Forest resource conservation necessitates a deeper understanding of forest ecosystem processes and how future management decisions and climate change may affect these processes. Argania spinosa (L.) Skeels is one of the most popular species in Morocco. Despite its ability to survive under harsh drought, it is endangered due to soil land removal and a lack of natural regeneration. Remote sensing offers a powerful resource for mapping, assessing, and monitoring the forest tree species at high spatio-temporal resolution. Multi-spectral Sentinel-2 and Synthetic Aperture Radar (SAR) time series combined with Digital Elevation Model (DEM) over the Argan forest in Essaouira province, Morocco, were subjected to pixel-based machine learning classification and analysis. We investigated the influence of different SAR data parameters and DEM layers on the performance of machine learning algorithms. In addition, we evaluated the synergistic effects of integrating remote sensing data, including optical, SAR, and DEM data, for identifying argan trees in the Smimou area. We collected data from Sentinel-2, Sentinel-1, SRTM DEM, and ground truth sources to achieve our goal. Testing different SAR parameters and integrating DEM layers of different resolutions with other remote sensing data showed that the Lee Sigma filter with a size of 11×11 and a DEM layer of 30 m resolution gave the best results using the Support Vector Machine algorithm. Significant improvements in overall accuracy (OA) and kappa index (K) were observed in the following phase. After applying a smoothing technique, the combined use of two Sentinel constellation products improved map accuracy and quality. For the best scenario (VV+NDVI), the OA was 88.32% (K = 0.85), while for scenarios NDVI+DEM and VH+NDVI+DEM, the OAs were 93.25% (K = 0.91) and 93.01% (K = 0.91), respectively. Integrating a DEM layer with SAR and optical data has significantly improved the accuracy in the classification of vegetation types, especially in our study area which is characterized by high environmental heterogeneity.

In the last decades, natural and anthropogenic pressures have caused observable changes in the argan landscape despite its significance in Morocco. Remote sensing data can be used to monitor these changes over time and provide information on vegetation health and land cover changes. This study assesses the performance of supervised methods (support vector machine, maximum likelihood, and minimum distance) and unsupervised classification method (Isodata) for mapping the argan forest in the Smimou area of Essaouira province using remote sensing data from Landsat-5 and Landsat-8 (1985 and 2019). Additionally, the impact of the resampling method and the digital elevation model (DEM) integration on the classification results have been examined. The ground truth data were collected and randomly divided into two categories: 234 samples to calibrate the classification algorithms and 340 samples for validation. Maximum likelihood supervised classification achieved an overall accuracy (OA) of 89.62% (kappa = 0.84) and 87.58% (kappa = 0.81) in 1985 and 2019, respectively. Using resampling techniques on normalized difference vegetation index (NDVI) products, aiming for a 10 m resolution, the NDVI results yielded an OA of 91.60% in 1985 and 88.85% in 2019. Further integration of DEM (30-m resolution) with NDVI, which was resampled to a 10 m resolution, achieved an OA of 92.27% and 92.37% for 1985 and 2019, respectively.

Although weather stations provide accurate measurements of ground climate conditions in close proximity to the station, they are expensive and require periodic recording of measurements. In addition, weather stations are therefore distributed sparsely, especially in developing countries. Conversely, satellites see entire landscapes and are therefore able to offer precise measurements at each location. This paper compares the performance of satellite products with weather station observations at three sites characterized by different climates Ounagha, Chichawa, and R3 in Morocco.Precipitation and temperature data over the period of 2 years (2018 - 2019) at the three sites were collected. Data based on satellite imagery were collected for two satellite products, namely ERA5 and POWER over a similar period. The data were compared and analyzed through inferential statistics such as the root-mean-square error (RMSE), and the coefficient of determination (R2 ). The results showed that the temperature minimum daily simulated using the ERA5 satellite reached the highest coefficient of determination R2 = 0.92, with RMSE=1.34 (daily for Ounagha), R2 = 0.94, and with RMSE=1.27 (daily for Chichawa), R2 = 0.96, and with RMSE=1.13 (daily for R3). The temperature maximum daily simulated through the POWER satellite showed the best coefficient of determination R2 =0.924 with RMSE=2.174 (Ounagha daily). In contrast, the ERA5 satellite presents a better coefficient of determination R2 =0.97 in Chichawa and R3 stations. The results of comparing the observed weather stations and the satellite data in terms of precipitation show that the acceptable performance was attributed to the ERA5 data for cumulative, decadaire, and monthly precipitation in the three sites.The use of satellite products is a good way to solve the lack of weather stations and to make data available to the scientific community for further investigation. Furthermore, since our interest in monitoring drought in the Smimou region and our need for climate data in this area, which lacks a meteorological station, the results of this study encourage us to use the ERA5 satellite to collect climate data for the region.

