Retrieval Of Chlorophyll-a Concentration Research Articles

Evaluation of Ocean Color Algorithms to Retrieve Chlorophyll-a Concentration in the Mexican Pacific Ocean off the Baja California Peninsula, Mexico

Mathematical algorithms relate satellite data of ocean color with the surface Chlorophyll-a concentration (Chl-a), a proxy of phytoplankton biomass. These mathematical tools work best when they are adapted to the unique bio-optical properties of a particular oceanic province. Ocean color algorithms should also consider that there are significant differences between datasets derived from different sensors. Common solutions are to provide different parameters for each sensor or use merged satellite data. In this paper, we use satellite data from the Copernicus merged product suite and in situ data from the southernmost part of the California Current System to test two widely used global algorithms, OCx and CI, and a regional algorithm, CalCOFI2. The OCx algorithm yielded the most favorable results. Consequently, we regionalized it and conducted further testing, leading to significant improvements, especially in eutrophic and oligotrophic waters. The database was then separated according to (a) dynamic boundaries in the area, (b) bio-optical properties, and (c) climatic conditions (El Niño/La Niña). Regional algorithms were obtained and tested for each partition. The Chl-a retrievals for each model were tested and compared. The best fit for the data was for the regional algorithms that considered the climatic conditions (El Niño/La Niña). These results will allow for the construction of consistent regionally adapted time series and, therefore, will demonstrate the importance of El Niño/La Niña events on the bio-optical properties of the area.

Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study

This paper analyses a time series of chlorophyll-a profiles in the Adriatic from 1997 to 2019, and compares the data with satellite products with the view of analysing and reducing uncertainties in the corresponding satellite products. Three sources of uncertainties in satellite chlorophyll-a concentration are examined: (a) the algorithm itself; (b) the vertical structure of the water column; and (c) the phytoplankton community structure. Global and regional algorithms were examined, along with a local algorithm tuned using the time series data. The global algorithm produced the largest uncertainties when compared with the in situ data, followed by the regional and local algorithms. Correlation coefficient for the local algorithm was 0.690 - a significant increase from regional’s 0.420 and global’s 0.042. Both the global and the regional algorithms exhibited systemic errors that inversely were related to chlorophyll-a concentration, while the local algorithm displayed some reduction in the systematic errors, highlighting the value of local in situ observations, for improving sub-regional and local algorithms for retrieval of chlorophyll-a concentration from satellite ocean colour data. While the mixed layer has not shown any direct correlation with the uncertainties, it may facilitate exceptionally strong vertical gradients in chlorophyll-a profiles after summer blooms that take role as the main source of high differences between satellite observations and surface chlorophyll-a concentration. As such, it is important to supplement satellite measurements with vertical profiles to ensure valid readings and exercise caution when dealing with data post-blooms. These instances occurred in less than 3% of all cases. Differences in the phytoplankton community structures have shown direct correlation to estimation error - Miozoa is associated with low error, Bacillariophyta with high error, while Phytoflagellates abundance tips the error between underestimation and overestimation.

Bio-optical properties of South Brazil Bight coastal waters and implications for satellite chlorophyll-a concentration retrieval

ABSTRACT Retrieving chlorophyll-a concentration (Chla) from remote-sensing data is especially challenging in coastal regions because of the presence of other co-dominant optically significant constituents (OSCs). In this study, we characterize bio-optical variability at a fixed coastal time series station in the South Brazil Bight (SBB), the ANTARES-Ubatuba site (São Paulo, Brazil) from radiometric measurements and water sample analyses, and evaluate the performance of ocean colour (OC) algorithms for Chla retrieval. In situ Chla and the spectral absorption of phytoplankton (aph), detritus, and coloured dissolved organic matter (CDOM), as well as the above-water remote-sensing reflectance (Rrs) were obtained from 2004 to 2019 at a quasi-monthly sampling frequency. Chla exhibited high variability, with a standard deviation twice as large as the mean (1.0 ± 1.83 mg m−3), and values ranging from 0.18 mg m−3 up to 18.09 mg m−3 during episodic bloom events. CDOM was the dominant OSC contributing to the absorption coefficient at 443 nm all-year round, suggesting the influence of continental sources, even though the station is located at the external limit of the inner shelf (40 m). Two optical water types were defined: one composed mainly of ‘blue’ waters with lower concentrations of the OSCs and another of ‘green’ waters with higher concentrations of OSCs. Despite the CDOM spectral dominance, the empirical OCx performed reasonably well with a positive bias for both in situ (17–25%) and satellite (25–31%) Chla retrievals. The performance was slightly better using the 3-band OC3 algorithm by selecting the best band ratio with lower influence of CDM (CDOM + detritus) absorption (Rrs (490/555)). The main sources of uncertainty were caused by higher CDM proportions and phytoplankton-specific absorption, yielding positive biases on the OC3 retrievals. The results suggest that standard satellite products of Chla can be used (with some caution) to monitor and study the dynamics of the phytoplankton biomass within the region, knowing the expected uncertainties.

