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MYU Tokyo
S & M 3448

1D Convolutional Neural Network-based Chlorophyll-a Retrieval
Algorithm for Sentinel-2 MultiSpectral Instrumentin Various Trophic States
Muhammad Salah,1 Hiroto Higa,2 Joji Ishizaka,3 and Salem Ibrahim Salem4,5*
1Graduate School of Engineering, Kyoto University of Advanced Science,
18 Gotanda-cho, Yamanouchi, Ukyo-ku, Kyoto-shi, Kyoto 615-8577, Japan
2Institute of Urban Innovation, Yokohama National University,
79-5 Tokiwadai, Hodogaya-ku, Yokohama 240-8501, Japan
3Institute for Space-Earth Environmental Research, Nagoya University,
Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
4Faculty of Engineering, Kyoto University of Advanced Science,
18 Gotanda-cho, Yamanouchi, Ukyo-ku, Kyoto-shi, Kyoto 615-8577, Japan
5Faculty of Engineering, Alexandria University,
Lotfy El-Sied st. off Gamal Abd El-Naser - Alexandria, Alexandria Governorate 11432, Egypt(Received January 20, 2023; accepted September 28, 2023)

Keywords: chlorophyll-a, Sentinel-2, MultiSpectral Instrument, deep learning, convolutional neuralnetwork, ocean color

Despite extensive research on chlorophyll-a (Chla) concentration retrieval methods fromremote sensing reflectance (Rrs, sr−1) data, there remains a need for more reliable Chla retrieval
techniques. In this study, we introduce a deep learning approach based on a 1D convolutional
neural network (1D CNN) architecture. In addition, we provide a new method of representing
the Rrs as a sequential vector. The model architecture targets the Sentinel-2 MultiSpectral
Instrument (MSI) sensor. The proposed model was trained and tested on simulated and in situ
data collected from broad trophic states in Japan and Vietnam waters with Chla concentrations
ranging from 0.02 to 148.26 mg/m3. The proposed model was evaluated against well-acceptedstate-of-the-art methods: ocean color three-band (OC3), ocean color index (OCI), two-band
ratio, Blend, and a neural network model with a mixture density network. The evaluation showsthat the proposed method outperforms other methods with a 7.48–38.02% reduction in root meansquared error (RMSE) and an 11.50–39.17% lower mean absolute error (MAE) than the othermethods. The promising performance of the proposed model suggests that more attention shouldbe paid to the domain of sequence modeling for Rrs and the architecture of 1D CNN.

1.

Introduction

Chlorophyll-a (Chla) concentration has been used as a direct indicator of the trophic state ofwater bodies.(1) It is also used as a representative of harmful algal blooms,(2) which makes*Corresponding author: e-mail: salem.ibrahim@kuas.ac.jp

https://doi.org/10.18494/SAM4331

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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Sensors and Materials, Vol. 35, No. 10 (2023)

