Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area

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

Physiological and biochemical responses of argan (Argania spinosa (L.)) seedlings from containers of different depths under water stress

DOI: 10.15835/nbha49412482 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

Hydraulic Modeling and Remote Sensing Monitoring of Floodhazard in Arid Environments—A Case Study of Laayoune City in Saquia El Hamra Watershed Southern Morocco

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