Retrieve Chl-a Concentration Research Articles

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.

Optimization of convolutional neural network with dual attention mechanism: Estimation of chlorophyll-a concentration in the Taiwan Strait using MODIS data

Chlorophyll-a (Chl-a) concentration plays a crucial role in monitoring marine phytoplankton, holding significant implications for marine ecosystems and human livelihoods. Remote sensing serves as a valuable method for directly estimating Chl-a concentration and facilitating marine monitoring activities. In order to accurately estimate Chl-a concentration and effectively monitor the marine ecological environment of the Taiwan Strait, this paper introduces a novel neural network model called Convolutional Neural Network with Dual Attention Mechanism Optimization (CNN-CBAM). Additionally, the spectral bands derived from MODIS 500 m imagery are subjected to various processing techniques to generate four distinct sets of feature data. These datasets are subsequently merged with in-situ measurements of Chl-a concentration obtained from buoy stations. Given the limited availability of buoy stations, the temporal continuity of data is leveraged to overcome the spatial distribution constraints. This enables the estimation of Chl-a concentration at various time intervals within the research area of the Taiwan Strait. To evaluate the performance of the model, metrics such as root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 are employed. The results demonstrate that the CNN-CBAM model, utilizing MODIS temporal data, effectively retrieves Chl-a concentration in the research area of the Taiwan Strait with high accuracy. For the CNN-CBAM model, the RMSE is 0.34 μg L−1, MAPE is 22%, and R2 is 0.86. In comparison, the traditional empirical model using the Band Ratio (BR) model has an RMSE of 0.61 μg L−1, MAPE of 57%, and R2 of 0.31. The results indicate that the CNN-CBAM model has improved R2 by 0.55 compared to the traditional BR model. Additionally, the RMSE and MAPE have decreased by 0.27 μg L−1 and 35%, respectively. The superiority of the CNN-CBAM model over the traditional BR model is evident, showcasing a substantial enhancement in the accuracy of Chl-a concentration retrieval. Given the challenges posed by the intricate and ever-changing marine environment, particularly the scarcity of in-situ buoy stations, this study offers a practical solution for monitoring Chl-a concentration. It establishes a framework for leveraging satellite-based multispectral sensors to enable large-scale and multi-temporal remote sensing retrieval of Chl-a concentration in the research area of the Taiwan Strait.

Retrieving Lake Chla concentration from remote Sensing: Sampling time matters

Remote sensing is a promising technology for global water eutrophication monitoring, and the quantity and representativeness of the observed samples influence its accuracy. Due to limitations in high costs in situ monitoring, current remote sensing of lakes often relies on models trained with water quality samples from a single season. However, the generalization ability of these single-season trained models to other seasons has not been extensively studied. In this study, focusing on 38 major lakes in the Yangtze River Basin, we utilized monthly chlorophyll-a (Chla) monitoring data and Sentinel-2 image datasets from 2016 to 2021 to assess the impact of different seasonal data-trained models on the performance of Chla concentration retrieval in lakes. The results indicate that: (a) The sampling time for lake Chla significantly affects the performance of the retrieval model. Based on the seasonal data, the trained models show an R2 range of 0.523 to 0.699, with a Bias ranging from −7.06 % to 7.74 %, while the models trained with full-season samples perform between the various seasonal models. (b) When models built with seasonal data are applied to full-season data retrieval, there is a noticeable decrease in performance. The R2 of the spring-winter data trained model drops by 18.2 %, with RMSL and MAPE increasing by 11.4 % and 4.2 %, respectively. Similarly, the model trained from summer-autumn data shows a 4.02 % decrease in R2, with RMSLE and MAPE rising by 2.76 % and 13.56 %, respectively. This suggests that using models established from seasonal samples to infer full-season Chla concentrations can amplify errors. (c) Compared to seasonal models, full-season models based on different seasonal sampling exhibit better performance (R2 = 0.585, RMSLE = 0.337, Bias = -3.12 %, MAPE = 34.71 %). R2, RMSLE, and MAPE are all superior to seasonal models. Therefore, when performing full-time and long-term retrieval of Chla concentrations in water bodies, it is necessary to use data sampled in different seasons to reduce the errors introduced by seasonal sampling. (d) Chla retrieval models were established for 38 typical lakes and reservoirs in the Yangtze River Basin using the full-time dataset, revealing that 25 % (7/28) of the lakes and 20 % (2/10) of the reservoirs showed significant alteration trends. The number of lakes with increasing Chla concentrations exceeded those with decreasing concentrations, indicating that there is still a substantial risk of eutrophication in the lakes and reservoirs of the Yangtze River Basin.

