Daily Chlorophyll-a Research Articles

Reconstruction of daily chlorophyll-a concentrations in the transit of severe tropical cyclone Hudhud using the ExDINEOF method

Tropical regions experience a diverse range of dense clouds, posing challenges for the daily reconstruction of chlorophyll-a concentration data. This underscores the pressing need for a practical method to reconstruct daily-scale chlorophyll-a concentrations in such regions. While traditional data reconstruction methods focus on single variables and rely on specific factors to infer missing data at specific locations, these single-variable methods may falter when applied to tropical oceans due to the scarcity of available data. Fortunately, all oceanographic variables undergo similar atmospheric and marine dynamic processes, creating internal relationships between them. This allows for the reconstruction of missing data through correlations between variables. Thus, this study introduces a multivariate reconstruction approach using the extended data interpolating empirical orthogonal function (ExDINEOF) method to reconstruct missing daily-scale chlorophyll-a concentration data. The ExDINEOF method considers the simultaneous relationships among multiple variables for data reconstruction in tropical oceans. To verify the method’s robustness, missing data were reconstructed during the formation and passage of severe tropical cyclone Hudhud through the Bay of Bengal. The results demonstrate that ExDINEOF outperforms traditional data reconstruction methods, exhibiting favorable spatial distribution and enhanced accuracy within the dynamic tropical marine environment. Furthermore, an assessment of marine physical environmental factors associated with chlorophyll-a concentration data provides additional evidence for the ExDINEOF method’s accuracy. Notably, the ExDINEOF method offers comprehensive spatial distribution aligned with underlying physical mechanisms governing phytoplankton distribution patterns, detailed phytoplankton growth, bloom, extinction variations in time series, satisfactory accuracy, and comprehensive local-level details.

Automated detection of coastal upwelling in the Western Indian Ocean: Towards an operational “Upwelling Watch” system

Coastal upwelling is an oceanographic process that brings cold, nutrient-rich waters to the ocean surface from depth. These nutrient-rich waters help drive primary productivity which forms the foundation of ecological systems and the fisheries dependent on them. Although coastal upwelling systems of the Western Indian Ocean (WIO) are seasonal (i.e., only present for part of the year) with large variability driving strong fluctuations in fish catch, they sustain food security and livelihoods for millions of people via small-scale (subsistence and artisanal) fisheries. Due to the socio-economic importance of these systems, an "Upwelling Watch" analysis is proposed, for producing updates/alerts on upwelling presence and extremes. We propose a methodology for the detection of coastal upwelling using remotely-sensed daily chlorophyll-a and Sea Surface Temperature (SST) data. An unsupervised machine learning approach, K-means clustering, is used to detect upwelling areas off the Somali coast (WIO), where the Somali upwelling – regarded as the largest in the WIO and the fifth most important upwelling system globally – takes place. This automatic detection approach successfully delineates the upwelling core and surrounds, as well as non-upwelling ocean regions. The technique is shown to be robust with accurate classification of out-of-sample data (i.e., data not used for training the detection model). Once upwelling regions have been identified, the classification of extreme upwelling events was performed using confidence intervals derived from the full remote sensing record. This work has shown promise within the Somali upwelling system with aims to expand it to the rest of the WIO upwellings. This upwelling detection and classification method can aid fisheries management and also provide broader scientific insights into the functioning of these important oceanographic features.

Prediction of daily chlorophyll-a concentration in rivers by water quality parameters using an efficient data-driven model: online sequential extreme learning machine

Prediction of daily chlorophyll-a concentration in rivers by water quality parameters using an efficient data-driven model: online sequential extreme learning machine

Revisiting the Intraseasonal Variability of Chlorophyll-a in the Adjacent Luzon Strait With a New Gap-Filled Remote Sensing Data Set

In the northern South China Sea of western Pacific Ocean during winter, clouds, sun glint, and other factors block optic sensors, leading to a high missing rate and hence a major concern in ocean color products such as the chlorophyll-a (CHL) data. These constraints inhibit the understanding of CHL variabilities at short (< seasonal) scales. Here, we introduce a new gap-filling method to reconstruct data gaps in a daily CHL remote sensing product. We applied discrete cosine transform with penalized least square (DCT-PLS) approach in the adjacent Luzon Strait, yielding a 15-year full-coverage daily 4-km CHL product. Against the cross-validation set and an independent observational data set collected from 34 cruises, evaluations suggest that DCT-PLS has outperformed the widely applied classical data-interpolating empirical orthogonal function (DINEOF) method. Besides, the DCT-PLS method is characterized by more efficient computation. The complete CHL product was analyzed with a particular focus on the intraseasonal (~30–60 days) control on the winter bloom by the Madden-Julian Oscillation (MJO). The MJO’s local signature on the CHL presents asymmetry. The CHL peaks at the late phases of MJO events, which could be explained by the relaxation after the MJO-induced wind strengthening. This gap-filling approach can be promisingly applied in other remote sensing gap-filling problems, which could shed light on the short-term variability of biological and physical dynamics in the ocean.

