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

Yang, Menglong; Ishizaka, Joji; Goés, Joaquim I.; Gomes, Helga do Rosário; Maúre, Elígio de Ráus; Hayashi, Masataka; Katano, Toshiya; Fujii, Naoki; Saitoh, Katsuya; Mine, Takayuki; Yamashita, Hirokazu; Mizuno, Akiko

doi:10.3390/rs10091335

Cited by 23 publications

(16 citation statements)

References 44 publications

(63 reference statements)

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“…However, that was not possible for logistical reasons. Oceanographic research is continuously improving the algorithms to accurately infer coastal phytoplankton abundance from satellite data (Bellacicco et al, 2016; Yang et al, 2018). Thus, future studies could re-examine our hypothesis as further improvements are made on that line.…”

Section: Discussionmentioning

confidence: 99%

A 5-year study (2014–2018) of the relationship between coastal phytoplankton abundance and intertidal barnacle size along the Atlantic Canadian coast

Scrosati

Ellrich

2019

PeerJ

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Benthic–pelagic coupling refers to the ecological relationships between benthic and pelagic environments. Studying such links is particularly useful to understand biological variation in intertidal organisms along marine coasts. Filter-feeding invertebrates are ecologically important on marine rocky shores, so they have often been used to investigate benthic–pelagic coupling. Most studies, however, have been conducted on eastern ocean boundaries. To evaluate benthic–pelagic coupling on a western ocean boundary, we conducted a 5-year study spanning 415 km of the Atlantic coast of Nova Scotia (Canada). We hypothesized that the summer size of intertidal barnacles (Semibalanus balanoides) recruited in the preceding spring would be positively related to the nearshore abundance (biomass) of phytoplankton, as phytoplankton constitutes food for the nauplius larvae and benthic stages of barnacles. Every year between 2014 and 2018, we measured summer barnacle size in clearings created before spring recruitment on the rocky substrate at eight wave-exposed locations along this coast. We then examined the annual relationships between barnacle size and chlorophyll-a concentration (Chl-a), a proxy for phytoplankton biomass. For every year and location, we used satellite data to calculate Chl-a averages for a period ranging from the early spring (when most barnacle larvae were in the water) to the summer (when barnacle size was measured after weeks of growth following spring benthic recruitment). The relationships were always positive, Chl-a explaining nearly half, or more, of the variation in barnacle size in four of the five studied years. These are remarkable results because they were based on a relatively limited number of locations (which often curtails statistical power) and point to the relevance of pelagic food supply to explain variation in intertidal barnacle size along this western ocean boundary coast.

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Section: Discussionmentioning

confidence: 99%

A 5-year study (2014–2018) of the relationship between coastal phytoplankton abundance and intertidal barnacle size along the Atlantic Canadian coast

Scrosati

Ellrich

2019

PeerJ

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“…is a concentration operation, ∈ , ∈ × , and ∈ × are parameters to learn, is the previous hidden state and is the memory cell state in the encoder LSTM unit. The spatial attention weight can be calculated by Equations (5) and (6) [16].…”

Section: Encoder With Multidimensional Spatial Attentionsmentioning

confidence: 99%

“…Machine learning methods were used to predict the occurrence of red tides by some scholars [5]. Yang et al proposed a new empirical switching algorithm to evaluate the root mean squared error (RMSE) for MODIS (MODerate resolution Imaging Spectroradiometer) Chl-a [6]. Among these methods, Artificial Neural Network (ANN) is the most widely used Machine Learning (ML) method to predict algal blooms [7,8], especially the back propagation (BP) neural network [9].…”

Section: Introductionmentioning

confidence: 99%

Unsteady Multi-Element Time Series Analysis and Prediction Based on Spatial-Temporal Attention and Error Forecast Fusion