A Novel Algorithm for the Retrieval of Chlorophyll a in Marine Environments Using Deep Learning

Chlorophyll a (Chla) is a crucial pigment in phytoplankton, playing a vital role in determining phytoplankton biomass and water nutrient status. However, in optically complex water bodies, Chla concentration is no longer the primary factor influencing remote sensing spectral reflectance signals, leading to significant errors in traditional Chla concentration estimation methods. With advancements in in situ measurements, synchronized satellite data, and computer technology, machine learning algorithms have become popular in Chla concentration retrieval. Nevertheless, when using machine learning methods to estimate Chla concentration, abrupt changes in Chla values can disrupt the spatiotemporal smoothness of the retrieval results. Therefore, this study proposes a two-stage approach to enhance the accuracy of Chla concentration estimation in optically complex water bodies. In the first stage, a one-dimensional convolutional neural network (1D CNN) is employed for precise Chla retrieval, and in the second stage, the regression layer of the 1DCNN is replaced with support vector regression (SVR). The research findings are as follows: (1) In the first stage, the performance metrics (R2, RMSE, RMLSE, Bias, MAE) of the 1D CNN outperform state-of-the-art algorithms (OCI, SVR, RFR) on the test dataset. (2) After the second stage, the performance further improves, with the metrics achieving values of 0.892, 11.243, 0.052, 1.056, and 1.444, respectively. (3) In mid- to high-latitude regions, the inversion performance of 1D CNN\SVR is superior to other algorithms, exhibiting richer details and higher noise tolerance in nearshore areas. (4) 1D CNN\SVR demonstrates high inversion capabilities in water bodies with medium-to-high nutrient levels.

A novel chlorophyll-a retrieval model based on suspended particulate matter classification and different machine learning

Chlorophyll-a (Chla) in inland waters is one of the most significant optical parameters of aquatic ecosystem assessment, and long-term and daily Chla concentration monitoring has the potential to facilitate in early warning of algal blooms. MOD09 products have multiple observation advantages (higher temporal, spatial resolution and signal-to-noise ratio), and play an extremely important role in the remote sensing of water color. For developing a high accuracy machine learning model of remotely estimating Chla concentration in inland waters based on MOD09 products, this study proposed an assumption that the accuracy of Chla concentration retrieval will be improved after classifying water bodies into three groups by suspended particulate matter (SPM) concentration. A total of 10 commonly used machine learning models were compared and evaluated in this study, including random forest regressor (RFR), deep neural networks (DNN), extreme gradient boosting (XGBoost), and convolutional neural network (CNN). Altogether, 41 basic bands and 820 band ratios between the 41 bands were filtered by measuring their correlation with Ln(Chla) and several bands brought into different machine learning models. Results demonstrated that the construction of Chla concentration remote estimation model based on SPM classification could significantly improve the correlation between Ln(Chla) and 41 basic spectral band combinations, the correlation between Ln(Chla) and 820 band ratios, and the model verification R2 from 0.41 to 0.83. Furthermore, B3, B20, and B32 were finally selected based on correlation with SPM to classify SPM and the classification accuracy could reach 0.9. Finally, we concluded that RFR model performed best via comparing the R2, RMSE, and MAPE. By comparing the relative contribution of input bands in different groups, B3 contributed most to three groups. The model constructed in this study has promising prospects for promotion and application in other inland waters, and could provide systematic research reference for subsequent research.

