# DeepRes-UpwellNet: A Robust Framework for Coastal Upwelling Monitoring Along the Moroccan Atlantic Coast

1st Belmajdoub Hanae

LRIT Laboratory, Faculty of Sciences in Rabat

Mohammed V University in Rabat

Rabat, Morocco

0000-0002-6283-6108

2nd Minaoui Khalid

LRIT Laboratory, Faculty of Sciences in Rabat

Mohammed V University in Rabat

Rabat, Morocco

0000-0002-3918-8552

**Abstract**—Upwelling, a pivotal oceanic process, serves as a crucial mechanism for transporting nutrients from the ocean depths to the surface, playing a fundamental role in enhancing primary productivity and bearing immense significance for coastal ecosystems. Within the scope of this study, we introduce a novel deep learning tool tailored to the monitoring of upwelling in the Moroccan coastal waters, utilizing satellite images encompassing both biological and physical attributes. Recent years have witnessed substantial advancements in the field of deep learning, particularly in the domain of segmentation techniques. These methods have proven their efficacy across a spectrum of ocean remote sensing applications, encompassing image segmentation, classification, and detection. Notably, the U-Net architecture has gained widespread recognition for its effectiveness in these tasks. In the context of our investigation, we present DeepRes-UpwellNet, a fully convolutional deep neural network architecture explicitly engineered to automate the detection and precise localization of upwelling regions in sea surface temperature (SST) and chlorophyll-a (chl-a) images. Our primary objective revolves around assessing the performance of deep learning in the accurate identification of upwelling regions. To achieve this, DeepRes-UpwellNet is meticulously trained and optimized using a database of satellite-derived SST and chlorophyll-a data sourced from the Moderate Resolution Imaging Spectroradiometer (MODIS). The empirical results, as obtained from experiments conducted along the Atlantic coast of Morocco, clearly underscore the superior performance of our proposed model in comparison to conventional segmentation methods. By harnessing deep learning-based upwelling detection systems, we usher in a cost-effective, precise, and practical approach to objectively analyze this pivotal oceanic phenomenon.

**Index Terms**—convolutional neural network, DeepRes-UpwellNet, Sea surface temperature, chlorophyll-a, Upwelling, Atlantic coast of Morocco.

## I. INTRODUCTION

The Canary Current Upwelling System (CCUS) is situated in the central region of Northwest Africa (NWA) and is renowned as one of the world's most productive marine ecosystems [1]. This system is a part of the extensive upwelling system running along the coast of North-West Africa, encompassing countries such as Morocco, Mauritania, Gambia, and Senegal. The remarkable productivity of this region

results in a significant fish biomass, primarily comprising small pelagic species [2]. The upwelling phenomenon in this area persists throughout most of the year, exhibiting continuous variability [3]. The driving force behind the upwelling in the North West Sea region is predominantly the equatorial trade winds, which facilitate the upward movement of cold, nutrient-rich intermediate waters [4]. As a consequence, a dominant surface current is observed along the coast, extending from north to south, with seasonal fluctuations in cold, nutrient-rich coastal waters being a prominent feature [5].

Within the scientific literature, various techniques and algorithms have been proposed for the automated identification of upwelling regions in SST images. For instance, [6] introduced an unsupervised classification method utilizing neural networks trained with k-means clustering [7]. Another approach, presented by [8], relied on a histogram-based separation method, interpreting the bimodality of the SST histogram as indicative of distinct water masses. In a similar context, [9] developed a specialized algorithm for extracting upwelling regions in the coastal waters of Portugal, using fuzzy clustering to achieve accurate segmentation in that specific area. More recent advancements by Tamim et al. [10], [11] and [12] improved upon these algorithms, delivering favorable results for the southern part of the Moroccan Atlantic coast. However, these methods exhibited limitations when applied to the entire Moroccan coast, including the central and northern regions. The primary challenge arose from the substantial variability in the latitudinal temperature distribution. To address this issue, [13] introduced a robust approach for identifying upwelling regions in SST images along the central part of the Canary Islands upwelling ecosystem. Their method relies on non-linear normalization of SST images, offering a straightforward implementation and addressing the limitations encountered in the methodology proposed by [10], [11].

The variability of upwelling in the Canary Current upwelling system has also been explored in the scientific literature using satellite images of ocean color. For example, studies by [14] and [15] delved into the non-linear correlation between surface mixing and biological productivity in the
upwelling phenomenon. Additionally, with a specific focus on Morocco's Atlantic coast, authors such as [16], [17] and [18] employed ocean color images to detect upwelling, utilizing hard clustering, fuzzy clustering, and expectation maximization algorithms, respectively.