Wang

2020

Future Internet

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Harmful algal blooms (HABs) often cause great harm to fishery production and the safety of human lives. Therefore, the detection and prediction of HABs has become an important issue. Machine learning has been increasingly used to predict HABs at home and abroad. However, few of them can capture the sudden change of Chl-a in advance and handle the long-term dependencies appropriately. In order to address these challenges, the Long Short-Term Memory (LSTM) based spatial-temporal attentions model for Chlorophyll-a (Chl-a) concentration prediction is proposed, a model which can capture the correlation between various factors and Chl-a adaptively and catch dynamic temporal information from previous time intervals for making predictions. The model can also capture the stage of Chl-a when values soar as red tide breaks out in advance. Due to the instability of the current Chl-a concentration prediction model, the model is also applied to make a prediction about the forecast reliability, to have a basic understanding of the range and fluctuation of model errors and provide a reference to describe the range of marine disasters. The data used in the experiment is retrieved from Fujian Marine Forecasts Station from 2009 to 2011 and is combined into 8-dimension data. Results show that the proposed approach performs better than other Chl-a prediction algorithms (such as Attention LSTM and Seq2seq and back propagation). The result of error prediction also reveals that the error forecast method possesses established advantages for red tides prevention and control.

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“…In this study, mathematical and statistical testing criteria are used in scientifically theoretical evidence to support the establishment of the proposed model. This study develops a self-adapting ANN based on remote sensing data to predict the contents of nitrogen, phosphorus, BOD, COD, turbidity, and Chla [39] using the modified spectral reflectance of water collected with a ground-based analytical spectral device (ASD). The proposed network is used to estimate the above parameters in the Shiqi River.…”

Section: Introductionmentioning

confidence: 99%

Mapping Water Quality Parameters in Urban Rivers from Hyperspectral Images Using a New Self-Adapting Selection of Multiple Artificial Neural Networks

Zhang

Ren

et al. 2020

Remote Sensing

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Protection of water environments is an important part of overall environmental protection; hence, many people devote their efforts to monitoring and improving water quality. In this study, a self-adapting selection method of multiple artificial neural networks (ANNs) using hyperspectral remote sensing and ground-measured water quality data is proposed to quantitatively predict water quality parameters, including phosphorus, nitrogen, biochemical oxygen demand (BOD), chemical oxygen demand (COD), and chlorophyll a. Seventy-nine ground measured data samples are used as training data in the establishment of the proposed model, and 30 samples are used as testing data. The proposed method based on traditional ANNs of numerical prediction involves feature selection of bands, self-adapting selection based on multiple selection criteria, stepwise backtracking, and combined weighted correlation. Water quality parameters are estimated with coefficient of determination R 2 ranging from 0.93 (phosphorus) to 0.98 (nitrogen), which is higher than the value (0.7 to 0.8) obtained by traditional ANNs. MPAE (mean percent of absolute error) values ranging from 5% to 11% are used rather than root mean square error to evaluate the predicting precision of the proposed model because the magnitude of each water quality parameter considerably differs, thereby providing reasonable and interpretable results. Compared with other ANNs with backpropagation, this study proposes an auto-adapting method assisted by the above-mentioned methods to select the best model with all settings, such as the number of hidden layers, number of neurons in each hidden layer, choice of optimizer, and activation function. Different settings for ANNS with backpropagation are important to improve precision and compatibility for different data. Furthermore, the proposed method is applied to hyperspectral remote sensing images collected using an unmanned aerial vehicle for monitoring the water quality in the Shiqi River, Zhongshan City, Guangdong Province, China. Obtained results indicate the locations of pollution sources.

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

Cited by 23 publications

References 44 publications

A 5-year study (2014–2018) of the relationship between coastal phytoplankton abundance and intertidal barnacle size along the Atlantic Canadian coast

A 5-year study (2014–2018) of the relationship between coastal phytoplankton abundance and intertidal barnacle size along the Atlantic Canadian coast

Unsteady Multi-Element Time Series Analysis and Prediction Based on Spatial-Temporal Attention and Error Forecast Fusion

Mapping Water Quality Parameters in Urban Rivers from Hyperspectral Images Using a New Self-Adapting Selection of Multiple Artificial Neural Networks

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