measuring Chla in water bodies a vital task to determine the quality, productivity, andenvironmental situation of water.
Various methods have been used to measure and retrieve Chla concentrations in water
bodies,(3) which can be divided into in situ measurements and remote sensing methods. Remotesensing methods provide an advantage over the complexity of in situ methods and labor-intensive
techniques involved in field measurements. However, remote sensing for Chla estimation isprone to errors and failures caused by several reasons, including cloud coverage, atmosphericcorrection, and the optical complexity of water.(4) Therefore, the estimation of Chla in waterusing remote sensing methods is a challenging task.
The estimation of Chla using satellite imagery has been widely explored owing to theaccessibility of monitoring methods, and several empirical algorithms have been developed forthis purpose. The majority of the currently used Chla retrieval algorithms are empiricalalgorithms that use the relationships between various remote sensing reflectance (Rrs) values atvarious bands to infer the corresponding Chla value. Band selection mainly relies on absorption
peaks of the phytoplankton using Rrs to directly estimate Chla, such as the band ratio between
the NIR or the red edge and the red bands,(5,6) or the ratio between the green and blue bands.(7)Ocean color (OCx) algorithms are fourth-order polynomial equations with different versions.(7–9)
The selected version of the OCx algorithm depends on the target sensor, selected bands (i.e., two
to six bands), and polynomial coefficients. The color index (CI) algorithm, which was developed
for case 1 water where Chla < 0.25 mg/m3, is used by the NASA Ocean Biology Processing
Group (OBPG; https://oceancolor.gsfc.nasa.gov) along with OCx as a blend algorithm.(10)However, blue and green algorithms are known to overestimate Chla concentrations in inlandand coastal waters, and red and NIR algorithms have been developed specifically for use in thesetypes of water.(11) The two-band ratio algorithm uses the linear relationship between the NIR or
red edge and red bands.(11) Other algorithms were developed by mixing different algorithms formore generalized solutions for case 1 and case 2 waters.(12) The previous algorithms mainly lack
generalization to different water characteristics as stated above, which makes it difficult to use
in all situations. Moreover, the lack of classification algorithms that can be used to determine thewater type and which algorithm should be used forces the use of a single algorithm for all watercases.
On the other hand, some studies have considered using different machine learning techniques,
such as support vector machines,(13–16) K-nearest neighbors,(14,16) and random forest,(14,16,17) toprovide more accurate Chla estimations for specific regions. In addition to traditional machinelearning methods, deep learning has attracted significant attention for water quality monitoring
and remote sensing. Few researchers have used deep learning methods to estimate Chla from
Rrs, such as feedforward neural networks,(18,19) which are multilayered neural networks used as
regression models and trained using the backpropagation algorithm. Convolutional neuralnetworks(20) operate on an image level instead of a pixel level, similar to other deep learningtechniques, and they consist of multiple filters that are also trained using the backpropagation
algorithm. The use of mixture density networks (MDNs) is a recent approach for estimating the
distribution of Chla(21–23) by estimating multiple Gaussian distributions and combining them;
they are basically feed-forward networks, but instead of estimating Chla directly, they try toSensors and Materials, Vol. 35, No. 10 (2023)

3745

estimate the parameters of several Gaussian distributions that can represent Chla. Althoughsome of the previously proposed deep learning models have shown some acceptable efficiency
in determining Chla concentration, a huge room for improvement exists when considering more
complex deep learning structures and when transforming the problem into other domains that
can improve the efficiency of retrieval.
In this study, we consider a novel approach by treating Rrs as a sequence of reflectancevalues that are related to each other instead of dealing with each band as a stand-alone value; onthe basis of this assumption, different sequence modeling techniques can be applied to determine
Chla from Rrs by modeling the sequence of Rrs bands and encoding it into the corresponding
Chla value. The method selected for this research uses 1D convolutional neural networks (1D
CNNs), which were first used to analyze and classify electrocardiography (ECG) signals.(24) To
the best of our knowledge, this is the first study to use 1D CNNs to model satellite data. First, webuilt a new model based on the 1D CNN architecture to estimate Chla concentrations in aquatic
environments using MultiSpectral Instrument (MSI) sensors by considering Rrs as a sequenceof related values. Next, we compared the proposed method with existing state-of-the-art
algorithms using both in situ and simulated datasets representing a wide range of trophic states.

2.

Materials and Methods

2.1

Datasets

The datasets used for developing this model are a combination of in situ and simulated
hyperspectral data. The in situ dataset contains radiometric and biogeochemical measurementsfrom various water bodies around Japan, Thailand, and Vietnam, with diverse Chlaconcentrations. Rrs measurements within the range of 350–900 nm were carried out using three
TriOS-RAMSES hyperspectral radiometers. These instruments have a spectral interval of 2 nm
and a field of view of 7 deg. Concurrently, Chla concentrations were determined using a Turner
Designs 10-AU fluorometer. The procedure involved filtering 20 mL of water samples through25 mm Whatman GF/F filters with a pore size of 0.7 µm. Subsequently, the pigments wereextracted by immersing the filter in 6 mL of N, N-dimethylformamide (DMF) and storing it in adark place at 4 ℃ for 4 h.(25) The locations of the sampling points are shown in Fig. 1, except for
the locations of the data points from the Gulf of Thailand because such locations could not be
verified. The simulated dataset is part of the CoastColour Round Robin dataset that includes
simulated measurements of hyperspectral Rrs and corresponding Chla concentrations.(26)The data used to create the generalized global Chla model included both in situ and simulated
data. The in situ data consisted of 958 samples collected from seven different water bodies,
which were divided into 660 training samples and 298 test samples (Table 1). The Rrs spectra of
the in situ dataset are shown in Fig. 2. The simulated data included 5000 samples, which wererandomly split into 159 training samples and 4841 test samples. The use of a small number ofsimulated data points was intentional to introduce randomness to the model training without
negatively impacting its performance on the in situ data. The histogram distribution shown in
Fig. 3 and the descriptive statistics in Table 1 demonstrate the variations in the characteristics of3746