In addition to the existing literature, authors in [19], [20] introduced alternative methodologies for identifying Moroccan coastal upwelling, using both SST and chl-a images. More specifically, they used the concept of image fusion to detect the upwelling region along the Moroccan Atlantic coast.

However, the aforementioned studies mainly use traditional image processing methods, which have two main limitations. Firstly, these methods are time-consuming, as they rely on optimization techniques. Secondly, they are highly dependent on the specific format of the input data. In this paper, we present a new deep learning approach that overcomes these limitations and enables effective upwelling monitoring, regardless of the characteristics of the input data.

Deep learning has emerged as a transformative approach to the segmentation of satellite-derived datasets [21], including SST and chl-a images. The integration of deep learning techniques with remote sensing data offers enormous potential for advancing our understanding of oceanographic processes and ecosystem dynamics [22]. By exploiting the power of deep neural networks, we are able to automatically extract and classify important features and patterns from SST and chl-a images. This enables the precise and efficient identification and delineation of regions of interest such as upwelling zones, frontal boundaries and phytoplankton blooms, as well as a better understanding of ecosystem dynamics. The application of deep learning to SST and chl-a segmentation facilitates the characterization of dynamic ocean phenomena at different spatial and temporal scales. It also offers the way for improved monitoring, analysis and modeling of marine ecosystems.

Recently, authors in [23] proposed to train the U-Net encoder/decoder network using both SST and chl-a images encompassing the entire Moroccan Atlantic coast. The main objective of this approach was to accurately identify upwelling regions within the same geographical area. Our study uses the DeepRes-UpwellNet, a modified version of the U-Net Encoder/Decoder network, for the segmentation of Moroccan upwelling regions. The use of the DeepRes-UpwellNet architecture offers significant advantages over previous methodologies. It facilitates efficient feature extraction, enabling the network to recognize the high-level features essential for accurate identification of upwelling regions. In addition, the DeepRes-UpwellNet is robust to the noise and variability commonly encountered in satellite images of the ocean. This robustness is particularly important for the accurate segmentation of upwelling regions, as the ocean environment can exhibit significant fluctuations and disturbances. In addition, the DeepRes-UpwellNet is capable of processing large datasets, a critical aspect when processing the many satellite images needed to segment upwelling regions. Its ability to process large volumes of data efficiently contributes to improved performance, and enables comprehensive analysis of the dynamic and extensive

nature of upwelling phenomena.

The structure of this paper is as follows: Section II presents the dataset and the specific area of interest used in this study. Section III presents an overview of the proposed methodology for the detection and extraction of upwelling zones from SST and chl-a images. Section IV focuses on the experimental results. Finally, Section V concludes the study.

## II. REGION OF INTEREST AND DATASET

In our research, we utilized a comprehensive training dataset for our deep neural network framework, employing remote sensing images of SST and chl-a. The dataset covered a substantial temporal span of a decade, from 2012 to 2021, focusing on coastal upwelling occurrences along the Moroccan Atlantic coast. The region of interest was meticulously delimited, encompassing geographical coordinates spanning from 21°N to 36°N latitude and from 6°E to 19°E longitude. The SST and chl-a images employed in our study were sourced from the MODIS sensor, hosted aboard the TERRA satellites, a mission overseen by NASA (National Aeronautics and Space Administration). Figure. 1 visually portrays the characteristic upwelling conditions captured in the images, distinctly highlighting the presence of a well-defined upwelling main front. The precision of the upwelling front's demarcation is uniformly evident across the entire study area, as exemplified in the chl-a image.

The data harnessed in this study encompassed 8-day MODIS-TERRA mapped products, extending spatial coverage at a resolution of 4 × 4 km. These data sets were publicly accessible through a dedicated database hosted at http://oceancolor.gsfc.nasa.gov/. To ensure comprehensive spatio-temporal coverage, we selected an 8-day temporal resolution for our analysis, a choice supported by previous work [3].

Each image within the SST/chl-a dataset was structured as 384 × 384 pixels, where SST pixels conveyed temperature values in degrees Celsius (°C), and chl-a pixels represented chlorophyll concentration (($mgm^{-3}$)). To accommodate potential challenges posed by terrestrial, cloudy, or sparsely populated regions, we opted to replace the standard fill value with zero, a modification designed to effectively mitigate the influence of outliers. Importantly, this adjustment does not interfere with the upwelling detection process, as upwelling typically manifests in regions characterized by non-zero SST/chl-a values.

