# Advanced Deep Learning Approach for Accurate Upwelling Detection Along Morocco's Atlantic Coast Using SST Imagery

Belmajdoub Hanae, El Belghiti Younes, Minaoui Khalid, Khalid Daoudi

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Belmajdoub Hanae, El Belghiti Younes, Minaoui Khalid, Khalid Daoudi. Advanced Deep Learning Approach for Accurate Upwelling Detection Along Morocco's Atlantic Coast Using SST Imagery. IEEE Geoscience and Remote Sensing Letters, 2024, 21, pp.1-5. 10.1109/LGRS.2024.3418880. hal-04638831

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# Advanced Deep Learning Approach for Accurate Upwelling Detection along Morocco's Atlantic Coast using SST Imagery

Belmajdoub Hanae, El Belghiti Younes, Minaoui Khalid, and Daoudi Khalid

***Abstract***—The study of coastal upwelling through the analysis of sea surface temperature (SST) satellite imagery has been a valuable approach because of its efficiency and practicality. Building on inception and residual structures, we introduce IncepRes<sub>up</sub>-Net, a novel deep learning model for identifying upwelling regions along Morocco's Atlantic coast. This model effectively addresses limitations in recent methods targeting the same upwelling system and outperforms them by more accurately detecting true upwelling areas, thereby minimizing false positives. Applied to SST data spanning from 2000 to 2022, IncepRes<sub>up</sub>-Net demonstrates superior performance over traditional and contemporary deep learning models, marked by its precise segmentation capabilities and robustness in real-world detection scenarios. Our findings highlight the model's effectiveness in leveraging SST imagery for upwelling detection, establishing a new benchmark in the application of deep learning within geoscience and remote sensing fields.

***Index Terms***—Coastal Upwelling, IncepRes<sub>up</sub>-Net, sea surface temperature, Morocco's Atlantic coast, Deep Learning.

## I. INTRODUCTION

The Canary Current Upwelling System (CCUS) is celebrated for its high productivity, significantly impacting the economies of various African nations, particularly through its fisheries [1]. This system spans an extensive coastline and ranks among the globe's most productive marine ecosystems, with the Moroccan and Mauritanian coasts being especially fertile [2]. Our study focuses on the Moroccan coast, where the interplay between the north-to-south trade winds and the surface currents infuses the coastal waters with cold, nutrient-rich waters, enhancing marine productivity [3]. This upwelling phenomenon is easily identified by the cooler temperatures in coastal areas compared to warmer offshore regions, monitored using satellite imagery that captures infrared thermal and ocean color data [4].

Sea surface temperature and chlorophyll-a (chl-a) concentrations are key indicators for studying upwelling dynamics. Several studies have been explored using ocean color satellite data, employing various algorithms for detecting upwelling events [5] [6].

To identify upwelling zones from SST imagery along Morocco's southern Atlantic coast, researchers have used a range of image processing methods, from traditional techniques to advanced clustering algorithms and region-growing segmentation [7], [8], [9]. However, these methods have limitations when applied to the wider Moroccan coast, including the central and northern regions. This is mainly due to significant variations in SST with latitude. To overcome this, recent

advancements have integrated physical and biological satellite data to better understand upwelling dynamics, focusing on spatial and temporal changes [10]. Additionally, the development of thermal upwelling indices has provided deeper insights into the seasonal and interannual trends in the area [3], [11].

Traditional image processing techniques, [7], [8], [9], while valuable in many contexts, encounter specific challenges when applied to the analysis of SST data along the diverse coastal regions of Morocco. One primary challenge is the significant latitudinal variations in SST, which can compromise the accuracy of traditional segmentation methods. Such methods may not be adequately calibrated to address this environmental diversity, often resulting in the incorrect identification of upwelling zones, particularly along the central and northern regions of the Moroccan coastline [3]. Additionally, these traditional techniques typically require extensive computational resources and manual intervention, which limits their scalability and feasibility for large-scale applications. They are also highly dependent on specific types of data, further restricting their flexibility and adaptability to different or evolving data sources. To address these limitations, recent developments in artificial intelligence offer promising alternatives.

