The urban heat island (UHI) phenomenon is a harmful environmental problem in urban areas affecting both climatic and ecological processes. This paper aims to highlight and monitor the spatial distribution of Surface UHI (SUHI) in the Casablanca region, Morocco, using remote sensing data. To achieve this goal, a time series of Landsat TM/ETM+/OLI-TIRS images was acquired from 1984 to 2016 and analyzed. In addition, nocturnal MODIS images acquired from 2005 to 2015 were used to evaluate the nighttime SUHI. In order to better analyze intense heat produced by urban core, SUHI intensity (SUHII) was computed by quantifying the difference of land surface temperature (LST) between urban and rural areas. The urban core SUHII appears more significant in winter seasons than during summer, while the pattern of SUHII becomes moderate during intermediate seasons. During winter, the average daytime SUHII gradually increased in the residential area of Casablanca and in some small peri-urban cities by more than 1 °C from 1984 to 2015. The industrial areas of the Casablanca region were affected by a significant rise in SUHII exceeding 15 °C in certain industrial localities. In contrast, daytime SUHII shows a reciprocal effect during summer with emergence of a heat island in rural areas and development of cool islands in urban and peri-urban areas. During nighttime, the SUHII remains positive in urban areas year-round with higher values in winter as compared to summer. The results point out that the seasonal cycle of daytime SUHII as observed in the Casablanca region is different from other mid-latitude cities, where the highest values are often observed in summer during the day.

We analyzed the urban development sustainability in five major urban areas of Algeria by the standard of the UN Sustainability Development Goal indicator SDG 11.3.1, which focuses on the ratio of land consumption rate to population growth rate. We utilized the annual global artificial impervious area (GAIA) dataset to characterize land-use and population data from the two censuses carried out by the National Office of Statistics (ONS) for 2008 and 2018. We discuss the prevailing relationship between urban land consumption rate and population growth rate at the smallest territorial and population census unit scale. We confirm that the indicator SDG 11.3.1 is nonlinear and that while, for example, the wilaya of Tlemcen as a whole appears to be on a sustainable path, twenty-one of its communes are not. We found that overall, and for most of its communes, the wilaya of Oran seems to have an urban land use commensurable to its population growth, but in the wilaya of Algiers, out of fifty-seven communes, only fourteen have a tendency towards sustainable development. However, the latter wilaya hosts the country’s capital and includes government buildings that are uninhabited but are accounted for as land consumed, and as such, the relationship between urban land consumption and population growth is biased. The wilaya of Annaba showed large discrepancies in terms of land use and population growth rates, and the evolution of these quantities is not homogenous across communes and not sustainable. In the Saharan wilaya of Ghardaia, the development is not homogeneous in all communes, with smaller communes undergoing buildup increases of more than 150% over the decade. Finally, in all communes where population growth exceeded urban land growth, there will be overcrowding, an aspect neither the SDG 11.3.1 nor the impervious surface per capita indicator captures. This result, in addition to other limitations, makes SDG 11.3.1 incomplete for the determination of the sustainable development in urban areas.

During the last decades, Morocco has recorded substantial urbanization and faced challenges related to urban sprawl and encroachment on fertile lands. This paper reviews several studies assessing urban sustainability development in 27 Moroccan urban areas using the UN’s Sustainable Development Goal (SDG) indicator 11.3.1, i.e., the ratio of Land Consumption Rate (LCR) to Population Growth Rate (PGR). Among the 27 urban areas reviewed, analysis of SDG 11.3.1 data revealed that only 7 exhibited trends towards sustainable urban development, while the remaining 20 showed a divergence from the sustainability objectives. We analyze the studies, conducted between 2020 and 2023, describing in depth the relationship between LCRs and PGRs, and provide a comprehensive summary of the SDG 11.3.1 outcome, discussing along the way methodological variations, and introducing a new concept for the evaluation of urban land consumption. The review reveals disparate regional urban development trends, emphasizing the need for local land tenure rules. Additionally, the study discusses how the SDG 11.3.1 reacts under the influence of several parameters such as land characteristics, climate, and geographic location. While the study acknowledges the limitations of SDG 11.3.1, it found that the indicator provides valuable insights into trends and comparisons, and should offer assistance to stakeholders, urban planners, and decision makers in their pursuit of sustainable development. However, these identified limitations underscore the need for a more comprehensive and multidimensional urban sustainability development indicator, capable of better addressing the complexities of the urban environment.

Keywords: Air temperature, land surface temperature, LST, MODIS, Casablanca, remote sensing, urban climate Air temperature is considered to be an essential variable for the study and analysis of meteorological regimes and chronics. However, the implementation of a daily monitoring of this variable is very difficult to achieve. It requires sufficient of measurements stations density, meteorological parks and favourable logistics. The present work aims to establish relationship between day and night land surface temperatures from MODIS data and the daily measurements of air temperature acquired between [2011-20112] and provided by the Department of National Meteorology [DMN] of Casablanca, Morocco. The results of the statistical analysis show significant interdependence during night observations with correlation coefficient of R²=0.921 and Root Mean Square Error RMSE=1.503 for T_min while the physical magnitude estimated from daytime MODIS observation shows a relatively coarse error with R²=0.775 and RMSE=2.037 for T_max. A method based on Gaussian process regression was applied to compute the spatial distribution of air temperature from MODIS throughout the city of Casablanca.