It is crucial to highlight that the creation of segmentation masks for training and testing our deep learning model relies on a training database comprising SST and chl-a images coupled with upwelling detection and segmentation outcomes (as illustrated in Fig.1). We adopted a method developed by [13], chosen for its track record of successfully identifying and delineating upwelling regions along the Moroccan coast, drawing on physical and biological satellite observations. Consequently, pixels were systematically labeled as "0" to represent areas devoid of upwelling, zones with no available data, or terrestrial regions, while "1" was designated for
areas exhibiting upwelling. For a visual representation of the segmentation map derived from the training dataset, (refer to Fig.1).

<figure><img src="image_1.png" /><figcaption>Fig. 1. Example of 8-day: (a) SST and its mask (b) chl-a and its mask selected from our database showing a typical upwelling scenario in the study area.The white color in the mask represent the upwelling region, and black color represent the non-upwelling region.</figcaption></figure>

### III. METHODS

The aim of this paper is to identify and extract upwelling zones from SST and chl-a images using the DeepRes-UpwellNet architecture. In this section, we provide details of the network architecture used.

#### A. Architecture

The proposed model, DeepRes-UpwellNet, represents a state-of-the-art end-to-end fully convolutional neural network meticulously engineered to identify upwelling regions in SST and chl-a satellite images. It is designed to improve upon the U-Net structure [24] by incorporating enhancements to handle more complex and deeper networks.

DeepRes-UpwellNet consists of two primary pathways: an encoder path, responsible for extracting features related to upwelling from the input SST and chl-a images, and a decoder path, which reconstructs these features to match the original input SST and chl-a map dimensions.

Deep neural networks with numerous stacked layers can enhance their discriminatory capabilities but are susceptible to the issue of vanishing gradients, which can impede training convergence. To combat this problem, the model employs residual units, as depicted in Figure. 2(b). These residual units

facilitate the direct propagation of gradients during backpropagation, leading to improved accuracy and convergence [25].

<figure><img src="image_2.png" /><figcaption>Fig. 2. Comparing Neural Network Building Blocks: (a): Original Plain Block in the Classic U-Net Architecture. (b): Residual Unit with Shortcut connections in the Proposed Network.</figcaption></figure>

Additionally, a Softmax layer is integrated at the network's end to generate the final binary segmentation map. For a more comprehensive understanding of DeepRes-UpwellNet's architectural details, (refer to Figure. 3).

The input layer of DeepRes-UpwellNet receives SST and chl-a satellite images, which contain temperature data in Celsius or chlorophyll concentration in $mgm^{-3}$. The output layer generates a binary segmentation map, where white pixels indicate upwelling regions, and black pixels represent non-upwelling areas, which may include cloud and land pixels.

The design of DeepRes-UpwellNet follows a symmetric structure akin to U-Net but distinguishes itself by employing residual network units instead of simple convolutional units. Each residual unit comprises a 3 × 3 convolutional layer, followed by batch normalization (BN) [26] and a ReLU activation function [27] to effectively capture non-linear features. The utilization of residual units ensures a smooth information flow through the network and mitigates the vanishing gradient problem, as depicted in Figure. 2(b).

Mathematically, the residual unit can be represented as follows:

Let's consider a neural network block, as shown in Figure. 2(a), with 'x' as the input and 'M(x)' as the true underlying distribution. The network learns the mapping from 'x' to 'y,' represented as:

$$M(x) = Y \qquad (1)$$

The residual function captures the difference between the mapping applied to 'x' (i.e., M(x)) and the original input 'x' instead of directly learning a direct mapping. This can be expressed as:

$$F(x) = M(x) - x \qquad (2)$$
The concept of shortcut connections is integral to this approach, represented by:

$$M(x) = F(x) + x \quad (3)$$

In this architecture, feature extraction from input SST and chl-a images is achieved using convolutional layers. To enhance the training process, batch normalization is applied after each convolutional layer. The convolution operations employ a 3 × 3 filter with a stride of one, resulting in convolved feature maps.

To downsample the feature maps obtained from the residual units in the encoder path, a 2 × 2 max-pooling operation is employed. This downsampling reduces the feature map size, reducing computational complexity while preserving input transformation invariance.

For the decoder path, transposed convolutions are used to restore the original resolution of the feature maps. The final layer of the network comprises a 1 × 1 convolutional layer, followed by a Softmax activation function, which converts the multi-channel feature maps into the ultimate binary segmentation map. In this segmentation map, pixels are labeled as '1' and '0' for upwelling and background regions, respectively.

