Hassan Mosaid

The Eddy Covariance station provides observations of agrometeorological variables and surface energy fluxes, collected from 2019 to 2022, in a citrus orchard located in the Souss-Massa plain, Morocco. The present dataset comprises measurements recorded via a set of aboveground and subsurface sensors. The aboveground setup consistently measures air temperature, relative humidity, wind speed, net radiation, and precipitation. Additionally, the subsurface setup continuously tracks soil temperature, moisture, and electrical conductivity at depths from 5 to 80 cm, along with soil heat flux. Moreover, these setups enable the measurement of turbulent fluxes (sensible and latent heat). Given the limited availability of long-term agrometeorological data in semi-arid regions of the Mediterranean, this paper addresses a critical data gap by providing a reliable agrometeorological dataset. The latter consists of two types of data: 30 min interval files and high-frequency files (20 Hz, i.e., one measurement every 50 ms). The processing of this data involved Card Convert, MATLAB EC-Pack, and Excel, with data quality control performed by removing outliers and excluding nighttime fluxes. The dataset is organized in a table and provided in a .csv format with standard metadata. It is designed for a wide range of applications, including evapotranspiration modeling, satellite product validation, agroclimatic monitoring, determining crop irrigation requirements, precision irrigation planning, and water management. Additionally, the dataset can be reused for crop and hydrological model calibration, as well as soil moisture and crop stress prediction using machine learning algorithms.

Soil plays a key role in storing organic carbon, which is a critical indicator of soil fertility and overall quality. Understanding the spatial distribution of soil organic carbon stock (SOCS) and its influencing factors is essential for promoting sustainable land management. This study applied four machine learning models such as Random Forest (RF), k-nearest neighbors (kNN), Support Vector Machine (SVM), and Cubist to enhance SOCS prediction in the Srou catchment, part of the Upper Oum Er-Rbia watershed in Morocco. A dataset of 120 samples was collected, with 80% used for model training and 20% for validation. Boruta’s feature selection and multicollinearity tests identified nine key factors influencing SOCS. Spatial maps generated from the models were validated using statistical indicators. The RF model showed the highest predictive accuracy (R² = 0.76, RMSE = 0.52 Mg C/ha), followed by SVM and Cubist, while kNN had the lowest performance (R² = 0.31, RMSE = 0.94 Mg C/ha). Key predictors for SOCS included bulk density, pH, electrical conductivity, and calcium carbonate. The proposed machine learning approach demonstrates significant potential for mapping SOCS in similar semi-arid environments.

The main objective of this study was to evaluate heavy metal contamination in volcanic and calcareous soils within Morocco’s semi-arid regions, focusing on the relationship between unique soil types and contamination dynamics. Using Geographic Information Systems (GIS), statistical analyses,...