Deep learning has significantly transformed marine ecosystem analysis, particularly through enhanced remote sensing applications. Convolutional neural networks (CNNs) have been pivotal, with studies like [12] using them to upscale sea surface measurements, and [13] employing them to forecast SST changes more accurately than traditional models. This technology's versatility extends to ecological and agricultural analyses, improving data analysis for habitat delineation and crop mapping as demonstrated by [14] and [15], respectively. In the study of upwelling detection, the authors of [16] implemented a CNN-based U-Net to identify upwelling zones using SST and chl-a imagery, while [17] introduced DeepResUpwellNet, an improved model for more precise upwelling segmentation along the Moroccan coast. These applications highlight deep learning's capability to enhance environmental monitoring's precision and effectiveness.

In this paper, we introduce IncepRes<sub>up</sub>-Net, a new deep learning architecture that integrates the strengths of inception and residual structures within a refined U-Net framework. This architecture is specifically designed to improve the detection and segmentation of upwelling currents along the Atlantic coastline of Morocco using SST images. Our model distinguishes itself by offering more robust and precise detection capabilities, significantly outperforming traditional methods. It
is exceptionally effective in accurately identifying upwelling events, overcoming the usual classification errors found in standard approaches, and significantly reducing false positives and negatives. Thus, our approach signifies a notable advancement in precision for detecting upwelling phenomena through remote sensing.

The rest of this presentation is organized in the following manner: Section II offers a detailed description of the region under study and the database used in this research. Section III describes the approach taken to identify and extract upwelling regions. Section IV discusses the findings from the experiments and their interpretation, followed by a conclusion.

## II. DESCRIPTION OF THE REGION OF INTEREST

The geographical scope of our study, as illustrated in figure.1(a), ranges from 6°W to 19°W longitude and 21°N to 36°N latitude. We employed a comprehensive dataset to train a deep neural network, utilizing satellite-based SST imagery. This dataset covers an extensive time frame from 2000 to 2022 and focuses on examining coastal upwelling events in the Morocco region. The data for this study are sourced from the MODIS-TERRA 8-day mapped products, which provide spatial coverage at a resolution of 4 × 4 km. These datasets are available through a specialized online database, ensuring easy public access. To achieve comprehensive spatial and temporal coverage, we chose an 8-day temporal resolution, a choice motivated by previous studies [3]. Each SST image in our dataset consists of 384 × 384 pixels, with each pixel representing temperature values in Celsius.

To address issues related to land, cloudy areas, or data-sparse regions, we assign a fill value of zero to these pixels. This reduces the impact of outliers without affecting the upwelling detection process, as upwelling typically occurs in regions with non-zero SST values. In the Moroccan coastal zone, historical SST data from 2000 to 2022 shows temperatures ranging between 12 and 45 degrees Celsius. Since zero-degree temperatures do not occur under normal conditions, using zero as a filler avoids confusion with natural SST readings. Creating segmentation masks was a key step in training and evaluating our deep learning model. These masks were developed using a dataset that included SST images. We used the methodology established by [10] for generalizing the masks. It was specifically chosen for its proven effectiveness in accurately identifying and defining upwelling regions along the Moroccan coastline. This method, referenced in existing literature, relies on the integration of physical and biological data obtained from satellite observations. For labeling, pixels in non-upwelling areas, including those with missing data or land, were assigned a value of 0, while pixels in upwelling regions were marked with a 1. Figure.1(b) displays the segmentation map generated from this labeled dataset.

## III. PROPOSED METHODS

In this study, we introduce a novel IncepRes<sub>up</sub>-Net architecture, specifically designed for segmenting SST images to detect upwelling in the Moroccan region. This architecture

<figure><img src="image_2.png" /><figcaption>Fig. 1. (a) 8-day SST (°C) example, sample from our database, exemplifying a standard upwelling event in the area of study. (b): The segmentation training, areas of upwelling are marked in white, while black is used to indicate regions where upwelling is not occurring.</figcaption></figure>

effectively combines inception and residual blocks within a U-Net framework, aiming to enhance the detection and extraction of upwelling features.