TRANSFORMATION OF THE NORMALIZED DIFFERENCE CHLOROPHYLL INDEX TO RETRIEVE CHLOROPHYLL-A CONCENTRATIONS IN MANILA BAY

Abstract. Manila Bay is one of the most significant harbors in the region, enabling commerce and trade between the Philippines and other countries. Its abundant natural resources have provided for generations of its inhabitants and have driven socio-economic development for centuries. Like other water bodies adjacent to highly urbanized cities, the increased organic and nutrient loading from untreated domestic, industrial, and agricultural wastes resulted to further degradation of its water quality. While frequent water quality monitoring is ideal, data from traditional field sampling methods might not be sufficient to assess the spatial and temporal variations of water quality in Manila Bay. Remote sensing fills the need for a frequent and full overview of the bay’s water quality. Sentinel-3 images were initially processed through the Case 2 Regional CoastColour (C2RCC) model to retrieve the remote sensing reflectance. The Normalized Difference Chlorophyll Index (NDCI) was computed for the images and were modeled against the C2RCC-derived chlorophyll-a estimates. From this, we were able to get a general equation (TNDCI – transformed NDCI) to retrieve chlorophyll-a concentrations from two reflectance bands (Oa8 and Oa11). TNDCI gave an R2 of 0.9 and RMSE = 5.12 µg/L as compared to C2RCC values, and an R2 = 0.85 and RMSE = 2.44 µg/L with field data.

Quality control based Chlorophyll-a estimation with two-band and three-band algorithms using Sentinel-2 MSI data in a complex inland lake, China

The estimation of Chlorophyll-a concentration remains challenging for inland waters owing to the optical complexity. Hence, a quality control-based Chlorophyll-a retrieval approach was proposed for the Sentinel-2 multispectral instrument. Two algorithms were selected: 2-band algorithm, which is suitable for extremely turbid waters, and improved 3-band algorithm designed for moderately turbid waters. Two scatter plots between Rrs560/Rrs490 and Rrs412/Rrs443 and between Rrs560/Rrs490 and Rrs560 were generated to determine boundary lines of the quality control test for the two selected algorithms. The quality control test was calibrated using 188 groups of field data collected from Daihai Lake. Results show that the two selected algorithms were considerably improved after the quality control test. The retrieval of Chlorophyll-a concentration was further enhanced by integrating the improved algorithms. The quality control test was also evaluated against twelve Sentinel-2 images. The integrated algorithm was found to be suitable for most areas in Daihai Lake.

Retrieval of Chlorophyll-a Concentrations Using Sentinel-2 MSI Imagery in Lake Chagan Based on Assessments with Machine Learning Models

Chlorophyll-a (Chl-a) is an important characterized parameter of lakes. Monitoring it accurately through remote sensing is thus of great significance for early warnings of water eutrophication. Sentinel Multispectral Imager (MSI) images from May to September between 2020 and 2021 were used along with in-situ measurements to estimate Chl-a in Lake Chagan, which is located in Jilin Province, Northeast China. In this study, the extreme gradient boosting (XGBoost) and Random Forest (RF) models, which had similar performances, were generated by six single bands and six band combinations. The RF model was then selected based on the assessments (R2 = 0.79, RMSE = 2.51 μg L−1, MAPE = 9.86%), since its learning of the input features in the model conformed to the bio-optical properties of Case 2 waters. The study considered Chl-a concentrations in Lake Chagan as a seasonal pattern according to the K-Nearest-Neighbors (KNN) classification. The RF model also showed relatively stable performance for three seasons (spring, summer and autumn) and it was applied to map Chl-a in the whole lake. The research presents a more reliable machine learning (ML) model with higher precision than previous empirical models, as shown by the effects of the input features linked with the biological mechanisms of Chl-a. Its robustness was revealed by the temporal and spatial distributions of Chl-a concentrations, which were consistent with in-situ measurements in the map. This research was capable of revealing the current ecological situation in Lake Chagan and can serve as a reference in remote sensing of inland lakes.