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(a)

(b)

Fig. 1. (Color online) Geographical distribution of the in situ measurements from different water bodies in (a)Japan and (b) Camranh Bay, Vietnam.
Table 1
Descriptive statistics of in situ and simulated Chla concentrations. The statistics include the minimum (Min),
maximum (Max), mean, standard deviation (Std.), and size of the data.
Data typesite
Train/Test
Min
Max
Mean
Std.
Size
Train
2.91
39.97
16.09
10.66
57
Ariake Bay
Test
1.53
37.50
15.44
11.09
18
Train
0.60
1.70
0.86
0.42
5
Biwa Lake
Test
0.63
0.63
0.63
0.00
1
Train
1.63
52.00
8.16
10.54
20
Camranh
Bay
Test
0.81
7.86
3.19
1.95
10
Train
3.05
39.79
14.21
11.17
21
Gulf ofIn situThailand
Test
5.12
37.63
12.37
10.28
8
Train
0.39
148.26
19.24
22.50
457
Ise-Mikawa
Bay
Test
0.53
131.74
19.55
22.60
218
Train
13.16
127.34
51.72
25.92
55
Lake
Kasumigaura
Test
21.63
94.49
48.72
18.60
22
Train
0.91
80.23
20.02
18.19
45
Tokyo Bay
Test
0.76
97.32
26.45
25.04
21
Train
0.22
60.78
6.16
7.92
159
Simulated
Test
0.02
129.08
6.15
10.06
4841

Fig. 2.

(Color online) Rrs spectra of in situ measurements.

Sensors and Materials, Vol. 35, No. 10 (2023)(a)Fig. 3.

3747(b)(Color online) Histogram distributions of (a) in situ and (b) simulated Chla concentrations.

the water bodies and data samples, which facilitate the development of a generalized global Chlamodel.
The target sensor of this study is the MSI onboard the Sentinel-2 satellite, which measures thereflectance at 13 different wavelengths, ranging from the visible and near-infrared regions to theshortwave infrared region.(27) MSI bands in the visible and NIR regions (Table 2) wereconsidered as inputs for the 1D CNN model. The hyperspectral Rrs values of the in situ andsimulated datasets were resampled to the MSI sensor for the training and testing of the evaluatedalgorithms by using the nominal bands.
2.2

Model development

The measured Rrs at each station is a sequential data vector with dependences andrelationships between its values. To model the sequential data, a 1D CNN was adopted toanalyze and detect different relationships and dependences between the different Rrs bands.
1D CNNs are effective deep learning models for sequential data processing, such as audio
signals, time-series data, and natural language text.(28) One key advantage of 1D CNNs is theirability to learn local patterns within the input data while preserving the overall structure andcontext of the input. This is accomplished using the convolutional kernels shown in Fig. 4, which
are small weight matrices that are applied to a sliding window of the input data, extracting local
features, and combining them to form higher-level representations. 1D CNNs can be stacked tocreate deeper networks, which can further improve their ability to learn more complex andabstract patterns within the data.
2.2.1 Feature engineering
The input to the proposed model is Rrs in sr−1, which consists of the first seven spectral
bands from the Sentinel-2 MSI sensor, and the output is the Chla concentration in mg/m3, which

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Table 2
Sentinel-2 MultiSpectral Instrument (MSI) bands in the visible and near-infrared regions.
Band
Nominal wavelength (nm)Spatial resolution (m)
1
443
60
2
490
10
3
560
10
4
665
10
5
705
20
6
740
20
7
783
20

Fig. 4.

(Color online) Simple 1D CNN architecture with two convolutional layers.

creates a regression problem. Various feature engineering techniques were applied to the data
before they were fed to the deep learning model to facilitate the learning progress andgeneralization of the model.
First, an Isolation Forest anomaly detection algorithm(29) was used to ensure that all data
samples do not include outliers. This algorithm is based on an isolation tree, which is constructedby randomly selecting a feature, splitting a value, and partitioning the data into two subsets. This
process is recursively repeated until each data point is isolated from its leaf node. After building
multiple isolation trees using different subsets of the data and measuring the average path lengthof the trees, data points that required fewer splits to be isolated in the tree were considered moreanomalous than data points that required more splits to be isolated in the tree. The algorithm
then generates an anomaly score for each data point based on its isolation in the tree, and thedata points with high scores are identified as anomalies. The threshold for considering a sample
as an anomaly is set to 0.55.
The Rrs values range from 4.27 e−6 to 2.49 e−2 sr−1 (Fig. 2), which is a massive range for deeplearning. Hence, a log transformation was applied to the inputs as a technique to minimize theinput range, which helps the model better represent the data and generalize for different Rrs
values. Applying the log transformation to the reflectance data narrows the input range to−12.36–−3.69.