### A. Architecture

The inception block as detailed in fig. 2(a) demonstrates remarkable efficacy in identifying and delineating upwelling regions within SST images. It allows the network to capture both the finest details and the broadest patterns simultaneously [18]. This block utilizes 1×1 convolutions to efficiently reduce dimensionality while maintaining spatial resolution. It then employs 3×3 and 5×5 convolutions to extract features at progressively larger scales, enabling a rich and varied feature representation. Additionally, the inception block incorporates a max pooling path to compress the input, enhancing robustness against input variations, followed by a 1×1 convolution that reintegrates the pooled information into the feature map. The output from these parallel paths is concatenated, resulting in a comprehensive feature map that includes information across multiple scales and spatial hierarchies.

The residual block, is specifically designed to address the vanishing gradient problem that is common in deep neural networks. It includes skip connections that allow the flow of information and gradients across layers without attenuation, ensuring that the signal and gradient are preserved even in deeper layers of the network. This is crucial for maintaining learning efficacy across the depth of the network.

The integration of these blocks as detailed in Fig. 2(b) forms the backbone of our Residual-Inception block, where each block combines the detailed feature extraction capabilities of the inception module with the gradient-preserving properties of the residual module. This synergy allows for enhanced feature learning without the risk of gradient vanishing, making the network robust to the complexity of SST data.

The proposed architecture of the IncepRes<sub>up</sub> network, as illustrated in Figure 3, is distinguished by its encoder-decoder structure. This architecture incorporates modified inception and residual blocks, enhancing the network's capability to capture context and achieve precise localization. The encoder path features successive residual-inception blocks paired with max pooling operations, progressively reducing spatial dimensions
<figure><img src="image_3.png" /><figcaption>Fig. 2. Structural scheme for (a): Inception block, (b): Residual-Inception block.</figcaption></figure>

while enriching the depth of features. The process begins with an initial reduction of the input SST images from 384x384 pixels to a feature map size of 32x32. This significant initial reduction aids in managing computational efficiency while capturing essential global features. Subsequently, the network navigates through a series of increasing resolutions 64x64, 128x128, and finally 512x512 prior to the onset of the decoding process. Each step in increasing resolution is meticulously designed to progressively enrich feature extraction, allowing the network to handle the complexities of the SST data and encode the detailed thermal behaviors essential for delineating upwelling zones. Conversely, the decoder path systematically restores spatial resolution by upsampling and merging features from the encoder path, utilizing skip connections to maintain spatial context. This multi-scale approach enables the architecture to balance the capture of broad contextual information with the detailed local features necessary for effective segmentation of upwelling zones.

The fusion of architectural elements in the proposed IncepRes\_up-Net model is specifically tailored to achieve precise demarcation of upwelling zones. By leveraging the strengths of each component, namely the inception and residual blocks within the encoder-decoder framework, the model forms a robust and sophisticated tool for analyzing this complex phenomenon. This synergistic integration enhances the model's ability to accurately identify and segment relevant features in the data, making it highly effective for the study of upwelling in SST images.

## B. Experimental Setup

The framework is built on Python, incorporating TensorFlow and Keras for its functionality. We utilized a dataset consisting of 1035 original SST images and their manually segmented counterparts for analysis. The dataset was split into two parts: 80% for training and 20% for validation purposes. We employed the ADAM optimizer to train our model, initiating with a learning rate of 0.0001 and conducted the training over 500 epochs.

Figure 4(e),(j) provide a segmentation results of SST images using our proposed method.

## IV. RESULTS AND VALIDATION

For assessing the efficiency of our approach, we have chosen to compare it with traditional masking methods, UNet, and Residual-UNet.