Research on Chlorophyll-a Concentration Retrieval Based on BP Neural Network Model—Case Study of Dianshan Lake, China

The Chlorophyll-a (Chl-a) concentration is an important indicator of water environmental conditions; thus, the simultaneous monitoring of large-area water bodies can be realized through the remote sensing-based retrieval of the Chl-a concentrations. The back propagation (BP) neural network learning method has been widely used for the remote sensing retrieval of water quality in first and second-class water bodies. However, many Chl-a concentration measurements must be used as learning samples with this method, which is constrained by the number of samples, due to the limited time and resources available for simultaneous measurements. In this paper, we conduct correlation analysis between the Chl-a concentration data measured at Dianshan Lake in 2020 and 2021 and synchronized Landat-8 data. Through analysis and study of the radiative transfer model and the retrieval method, a BP neural network retrieval model based on multi-phase Chl-a concentration data is proposed, which allows for the realization of remote sensing-based Chl-a monitoring in third-class water bodies. An analysis of spatiotemporal distribution characteristics was performed, and the method was compared with other constructed models. The research results indicate that the retrieval performance of the proposed BP neural network model is better than that of models constructed using multiple regression analysis and curve estimation analysis approaches, with a coefficient of determination of 0.86 and an average relative error of 19.48%. The spatial and temporal Chl-a distribution over Dianshan Lake was uneven, with high concentrations close to human production and low concentrations in the open areas of the lake. During the period from 2020 to 2021, the Chl-a concentration showed a significant upward trend. These research findings provide reference for monitoring the water environment in Dianshan Lake.

Retrieval of Chlorophyll-a Concentrations of Class II Water Bodies of Inland Lakes and Reservoirs Based on ZY1-02D Satellite Hyperspectral Data

Chlorophyll-a is an important parameter that characterizes the eutrophication of water bodies. The advantage of ZY1-02D hyperspectral satellite subdivision in the visible light and near-infrared bands is that it highlights the unique characteristics of water bodies in the spectral dimension, and it helps to assess the Class II water bodies of inland lakes and reservoirs, making it an important tool for refined remote sensing detection of the environment. In this study, the Baiyangdian Nature Reserve in northern China, which contains a typical inland lake and wetland, was chosen as the study area. Using ZY1-02D hyperspectral synchronization transit images and in situ measured chlorophyll-a concentration as the data source, remote sensing of the chlorophyll-a concentration of inland lakes was conducted. By analyzing the correlation between the spectral reflectance of the ZY1-02D hyperspectral image and the chlorophyll-a concentration and using algorithms such as the single band, band ratio, and three bands to compare and filter characteristic wavelengths, a quantitative hyperspectral model of the chlorophyll-a concentration was established to determine the chlorophyll-a concentration of Baiyangdian Lake. The dynamic monitoring of the water body and the assessment of the nutritional status of the water body were determined. The results revealed that the estimation of the chlorophyll-a concentration of Baiyangdian Lake based on the hyperspectral Fluorescence Line Height (FLH) model was ideal, with an R2 value of 0.78. The FLH model not only comprehensively considers the effects of suspended solids, yellow substances, and backscattering of the water body on the estimation of the chlorophyll-a concentration, but also considers the influence of the elastic scattering efficiency of the chlorophyll. Based on the ZY1-02D hyperspectral data, a spatial distribution map of the chlorophyll-a concentration of Baiyangdian Lake was created to provide new ideas and technical support for monitoring inland water environments.

Remote sensing retrieval of chlorophyll-a concentration in Dianchi lake based on orbita hyperspectral imagery

Remote sensing retrieval of chlorophyll-a concentration in Dianchi lake based on orbita hyperspectral imagery

Retrieval of Chlorophyll-a concentration and associated product uncertainty in optically diverse lakes and reservoirs

