Dataset on Agrometeorological Parameters in the Souss-Massa Plain
Hamza Ait-Ichou, Mohammed Hssaisoune, Abdelwahed Chaaou, Mohammed El Hafyani, Asma Abou Ali, Adnane Chakir, Yassine Ait-Brahim, Khaoula Bakas, Amine Saddik, Ilham Elhaid, Soufiane Taia, Said El Hachemy, Aya Rais, Adnane Labbaci, Salwa Belaqziz, Abdellaali Tairi, Safae Ijlil, Houria Abahous, Elhousna Faouzi, Ismail Ait Lahssaine, Rachid El Moumen, Moussa Ait El Kadi, Fatima Abdelfadel, Sofyan Sbahi, Sokaina Tadoumant, Brahim Meskour, Soumia Gouahi, Chaima Aglagal, Hamza Ait Moh, Hassan Mosaid, Lhoussaine BouchaouThe 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.