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2.2.2 Model architecture
The architecture of the proposed model is shown in Fig. 5. The model construction can be
described in three steps. First, feature engineering and normalization were applied to the Rrs
data to produce a normalized input. Second, the output from the first step was fed to two-layered
1D CNNs that can be considered as the encoding part of the model, which captures therelationship between the input reflectance and the output Chla, and encodes the input values toan intermediate vector representation of Chla. Finally, the vector is fed to a two-layered fully
connected feed-forward network with an output layer that attempts to estimate the Chlaconcentration for the corresponding Rrs values.
Different hyperparameters were tuned to obtain the current structure and parameters of the
proposed model. The hyperparameters include the number of layers for both the CNN and thefully connected network, kernel size, the number of filters for the CNN layers, and the numberof units in each of the fully connected layers. Beyond the architectural parameters, featureengineering parameters were also carefully adjusted. This involved setting the anomaly
threshold to 0.55 for the Isolation Forest anomaly detection algorithm and selecting a logarithmictransformation as the normalization technique for Rrs. Figure 5 shows the different
hyperparameters used for each layer in the deep learning model.
2.3. Comparison with other Chla algorithms
The performance of the proposed model was evaluated using state-of-the-art algorithms. Six
well-known standard algorithms used for Chla estimation were selected for comparison: thethree-band version of OC3;(8) the OCI algorithm that blends the CI algorithm with the OC3algorithm;(10) the two-band ratio algorithm (2 Band), which indicates that the ratio of the near
705 nm band to the near 675 band is directly related to the Chla ratio;(5) the three-band ratio
algorithm (3 Band), which uses the spectral bands 665, 705, and 740;(30) the four-band ratio
algorithm (4 Band) using the relationship among the 560, 665, 705, and 740 bands to estimate
Chla;(31) and the blend algorithm that blends the OC3 algorithm with the two-band algorithm.(12)For the band ratio algorithms, the relationship between the band ratios and the Chla value waslinear. The equations and tuned parameters for the six algorithms are presented in Appendix A.
In addition to the six classical algorithms, the state-of-the-art deep learning model proposed
by Pahlevan et al., which is based on the MDN architecture to estimate the distribution of Chlausing several Gaussian distributions, was also included in the comparison.(23)Fig. 5. (Color online) Schematic diagram illustrating the overall architecture of the proposed 1D CNN, including
the feature engineering step followed by the deep learning model's main components.

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2.4

Sensors and Materials, Vol. 35, No. 10 (2023)

Evaluation metrics

The evaluation metrics used in this study were the root mean squared error (RMSE), mean
absolute error (MAE), mean absolute percentage error (MAPE), and BIAS. The proposed model,
state-of-the-art deep learning model, and six standard algorithms were evaluated using the sametest data. All four evaluation metrics were calculated for each model’s prediction against the insitu measurements of Chla for comparison.
n1( yˆi − yi )2(1)

RMSE =

∑

MAE =

1 n
∑ yˆi − yi
n 1

(2)

100% n yˆi − yi
∑1 y
n
i

(3)

BIAS = 1 ∑ n ( yi − yˆi )
n 1

(4)

MAPE =

nHere, yi and yˆi represent the measured and model retrieved Chla values, respectively, and nrefers to the number of samples.

3.

