spherical particles to simulate the bbp(λ) of the vertical profile by Aden-Kerker method and Mie theory, and the results also indicated that the backscattering caused by particles less than 20 µm were closer to the measured data when they were below and above the DCM layer, respectively. This relationship also reflects the bbp(λ) of particles in the upper water was significantly affected particle size, but bbp(λ) in the lower water was significantly affected by Chla concentration. This effect may have relationship with phytoplankton photoacclimation and the relationship of a phytoplankton biomass maximum with particle size distribution in the water column according to the previous relevant studies. These characteristics also had spatial and seasonal variations due to changes of Chla concentration at the surface and at different depths. There was mostly a linear relationship between Chla and bbp(700) during winter. During other seasons, the relationship between these two variables was better characterized by a power function (or a logarithmic function) in the lower layer of the DCM. The spatial and vertical relationships between the bbp(λ) and Chla and the corresponding variations in the types of particles described in this study provide parameters that can be used for accurate estimation of regional geochemical processes.

Evaluation of Sentinel-2 Based Chlorophyll-a Estimation in a Small-Scale Reservoir: Assessing Accuracy and Availability

Small-scale reservoirs located in river estuaries are a significant water resource supporting agricultural and industrial activities; however, they face annual challenges of eutrophication and algal bloom occurrences due to excessive nutrient accumulation and watershed characteristics. Efficient management of algal blooms necessitates a comprehensive analysis of their spatiotemporal distribution characteristics. Therefore, this study aims to develop a chlorophyll-a (Chl-a) estimation model based on high-resolution satellite remote sensing data from Sentinel-2 multispectral sensors and multiple linear regression. The multiple linear regression (MLR) models were constructed using multiple reflectance-based variables that were collected over 2 years (2021–2022) in an estuarine reservoir. A total of 21 significant input variables were selected by backward elimination from the 2–4 band algorithms as employed in previous Chl-a estimation studies, along with the Sentinel-2 B1-B8A wavelength ratio. The developed algorithm exhibited a coefficient of determination of 0.65. Spatiotemporal variations in Chl-a concentration generated by the algorithm reflected the movement of high Chl-a concentration zones within the body of water. Through this analysis, it turned out that Sentinel-2-based spectral images were applicable to a small-scale reservoir which is relatively long and narrow, and the algorithm estimated changes in concentration levels over the seasons, revealing the dynamic nature of Chl-a distributions. The model developed in this study is expected to support effective algal bloom management and water quality improvement in a small-scale reservoir or similar complex water quality water bodies.

Decision tree ensemble with Bayesian optimization to predict the spatial dynamics of chlorophyll-a concentration: A case study in Bay of Bengal

An accurate prediction of the spatial distribution of phytoplankton biomass, as represented by Chlorophyll-a (CHL-a) concentrations, is important for assessing ecological conditions in the marine environment. This study developed a hyperparameter-optimized decision tree-based machine learning (ML) models to predict the geographical distribution of marine phytoplankton CHL-a in the Bay of Bengal. To predict CHL-a over a large spatial extent, satellite-derived remotely sensed data of ocean color features (CHL-a, colored dissolved organic matter, photosynthetically active radiation, particulate organic carbon) and climatic factors (nighttime sea surface temperature, surface absorbed longwave radiation, sea level pressure) from 2003 to 2022 are used to train and test the models. Results obtained from this study have shown the highest concentrations of CHL-a occurred near the Bay's coastal belts and river estuaries. Analysis revealed that aside from photosynthetically active radiation, organic components exhibited a stronger positive relationship with CHL-a than climatic features, which are correlated negatively. Results showed the chosen decision tree methods to all possess higher R2 and lower root mean square error (RMSE) errors. Furthermore, XGBoost outperforms all other models in predicting the geographic distribution of CHL-a. To assess the model efficacy on seasonal basis, a best performing XGBoost model was validated in the Bay of Bengal region which has shown a good performance in predicting the spatial distribution of Chl-a as well as the pixel values during the summer, winter and monsoon seasons. This study provides the best ML model to researchers for predicting CHL-a in the Bay of Bengal. Further it helps to improve our knowledge of CHL-a spatial dynamics and assist in monitoring marine resources in the Bay of Bengal. It worth noting that the water quality in the Indian Ocean is very dynamic in nature, therefore, additional efforts are needed to test the efficacy of this study model over different seasons and spatial gradients.

Improvement in Spatiotemporal Chl-a Data in the South China Sea Using the Random-Forest-Based Geo-Imputation Method and Ocean Dynamics Data

The accurate estimation of the spatial and temporal distribution of chlorophyll-a (Chl-a) concentrations in the South China Sea (SCS) is crucial for understanding marine ecosystem dynamics and water quality assessment. However, the challenge of missing values in satellite-derived Chl-a data has hindered obtaining complete spatiotemporal information. Traditional methods for deriving Chl-a are based on the modeling of measured sensor data and in situ measurements. Spatiotemporal imputation of Chl-a is difficult due to the inaccessibility of the measured Chl-a. In this study, we introduce an innovative approach that incorporates an ocean dynamics dataset and utilizes the random forest algorithm for predicting the Chl-a concentration in the SCS. The method combines the spatiotemporal feature pattern of Chl-a and the main influencing factors, and it introduces ocean dynamics data, which has a high correlation with the spatiotemporal distribution of Chl-a, as the input data through feature engineering. Also, we compared Random Forest (RF) with other Machine Learning (ML) methods. The results show that (1) ocean dynamics datasets can provide important data support for Chl-a imputation by capturing the impact of dynamical processes on ecological roles in the South China Sea. (2) The RF method is the superior imputation method for the reconstruction of Chl-a in the South China Sea, with better model performance and smaller errors. This study provides valuable insight for researchers and practitioners in choosing suitable machine learning methods for the imputation of the Chl-a concentration in the SCS, facilitating a better understanding of the region’s marine ecosystems and supporting effective environmental management.