Remote sensing-based spatiotemporal variation and driving factor assessment of chlorophyll-a concentrations in China’s Pearl River Estuary

Climate change and intensive anthropogenic activities have severely challenged the water quality of China’s Pearl River Estuary (PRE). Further investigations into long-term water quality variation and associated driving mechanisms are therefore necessary to support the sustainable development of the PRE’s Greater Bay Area (GBA). This study used remote sensing retrieval to address long-term spatiotemporal chlorophyll-a (Chl-a) variation characteristics in the PRE and the relationship between Chl-a concentrations and socioeconomic/environmental indicators. Three decades of Landsat satellite images and measured data were collected, and a two-band global algorithm was used to retrieve Chl-a concentration data. Results reveal significant spatiotemporal variability in Chl-a concentrations. The space-averaged Chl-a concentration exhibited a slight downward trend during the past three decades, and the multi-year mean value was 5.20 mg/L. Changes to environmental protection policies in recent years have improved overall PRE water quality. The western section of the PRE had the highest Chl-a concentration (i.e., 5.92 mg/L average) while the eastern section had the lowest (i.e., 3.98 mg/L average). This discrepancy was likely caused by the western section’s more intensive industrial activities, resulting in a higher overall wastewater discharge volume. Affected by climatic conditions, winter Chl-a concentrations were evenly distributed while summer concentrations were significantly higher. Additionally, Chl-a concentrations significantly and positively correlated with total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH3-N), and the biotic oxygen demand (BOD5). Chl-a concentrations also correlated with external factors (i.e., climate and anthropogenic activities). Among these factors, industrial wastewater discharge and the proportion of primary industries in coastal cities significantly and positively correlated with water quality. This study is intended to help direct water quality improvement management and urban sustainable development in the GBA.

Assessment of Estimated Phycocyanin and Chlorophyll-a Concentration from PRISMA and OLCI in Brazilian Inland Waters: A Comparison between Semi-Analytical and Machine Learning Algorithms

The aim of this work is to test the state-of-the-art of water constituent retrieval algorithms for phycocyanin (PC) and chlorophyll-a (chl-a) concentrations in Brazilian reservoirs from hyperspectral PRISMA images and concurrent in situ data. One near-coincident Sentinel-3 OLCI dataset has also been considered for PC mapping as its high revisit time is a relevant element for mapping cyanobacterial blooms. The testing was first performed on remote sensing reflectance (Rrs), as derived by applying two atmospheric correction methods (6SV, ACOLITE) to Level 1 data and as provided in the corresponding Level 2 products (PRISMA L2C and OLCI L2-WFR). Since PRISMA images were affected by sun glint, the testing of three de-glint models was also performed. The applicability of Semi-Analytical (SA) and Mixture Density Network (MDN) algorithms in enabling PC and chl-a concentration retrieval was then tested over three PRISMA scenes; in the case of PC concentration estimation, a Random Forest (RF) algorithm was further applied. Regarding OLCI, the SA algorithm was tested for PC estimation; notably, only SA was calibrated with site-specific data from the reservoir. The algorithms were applied to the Rrs spectra provided by PRISMA L2C products—and those derived with ACOLITE, in the case of OLCI—as these data showed better agreement with in situ measurements. The SA model provided low median absolute error (MdAE) for PRISMA-derived (MdAE = 3.06 mg.m−3) and OLCI-derived (MdAE = 3.93 mg.m−3) PC concentrations, while it overestimated PRISMA-derived chl-a (MdAE = 42.11 mg.m−3). The RF model for PC applied to PRISMA performed slightly worse than SA (MdAE = 5.21 mg.m−3). The MDN showed a rather different performance, with higher errors for PC (MdAE = 40.94 mg.m−3) and lower error for chl-a (MdAE = 23.21 mg.m−3). The results overall suggest that the model calibrated with site-specific measurements performed better and indicates that SA could be applied to PRISMA and OLCI for remote sensing of PC in Brazilian reservoirs.