Satellite product uncertainty estimates are critical for the further development and evaluation of remote sensing algorithms, as well as for the user community (e.g., modelers, climate scientists, and decision-makers). Optical remote sensing of water quality is affected by significant uncertainties stemming from correction for atmospheric effects as well as a lack of algorithms that can be universally applied to waterbodies spanning several orders of magnitude in non-covarying substance concentrations. We developed a method to produce estimates of Chlorophyll-a (Chla) satellite product uncertainty on a pixel-by-pixel basis within an Optical Water Type (OWT) classification scheme. This scheme helps to dynamically select the most appropriate algorithms for each satellite pixel, whereas the associated uncertainty informs downstream use of the data (e.g., for trend detection or modeling) as well as the future direction of algorithm research. Observations of Chla were related to 13 previously established OWT classes based on their corresponding water-leaving reflectance (Rw), each class corresponding to specific bio-optical characteristics. Uncertainty models corresponding to specific algorithm - OWT combinations for Chla were then expressed as a function of OWT class membership score. Embedding these uncertainty models into a fuzzy OWT classification approach for satellite imagery allows Chla and associated product uncertainty to be estimated without a priori knowledge of the biogeochemical characteristics of a water body. Following blending of Chla algorithm results according to per-pixel fuzzy OWT membership, Chla retrieval shows a generally robust response over a wide range of class memberships, indicating a wide application range (ranging from 0.01 to 362.5 mg/m3). Low OWT membership scores and high product uncertainty identify conditions where optical water types need further exploration, and where biogeochemical satellite retrieval algorithms require further improvement. The procedure is demonstrated here for the Medium Resolution Imaging Spectrometer (MERIS) but could be repeated for other sensors, atmospheric correction methods and optical water quality variables.

Deep Learning for Chlorophyll-a Concentration Retrieval: A Case Study for the Pearl River Estuary

The abundance of phytoplankton is generally estimated by measuring the chlorophyll-a concentration (Cchla), which is an important factor in photosynthesis and can be used to analyze the density and biomass of phytoplankton in the ecosystem. The band-ratio-based empirical or semi-analytical algorithms are operationally applied to retrieve Cchla in global oceans, which generally experience difficulties from the diversity of optical properties and the complexity of the radiative transfer equations in analytical analyses, respectively. With an attempt to develop an accurate Cchla retrieval model for the optically complex coastal and estuarine waters, this study aimed to explore the deep learning (DL) methods in satellite retrieval of Cchla. A two-stage convolutional neural network (CNN), named Cchla-Net, was proposed, which utilized the spectral information of remote sensing reflectances at MODIS/Aqua’s visible bands. In the first-stage phase, the Cchla-Net was pretrained by a set of remote sensing patches, in which the Cchla was generated from an existing model (OC3M). The pretrained results were than used as the initial values to refine the network with the synthetic oversampled in-situ dataset in the second-stage training phase. Using in-situ samples for training with the new initial values has a higher probability to reach the global optimum. The quantitative analyses showed that the two-stage training was more likely to achieve a global optimum in the optimization than the one-stage training. Matchups of the in-situ Cchla measurements were used to evaluate the retrieval models. Results showed that the proposed Cchla-Net produced obvious better performance than the empirical and semi-analytical algorithms, implying the DL method was more effective for optically complex waters with extremely high Cchla. This study provided an applicable method for remote sensing retrieval of Cchla, which should be helpful for studying the spatial distribution and temporal variability in the productive Pearl River estuary (PRE) waters.

Remote sensing inversion modeling of chlorophyll-a concentration in Wuliangsuhai Lake based on BP neural network

Monitoring chlorophyll-a concentration in Wuliangsuhai Lake can effectively grasp the primary production of Wuliangsuhai Lake. The traditional manual sampling monitoring method is time-consuming and laborious, compared with the monitoring method, the monitoring range of chlorophyll-a in lake water by remote sensing is not only wide, long time sequence, but also lower than that of the original. Based on the Landsat 8 remote sensing image data of Wuliangsuhai Lake and the measured data of chlorophyll-a concentration sampling points in Wuliangsuhai Lake, the spectral reflectance of each sampling point was extracted through the longitude and latitude coordinates of each sampling point, the first to fifth band spectral reflectance combination of Landsat 8 remote sensing image data is used as input, and the measured chlorophyll-a concentration is used as output, 26 BP neural network models were constructed to Retrieve Chlorophyll-a Concentration in Wuliangsuhai Lake. Through the verification and analysis of all the models, it is found that the inversion model with spectral reflectance of band 1, 2, 3, 4 and 5 as input has the best performance.

