The monitoring of cultivated crops and the types of different land covers is a relevant environmental and economic issue for agricultural lands management and crop yield prediction. In this context, this paper aims to use and evaluate the contribution of multisensors classification based on machine learning classifiers to crop-type identification in a semiarid area of Morocco. It is a very heterogeneous zone characterized by mixed crops (tree crops with annual crops, same crop with different phenological states during the same agricultural season, crop rotation, etc.). Therefore, such heterogeneity made the crop-type discrimination more complicated. To overcome these challenges, the present work is the first study in this area which used the fusion of high spatiotemporal resolution Sentinel-1 and Sentinel-2 satellite images for land use and land cover mapping. Three machine learning classifier algorithms, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML), were applied to identify and map crop types in irrigated perimeter. In situ observations of the year 2018, for the R3 perimeter of Haouz plain in central Morocco, were used with satellite data of the same year to perform this work. The results showed that combined images acquired in C-band and the optical range improved clearly the crop-type classification performance (overall accuracy = 89%; Kappa = 0.85) compared to the classification results of optical or SAR data alone.

Argania spinosa also known as the argan tree is an endemic plant of Morocco. Despite having the ability to subsist in extreme drought conditions, it is threatened by soil land clearing, overexploitation, and absence of natural regeneration, causing a worrying decline in both spatial extent and density. The spatial extent of dryland forests is debated, as estimates of forest areas in drylands are uncertain. The present study aims to map and locate the spatial distribution of the argan trees at Smimou community located in Essaouira province, south-eastern Morocco, using satellite images and a double-classification process to overcome separability problems. The work focuses on the characterization and comparison of the unique phenological patterns of argan with the other present land-cover classes. NDVI products were derived from a Sentinel-2 time-series covering one year (2018 to 2019), then ground samples were used to extract phenological profiles at parcel level then at tree level, to feed representative calibration samples to Support Vector Machine classifier. The outcome was integrated with an elevation model in a Decision Tree to reclassify mixed areas. The results indicated an F1-score and an overall accuracy of 91.27% and 92.60% respectively, a promising technique for updating argan extent at national scale.

Tree species identification and their geospatial distribution mapping are crucial for forest monitoring and management. The satellite‐based remote sensing time series of Sentinel missions (Sentinel‐1 and Sentinel‐2) are a perfect tool to map the type, location, and extent of forest cover over large areas at local or global scale. This study is focused on the geospatial mapping of the endemic argan tree (Argania spinosa (L.) Skeels) and the identification of two other tree species (sandarac gum and olive trees) using optical and synthetic aperture radar (SAR) time series. The objective of the present work is to detect the actual state of forest species trees, more specifically the argan tree, in order to be able to study and analyze forest changes (degradation) and make new strategies to protect this endemic tree. The study was conducted over an area located in Essaouira province, Morocco. The support vector machine (SVM) algorithm was used for the classification of the two types of data. We first classified the optical data for tree species identification and mapping. Second, the SAR time series were used to identify the argan tree and distinguish it from other species. Finally, the two types of satellite images were combined to improve and compare the results of classification with those obtained from single‐source data. The overall accuracy (OA) of optical classification reached 86.9% with a kappa coefficient of 0.84 and declined strongly to 37.22% (kappa of 0.29) for SAR classification. The fusion of multisensor data (optical and SAR images) reached an OA of 86.51%. A postclassification was performed to improve the results. The classified images were smoothed, and therefore, the quantitative and qualitative results showed an improvement, in particular for optical classification with a highest OA of 89.78% (kappa coefficient of 0.88). The study confirmed the potential of the multitemporal optical data for accurate forest cover mapping and endemic species identification.

Plant species characteristic of arid and semi-arid zones, such as Argania spinosa (L.) Skeels, have a taproot that allows them to reach the soil horizons more quickly. Unfortunately, in the nursery, the containers of culture used for the production of seedlings do not support an excellent development of the root architecture that can be able to resist the shock of transplantation, in particular of the hydric stress. This study aimed to evaluate the physiological and biochemical behavior of Argania spinosa seedlings grown in containers of different depths under water stress. An experiment was conducted with 90 seedlings from the different containers (P1 for depth of 16 cm, P2 for depth of 30 cm, and P3 for depth of 60 cm), and three watering treatments (well-watered 100% of field capacity, moderate stress with 50% of field capacity and severe stress with 25% of the field capacity). Our results showed that seedlings from the 16 cm container had lower values of water status. Malondialdehyde content, electrolyte leakage, hydrogen peroxide, and superoxide radical content gave higher values on seedlings from the shallow container. The benefits of increasing the container depth of nursery seedlings contribute to the improvement of physiological and biochemical responses of seedlings under water stress. To fully validate our findings, a long-term field study must be conducted.

With the recent launch of Sentinel-2, the possibilities of image analysis increased especially in the field of land cover and crops mapping. The identification of different crop types is important particularly for agricultural field management and water resource monitoring. This paper aims to select a limited number of acquisition dates, from Sentinel-2 time series, to improve the accuracy of crop mapping method in the Haouz plain Marrakech, Morocco. First, the collected reference data was exploited to build the phenological shifts of the different crops present in the area of interest, namely: Apricot, Citrus, Olive, Cereals, watermelon and bare soil/fallow, using the derived NDVI layers. As a second step, the NDVI profiles were employed to distinguish different periods based on the spectral separability between the thematic classes. Then, crop classification independently for the 20acquired images, per period, was performed and evaluated in order to extract Overall Accuracy (OA) and kappa coefficient temporal evolution. The optimal acquisition dates per period were determined, relying on the calculated OA values, and classified based on the corresponding NDVI time series. By minimizing the number of optimal acquisition dates, different combinations were tested and analysed (3 dates per period, 2 dates per period, 1 date per period). The classification results, proved that the proposed approach found out that three acquisition dates, well distributed through the agricultural season achieve the best differentiation between five crops and bare soil with an OA of 85%.

Morocco often faces significant intense rainfall periods that can generate flash floods and raging torrents, causing serious damage in a very short period of time. This study aims to monitor wetland areas after a flash-flood event in an arid region, Saquia El hamra Saharan of Morocco, using a technique that combines hydraulic modeling and remote sensing technology, namely satellite images. The hydrological parameters of the watershed were determined by the WMS software. Flood flow was modeled and simulated using HEC HMS and HEC-RAS software. To map the flooded areas, two satellite images (Sentinel-2 optical images) taken before and after the event were used. Three classifications were carried out using two powerful classifiers: support vector machines and decision tree. The first classifier was applied on both dates’ images, and the resulting maps were used as input for a constructed decision tree model as a post-classification change detection process.