This comprehensive review explores the ecological significance of the Argane stands (Argania spinosa) in southwestern Morocco and the pivotal role of remote sensing technology in monitoring forest ecosystems. Argane stands, known for their resilience in semi-arid and arid conditions, serve as a keystone species, preventing soil erosion, maintaining ecological balance, and providing habitat and sustenance to diverse wildlife species. Additionally, they produce an extremely valuable Argane oil, offering economic opportunities and cultural significance to local communities. Remote sensing tools, including satellite imagery, LiDAR, drones, radar, and GPS precision, have revolutionized our capacity to remotely gather data on forest health, cover, and responses to environmental changes. These technologies provide precise insights into canopy structure, density, and individual tree health, enabling assessments of Argane stand populations and detection of abiotic stresses, biodiversity, and conservation evaluations. Furthermore, remote sensing plays a crucial role in monitoring vegetation health, productivity, and drought stress, contributing to sustainable land management practices. This review underscores the transformative impact of remote sensing in safeguarding forest ecosystems, particularly the Argane forest stands, and highlights its potential for continued advancements in ecological research and conservation efforts.

The Argane tree (Argania spinosa (L.) Skeels) is an endemic species of Morocco, widely adapted to the arid and semi-arid climate of the southwest. The Argane may serve as an oilseed crop in desert areas threatened by drought and salinity. Its domestication will open up important economic opportunities for Morocco in the face of the growing demand for Argane oil and will help reduce the pressure on the wild Argane forest. In this research, we aimed to study and characterize some physiological and biochemical traits of Argane tolerance to drought stress. We measured physiological parameters related to the water content (foliar water potential and relative water content of the leaves), biochemical parameters involved in osmoregulation (proline and total sugars), and photosynthesis (chlorophylls) in Argane seedlings from four contrasting provenances (Bouizakarne, Agadir, Essaouira and Berkane) cultivated under drought stress induced by cessation of irrigation. The results showed that the basic and minimal foliar water potential, relative water content as well as chlorophyll content significantly decreased in seedlings under severe drought stress compared to control ones, whereas a significant accumulation of proline and total soluble sugars was noted in stressed seedlings. Nonetheless, inter-provenance differences were recorded for some parameters studied. Provenance effect was determinant for variation in drought stress responses of A. spinosa. The study of drought stress-adaptive traits in Argane tree can help to understand the tolerance mechanisms and discriminate between the most drought tolerant provenances in order to rehabilitate degraded Argane forests. It is also relevant for domestication and conservation programs in others abiotic stress conditions resulting from climate change.

In this study, we explored the efficacy of random forest algorithms in downscaling CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation data to predict Argane stand traits. Nonparametric regression integrated original CHIRPS data with environmental variables, demonstrating enhanced accuracy aligned with ground rain gauge observations after residual correction. Furthermore, we explored the performance of range machine learning algorithms, encompassing XGBoost, GBDT, RF, DT, SVR, LR and ANN, in predicting the Leaf Area Index (LAI) and crop yield of Argane trees using condition index-based drought indices such as PCI, VCI, TCI and ETCI derived from multi-sensor satellites. The results demonstrated the superiority of XGBoost in estimating these parameters, with drought indices used as input. XGBoost-based crop yield achieved a higher R² value of 0.94 and a lower RMSE of 6.25 kg/ha. Similarly, the XGBoost-based LAI model showed the highest level of accuracy, with an R² of 0.62 and an RMSE of 0.67. The XGBoost model demonstrated superior performance in predicting the crop yield and LAI estimation of Argania sinosa, followed by GBDT, RF and ANN. Additionally, the study employed the Combined Drought Index (CDI) to monitor agricultural and meteorological drought over two decades, by combining four key parameters, PCI, VCI, TCI and ETCI, validating its accuracy through comparison with other drought indices. CDI exhibited positive correlations with VHI, SPI and crop yield, with a particularly strong and statistically significant correlation observed with VHI (r = 0.83). Therefore, CDI was recommended as an effective method and index for assessing and monitoring drought across Argane forest stands area. The findings demonstrated the potential of advanced machine learning models for improving precipitation data resolution and enhancing agricultural drought monitoring, contributing to better land and hydrological management.

Forest degradation has been accentuated in recent years by climate change and drought. This study consists of producing quality plants and understanding their ecophysiological behavior in the face of water stress. The plants of two broadleaves (Quercus suber, Ceratonia siliqua) and two conifers (Tetraclinis articulata, Cedrus atlantica) aged 6 months were subjected to water stress. The basic (Ψb) and minimum (Ψm) leaf water potential was measured and processed according to climatic factors for the 4 forest species. The results show that the leaf water potential in Quercus suber presents more negative values (−0.42 MPa) for basic leaf water potential and (−1.43 MPa) for minimum leaf water potential than the other forest species studied. While Cedrus atlantica presents the least negative values for minimum leaf water potential (−0.89 MPa). These results allowed us to define the species that is more resistant to water stress and climate change. However, they refer us to more general questions concerning the mechanisms of water use in forest plants.