Satellite-derived global chlorophyll-a anomaly products

We describe our effort to produce the first-ever near-real-time (NRT) global daily chlorophyll-a (Chl-a) anomaly products that can be used to detect and identify algae blooms from satellite ocean color measurements such as those from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20. Specifically, the production of the two Chl-a anomaly products, one for the Chl-a anomaly in difference and another for the Chl-a anomaly ratio (or in its relative difference) compared to the 61-day Chl-a median value from the previous time period (as a Chl-a reference), has been implemented in the daily global NRT satellite data processing. These two Chl-a anomaly products represent the global ocean and coastal/inland water Chl-a abnormal condition for a given location and can provide more complete characteristics for the daily phytoplankton (or algae) biomass status by comparing to the normal condition. Detailed satellite Chl-a anomaly algorithms, the implementation of the algorithms in the routine satellite data processing, and the global data processing procedure are described and discussed. In addition, we provide several specific examples from the VIIRS-measured global daily Chl-a anomaly products to demonstrate various applications in the detection of algae blooms, including the harmful algal blooms (HABs), in various ocean and coastal/inland waters. Some quantitative seasonal and interannual evaluations and characterizations of Chl-a anomalies over global oceans and several coastal/inland waters are also provided and discussed.

On the implementation of a novel data-intelligence model based on extreme learning machine optimized by bat algorithm for estimating daily chlorophyll-a concentration: Case studies of river and lake in USA

Chlorophyll-a is one of the main indicators for water quality (WQ) analysis in environmental monitoring of aquatic ecosystems. WQ degradation is mostly a result of the increase of the concentration of chlorophyll-a in a waterbody, however, proper estimation of daily chlorophyll-a concentration is a complex problem. In this study, the classic extreme learning machine (ELM), group method of data handling (GMDH), random forest (RF), classification and regression tree (CART), and a novel integrated Bat-ELM model (with the bat optimization algorithm) were developed and applied to predict daily chlorophyll-a (Chl-a) concentration in river and lake ecosystems. Input parameters including turbidity (TU), pH, specific conductance (SC), water temperature (TE), and periodicity were applied as the influential elements for estimating daily Chl-a concentration for two different USGS stations. General results based on RMSE values indicated that the Bat-ELM as the most robust model improved the performance of standard ELM, GMDH, RF, and CART models during the testing procedure by 20.7%, 23.9%, 18.3%, and 27.4% in USGS no. 05543010 and 13.8%, 16.8%, 17.5%, and 52.0% in USGS no. 09014050 in terms of the 7th input-combination, respectively. Moreover, the results revealed that periodicity is the most effective input parameter that considered as the last scenario (input combination) on daily chlorophyll-a (Chl-a) concentration.

Merged-LSTM and multistep prediction of daily chlorophyll-a concentration for algal bloom forecast

Algal blooms are significant environmental problems which threaten the water supply system and ecosystem. To manage the problem, the effective forecasting model is necessary, but it is still challenging to predict the algal bloom due to its uncertainty and complexity. To improve the prediction performance, this study proposed the advanced model based on LSTM networks. Merged-LSTM model contains the three parallel LSTM layers and merged layers which is available to use the additional data from the diverse sources without problem in the training process. To predict the chlorophyll-a of target area, data from an additional monitoring station in upstream and auxiliary environmental data were put into parallel layers as well as data from the target area. The prediction result of the proposed model outperforms the existing models, and also shows a better training process with larger data dimensions. The proposed model and its result also suggest that the possibility of prediction of algal bloom with more advanced models and corresponding data sources.

Twenty years of satellite and in situ observations of surface chlorophyll-a from the northern Bay of Biscay to the eastern English Channel. Is the water quality improving?

The variability of the phytoplankton biomass derived from daily chlorophyll-a (Chl-a) satellite images was investigated over the period 1998–2017 in the surface waters of the English Channel and the northern Bay of Biscay. Merged satellite (SeaWiFS-MODIS/Aqua-MERIS-VIIRS) Chl-a was calculated using the OC5 Ifremer algorithm which is optimized for moderately-turbid waters. The seasonal cycle in satellite-derived Chl-a was compared with in situ measurements made at seven coastal stations located in the southern side of the English Channel and in the northern Bay of Biscay. The results firstly showed that the satellite Chl-a product, derived from a suite of space-borne marine reflectance data, is in agreement with the coastal observations. For compliance with the directives of the European Union on water quality, time-series of 6-year moving average of Chl-a were assessed over the region. A clear decline was observed in the mean and 90th percentile of Chl-a at stations located in the mixed waters of the English Channel. The time-series at the stations located in the Bay of Biscay showed yearly fluctuations which correlated well with river discharge, but no overall Chl-a trend was observed. In the English Channel, the shape of the seasonal cycle in Chl-a changed over time. Narrower peaks were observed in spring at the end of the studied period, indicating an earlier limitation by nutrients. Monthly averages of satellite Chl-a, over the periods 1998–2003 and 2012–2017, exhibited spatial and temporal patterns in the evolution of the phytoplankton biomass similar to these observed at the seven coastal stations. Both the in situ and satellite Chl-a times-series showed a decrease in Chl-a in the English Channel in May, June and July. This trend in phytoplankton biomass is correlated with lower river discharges at the end of the period and a constant reduction in the riverine input of phosphorus through improvements in the water quality of the surrounding river catchments.