Application of a PLS-Augmented ANN Model for Retrieving Chlorophyll-a from Hyperspectral Data in Case 2 Waters of the Western Basin of Lake Erie

We present results that demonstrate the utility of machine learning techniques that are based on partial least squares (PLS) and artificial neural networks (ANNs) for estimating low-moderate chlorophyll-a (chl-a) concentrations in the western basin of Lake Erie (WBLE). Previous ocean color studies have resulted in a large number of algorithms that are based on spectral indices to estimate water quality parameters (WQPs) such as chl-a concentration from remote sensing reflectance. However, these spectral index algorithms are based on reflectance features at specific wavelengths and do not take advantage of the wealth of spectral information that is contained in hyperspectral data, and are often not easily adaptable to waters with conditions that are different from those in the datasets that were used to originally calibrate the indices. Recently, there have been efforts to use machine learning techniques that are based on ANNs and PLS regression to exploit the spectral richness contained in hyperspectral data and retrieve WQPs. In this study, we have combined an ANN model with output from PLS regression to retrieve chl-a concentration from hyperspectral data in the WBLE. We compared the results from the PLS-ANN method to those that were obtained from a band-ratio algorithm that is based on reflectances in the blue and green spectral regions, a band ratio algorithm that is based on reflectances in the red and near-infrared (NIR) spectral regions, and a PLS-only approach. For a dataset that was collected in 2012, with chl-a concentrations ranging from 0.48 to 21.2 µg/L, the PLS-ANN method yielded a root mean square error (RMSE) of 1.22 µg/L, whereas the blue-green ratio algorithm yielded an RMSE of 1.75 µg/L, the NIR-red ratio algorithm yielded an RMSE of 1.95 µg/L, and the PLS-only approach yielded an RMSE of 1.95 µg/L. The PLS-ANN method takes advantage of the PLS regression to identify specific wavelengths that contain most information about the variation in chl-a concentration, minimize spectral collinearity and redundancy in the data, and simplify the neural network’s input structure. The better performance of the PLS-ANN method can also be attributed to the neural network’s ability to account for nonlinearity in the relationship between chl-a concentration and spectral reflectance. The results indicate that the PLS-ANN method can be reliably used to estimate and monitor low-moderate chl-a concentrations in optically complex waters.

Improvement and Assessment of Ocean Color Algorithms in the Northwest Pacific Fishing Ground Using Himawari-8, MODIS-Aqua, and VIIRS-SNPP

Chlorophyll-a (Chl-a) is an important marine indicator, and the improvement in Chl-a concentration retrieval for ocean color remote sensing is always a major challenge. This study focuses on the northwest Pacific fishing ground (NPFG) to evaluate and improve the Chl-a products of three mainstream remote sensing satellites, Himawari-8, MODIS-Aqua, and VIIRS-SNPP. We analyzed in situ data and found that an in situ Chl-a concentration of 0.3 mg m−3 could be used as a threshold to distinguish the systematic deviation of remote sensing Chl-a data in the NPFG. Based on this threshold, we optimized the Chl-a algorithms of the three satellites by data grouping, and integrated multisource satellite Chl-a data by weighted averaging to acquire high-coverage merged data. The merged data were thoroughly verified by Argo Chl-a data. The Chl-a front of merged Chl-a data could be represented accurately and completely and had a good correlation with the distribution of the NPFG. The most important marine factors for Chl-a are nutrients and temperature, which are affected by mesoscale eddies and variations in the Kuroshio extension. The variation trend of merged Chl-a data is consistent with mesoscale eddies and Kuroshio extension and has more sensitive responses to the marine climatic conditions of ENSO.

Spatiotemporal Change of Chl-a Concentration in Jakarta Bay using Sentinel-2 Multispectral Imageries

Jakarta Bay is the estuary of 13 rivers that flows through Jakarta Metropolitan areas. Consequently, nutrients and pollutants accumulate in Jakarta Bay and lead to eutrophication. Therefore, monitoring the water quality of Jakarta Bay is essential to support integrated river coastal management. Chlorophyll a (Chl-a) concentration is one of the essential parameters in describing water environment conditions. For large and dynamic areas, Chl-a concentration distribution monitoring requires multi-temporal remote sensing data. This study examines remote sensing data and methods combined with on-site measurement to get more accurate and effective Chl-a spatiotemporal analysis approaches. We estimate Chl-a concentration of Jakarta Bay during 2016-2021 using selected Sentinel-2A and Sentinel-2B images. We conducted an atmospheric correction using a C2RCC (Case 2 Regional Coast Colour) processor and obtained Chl-a concentration from the embedded Neural Network model. The results showed that the model could retrieve Chl-a concentration with an R2 of 0.79 and the root mean square error of 31.75 mg/m3. Time-series results reveal an increase of maximum Chl-a concentration during 2016-2021. Our remotely sensed estimation results well-captured the change of temporal and spatial of Chl- a concentration in the Jakarta Bay. It provides the public and policymakers valuable information to support integrated river coastal sustainable management of Jakarta Metropolitan