Results and Discussion

The inclusion of simulated data in our study seeks to address the inherent limitations posedby the relatively small number of in situ Rrs–Chla pairs, allowing for the exploration of Chla
distributions not fully captured by in situ data alone. To evaluate the efficacy of this approach,
we first compared the performance characteristics of the 1D CNN model trained with andwithout the inclusion of 159 simulated samples. As illustrated in Fig. 6, the performance results
derived from in situ data were similar, with a slight improvement observed in the nonsimulated
data scenario, indicating that the inclusion of simulated data does not significantly affect themodel’s performance on in situ data.
In contrast, a marked change in performance was observed when assessing the model’sability to handle simulated data. The 1D CNN model, when trained exclusively on in situ data,
showed a significant performance decrease when tested against simulated data, as demonstrated
by a 100% increase in RMSE and 7-fold and 20-fold decreases in MAE and BIAS, respectively, as
demonstrated in Fig. 6.
We further assessed the quantity of simulated data incorporated into the 1D CNN model.
While a small portion of simulated data significantly enhances the model’s performance on
simulated data, expanding this simulated dataset negatively impacted the performance on in situ

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3751

(a)

(b)

(c)

(d)

Fig. 6. (Color online) Evaluation of model performance based on training scenarios: (a) & (b) Model performance
characteristics on in situ data, trained using in situ data only, and both in situ and simulated data, respectively. (c) &(d) Model performance characteristics on simulated data, trained using only in situ, and both in situ and simulateddata, respectively.

data. Accordingly, we infer that our selected mix of in situ and simulated data optimizes the 1D
CNN model’s capacity to capture the variability present in both in situ and simulated
distributions.
We further investigated the proposed 1D CNN’s performance for Chla retrieval by comparing
it with those of six standard algorithms [i.e., OC3, OCI, 2 Band, 3 Band, 4 Band, and Blend (2Band & OC3)] and the MDN model. The models were evaluated using in situ and simulated
datasets as discussed in the subsequent sections.

3.1

Performance evaluation based on simulated data

The performance of all models was evaluated using a simulated dataset with 4841 samples.
The evaluation metrics, including RMSE, MAE, and MAPE, were calculated for each model on
the basis of the estimated Chla values and the Chla values associated with the simulated Rrs. The
overall performance of the evaluated models, shown in Table 4, indicates that the proposedmodel outperforms other models with a 40.97–73.74% improvement in RMSE. In addition, MAE
and MAPE indicated that Chla retrieval was improved by at least two folds. The statistics

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Table 4Overall evaluation for all models based on the simulated test dataset.
Model
RMSE
MAE
Proposed Model
4.12
1.06
MDN
6.98
3.28
OC3
10.72
5.76
OCI
10.72
5.76
2 Band Ratio
8.10
6.77
3 Band Ratio
15.69
14.24
4 Band Ratio
14.64
12.65
Blend (2 Band & OC3)
9.25
5.36

MAPE
27.74
68.93
141.41141.36468.261146.05
1011.27140.19

BIAS
0.30
0.25
−3.32
−3.29
−5.41
−12.04−9.75−3.47

tabulated in Table 4 using the simulated dataset indicate that the MDN model provides the least
BIAS (0.25; compared with 0.30 for the proposed model). Among the standard algorithms, 2Band and Blend are the most accurate, whereas 3 Band and 4 Band have the least retrieval
accuracy with a MAPE of up to 1146%.
Figure 7 shows the scatterplots of the top-performing standard algorithms, along with the
MDN and proposed models. The scatterplots indicate that Blend and the MDN model bettermodel Chla in the range of 0.01–0.2 mg/m3; however, the proposed model provides better
agreement for Chla > 1 mg/m3. Table 5 shows a detailed comparison between the different
models in different Chla ranges, which clearly shows that the proposed model outperforms theother models in all evaluation metrics across all Chla ranges by multiple folds. Even though theproposed model fails to capture the very low Chla values (less than 0.2 mg/m3), the model still
performs very well in the 0–5 mg/m3 range with a 76.97–87.18% reduction in RMSE comparedwith other models and a 78.53–89.24% reduction in MAE.
3.2

Performance evaluation based on in situ data

The performance characteristics of various models, including the proposed model, were
evaluated using 298 samples of in situ-measured Rrs and Chla pairs. The same evaluation
procedures used for the simulated data were applied to the in situ data, and the results arepresented in Table 6, which compares the overall performance characteristics of all the models
considered in this study. The results indicate a significant improvement over the standard
algorithms and the MDN model, with a 7.48–38.02% reduction in RMSE and an 11.50–39.17%
improvement in MAE. MAPE was also reduced significantly. Figure 8 shows the scatterplots ofthe top-performing models, which demonstrate that the scatter points are close to the unity line
compared with the other algorithms.
Table 7 shows the proposed Chla retrieval model with the other algorithms for various Chlaranges. For Chla values between 0 and 5 mg/m3, the MDN model performed slightly better thanthe proposed model, with lower RMSE and MAE values. Both deep-learning-based algorithms
significantly outperformed the standard algorithms in this range. When considering the range of
5–10 mg/m3, the proposed model outperforms the other algorithms, with a 15.98–31.26%