The hydraulic basin of Tensift is a concrete example of diversification and increase of pollutants discharged without treatment into the natural environment. This issue strongly threatens the water resources of this basin and makes it extremely sensitive to pollution, including groundwater that is a strategic resource in this area. The purpose of this study is to simulate two phenomena, which are hydrodynamic operation and leaching of solute in the conditions of the Haouz region.The study was conducted on the R3 perimeter. It is an irrigated agricultural sector in the region of Sidi Rahal, about 40 km east of Marrakech city, Morocco. To carry out this work, the VS2DI model was chosen for the following reasons: accessibility, reliability, and free of charge. This model popularly uses Cartesian or radial coordinates and allows solving the Richards equation to model the water transfer and the convection-dispersion equation to model the transport of solutes and heat in a porous and variably saturated medium. Our research team collects the data needed for the VS2DI model during the agricultural season of 2002/2003. The collected data is related to climatic conditions, soil, plant, and cultivation practice. The results obtained showed that for the scenarios studied the moisture of the upper layers increases and tends towards saturation depending on the value of the flux imposed on the surface. However, the deep layers remain unsaturated for a long time because of drainage. Thus, after one day and for a flux of 12 cm/d, the first 40cm of the soil is saturated. For the 4 cm/d flow, the saturation, during 24h, did not exceed the 30 cm depth. Knowing that these upper layers are subject to strong thermal gradients and root extractions. On the other hand, in the simulations of solute transport, we try to describe the evolution of the degree of contamination of a layer after a period of one day as a function of the imposed water flow and the concentration of solute on the surface.The results obtained by these simulations show that at 20cm depth the solute concentration starts to change only after a period of 4h and that the rate of change of the concentration is almost linear with time for each given water flux. Beyond 4h, the rate of change of the solute at this depth decreases in a non-linear way with the increase of the water flux imposed on the surface. From these first tests, we can say that this model performs water balances in an acceptable way. It has also been proved that the saturation rate of the soil increases with the increase of the imposed flux and of the moisture of this layer. Finally, it was found that the rate of change of the solute at a given depth decreases non-linearly with the imposed water flux at the surface.

In Morocco, agriculture accounts for 80-90% of water resources. Available data show that the performance of current irrigation systems remains low to medium, with water losses at plots ranging from 30 to 40%, divided between percolation and evaporation. Gravity irrigation is almost total in the study area, resulting in significant percolation losses. In principle, this percolation contributes mainly to the recharge of the aquifer.The purpose of this study was to evaluate, by simulation, the impact of irrigation techniques on wheat yield and growth using the generic agro-environmental model SALTMED under the climatic and soil conditions of zone R3, which is an irrigation area located in the region of Sidi Rahal about 40 km east of the city of Marrakech in the plain of Haouz. We started the study by calibrating the model based on two parameters: photosynthetic efficiency and harvest index. After calibration, we compared different irrigation techniques implemented in the model (surface irrigation, sprinkler irrigation, and drip irrigation). Simulation results showed that the drip irrigation technique is the most economical, exhibiting the lowest losses attributed to percolation and soil evaporation. Notably, percolation, a significant contributor to groundwater recharge, measured approximately 255.5 mm/season. In addition, the irrigation practice in the study area appears to be overestimated during the observed season and could be reduced by half, according to SALTMED. When the irrigation dose is halved, the simulated yield (grain and total biomass) decreases by only 1.33%.