Retrieval and Evaluation of Chlorophyll-A Spatiotemporal Variability Using GF-1 Imagery: Case Study of Qinzhou Bay, China

Chlorophyll-a (Chl-a) concentration is a measure of phytoplankton biomass, and has been used to identify ‘red tide’ events. However, nearshore waters are optically complex, making the accurate determination of the chlorophyll-a concentration challenging. Therefore, in this study, a typical area affected by the Phaeocystis ‘red tide’ bloom, Qinzhou Bay, was selected as the study area. Based on the Gaofen-1 remote sensing satellite image and water quality monitoring data, the sensitive bands and band combinations of the nearshore Chl-a concentration of Qinzhou Bay were screened, and a Qinzhou Bay Chl-a retrieval model was constructed through stepwise regression analysis. The main conclusions of this work are as follows: (1) The Chl-a concentration retrieval regression model based on 1/B4 (near-infrared band (NIR)) has the best accuracy (R2 = 0.67, root-mean-square-error = 0.70 μg/L, and mean absolute percentage error = 0.23) for the remote sensing of Chl-a concentration in Qinzhou Bay. (2) The spatiotemporal distribution of Chl-a in Qinzhou Bay is varied, with lower concentrations (0.50 μg/L) observed near the shore and higher concentrations (6.70 μg/L) observed offshore, with a gradual decreasing trend over time (−0.8).

Bio-optical studies on chlorophyll-a concentration in Hooghly River, India

The concentration of chlorophyll-a in the aquatic environment is a good indicator to understand its tropic status and also used to monitor the biogeochemical parameter. The aim of this study is to identify the spectral signature of Hooghly River, India using Satlantic hyperspectral ocean colour radiometer and developing the regional algorithm for retrieval for chlorophyll-a concentration. This approach is based on the development of an empirical band ratio algorithm. The calibrated results between the in-situ chlorophyll-a and in-situ remote sensing reflectance show a good correlation with R2 = 0.77. In-situ and satellite validation also gives better accuracy (R2 = 0.78&0.73) in the estimation of chlorophyll-a concentration. So the satellite-based approach provides a good correlation with in-situ data. This study was helped in monitoring and retrieval of chl-a concentration in the Hooghly River, Kolkata.

Ocean Chlorophyll-a retrieval using GF1-WFV data-a case study of the central Bohai Sea

Chlorophyll-a (Chl-a) concentration is an important indicator of phytoplankton bloom and water eutrophication. It is also a basic parameter of water quality. Remote sensing technology has been widely used for water quality monitoring in the past. In this study, the Chinese GF1 (Gaofen-1) WFV (Wide Field of View) images acquired on May 19th, 2016 were used to retrieve Chl-a concentration of central Bohai sea in order to assess the recovery condition of marine ecosystem which has been seriously polluted by the Penglai 19-3 platform oil spill accident in 2011. The remote sensing model of Chl-a concentration was developed using the linear regression method. The leave-one-out cross-validation (LOOCV) method showed that the average relative error and root mean square error (RMSE) between retrieved Chl-a concentration and in situ measurement was 11.67% and 0.1808, respectively. It indicated that the remote sensing model could correctly reflect the Chl-a concentration and its spatial distribution of the study area. The average Chl-a concentration in the buffer regions with the distance of 2km, 4km, 6km to the Penglai 19-3 platform was 0.32μg/L, 0.37μg/L and 0.40μg/L, respectively. They were in normal range and lower than that of the coastal waters. The spatial distribution of the result was similar to previous studies of Bohai sea in spring. These results indicated that Chl-a concentration of central Bohai sea was returned to normal after oil spill accident occurred for 5 years. The growth of phytoplankton in surface seawater has been returned to normal. The major causes for Chl-a concentration in the Bohai Sea was the land-sourced pollutant during the study period.