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3753

(a)

(b)

(c)

(d)

Fig. 7. (Color online) Scatterplots of the top-performing algorithms showing the comparison between theevaluation metrics for the (a) proposed model, (b) MDN model, (c) 2 Band algorithm, and (d) Blend algorithm
evaluated on the basis of the simulated dataset.

Table 5
Detailed comparison between the top-performing Chla retrieval algorithms against different Chla ranges based on
the simulated dataset.
Chla range (mg/m3)
Model
RMSE
MAE
MAPE
BIAS
Proposed Model
0.76
0.38
36.43
−0.21
MDN
3.30
1.77
83.09
−1.39
0 to 5
OCI
5.93
3.53
168.99−3.49
Blend (2 Band & OC3)
5.93
3.53
169.07−3.50
Proposed Model
1.53
0.69
10.06
−0.35
MDN
4.12
3.04
45.62
−1.62
5 to 10
OCI
10.73
7.67
114.38−7.44
Blend (2 Band & OC3)
10.73
7.66
114.33−7.44
Proposed Model
2.18
0.93
6.55
0.08
MDN
4.83
3.94
28.28
3.01
10 to 20
OCI
12.73
8.06
61.13
−5.57
Blend (2 Band & OC3)
11.62
7.43
56.95
−5.29
Proposed Model
16.53
10.20
21.69
8.69
MDN
25.10
20.29
52.06
20.29
20 to 150
OCI
30.78
21.28
50.44
16.31
Blend (2 Band & OC3)
22.00
15.33
36.48
12.68

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Table 6Overall evaluation for all models based on the in situ test dataset.
Model
RMSE
MAE
Proposed Model
20.05
10.00
MDN
22.58
12.44
OC3
32.35
14.98
OCI
32.35
14.98
2 Band Ratio
21.67
11.30
3 Band Ratio
23.21
16.44
4 Band Ratio
22.28
15.11
Blend (2 Band & OC3)
22.78
11.36

MAPE
66.89
72.40
96.06
96.06
124.04223.44209.01
85.68

(a)

(b)

(c)

(d)

BIAS
5.54
8.92
8.04
8.04
1.40
2.94
2.05
4.61

Fig. 8. (Color online) Scatterplots of the top-performing algorithms showing the comparison between theevaluation metrics for (a) the proposed model, (b) MDN model, (c) 2 Band algorithm, and (d) Blend algorithm
evaluated on the basis of the in situ test dataset.

reduction in RMSE and a 21.18–47.34% reduction in MAE. Furthermore, the proposed model
also excels in higher Chla ranges; in the range of 10–20 mg/m3, RMSE is 23.5% less than that of
the MDN model and 23.2–29.27% less than that of the standard algorithms. MAE is also reducedby 10.85–36.63% compared with the other models, as reflected in the scatterplots of Fig. 8. The
standard algorithms showed better results than the MDN model in this range in terms of MAE.
Finally, 2 Band performed better than the other models in the highly turbid water Chla range(20–150 mg/m3).

Sensors and Materials, Vol. 35, No. 10 (2023)

3755

Table 7
A detailed comparison between the top-performing Chla retrieval algorithms against different ranges of Chla basedon the in situ test dataset.
Chla range (mg/m3)
Model
RMSE
MAE
MAPE
BIAS
Proposed Model
18.00
4.53
160.42
−4.11
MDN
16.11
4.00
128.74−2.28
0 to 52 Band Ratio
27.66
9.60
380.02−8.89
Blend (2 Band & OC3)
26.88
6.01
206.08−5.56
Proposed Model
6.73
3.46
46.84
−1.57
MDN
8.01
4.39
59.17
1.54
5 to 10
2 Band Ratio
9.43
6.57
93.19
−6.48
Blend (2 Band & OC3)
9.79
4.97
69.27
−3.35
Proposed Model
7.78
5.26
37.18
1.72
MDN
10.17
8.30
59.89
5.91
10 to 20
2 Band Ratio
10.13
5.90
43.56
−3.68
Blend (2 Band & OC3)
11.00
6.92
50.36
−0.03
Proposed Model
29.25
19.96
41.23
17.90
MDN
33.98
24.94
55.45
22.02
20 to 150
2 Band Ratio
27.20
18.08
35.80
15.24
Blend (2 Band & OC3)
29.76
21.00
44.47
18.23

