scholarly journals 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

2019 ◽  
Vol 11 (18) ◽  
pp. 2076 ◽  
Author(s):  
Katalin Blix ◽  
Juan Li ◽  
Philippe Massicotte ◽  
Atsushi Matsuoka
Keyword(s):  
Machine Learning ◽  
Chlorophyll A ◽  
Environmental Changes ◽  
Learning Algorithm ◽  
Atmospheric Correction ◽  
Radiometric Calibration ◽  
High Northern Latitude ◽  
Chl A ◽  
Northern Latitude ◽  
Level 2

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.

scholarly journals Improved MODIS-Aqua Chlorophyll-a Retrievals in the Turbid Semi-Enclosed Ariake Bay, Japan

2018 ◽  
Vol 10 (9) ◽  
pp. 1335 ◽  
Author(s):  
Meng Meng Yang ◽  
Joji Ishizaka ◽  
Joaquim I. Goes ◽  
Helga do R. Gomes ◽  
Elígio de Raús Maúre ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Red Tide ◽  
Atmospheric Correction ◽  
Ariake Bay ◽  
Band Ratio ◽  
Simple Method ◽  
Chl A ◽  
Switching Algorithm ◽  
Economic Significance

The accurate retrieval of chlorophyll-a concentration (Chl-a) from ocean color satellite data is extremely challenging in turbid, optically complex coastal waters. Ariake Bay in Japan is a turbid semi-enclosed bay of great socio-economic significance, but it suffers from serious water quality problems, particularly due to red tide events. Chl-a derived from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on satellite Aqua in Ariake Bay was investigated, and it was determined that the causes of the errors were from inaccurate atmospheric correction and inappropriate in-water algorithms. To improve the accuracy of MODIS remote sensing reflectance (Rrs) in the blue and green bands, a simple method was adopted using in situ Rrs data. This method assumes that the error in MODIS Rrs(547) is small, and MODIS Rrs(412) can be estimated from MODIS Rrs(547) using a linear relation between in situ Rrs(412) and Rrs(547). We also showed that the standard MODIS Chl-a algorithm, OC3M, underestimated Chl-a, which was mostly due to water column turbidity. A new empirical switching algorithm was generated based on the relationship between in situ Chl-a and the blue-to-green band ratio, max(Rrs(443), Rrs(448)/Rrs(547), which was the same as the OC3M algorithm. The criterion of Rrs(667) of 0.005 sr−1 was used to evaluate the extent of turbidity for the switching algorithm. The results showed that the switching algorithm performed better than OC3M, and the root mean square error (RMSE) of estimated Chl-a decreased from 0.414 to 0.326. The RMSE for MODIS Chl-a using the recalculated Rrs and the switching algorithm was 0.287, which was a significant improvement from the RMSE of 0.610, which was obtained using standard MODIS Chl-a. Finally, the accuracy of our method was tested with an independent dataset collected by the local Fisheries Research Institute, and the results revealed that the switching algorithm with the recalculated Rrs reduced the RMSE of MODIS Chl-a from 0.412 of the standard to 0.335.

scholarly journals Comparison of In-Situ Chlorophyll-a Time Series and Sentinel-3 Ocean and Land Color Instrument Data in Slovenian National Waters (Gulf of Trieste, Adriatic Sea)

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1903
Author(s):  
El Khalil Cherif ◽  
Patricija Mozetič ◽  
Janja Francé ◽  
Vesna Flander-Putrle ◽  
Jana Faganeli-Pucer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Adriatic Sea ◽  
Global Ocean ◽  
Learning Approach ◽  
Chl A ◽  
Gulf Of Trieste ◽  
Machine Learning Approach