Inversion of Chlorophyll-a Concentration in Donghu Lake Based on Machine Learning Algorithm

Machine learning algorithm, as an important method for numerical modeling, has been widely used for chlorophyll-a concentration inversion modeling. In this work, a variety of models were built by applying five kinds of datasets and adopting back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM). The results revealed that modeling with multi-factor datasets has the possibility to improve the accuracy of inversion model, and seven band combinations are better than seven single bands when modeling, Besides, SVM is more suitable than BPNN and ELM for chlorophyll-a concentration inversion modeling of Donghu Lake. The SVM model based on seven three-band combination dataset (SVM3) is the best inversion one among all multi-factor models that the mean relative error (MRE), mean absolute error (MAE), root mean square error (RMSE) of the SVM model based on single-factor dataset (SF-SVM) are 30.82%, 9.44 μg/L and 12.66 μg/L, respectively. SF-SVM performs best in single-factor models, MRE, MAE, RMSE of SF-SVM are 28.63%, 13.69 μg/L and 16.49 μg/L, respectively. In addition, the simulation effect of SVM3 is better than that of SF-SVM. On the whole, an effective model for retrieving chlorophyll-a concentration has been built based on machine learning algorithm, and our work provides a reliable basis and promotion for exploring accurate and applicable chlorophyll-a inversion model.

Atmospheric correction of Sentinel-3/OLCI data for mapping of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters

The performance of different atmospheric correction algorithms for the Ocean and Land Colour Instrument (OLCI) on board of Sentinel-3 (S3) is evaluated for retrieval of water-leaving radiance reflectance, and derived parameters chlorophyll-a concentration and turbidity in turbid coastal waters in the Belgian Coastal Zone (BCZ). This is performed using in situ measurements from an autonomous pan-and-tilt hyperspectral radiometer system (PANTHYR). The PANTHYR provides validation data for any satellite band between 400 and 900 nm, with the deployment in the BCZ of particular interest due to the wide range of observed Near-InfraRed (NIR) reflectance. The Dark Spectrum Fitting (DSF) atmospheric correction algorithm is adapted for S3/OLCI processing in ACOLITE, and its performance and that of 5 other processing algorithms (L2-WFR, POLYMER, C2RCC, SeaDAS, and SeaDAS-ALT) is compared to the in situ measured reflectances. Water turbidities across the matchups in the Belgian Coastal Zone are about 20–100 FNU, and the overall performance is best for ACOLITE and L2-WFR, with the former providing lowest relative (Mean Absolute Relative Difference, MARD 7–27%) and absolute errors (Mean Average Difference, MAD -0.002, Root Mean Squared Difference, RMSD 0.01–0.016) in the bands between 442 and 681 nm. L2-WFR provides the lowest errors at longer NIR wavelengths (754–885 nm). The algorithms that assume a water reflectance model, i.e. POLYMER and C2RCC, are at present not very suitable for processing imagery over the turbid Belgian coastal waters, with especially the latter introducing problems in the 665 and 709 nm bands, and hence the chlorophyll-a and turbidity retrievals. This may be caused by their internal model and/or training dataset not being well adapted to the waters encountered in the BCZ. The 1020 nm band is used most frequently by ACOLITE/DSF for the estimation of the atmospheric path reflectance (67% of matchups), indicating its usefulness for turbid water atmospheric correction. Turbidity retrieval using a single band algorithm showed good performance for L2-WFR and ACOLITE compared to PANTHYR for e.g. the 709 nm band (MARD 15 and 17%), where their reflectances were also very close to the in situ observations (MARD 11%). For the retrieval of chlorophyll-a, all methods except C2RCC gave similar performance, due to the RedEdge band-ratio algorithm being robust to typical spectrally flat atmospheric correction errors. C2RCC does not retain the spectral relationship in the Red and RedEdge bands, and hence its chlorophyll-a concentration retrieval is not at all reliable in Belgian coastal waters. L2-WFR and ACOLITE show similar performance compared to in situ radiometry, but due to the assumption of spatially consistent aerosols, ACOLITE provides less noisy products. With the superior performance of ACOLITE in the 490–681 nm wavelength range, and smoother output products, it can be recommended for processing of S3/OLCI data in turbid waters similar to those encountered in the BCZ. The ACOLITE processor for OLCI and the in situ matchup dataset used here are made available under an open source license.