Remote Sensing Estimation of Chlorophyll-A in Case-II Waters of Coastal Areas: Three-Band Model Versus Genetic Algorithm–Artificial Neural Networks Model

Chlorophyll-a (Chl-a), an important indicator of phytoplankton biomass and eutrophication, is sensitive to water constitutes and optical characteristics. An integrated machine learning method of genetic algorithm and artificial neural networks (GA–ANN) was developed to retrieve the concentration of Chl-a. In situ spectra and simultaneous water quality parameters of 107 samples from two reservoirs (Res) and coastal waters (CW) were used to calibrate GA–ANN and three-band models (TBM) for comparison of Chl-a estimation. Both GA–ANN and TBM methods perform well for the joint dataset (WGD) of Res and CW with the R 2 exceeding 0.90, and the root mean square error (RMSE) of corresponding validation ( N = 35) are 4.40 and 5.23 μ g/L, respectively. Similarly, for independent dataset of Res ( N = 45), GA–ANN and TBM methods show robust performance: the R 2 values are 0.87 and 0.80, respectively; and the corresponding RMSE values are 7.79 and 7.73 μ g/L, respectively. For CW dataset ( N = 62), the R 2 values of two methods are 0.81 and 0.62, respectively; and the corresponding RMSE values are 0.79 and 1.32 μ g/L, respectively. When the GA–ANN and TBM models were applied to retrieve Chl-a concentration from the calibrated Sentinel 2 MSI reflectance data in two Res on October 20, 2019, however, the validated results of MSI-derived Chl-a concentrations using quasi-synchronous in situ data ( N = 36) indicated that the GA–ANN model outperforms TBM with higher R 2 value (0.91 vs. 0.26) and smaller RMSE (4.41 vs. 13.85 μ g/L) and mean absolute errors (3.40 vs. 11.87 μ g/L) values. Although TBM has obvious overestimation of Chl-a concentration when applied to remote sensing image, we still thought that both GA–ANN and TBM are useful methods for Chl-a estimation in case-II waters, and GA–ANN performs marginally better with less deviation to measured Chl-a for multispectral remote sensing data. The ratio of TSS to Chl-a, experimental measurements, abundance of sampling points, and Chl-a concentration range are several important factors affecting the accuracy and robustness of GA–ANN and TBM methods.

Spatial Variability Assessment on The High-resolution Chlorophyll-a Extraction from Landsat 8and Sentinel 2 Imageries in Johor Waters

High resolution Chlorophyll-a (Chl-a) can indicate the trophic status of the water and provide useful information on optical features of water body in water quality monitoring. Remote sensing has great potential to offer the spatial and temporal coverage needed. Over the last decades the Sea WIFS and MODIS were applied, but not suitable due to the low spatial resolution for monitoring Chl-a in coastal area. However, the retrieval of Chl-a in the coastal region is usually challenging due to the other in-water substances regardless of Chl-a, hence resulting in the satellite retrieved Chl-a overestimation. By the advancement of the Sentinel-2 and Landsat 8 satellites, continuous high resolution optical imageries have served for remarkable coastal mapping capability thanks to the spectroscopic capability certain spectral bands and as high as 10-meter spatial resolution. This paper aims to evaluate the performance of the SEADASS and SNAP processor for Chl-a estimation from the Operational Land Imager (OLI)and MultiSpectral Instrument(MSI) data in Johor waters. The representative models, in standard algorithm OC3and C2RCC, were adapted to retrieve Chl-a concentration. The statistical regression has shown that these algorithms give an acceptable Chl-a estimation at medium and high resolution with R2=0.44 from OC3and R2=0.55from C2RCC comparing to the in-situ data. Despite of the spatial, temporal and spectral variability, this paper shows that OLI and MSI could provide Chl-a mapping capability at suitable Chl-a estimation techniques.