While satellite remote sensing of ocean color is a viable tool for estimating large-scale patterns of chlorophyll-a (Chl-a) and global ocean primary production, its application in coastal waters is limited by the complex optical properties. An exploratory study was conducted in the Gulf of Trieste (Adriatic Sea) to assess the usefulness of Sentinel-3 satellite data in the Slovenian national waters. OLCI (Ocean and Land Colour Instrument) Chl-a level 2 products (OC4Me and NN) were compared to monthly Chl-a in-situ measurements at fixed sites from 2017 to 2019. In addition, eight other methods for estimating Chl-a concentration based on reflectance in different spectral bands were tested (OC3M, OC4E, MedOC4, ADOC4, AD4, 3B-OLCI, 2B-OLCI and G2B). For some of these methods, calibration was performed on in-situ data to achieve a better agreement. Finally, L1-regularized regression and random forest were trained on the available dataset to test the capabilities of the machine learning approach. The results show rather poor performance of the two originally available products. The same is true for the other eight methods and the fits to the measured values also show only marginal improvement. The best results are obtained with the blue-green methods (OC3, OC4 and AD4), especially the AD4SI (a designated fit of AD4) with R = 0.56 and RMSE = 0.4 mg/m³, while the near infrared (NIR) methods show underwhelming performance. The machine learning approach can only explain 30% of the variability and the RMSE is of the same order as for the blue-green methods. We conclude that due to the low Chl-a concentration and the moderate turbidity of the seawater, the reflectance provided by the Sentinel-3 OLCI spectrometer carries little information about Chl-a in the Slovenian national waters within the Gulf of Trieste and is therefore of limited use for our purposes. This requires that we continue to improve satellite products for use in those marine waters that have not yet proven suitable. In this way, satellite data could be effectively integrated into a comprehensive network that would allow a reliable assessment of ecological status, taking into account environmental regulations.

scholarly journals Simulation and Assessment of the Capabilities of Orbita Hyperspectral (OHS) Imagery for Remotely Monitoring Chlorophyll-a in Eutrophic Plateau Lakes

2021 ◽  
Vol 13 (14) ◽  
pp. 2821
Author(s):  
Runfei Zhang ◽  
Zhubin Zheng ◽  
Ge Liu ◽  
Chenggong Du ◽  
Chao Du ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll A ◽  
Spatial Resolution ◽  
Atmospheric Correction ◽  
Dianchi Lake ◽  
Inland Waters ◽  
Eutrophic Lakes ◽  
Correction Model ◽  
Chl A

The chlorophyll-a (Chl-a) concentration of eutrophic lakes fluctuates significantly due to the disturbance of wind and anthropogenic activities on the water body. Consequently, estimation of the Chl-a concentration has become an immense challenge. Due to urgent demand and rapid development in high-resolution earth observation systems, it has become crucial to assess hyperspectral satellite imagery capabilities on inland water monitoring. The Orbita hyperspectral (OHS) satellite is the latest hyperspectral sensor with both high spectral and spatial resolution (2.5 nm and 10 m, respectively), which could provide great potential for remotely estimating the concentration of Chl-a for inland waters. However, there are still some deficiencies that are mainly manifested in the Chl-a concentration remote sensing retrieval model assessment and accuracy validation, as well as signal-to-noise ratio (SNR) estimation of OHS imagery for inland waters. Therefore, the radiometric performance of OHS imagery for water quality monitoring is evaluated in this study by comparing different atmospheric correction models and the SNR with several remote sensing images. Several crucial findings can be drawn: (1) the three-band model ((1/B15-1/B17)B19) developed by OHS imagery is most suitable for estimating the Chl-a concentration in Dianchi Lake, with the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE) of 15.55 µg/L and 16.31%, respectively; (2) the applicability of the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model for OHS imagery in a eutrophic plateau lake (Dianchi Lake) was better than the 6S (Second Simulation of Satellite Signal in the Solar Spectrum) model, and QUAC (Quick Atmospheric Correction) model, as well as the dark pixel method; (3) the SNR of the OHS imagery was similar to that of Hyperion imagery and was significantly higher than SNR of the HSI imagery; (4) the spatial resolution showed slight influence on the SNR of the OHS imagery. The results show that OHS imagery could be applied to remote sensing retrieval of Chl-a in eutrophic plateau lakes and presents a new tool for dynamic hyperspectral monitoring of water quality.