Remote sensing retrieval of chlorophyll-a concentration in the coastal waters of Hong Kong based on Landsat-8 OLI and Sentinel-2 MSI sensors

Using Landsat-8 OLI remote sensing data and Sentinel-2 MSI remote sensing data as data sources, selecting the coastal waters of Hong Kong as the study area, using semi-analytical model as the method, selecting the same time as the measured chlorophyll-a concentration at the monitoring point and the cloud coverage rate of remote sensing images two types of remote sensing images with clear images less than 10%. For the two types of remote sensing images, two thirds of the remote sensing image data are selected after preprocessing, and the remote sensing reflectance of the monitoring point location corresponding to the actual date is extracted for correlation analysis, and the most relevant inversion factor is obtained for modeling. Meanwhile the remaining 1/3 of the data is used to test the accuracy of its inversional regression model. The best inversional regression model based on Landsat-8 remote sensing data is Y=6.8x2-20.77x+17.02, R2=0.906 which is slightly higher than the best inversional regression model based on Sentinel-2 remote sensing data Y=-3.345e +05x2+3826x-3.44, R2=0.801, which proves the feasibility of inverting the two types of remote sensing data for the chlorophyll-a concentration in the Hong Kong offshore waters, and the inversional results of the two types of data show that the chlorophyll-a concentration in the internal waters of the Hong Kong offshore waters is higher than the phenomenon of external chlorophyll-a concentration.

Global Ocean Chlorophyll-a Concentrations Derived From COCTS Onboard the HY-1C Satellite and Their Preliminary Evaluation

The Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite was launched on September 7, 2018, and has been providing global multispectral Earth observation data as a new spaceborne sensor for ocean color detection since September 10, 2018. In this study, an atmospheric correction algorithm using a composite of the algorithms developed by Wang and Gordon (1994) and He <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2004) and a chlorophyll-a concentration retrieval method that is a composite of the OC4 and color index (CI) algorithms are used to derive the global chlorophyll-a concentration from COCTS. The retrieval products are validated against <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> data measured in the East China Sea and the South China Sea during the in-orbit testing activity for the HY-1C satellite with an unbiased percentage difference (UPD) of 39%, and the data are also validated against the Aerosol Robotic Network-Ocean Color (AERONET-OC) data with a UPD of 39%. The daily global chlorophyll-a concentration from COCTS with a gridded resolution of 9.2 km and a time period from September 10, 2018 to February 29, 2020 is compared with the same products from MODIS and VIIRS. The UPDs of COCTS against MODIS onboard Terra are approximately 20%, and the mean biases (in logarithm form) are approximately zero. Examples of chlorophyll-a concentration retrieval results in specific cases along the eastern coast of China and the Kuroshio Current are also presented to show the performance of the algorithms used in this study. The retrieval and evaluation results show that COCTS onboard HY-1C demonstrates satisfactory performance for global chlorophyll-a concentration observations.

A single semi-analytical algorithm to retrieve chlorophyll-a concentration in oligo-to-hypereutrophic waters of a tropical reservoir cascade

Previous studies have shown the challenges in using a single model to estimate chlorophyll-a concentration (Chl-a) in water bodies with widely differing characteristics. A single model based on remote sensing to map the Chl-a distribution across the entire Tietê River Cascade System (TRCS) serves as a cost and time-efficient alternative to the conventional monitoring by providing trophic status over space and time. The Tietê River contains one of the largest cascade reservoir systems in the world, which sustains important ecological and socio-economic activities in the São Paulo State, Brazil. Surplus nutrients in water draining its surrounding catchments have been the main cause of eutrophication in the reservoirs of the TRCS. To assess the trophic state of the reservoirs, Chl-a has been regularly monitored by sampling points. However, they are limited by operational costs and dependent on weather conditions. Moreover, the current sampling method only produces point-based measurements. In this paper, we calibrate remote sensing images based on the absorption coefficient to map the spatial distribution patterns of Chl-a levels in the reservoirs. Mapping is done by estimating the Chl-a concentration. The absorption coefficients were retrieved from OLI/Landsat images using the Quasi-Analytical Algorithm (QAA). The total absorption (at) in 482 nm and 655 nm retrieved by QAA presented NRMSE of 17% and 18.5%, respectively. Both at (482 and 655 nm) were used in the model calibration and presented a satisfactory result covering all data ranges, with R2 of 0.646 and NRMSE of 15.3%. The proposed model in this study to retrieve Chl-a maps with relatively high accuracy can be incorporated into the operational monitoring system of the TRCS at a low cost that can provide timely information for reservoir managers to carry out necessary actions. This may include mitigating environmental impacts caused by sudden algae blooms.