Figure 4 provides a comparative analysis of SST segmentation, demonstrating the superior efficiency of the IncepRes\_up-Net algorithm compared to the traditional masking method described by [10]. In our study, we employed the masking technique from [10] to identify upwelling regions along the Moroccan coast, due to its proven effectiveness in identifying and defining these regions. This method efficiently integrates physical and biological data obtained from satellite observations, providing a solid foundation for the initial detection of upwelling phenomena. However, although this approach reliably captures upwellings in the central and northern regions of Morocco, it does not consistently identify upwellings across all analyzed images. Our IncepRes\_up-Net model enhances the accuracy and consistency of upwelling detection, overcoming the limitations of traditional methods in the face of the variability of coastal SST data. Consequently, the IncepRes\_up-Net offers significant improvements in terms of precision and reliability in a complex geographical context. This analysis includes comparisons with UNet and Residual-UNet models in delineating upwelling zones along the Moroccan Atlantic coast. The blue circle in the mask visualization (Figure 4(b)) highlights regions of false positives where upwelling was erroneously detected. In contrast, the IncepRes\_up-Net algorithm effectively eliminates these inaccuracies (Figure 4(e), red circle). Both UNet and Residual-UNet models also partially remove these erroneously identified areas (Figures 4(c),(d), red circles). Additionally, the IncepRes\_up-Net algorithm accurately detects upwelling in the central and northern Moroccan coast (Figures 4(e),(j), gray circles), a capability not shown by the traditional masking method (Figures 4(b),(g), green circles). UNet and Residual-UNet models successfully detect upwelling in the central coast (Figures 4(h),(i), gray circles), with Residual-UNet showing an improvement by eliminating isolated pixels (Figures 4(h),(i), black circle). These results underscore the IncepRes\_up-Net algorithm's enhanced accuracy in identifying true upwelling events and reducing inaccuracies inherent in conventional segmentation methods.

### A. Quantitative Validation

In our study, we analyzed 1035 SST images from the Moroccan region, spanning from 2000 to 2022. Our focus was on accurately segmenting upwelling zones, achieved by measuring a standard gradient at their boundaries. These boundaries were identified by the temperature difference between adjacent pixels: one in the offshore zone, the other in the upwelling zone. As previously mentioned, upwelling zones typically show cooler temperatures than offshore areas. A positive gradient in SST images indicates correct upwelling detection termed a "good", while a negative gradient suggests unreliable detection, termed a "bad" gradient value.

The upwelling validation index ($V_{up}$), as introduced by [4], was employed to quantitatively assess the segmentation accuracy. This index is formulated as follows:
<figure><img src="image_4.png" /><figcaption>Fig. 3. Detailed framework overview: The advanced IncepRes<sub>up</sub>-Net system for coastal upwelling monitoring.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 4. Segmentation results in the Moroccan upwelling region: (a),(d) represents the SST input data; (b),(e) shows the mask segmentation; (c),(f) depicts the predicted segmentation using the IncepRes<sub>up</sub>-Net model. The Upwelling Region is delineated with a black line.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 5. (a) Temporal analysis of the $V_{up}$ index applied to SST data in Moroccan region using: (a): IncepRes<sub>up</sub>-Net, (b): Mask segmentation, (c): Residual-Unet, (d): Unet. Green indicates areas where the detection of upwelling is correct (true positives), while red indicates incorrect detections (false positives).</figcaption></figure>

$$V_{up} = \frac{N_C}{N_L * T_I} \qquad (1)$$

where, $N_C$ (the total number of accurately classified latitude steps from SST images). $N_L$ is the number of latitude steps

along the Moroccan Atlantic coast, and $T_I$ is the total number of processed SST images, which is 1035. A higher $V_{up}$ value indicates a greater number of correctly identified upwelling zones.

The findings from the validation of the upwelling index are detailed in Table. I.