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

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1179
Author(s):  
Xiaodong Tang ◽  
Mutao Huang
Keyword(s):  
Machine Learning ◽  
Chlorophyll A ◽  
Learning Algorithm ◽  
Factor Models ◽  
Machine Learning Algorithm ◽  
Single Factor ◽  
Chlorophyll A Concentration ◽  
Donghu Lake ◽  
Svm Model ◽  
Better Than

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.

scholarly journals Carbon fluxes forced by anticyclonic mesoscale eddies generated by islands at the subtropical NE Atlantic Ocean

2012 ◽  
Vol 9 (8) ◽  
pp. 10241-10283 ◽  
Author(s):  
S. Lasternas ◽  
M. Piedeleu ◽  
P. Sangrà ◽  
C. M. Duarte ◽  
S. Agustí
Keyword(s):  
Chlorophyll A ◽  
Environmental Changes ◽  
Heterotrophic Bacteria ◽  
Early Stage ◽  
Carbon Fluxes ◽  
Ammonium Concentration ◽  
Chl A ◽  
Cell Mortality ◽  
Phytoplankton Cell ◽  
Nutrients Concentration

Abstract. The carbon fluxes mediated by planktonic communities in two cyclonic eddies (CEs) and two anticyclonic eddies (AEs) at the Canary Eddy Corridor were studied and compared with the dynamics in two far-field (FF) stations located outside the eddies. We observed favorable conditions and signs for upwelling at the center of CEs and for downwelling and mixing at the centers of AEs. CEs were characterized by higher nutrients concentration and highest chlorophyll a concentration, associated with highest microphytoplankton and diatoms abundance. AEs displayed similar chlorophyll a values and nutrients concentration (except highest ammonium concentration) to those of the FF stations and were characterized by increasing abundance of picophytoplankton and heterotrophic bacteria. While primary production was similar between the systems, the production of dissolved organic carbon (PDOC) was significantly higher at AEs. Phytoplankton cell mortality was lowest in CEs and we found higher cell mortality in AE than FF, despite similar chl a concentration. Environmental changes at the AEs presented significant prejudicial effects on the phytoplankton health as indicated by higher phytoplankton mortality (e.g. 60% of dead diatoms cells) and higher cell lysis rates observed at AEs than at two other systems. The adverse conditions associated to the early-stage anticyclonic systems, mainly triggered by active downwelling, resulted in higher consequent PDOC production, corresponding to forcing of the carbon flux to the dissolved pool and a weakness of the carbon pump.

scholarly journals Machine Learning-Based Prediction of Chlorophyll-a Variations in Receiving Reservoir of World’s Largest Water Transfer Project—A Case Study in the Miyun Reservoir, North China

Water ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 2406
Author(s):  
Zhenmei Liao ◽  
Nan Zang ◽  
Xuan Wang ◽  
Chunhui Li ◽  
Qiang Liu
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Chlorophyll A ◽  
Trophic State ◽  
Water Transfer ◽  
Support Vector ◽  
Miyun Reservoir ◽  
Chl A ◽  
Svm Model

Although water transfer projects can alleviate the water crisis, they may cause potential risks to water quality safety in receiving areas. The Miyun Reservoir in northern China, one of the receiving reservoirs of the world’s largest water transfer project (South-to-North Water Transfer Project, SNWTP), was selected as a case study. Considering its potential eutrophication trend, two machine learning models, i.e., the support vector machine (SVM) model and the random forest (RF) model, were built to investigate the trophic state by predicting the variations of chlorophyll-a (Chl-a) concentrations, the typical reflection of eutrophication, in the reservoir after the implementation of SNWTP. The results showed that compared with the SVM model, the RF model had higher prediction accuracy and more robust prediction ability with abnormal data, and was thus more suitable for predicting Chl-a concentration variations in the receiving reservoir. Additionally, short-term water transfer would not cause significant variations of Chl-a concentrations. After the project implementation, the impact of transferred water on the water quality of the receiving reservoir would have gradually increased. After a 10-year implementation, transferred water would cause a significant decline in the receiving reservoir’s water quality, and Chl-a concentrations would increase, especially from July to August. This led to a potential risk of trophic state change in the Miyun Reservoir and required further attention from managers. This study can provide prediction techniques and advice on water quality security management associated with eutrophication risks resulting from water transfer projects.