The DeepRes-UpwellNet model represents a significant leap forward in upwelling region detection from SST and chl-a satellite images. It combines the power of residual units with a symmetric U-Net architecture to deliver more accurate and reliable results when compared to traditional methodologies.

## B. Training the Model

The segmentation model employed for the extraction and identification of upwelling regions in SST and chl-a images was developed using Python, TensorFlow, and Keras, serving as a high-level neural network API, operates seamlessly on top of TensorFlow. Leveraging Keras facilitates rapid experimentation without the need for extensive code development.

The training dataset utilized in this study comprises original SST and chl-a images alongside manually segmented mask images. Specifically, the dataset consists of 450 sets of both masked and unmasked SST/chl-a images, which were partitioned into training and validation subsets. The training subset encompassed (70%) of the images, while the remaining (30%) were reserved for validation purposes. We use ADAM optimization function, with an initial learning rate of 0.0001, and 300 epochs.

## C. Results and Discussion

In this section, we describe the results obtained from our experiments. To evaluate the effectiveness of our method, we have chosen to compare it with the method developed by [13] and by [23].

The results of the visual inspection reveal that the DeepRes-UpwellNet outperforms the other two methods in terms of performance evaluation. Figure 4 provides a clear illustration of the DeepRes-UpwellNet's remarkable ability to accurately extract the primary upwelling area along the Moroccan Atlantic coast. In the realm of detecting upwelling in both SST

and chl-a images, both the U-Net and DeepRes-UpwellNet models exhibit superior effectiveness compared to the traditional method. Notably, these models successfully detected upwelling in the northern region, particularly in the case of SST images, where the conventional method fell short.

Moreover, upon a detailed visual examination, it becomes evident that the DeepRes-UpwellNet model surpasses the U-Net framework. Notably, the DeepRes-UpwellNet excels at identifying even the smallest areas of upwelling, as indicated by the red circle in Figure 4, which the U-Net fails to detect.

<figure><img src="image_3.png" /><figcaption>Fig. 3. DeepUpwell Res-Net Architecture: Binary Segmentation from SST Images. Input layer contains temperature values in (°C) or chlorophyll-concentration in ($mgm^{-3}$)). The left part depicts the Encoder Path, while the right part represents the Decoder Path.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Fig. 4. Comparative visualization of upwelling segmentation results using three approaches to (a) SST and (b) chl-a image.</figcaption></figure>

To establish the quantitative validity of our proposed method, we computed the upwelling validation index (Vup) on 92 SST images and 92 chl-a images collected over two consecutive years (2020-2021). The results for Vup are presented in Table I and are compared with the findings from previous works, as reported in [23] and [13].

<table><caption>TABLE I
RESULTS OF V<sub>up</sub> ON THE PROPOSED METHODOLOGY ON SST AND CHL-A IMAGES FROM 2020 TO 2021.</caption><thead><tr><td></td><td>time</td><td>V<sub>up</sub></td></tr></thead><tbody><tr><td>DeepRes-UpwellNet</td><td>3s</td><td>SST= 79.18%; chl-a= 79.34%</td></tr><tr><td>Methodology of [23]</td><td>3s</td><td>SST= 78.30%; chl-a= 78.97%</td></tr><tr><td>Methodology of [13]</td><td>1min9s</td><td>SST= 76.74%; chl-a= 77.88%</td></tr></tbody></table>

The calculated index substantiates the effectiveness of our approach in accurately delineating the primary Moroccan upwelling fronts. Notably, for both chl-a and SST, our method exhibits a significant improvement of over 1.5% in V<sub>up</sub> compared to the approaches developed in the previous works. This substantial enhancement underscores the superior upwelling detection capabilities of the DeepRes-UpwellNet, as it adeptly identifies more upwelling regions than the conventional U-Net method.

## IV. CONCLUSION

In this study, we have introduced a novel deep learning tool called the DeepRes-UpwellNet, specifically designed for accurately detecting and localizing upwelling regions in satellite images of SST and chl-a along the Moroccan Atlantic coast. The performance evaluation demonstrated that the proposed model surpasses traditional segmentation methods, offering an effective and objective analysis of this oceanic phenomenon. Additionally, a comparative analysis was conducted between the segmentation results obtained by the DeepRes-UpwellNet and the U-Net. The DeepRes-UpwellNet model outperformed

the U-Net framework, showcasing its enhanced capability in identifying even the smallest areas of upwelling that were not detectable by the U-Net. To quantitatively validate our proposed method, we computed the V<sub>up</sub> index over the years between 2020 and 2021. The results confirm the effectiveness and reliability of our approach to detecting upwelling.

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