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. 2023 Jul 7;23(13):6229.
doi: 10.3390/s23136229.

Spatiotemporal Thermal Variations in Moroccan Cities: A Comparative Analysis

Ahmed Derdouri  1 , Yuji Murayama  1 , Takehiro Morimoto  1
Affiliations

Spatiotemporal Thermal Variations in Moroccan Cities: A Comparative Analysis

Ahmed Derdouri et al. Sensors (Basel). .

Abstract

This study examines the Land Surface Temperature (LST) trends in eight key Moroccan cities from 1990 to 2020, emphasizing the influential factors and disparities between coastal and inland areas. Geographically weighted regression (GWR), machine learning (ML) algorithms, namely XGBoost and LightGBM, and SHapley Additive exPlanations (SHAP) methods are utilized. The study observes that urban areas are often cooler due to the presence of urban heat sinks (UHSs), more noticeably in coastal cities. However, LST is seen to increase across all cities due to urbanization and the degradation of vegetation cover. The increase in LST is more pronounced in inland cities surrounded by barren landscapes. Interestingly, XGBoost frequently outperforms LightGBM in the analyses. ML models and SHAP demonstrate efficacy in deciphering urban heat dynamics despite data quality and model tuning challenges. The study's results highlight the crucial role of ongoing urbanization, topography, and the existence of water bodies and vegetation in driving LST dynamics. These findings underscore the importance of sustainable urban planning and vegetation cover in mitigating urban heat, thus having significant policy implications. Despite its contributions, this study acknowledges certain limitations, primarily the use of data from only four discrete years, thereby overlooking inter-annual, seasonal, and diurnal variations in LST dynamics.

Keywords: Land Surface Temperature (LST); Moroccan urban landscapes; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Map of Morocco Köppen-Geiger climate classification [14] highlighting target cities and their geographical settings: Casablanca, Tangier, Agadir, Fes, Marrakech, Oujda, Laayoune, Errachidia. Landsat 8 and 9 images produced by the U.S. Geological Survey. Industrial zones and transportation networks data © OpenStreetMap contributors [15].
Figure 2
Figure 2
Methodology flowchart.
Figure 3
Figure 3
Temporal evolution of LST during summer (June–August) for coastal Moroccan cities (Casablanca, Tangier, and Agadir) from 1990 to 2020. The color gradient represents LST intervals from 15 to 60 °C, with blue indicating lower temperatures, yellow medium temperatures, and red higher temperatures.
Figure 4
Figure 4
Temporal evolution of LST during summer (June–August) for inland Moroccan cities (Fes, Marrakech, Oujda, Laayoune, and Errachidia) from 1990 to 2020. The color gradient represents LST intervals from 15 to 60 °C, with blue indicating lower temperatures, yellow medium temperatures, and red higher temperatures.
Figure 5
Figure 5
Spectral indices (NDVI, NDBI, NDWI) plotted against LST over time (1990–2020) for the coastal cities Casablanca, Tangier, and Agadir. The color of the dots represents LST using the jet colormap, with darker blue indicating lower temperatures and reddish colors indicating higher temperatures. Note: data for Agadir in 1990 is unavailable; hence, data from 1995 is used for initial representation.
Figure 6
Figure 6
Spectral indices (NDVI, NDBI, NDWI) plotted against LST over time (1990–2020) for the inland cities Fes, Marrakech, Oujda, Laayoune, and Errachidia. The color of the dots represents LST using the jet colormap, with darker blue indicating lower temperatures and reddish colors indicating higher temperatures.
Figure 7
Figure 7
Bar plots illustrating temporal trends of GWR coefficients for spectral indices and geographical features across the target Moroccan cities for the years 1990, 2000, 2010, and 2020. The table below shows corresponding adjusted R-squared values, indicating model fit for each city and year. Note that DEM, ASPECT, SLOPE, and HILLSHADE are included in the legend for completeness, even though they may not be discernible in the figure due to their values.
Figure 8
Figure 8
SHAP-value-based importance of LST driving factors in coastal Moroccan cities.
Figure 9
Figure 9
SHAP-value-based importance of LST driving factors in inland Moroccan cities.
Figure 10
Figure 10
Heatmap plots illustrating the interactions of different features influencing LST across coastal Moroccan cities from 1990 to 2020. The intensity of color represents the interaction score, with higher values indicating stronger interactions. Notable interactions are discussed in the text.
Figure 11
Figure 11
Heatmap plots illustrating the interactions of different features influencing LST across inland Moroccan cities from 1990 to 2020. The intensity of color represents the interaction score, with higher values indicating stronger interactions. Notable interactions are discussed in the text.
Figure 12
Figure 12
Dependence plots illustrating the most notable interactions between different factors influencing LST across coastal Moroccan cities from 1990 to 2020.
Figure 13
Figure 13
Dependence plots illustrating the most notable interactions between different factors influencing LST across inland Moroccan cities from 1990 to 2020.
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