scholarly journals Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning

2021 ◽  
Vol 13 (4) ◽  
pp. 576
Author(s):  
Hua Su ◽  
Xuemei Lu ◽  
Zuoqi Chen ◽  
Hongsheng Zhang ◽  
Wenfang Lu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Time Series ◽  
Chlorophyll A ◽  
Gradient Boosting ◽  
Percentage Error ◽  
Neural Net ◽  
Spectral Indices ◽  
Chl A ◽  
Light Gradient

Chlorophyll-a (chl-a) is an important parameter of water quality and its concentration can be directly retrieved from satellite observations. The Ocean and Land Color Instrument (OLCI), a new-generation water-color sensor onboard Sentinel-3A and Sentinel-3B, is an excellent tool for marine environmental monitoring. In this study, we introduce a new machine learning model, Light Gradient Boosting Machine (LightGBM), for estimating time-series chl-a concentration in Fujian’s coastal waters using multitemporal OLCI data and in situ data. We applied the Case 2 Regional CoastColour (C2RCC) processor to obtain OLCI band reflectance and constructed four spectral indices based on OLCI feature bands as supplementary input features. We also used root-mean-square error (RMSE), mean absolute error (MAE), median absolute percentage error (MAPE), and R2 as performance indicators. The results indicate that the addition of spectral indices can easily improve the prediction accuracy of the model, and normalized fluorescence height index (NFHI) has the best performance, with an RMSE of 0.38 µg/L, MAE of 0.22 µg/L, MAPE of 28.33%, and R2 of 0.785. Moreover, we used the well-known band ratio and three-band methods for chl-a estimation validation, and another two OLCI chl-a products were adopted for comparison (OC4Me chl-a and Inverse Modelling Technique (IMT) Neural Net chl-a). The results confirmed that the LightGBM model outperforms the traditional methods and OLCI chl-a products. This study provides an effective remote sensing technique for coastal chl-a concentration estimation and promotes the advantage of OLCI data in ocean color remote sensing.

scholarly journals A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks

2021 ◽  
Vol 1 ◽  
Author(s):  
Brandon Smith ◽  
Nima Pahlevan ◽  
John Schalles ◽  
Steve Ruberg ◽  
Reagan Errera ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chlorophyll A ◽  
Atmospheric Correction ◽  
Support Vector ◽  
Landsat 8 ◽  
Type B ◽  
Data Set ◽  
Zero Bias ◽  
Near Surface ◽  
Mixture Density

Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chlorophyll-a (Chla) in inland and coastal waters via remote observations, has long been regarded as a challenging task. This manuscript applies Mixture Density Networks (MDN) that use the visible spectral bands available by the Operational Land Imager (OLI) aboard Landsat-8 to estimate Chla. We utilize a database of co-located in situ radiometric and Chla measurements (N = 4,354), referred to as Type A data, to train and test an MDN model (MDNA). This algorithm’s performance, having been proven for other satellite missions, is further evaluated against other widely used machine learning models (e.g., support vector machines), as well as other domain-specific solutions (OC3), and shown to offer significant advancements in the field. Our performance assessment using a held-out test data set suggests that a 49% (median) accuracy with near-zero bias can be achieved via the MDNA model, offering improvements of 20 to 100% in retrievals with respect to other models. The sensitivity of the MDNA model and benchmarking methods to uncertainties from atmospheric correction (AC) methods, is further quantified through a semi-global matchup dataset (N = 3,337), referred to as Type B data. To tackle the increased uncertainties, alternative MDN models (MDNB) are developed through various features of the Type B data (e.g., Rayleigh-corrected reflectance spectra ρs). Using held-out data, along with spatial and temporal analyses, we demonstrate that these alternative models show promise in enhancing the retrieval accuracy adversely influenced by the AC process. Results lend support for the adoption of MDNB models for regional and potentially global processing of OLI imagery, until a more robust AC method is developed. Index Terms—Chlorophyll-a, coastal water, inland water, Landsat-8, machine learning, ocean color, aquatic remote sensing.

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