Retrieval and mapping of chlorophyll-a concentration from Sentinel-2 images in an urban river in the semiarid region of Brazil

Recently, the Poti river mouth region has experienced environmental impacts that resulted in a change of landscape in its dry season, highlighting the eutrophication and proliferation of phytoplankton, algae, cyanobacteria and aquatic plants. Considering the aspects related to water-quality monitoring in the semiarid region of Brazil from remote sensing, this study aimed to evaluate the performance of Sentinel-2A satellite data in the retrieval of chlorophyll-a concentration in Poti River in Teresina, Piaui, Brazil. The chlorophyll-a concentration retrieval and mapping methodology involved the study of the water surface reflectance in Sentinel-2A images and their correlation with the chlorophyll-a data collected in situ during the years 2016 and 2017. The results generated by the Chl-1, Ha et al. (2017), Chl-2, Page et al. (2018), and Chl-3, Kuhn et al. (2019) equations show the need for calibrating the algorithms used for the Poti River water components. However, the empirical algorithm Chl-2 shows a correlation has been established to identify the spatiotemporal variation of chlorophyll-a concentration along the Poti River broadly and not punctually. The spatial distribution of this pigment in maps derived from Sentinel-2A is consistent with the pattern of occurrence determined by the in situ data. Therefore, the MSI sensor proved to be a tool suitable for the retrieval and monitoring of chlorophyll-a concentration along the Poti River.

OPTMIZATION OF BIO-OPTICAL MODEL PARAMETERS FOR TURBID LAKE WATER QUALITY ESTIMATION USING LANDSAT 8 AND WASI-2D

Abstract. Laguna Lake, the Philippines’ largest freshwater lake, has always been historically, economically, and ecologically significant to the people living near it. However, as it lies at the center of urban development in Metro Manila, it suffers from water quality degradation. Water quality sampling by current field methods is not enough to assess the spatial and temporal variations of water quality in the lake. Regular water quality monitoring is advised, and remote sensing addresses the need for a synchronized and frequent observation and provides an efficient way to obtain bio-optical water quality parameters. Optimization of bio-optical models is done as local parameters change regionally and seasonally, thus requiring calibration. Field spectral measurements and in-situ water quality data taken during simultaneous satellite overpass were used to calibrate the bio-optical modelling tool WASI-2D to get estimates of chlorophyll-a concentration from the corresponding Landsat-8 images. The initial output values for chlorophyll-a concentration, which ranges from 10–40 μg/L, has an RMSE of up to 10 μg/L when compared with in situ data. Further refinements in the initial and constant parameters of the model resulted in an improved chlorophyll-a concentration retrieval from the Landsat-8 images. The outputs provided a chlorophyll-a concentration range from 5–12 μg/L, well within the usual range of measured values in the lake, with an RMSE of 2.28 μg/L compared to in situ data.

Evaluation of Satellite-Based Algorithms to Retrieve Chlorophyll-a Concentration in the Canadian Atlantic and Pacific Oceans

Remote-sensing reflectance data collected by ocean colour satellites are processed using bio-optical algorithms to retrieve biogeochemical properties of the ocean. One such important property is the concentration of chlorophyll-a, an indicator of phytoplankton biomass that serves a multitude of purposes in various ocean science studies. Here, the performance of two generic chlorophyll-a algorithms (i.e., a band ratio one, Ocean Colour X (OCx), and a semi-analytical one, Garver–Siegel Maritorena (GSM)) was assessed against two large in situ datasets of chlorophyll-a concentration collected between 1999 and 2016 in the Northeast Pacific (NEP) and Northwest Atlantic (NWA) for three ocean colour sensors: Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). In addition, new regionally-tuned versions of these two algorithms are presented, which reduced the mean error (mg m−3) of chlorophyll-a concentration modelled by OCx in the NWA from −0.40, −0.58 and −0.45 to 0.037, −0.087 and −0.018 for MODIS, SeaWiFS, and VIIRS respectively, and −0.34 and −0.36 to −0.0055 and −0.17 for SeaWiFS and VIIRS in the NEP. An analysis of the uncertainties in chlorophyll-a concentration retrieval showed a strong seasonal pattern in the NWA, which could be attributed to changes in phytoplankton community composition, but no long-term trends were found for all sensors and regions. It was also found that removing the 443 nm waveband for the OCx algorithms significantly improved the results in the NWA. Overall, GSM performed better than the OCx algorithms in both regions for all three sensors but generated fewer chlorophyll-a retrievals than the OCx algorithms.