Understanding the spatial variability of soil organic matter (SOM) is critical for studying and assessing soil fertility and quality. This knowledge is important for soil management, which results in high crop yields at a reduced cost of crop production and helps to protect the environment. The...

Land surface temperature plays an essential role in estimating radiation budgets, in heat balance studies, as a control for climate dynamics and for soil–vegetation–atmosphere transfer modeling, and in studying the impact of land use/land cover (LULC) changes at the regional level. This study provides a comprehensive evaluation of the relationship between land use and land surface temperature (LST), through a landscape dynamics assessment based on multi-source and multi-sensor remote sensing technologies. In particular, the study was performed using Landsat satellite 5 TM, ETM, and OLI 8 data for three different dates (1985, 2001 and 2017) and aimed to assess the effects of land use/land cover changes on the LST distribution in the region of Taroudant, Morocco. Spatial and statistical analysis and comparison of maps generated from remotely sensed data using GIS indicate the existence of different changes in the Taroudant region between 1985 and 2017. These changes are predominantly characterized by an increase in built-up areas and bare ground and a decrease in natural areas (vegetation, forest, etc.). The average temperatures in 1985, 2001 and 2017 in open forests were 32.74 °C, 34.37 °C and 39.17 °C, respectively. The farming greenhouse temperatures were 24.09 °C, 28.5 °C, 35.58 °C, and barren soils 37.14 °C, 38.38 °C, 40.01 °C. The average land surface temperatures of farming lands were 24.31 °C, 27.87 °C and 28.61 °C, respectively. As a result, soil artificialization and everything associated with it, such as greenhouse gas emissions, and abusive consumption of farming and natural land, are likely to be the origin of environmental problems and climate change marked mainly by these changes in surface temperature, irregular rainfall, and unprecedented periods of drought.

The pump-irrigated soils of the coastal Chaouia in Morocco are subject to changes in their qualities due to the quality of the irrigation water and their proximity to the sea. This work aims to approach irrigation water quality and the influence of these waters on the quality of agricultural soils. The study is based on the systematic and selective sampling of water and soils (19 water samples and 19 soil samples). Analyzed parameters mainly include the salinity and pH of the soil and water samples. The main results show that soil pH varies from 6.81 to 7.7. However, the pH of the water varies from 6.22 to 8.13. The electrical conductivity in soils varies from 12,260 μs/cm to 700 μs/cm and from 1123 μs/cm to 8120 μs/cm. The results of the analysis of soil salinity show that in moving away from the sea, the salinity decreases, and the salinity of the water samples follows the same trend. The Richard and Wilcox graphs show that most water samples taken near the sea are highly mineralized and have poor water quality. This paper presents important aspects of the feasibility of coastal agriculture and can be a source of inspiration for future research and planning of coastal agriculture.

Recent advancements in remote sensing and geospatial processing tools have ushered in a new era of mapping and monitoring landscape changes across various scales. This progress is critical for understanding and anticipating the underlying drivers of environmental change. In particular, large-scale Land Use and Land Cover (LULC) mapping has become an indispensable tool for territorial planning and monitoring. This study aims to map and evaluate LULC changes in the El Jadida region of Morocco between 1985 and 2020. Utilizing multispectral Landsat imagery, we applied and compared three supervised machine learning classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NNET). Model performance was assessed using statistical metrics, including overall accuracy, the Kappa coefficient, and the F1 score. The results indicate that the RF algorithm was the most effective, achieving an overall accuracy of 90.3% and a Kappa coefficient of 0.859, outperforming both NNET (81.3%; Kappa = 0.722) and SVM (80.2%; Kappa = 0.703). Analysis of explanatory variables underscored the decisive contribution of the NDWI, NDBI, and SWIR and thermal bands in discriminating land cover classes. The spatio-temporal analysis reveals significant urban expansion, primarily at the expense of agricultural land, while forested areas and water bodies remained relatively stable. This trend highlights the growing influence of anthropogenic pressure on landscape structure and underscores its implications for sustainable resource management and land use planning. The findings demonstrate the high efficacy of machine learning, particularly the RF algorithm, for accurate LULC mapping and change detection in the El Jadida region. This study provides a critical evidence base for regional planners to address the ongoing loss of agricultural land to urban expansion.