<table><caption>TABLE I
RESULTS OF $V_{up}$ INDEX ON SST IMAGES FROM 2000 TO 2022.</caption><thead><tr><th>Method</th><th>IncepRes<sub>up</sub>-Net</th><th>Residual-Unet</th><th>U-Net</th><th>Mask</th></tr></thead><tbody><tr><td>$V_{up}$</td><td>SST= 97.76%</td><td>SST= 92.69%</td><td>SST= 90.31%</td><td>SST= 88.44%</td></tr></tbody></table>

The four methods tested were IncepRes<sub>up</sub>-Net, Residual-Unet, U-Net, and a mask segmentation technique. The validation results over 23 years, as represented graphically in the figure. 5 and detailed in Table. I, highlight that the IncepResup-Net achieved the highest validation index, indicating superior performance in correctly identifying upwelling zones. The validation indices for the tested methods are as follows: IncepResup-Net at the highest, followed by Residual-UNet with a 92.69% index, U-Net at 90.31%, and the mask segmentation method at 88.44%. This ordering reflects a progressive decline in the $V_{up}$ score, suggesting a corresponding decrease in the effectiveness of each subsequent method in accurately segmenting the upwelling zones.

## B. Evaluation Metrics

To ensure a comprehensive and reliable evaluation of our model, IncepResup-Net, we have implemented a diverse set of evaluation metrics. Three widely recognized metrics have been selected for this evaluation: overall accuracy, F1-score, and Intersection over Union (IoU). In our comparative analysis of methodologies for detecting and characterizing upwelling zones in SST images, the IncepResup-Net, along with Residual-Unet and Unet, were assessed using a variety of performance indicators. The results of this evaluation are outlined in table. II.

Our IncepRes<sub>up</sub>-Net model exhibited superior performance, achieving a near-perfect accuracy rate of 99.87%, which signifies its exceptional capability in accurately identifying upwelling zones. This high accuracy is further supported by an Intersection over Union (IoU) score of 97.68% and an F1-score
<table><caption>TABLE II
PERFORMANCE MODELS.</caption><thead><tr><td>Method</td><td>Accuracy (%)</td><td>IoU (%)</td><td>F1-Score (%)</td></tr></thead><tbody><tr><td>IncepRes<sub>up</sub>-Net</td><td>99.87</td><td>97.68</td><td>94.23</td></tr><tr><td>Residual-Unet</td><td>97.80</td><td>85.66</td><td>90.82</td></tr><tr><td>Unet</td><td>96.95</td><td>81.23</td><td>86.12</td></tr></tbody></table>

of 94.23%, both of which demonstrate an outstanding level of agreement with the actual upwelling regions. These metrics collectively highlight the model's precision and reliability in segmenting SST images, indicating its effectiveness in delineating upwelling areas with minimal error. Contrastingly, the Residual-Unet model, although still demonstrating high accuracy at 97.80%, showed a decrease in IoU to 85.66% and an F1-score of 90.82%. The lower IoU suggests a reduced precision in accurately identifying upwelling zones, potentially leading to a higher incidence of misclassification—either falsely identifying non-upwelling areas as upwelling or failing to detect some upwelling regions. The Unet model, marked by an accuracy of 96.95%, the lowest IoU of 81.23%, and an F1-score of 86.12%, was the least effective among the three methods tested. These metrics reflect a diminished capacity to accurately and precisely segment and identify upwelling areas within SST images. Compared to its counterparts, the Unet's lower performance metrics underscore its limitations in reliably distinguishing upwelling zones.

## V. CONCLUSION

In this research, we introduced IncepRes<sub>up</sub>-Net, a novel deep learning model that significantly enhances the segmentation of upwelling systems along Morocco's Atlantic coast. By integrating SST data with inception and residual structures, IncepRes<sub>up</sub>-Net improves precision and detail in segmenting upwelling zones and excels in detecting upwelling events, surpassing traditional methods and previous deep learning models. Extensive benchmarking and quantitative validation using SST images from 2000 to 2022 showed the model's superiority in key performance metrics such as Accuracy, IoU, and Dice-Metric, reducing false positives and negatives. However, the model's generalizability to other regions and its computational efficiency in real-time applications require further investigation. Future work will extend IncepRes<sub>up</sub>-Net to other coastal regions and integrate multi-modal data sources to enhance predictive accuracy and robustness under changing climatic conditions.

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