Accurate and rapid crop type mapping is critical for agricultural sustainability. The growing trend of cloud-based geospatial platforms provides rapid processing tools and cloud storage for remote sensing data. In particular, a variety of remote sensing applications have made use of publicly accessible data from the Sentinel missions of the European Space Agency (ESA). However, few studies have employed these data to evaluate the effectiveness of Sentinel-1, and Sentinel-2 spectral bands and Machine Learning (ML) techniques in challenging highly heterogeneous and fragmented agricultural landscapes using the Google Earth Engine (GEE) cloud computing platform. This work aims to map, accurately and early, the crop types in a highly heterogeneous and fragmented agricultural region of the Tadla Irrigated Perimeter (TIP) as a case study using the high spatiotemporal resolution of Sentinel-1, Sentinel-2, and a Random Forest (RF) classifier implemented on GEE. More specifically, five experiments were performed to assess the optical band reflectance values, vegetation indices, and SAR backscattering coefficients on the accuracy of crop classification. Besides, two scenarios were used to assess the monthly temporal windows on classification accuracy. The findings of this study show that the fusion of Sentinel-1 and Sentinel-2 data can accurately produce the early crop mapping of the studied area with an Overall Accuracy (OA) reaching 95.02%. The scenarios prove that the monthly time series perform better in terms of classification accuracy than single monthly windows images. Red-edge and shortwave infrared bands can improve the accuracy of crop classification by 1.72% when compared to only using traditional bands (i.e., visible and near-infrared bands). The inclusion of two common vegetation indices (The Normalized Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI)) and Sentinel-1 backscattering coefficients to the crop classification enhanced the overall classification accuracy by 0.02% and 2.94%, respectively, compared to using the Sentinel-2 reflectance bands alone. The monthly windows analysis indicated that the improvement in the accuracy of crop classification is the greatest when the March images are accessible, with an OA higher than 80%.

The Central Jebilet Massif is one of the main Palaeozoic outcrops in Morocco. This massif is characterized by its arid climate, its significant mining potential and the absence of plant cover, which favors the use of spatial remote sensing for geological mapping and mineral prospecting in this site. The objective of this study is the comparison of hyperspectral data from the Hyperion sensor of the Earth Observing-1 (EO-1) satellite and multispectral data from the Operational Land Imager (OLI) sensor of Landsat 8 in the discrimination of geological units and detection of iron caps in the study area. The classification by the Support Vector Machine (SVM) method allowed for a good mapping of the lithological units in the study area. The accuracy of the SVM classification of hyperspectral data is higher than that of multispectral data, which was demonstrated by the confusion matrix, notably an overall accuracy of 93.05% and 89.24%, respectively, and a kappa coefficient of 91.25% and 84.36%, respectively. Concerning the iron detection, the band rationing using both sensors have demonstrated a performance of detecting areas that contain more iron ores, especially, the iron caps of Kettara mine, with a small advantage of hyperspectral data. In overall, our results highlight the efficiency of machine learning classifier and hyperspectral data for the detection of iron ores and the discrimination of lithological units in arid regions. The use of hyperspectral and multispectral images has been shown to be a good technique for the characterization of iron deposits and lithological units, which may help in in mineral exploration engineering with reduced fieldwork and geochemistry.

In arid and semi-arid regions, agriculture is an important element of the national economy, but this sector is a large consumer of water. In a context of high pressure on water resources, appropriate management is required. In semi-arid, intensive agricultural systems, such as the Tadla irrigated perimeter in central Morocco, a large amount of water is lost by evapotranspiration (ET), and farmers need an effective decision support system for good irrigation management. The main objective of this study was to combine a high spatial resolution Sentinel-2 satellite and meteorological data for estimating crop water requirements in the irrigated perimeter of Tadla and qualifying its irrigation strategy. The dual approach of the FAO-56 (Food and Agriculture Organization) model, based on the modulation of evaporative demand, was used for the estimation of crop water requirements. Sentinel-2A temporal images were used for crop type mapping and deriving the basal crop coefficient (Kcb) based on NDVI data. Meteorological data were also used in crop water requirement simulation, using SAMIR (satellite monitoring of irrigation) software. The results allowed for the spatialization of crop water requirements on a large area of irrigated crops during the 2016–2017 agricultural season. In general, the crops’ requirement for water is at its maximum during the months of March and April, and the critical period starts from February for most crops. Maps of water requirements were developed. They showed the variability over time of crop development and their estimated water requirements. The results obtained constitute an important indicator of how water should be distributed over the area in order to improve the efficiency of the irrigation scheduling strategy.

The present study demonstrates the feasibility of producing crop type maps using a single high spatial resolution image from Sentinel-2A (S-2A). Our main objectives focus on the evaluation of the potential of S-2A data for crop mapping in the Tadla Plain and the determination of the most suitable period for discriminating different crops using a single S-2A image. In order to achieve these objectives, we have acquired a set of seven images of the S-2A and twelve vegetation indices. Then, we produced crop type maps at a spatial resolution of 10 m using (SVM) and (RF) classifiers for each image. The accuracy and performance of classification were assessed using field data. The optimal period for mapping was done during March with an overall accuracy and kappa coefficient of 91.49%, 0.90 respectively. The results show that a single S-2A image should be sufficient for the discrimination of different crops.

Lineament mapping is a very important step in geological studies and mineral exploration, with the evolution of remote sensing, processing methods as well as the availability of several optical and radar satellite data, lineaments can be detected without using traditional methods. The objective of this work is to recommend the most suitable data for lineaments to be used in the field of geological science, by comparing the performance of three different RADAR data, namely ALOS PALSAR (The Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar), Radarsat-1 and Sentinel-1 for automatic lineament extraction using a combination of edge detection and line linking algorithms. The methodology consists to link between the length, number, orientation and density of lineaments with surface characteristics such as the slope, faults, lithology, discontinuities and mineral veins. The results obtained show that the extracted lineaments from Sentinel-1 VH polarization have a better correlation with geological units, tectonic system direction, as well as shading and slope maps, which is due to the effectiveness of VH polarization, which is independent of soil properties compared to the other polarizations that have shown an overestimation of the lineaments.