3.3. Models’ performance
In this study, we compared the performance of the proposed 1D CNN model for Chlaretrieval with those of six standard algorithms and the MDN model using simulated and in situ
datasets. The results indicate that the proposed model outperforms the other models, withsignificant improvements in terms of RMSE, MAE, and MAPE. The results from the simulated
dataset showed that the proposed model provided the highest accuracy across various Chlaranges. However, when considering the in situ dataset, the proposed model performed best in the
mid-Chla range (5 < Chla < 20 mg/m3). The statistical analysis of the in situ dataset highlights
the need to improve the accuracy of the proposed model in the future by increasing the size ofthe trained dataset, particularly for Chla < 1 mg/m3, which has few samples, as shown in Fig. 3.
The results emphasize that machine-learning-based models, such as the 1D CNN and MDN,
consistently outperform the six standard empirical algorithms because they can learn from thetrained data, find complex patterns and relationships, automatically extract features from thedata, and improve as the dataset size increases, which allows them to detect even more complex
relationships in the data. In contrast, standard algorithms rely on predefined equations that may
not capture the complexity of the problem. On the other hand, this research proved theapplicability of using sequential models to handle Rrs, and that it is more efficient than normal
feed forward networks.

4.

Conclusion

In this study, we propose a groundbreaking approach for the retrieval of Chla concentrations
from different water bodies using satellite imagery and a novel perspective on satellite data in3756

Sensors and Materials, Vol. 35, No. 10 (2023)

terms of sequential reflectance values. The algorithm proposed in this research is a 1D
convolutional deep neural network that is known to excel in modeling simple sequential dataapplied to the domain of water quality monitoring to obtain breakthrough results in the retrievalof Chla using Rrs. The proposed model was trained and tested using Rrs–Chla pairs from both
simulated and in situ data collected using measurements in different water bodies, and wasproven to outperform the standard empirical algorithms and state-of-the-art deep learning
methods significantly in most cases and Chla ranges. The model excels in the high ranges of
Chla (i.e., higher than 5 mg/m3) with a 7.48–38.02% overall improvement of in RMSE and an
11.50–39.17% lower MAE than the other algorithms in this study, which proves that using
sequence modeling methods such as 1D CNN for Chla concentration retrieval using Rrs is moreaccurate than using standard algorithms and basic deep learning methods. Limitations of the
proposed model lie mainly in the low Chla range performance, which can be mitigated byproviding a model with more balanced data between low and high Chla concentrations. Future
research could explore the potential of using 1D CNNs for other satellite products, such as totalsuspended matter or colored dissolved organic matter, and expand the dataset of Rrs–Chla pairs
to enhance the robustness of deep learning methods in estimating Chla. Real-time Chla and Rrs
measurements can also be used in an online learning manner to further improve the proposedmodel.

Acknowledgments
The study was partially funded by the Global Change Observation Mission for Climate(GCOM-C) RA7 grant, managed by the Japan Aerospace Exploration Agency (JAXA). We are
grateful for the valuable and constructive input given by two anonymous reviewers, which
considerably improved the quality and presentation of this manuscript.

Appendix A
The detailed implementations of the standard Chla algorithms are listed below.
OC3:
 Rrs443 Rrs490 x=, Rrs560 Rrs560 ( ChlaOC 3 ) =a0 + a1x + a2 x 2 + a3 x3 + a4 x 4
a0 =0.3308, a1 =−2.6684, a2 =1.5990, a3 =0.5525, a4 =−1.4876Sensors and Materials, Vol. 35, No. 10 (2023)

3757

CI:
 560 − 443
CI = Rrs560 − Rrs443 − * ( Rrs665 − Rrs443 )  665 − 443
−0.4909 + (191.6590 * CI )( ChlaCI ) =OCI (CI & OC3):w=

ChlaCI − 0.15
0.2 − 0.15

ChlaCI(ChlaCI < 0.15)
=
ChlaOCI ChlaOC 3(ChlaCI > 0.2)
 w * Chla
OC 3 + w − 1 * ChlaCI (otherwise)
2 Band ratio:
Chla2 B= a0 + a1

Rrs705
Rrs665

By regression on the datasets used in this study, the parameters for the above linear equation
were found to bea0 =−23.6481638, a1 =51.54954222.
With RMSE = 16.66, MAPE = 161.93%, R2 = 0.42.
3 Band ratio:
 1
1 
Chla3 B =a0 + a1 − Rrs740
 Rrs665 Rrs705 

By regression on the datasets used in this study, the parameters for the above linear equation
were found to be=
a0 18.19501540,=
a1 −0.00312166 .