A Semi-Empirical Chlorophyll-a Retrieval Algorithm Considering the Effects of Sun Glint, Bottom Reflectance, and Non-Algal Particles in the Optically Shallow Water Zones of Sanya Bay Using SPOT6 Data

Chlorophyll-a (Chl-a) concentration retrieval is essential for water quality monitoring, aquaculture, and guiding coastline infrastructure construction. Compared with common ocean color satellites, land observation satellites have the advantage of a higher resolution and more data sources for retrieving the concentration of Chl-a from optically shallow waters. However, the sun glint (Rsg), bottom reflectance (Rb), and non-algal particle (NAP) derived from terrigenous matter affect the accuracy of Chl-a concentration retrieval using land observation satellite image data. In this paper, we propose a semi-empirical algorithm based on the remote sensing reflectance (Rrs) of SPOT6 to retrieve the Chl-a concentration in Sanya Bay (SYB), considering the effect of Rsg, Rb, and NAP. In this semi-empirical algorithm, the Cox–Munk anisotropic model and radiative transfer model (RTM) were used to reduce the effects of Rsg and Rb on Rrs, and the Chl-a concentration was retrieved by the Chl-a absorption coefficient at 490 nm (aphy(490)) to remove the effect of NAP. The semi-empirical algorithm was in the form of Chl-a = 43.3[aphy(490)]1.454, where aphy (490) was calculated by the total absorption coefficient and the absorption coefficients of each component by empirical algorithms. The results of the Chl-a concentration retrieval show the following: (1) SPOT6 data are available for Chl-a retrieval using this semi-empirical algorithm in oligotrophic or mesotrophic coastal waters, and the accuracy of the algorithm can be improved by removing the effects of Rsg, Rb, and NAP (R2 from 0.71 to 0.93 and root mean square error (RMSE) from 0.23 to 0.11 ug/L); (2) empirical algorithms based on the blue-green band are suitable for oligotrophic or mesotrophic coastal waters, and the algorithm based on the blue-green band difference Chl-a index (DCI) has stronger anti-interference in terms of the effects of sun glint and bottom reflectance than the algorithm based on the blue-green ratio (BGr); (3) in the case of ignoring Rsg unrelated to inherent optical properties (IOPs), NAP is the biggest interference factor when >9.5 mg/L and the effect of bottom reflectance should be considered when the water depth (H) <5 m in SYB; and (4) the inherent optical properties of the waters in SYB are dominated by NAP (Chl-a = 0.2–2.6 ug/L and NAP = 2.2–30.1 mg/L), and the nutrients are concentrated by enclosed terrain and southeast current. This semi-empirical algorithm for Chl-a concentration retrieval has the potential to monitor Chl-a in oligotrophic and mesotrophic coastal waters using other land observation satellites (e.g., Landsat8 OLI, ASTER, and GaoFen2).

WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval

The retrieval of chlorophyll-a (Chl-a) concentrations relies on empirical or analytical analyses, which generally experience difficulties from the diversity of inland waters in statistical analyses and the complexity of radiative transfer equations in analytical analyses, respectively. Previous studies proposed the utilization of artificial neural networks (ANNs) to alleviate these problems. However, ANNs do not consider the problem of insufficient in situ samples during model training, and they do not fully utilize the spatial and spectral information of remote sensing images in neural networks. In this study, a two-stage training is introduced to address the problem regarding sample insufficiency. The neural network is pretrained using the samples derived from an existing Chl-a concentration model in the first stage, and the pretrained model is refined with in situ samples in the second stage. A novel convolutional neural network for Chl-a concentration retrieval called WaterNet is proposed which utilizes both spectral and spatial information of remote sensing images. In addition, an end-to-end structure that integrates feature extraction, band expansion, and Chl-a estimation into the neural network leads to an efficient and effective Chl-a concentration retrieval. In experiments, Sentinel-3 images with the same acquisition days of in situ measurements over Laguna Lake in the Philippines were used to train and evaluate WaterNet. The quantitative analyses show that the two-stage training is more likely than the one-stage training to reach the global optimum in the optimization, and WaterNet with two-stage training outperforms, in terms of estimation accuracy, related ANN-based and band-combination-based Chl-a concentration models.