Assessing the Potential Benefits of the Geostationary Vantage Point for Generating Daily Chlorophyll-a Maps in the Baltic Sea

Currently, observations from low-Earth orbit (LEO) ocean color sensors represent one of the most used tools to study surface optical and biogeochemical properties of the ocean. LEO observations are available at daily temporal resolution, and are often combined into weekly, monthly, seasonal, and annual averages in order to obtain sufficient spatial coverage. Indeed, daily satellite maps of the main oceanic variables (e.g., surface phytoplankton chlorophyll-a) generally have many data gaps, mainly due to clouds, which can be filled using either Optimal Interpolation or the Empirical Orthogonal Functions approach. Such interpolations, however, may introduce large uncertainties in the final product. Here, our goal is to quantify the potential benefits of having high-temporal resolution observations from a geostationary (GEO) ocean color sensor to reduce interpolation errors in the reconstructed hourly and daily chlorophyll-a products. To this aim, we used modeled chlorophyll-a fields from the Copernicus Marine Environment Monitoring Service’s (CMEMS) Baltic Monitoring and Forecasting Centre (BAL MFC) and satellite cloud observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor (on board the geostationary satellite METEOSAT). The sampling of a GEO was thus simulated by combining the hourly chlorophyll fields and clouds masks, then hourly and daily chlorophyll-a products were generated after interpolation from neighboring valid data using the Multi-Channel Singular Spectral Analysis (M-SSA). Two cases are discussed: (i) A reconstruction based on the typical sampling of a LEO and, (ii) a simulation of a GEO sampling with hourly observations. The results show that the root mean square and interpolation bias errors are significantly reduced using hourly observations.

Binning algorithm for high-resolution IRS-P4 OCM chlorophyll image

Daily chlorophyll-a concentration from the Ocean Colour Monitor (OCM) sensor onboard the Indian Remote Sensing satellite (IRS-P4) is used to make weekly and monthly chlorophyll-a concentration maps. The pathwise swath data at 12 noon for every alternate day over the north Indian Ocean (NIO) during February 2004 and February 2005 were used to compare the existing algorithms for binning the data. Atmospherically corrected and geocorrected OCM data were used in the comparative study of three averaging algorithms – arithmetic mean (AVG), geometric mean (GEO) and maximum likelihood estimator (MLE). The analysis shows that the AVG algorithm is best suited when compared with the two other algorithms. However, for case 1 water, MLE gives nearly the same value as AVG. Based on this result, AVG was selected for operational weekly and monthly averaging of OCM data over the NIO. These high-resolution-derived chlorophyll-a weekly and monthly products will be useful to resolve inter-annual-to-decadal changes in chlorophyll-a concentration over the NIO.

Phytoplankton Variability in the Bay of Bengal During Winter Monsoon Using Oceansat-1 Ocean Colour Monitor Data

Quantitative assessment of chlorophyll-a concentration and its variability is an important input for the oceanic primary productivity modeling and also a key parameter in the global carbon cycle studies. This present work is focused to understand the spatial and temporal variability of phytoplankton in the Bay of Bengal (BOB) during winter monsoon season of October 1999 to March 2000 using Ocean Colour Monitor (OCM) sensor onboard OCEANSAT-1 satellite. Daily chlorophyll-a images from OCM sensor were used in the study. Efforts were also put to study the correlation between chlorophyll-a concentrations; NOAA-AVHRR derived Sea Surface Temperature (SST) and QuickSCAT scatterometer derived wind stress data. Analysis of the chlorophyll-a images shows the presence of extensive phytoplankton blooms during mid December 1999 to early January 2000 in the western part of BOB. The bloom dominated regions also exhibit reduced SST (∼24–27°C) and enhanced wind stress indicating upwelling processes leading nutrient entrainment in the upper column of the sea surface. Apart from this, higher phytoplankton biomass associated with the fresh water reverine plumes has also been observed. During October 1999 a super cyclone was active in the BOB, as increase in the productivity was observed in the early November 1999 images of OCM data due to the cyclone induced churning of the water column.