With RMSE = 16.18, MAPE = 182.43, R2 = 0.46.

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4 Band ratio:

1
1−
Rrs560 Rrs665
Chla4 B= a0 + a1
1
1−
Rrs740 Rrs705

By regression on the datasets used in this study, the parameters for the above linear equation
were found to bea0 = 6.0385872814, a1 = −72.97858459 .
With RMSE = 20.33, MAPE = 312.17, R2 = 0.14.
Blend (2 Band & OC3):r=

Rrs705
Rrs665

0.75 r < 0.75
=∅ 1.15 r > 1.15
rotherwisew=∅ − 0.75
1.15 − 0.75=
Chla w * Chla2 B + w − 1 * ChlaOC 3

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About the Authors
Muhammad Salah is a master’s student at the School of Engineering at Kyoto
University of Advanced Science, expected to graduate in September 2024.
Also working as a teaching assistant in the Department of Electrical andMechanical Systems Engineering at Kyoto University of Advanced Sciencesince 2022. Earned his B.Sc. in Computer and Systems Engineering from
Alexandria University in 2021. His main research interests include utilizing
deep learning methods for water quality monitoring, generative methods indeep learning, and sequential analysis and modeling of data.
Hiroto Higa earned his Ph.D. in Environmental Studies from The Universityof Tokyo, Japan, in 2015. He earned his B.Sc. in Civil Engineering from Tokyo
University of Science, Japan, in 2010, and his M.Sc. in Environmental Studies
from The University of Tokyo, Japan, in 2012. Higa served as a Special
Researcher at the Japan Society for the Promotion of Science from 2015 to2016. From 2016 to 2021, he held the position of assistant professor at
Yokohama National University. Since 2022, Higa has been serving as an
associate professor at the Institute of Urban Innovation, Yokohama National
University, Japan. Additionally, he acts as an ad-hoc reviewer for several
international journals. Higa's research primarily focuses on coastal
environmental engineering studies and ocean color remote sensing for turbid
water areas.
Joji Ishizaka earned his Ph.D. in Geoscience from Texas A&M University,
US, in 1989. He received his B.Sc. in Biology and M.Sc. in Environmental
Studies, Tsukuba University, Japan, in 1981 and 1983, respectively. Ishizaka
served as a Research Scientist at the Agency of Industrial Sciences andTechnology, Ministry of International Trade and Industry, for 11 years (1989–
1998). From 1998 to 2009, he worked as a professor at Faculty of Fisheries,
Nagasaki University, Japan. From 2009 to 2015, Ishizaka worked as a professor
at the Hydrospheric-Atomspheric Research Center, Nagoya University, Japan,
and also served as the director of the center in 2013–2015. Since 2015, heworked as a professor at the Institute for Space-Earth Environmental Reseach,
Nagoya University, and served as the vice director of the institute in 2015 to2019. Ishizaka's research focus on the variation of phytoplankton and primary
production associated with natural and anthropogenic effects using ocean
color remote sensing.

Sensors and Materials, Vol. 35, No. 10 (2023)

3761

Salem Ibrahim Salem earned his Ph.D. in Civil Engineering from The
University of Tokyo, Japan, in 2017. He received his B.Sc. and M.Sc. in Civil
Engineering from Alexandria University, Egypt, in 2005 and 2011,
respectively. Salem served as a full-time teaching assistant at the School ofEngineering, Alexandria University, for eight years (2006–2014). From 2017 to
2019, he held a postdoctoral researcher position at The University of Tokyo.
Since 2019, Salem has been working as a junior associate professor at theFaculty of Engineering, Kyoto University of Advanced Science, Kyoto, Japan.
He also acts as an ad-hoc reviewer for several international journals. Salem's
research primarily centers on remote sensing for water resources andenvironmental studies, integrating the latest advances in machine learning
into his work.