CHLOROPHYLL-A CONCENTRATION RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS IN LAGUNA LAKE, PHILIPPINES

Abstract. Two existing chlorophyll-a (chl-a) concentration retrieval procedures, which are analytical and empirical, are hindered by the complexity in radiative transfer equation (RTE) and in statistical analyses, respectively. Another promising model in this direction is the use of artificial neural networks (ANN). Mostly, a pixel-to-pixel with one-layer ANN model is used; where in fact that the satellite instrumental errors and man-made objects in water bodies might affect the retrieval and should be taken into account. In this study, the mask-based neural structure, called convolutional neural networks (CNN) model containing both the target and neighborhood pixels, is proposed to reduce the influence of the aforementioned premises. The proposed model is an end-to-end multiple-layer model which integrates band expansion, feature extraction, and chl-a estimation into the structure, leading to an optimal chl-a concentration retrieval. In addition to that, a two-stage training is also proposed to solve the problem of insufficient in-situ samples which happens in most of the time. In the first stage, the proposed model is trained by using the chl-a concentration derived from the water product, provided by satellite agency, and is refined with the in-situ samples in the second stage. Eight Sentinel-3 images from different acquisition time and coincide in-situ measurements over Laguna Lake waters of Philippines were utilized to conduct the model training and testing. Based on quantitative accuracy assessment, the proposed method outperformed the existing dual- and triple- bands combinations in chl-a concentration retrieval.

Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI

The monitoring of Chlorophyll-a (Chl-a) concentration in high northern latitude waters has been receiving increased focus due to the rapid environmental changes in the sub-Arctic, Arctic. Spaceborne optical instruments allow the continuous monitoring of the occurrence, distribution, and amount of Chl-a. In recent years, the Ocean and Land Color Instruments (OLCI) onboard the Sentinel 3 (S3) A and B satellites were launched, which provide data about various aquatic environments on advantageous spatial, spectral, and temporal resolutions with high SNR. Although S3 OLCI could be favorable to monitor high northern latitude waters, there have been several challenges related to Chl-a concentration retrieval in these waters due to their unique optical properties coupled with challenging environments including high sun zenith angle, presence of sea ice, and frequent cloud covers. In this work, we aim to overcome these difficulties by developing a machine-learning (ML) approach designed to estimate Chl-a concentration from S3 OLCI data in high northern latitude optically complex waters. The ML model is optimized and requires only three S3 OLCI bands, reflecting the physical characteristic of Chl-a as input in the regression process to estimate Chl-a concentration with improved accuracy in terms of the bias (five times improvements.) The ML model was optimized on data from Arctic, coastal, and open waters, and showed promising performance. Finally, we present the performance of the optimized ML approach by computing Chl-a maps and corresponding certainty maps in highly complex sub-Arctic and Arctic waters. We show how these certainty maps can be used as a support to understand possible radiometric calibration issues in the retrieval of Level 2 reflectance over these waters. This can be a useful tool in identifying erroneous Level 2 Remote sensing reflectance due to possible failure of the atmospheric correction algorithm.

Retrieval Algorithm of Chlorophyll-a Concentration in Turbid Waters from Satellite HY-1C Coastal Zone Imager Data

Chen, X.-Y.; Zhang, J.; Tong, C.; Liu, R.-J.; Mu, B., and Ding, J., 2019. Retrieval algorithm of chlorophyll-a concentration in turbid waters from satellite HY-1C coastal zone imager data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 146-155. Coconut Creek (Florida), ISSN 0749-0208.The satellite HY-1C was successfully launched in September 2018 with the Coastal Zone Imager (CZI) onboard, which can provide optical images of 50-m resolution at four broad bands (blue, green, red and near-infrared). In this study, 305 sets of in-situ measurements collected between 2005 and 2015 in the Bohai, Yellow and East China seas were used to assess the performance of HY-1C in retrieving chlorophyll-a concentration in the coastal waters. The results showed that two algorithms based on blue-green band ratios (OC3 and OC3L) performed well. The median of the ratio of retrieved to in-situ values was about 1.1, and the median absolute percentage difference was about 40 %. After applying the two algorithms to HY-1C CZI data of Jiaozhou Bay (Qingdao, China), the spatial distribution of retrieved Chl-a data was found to be in good agreement with results based on data from another satellite (Landsat-8 OLI) with a median absolute percentage difference less than 15 %. The results showed that the broadband channels of the HY-1C CZI can retrieve Chl-a concentration in turbid waters, which indicates potential for its application in coastal water quality and eutrophication monitoring.