Digital Soil Mapping
Healthy soils are essential for food security, climate resilience, and sustainable agricultural production. At DALab, we are developing next-generation digital soil mapping approaches that combine spectroscopy, remote sensing, machine learning, and advanced spatial analysis to characterize soil variability across landscapes.
Our research focuses on predicting soil properties such as organic carbon, texture, nutrient status, and fertility indicators. By integrating field measurements with proximal and satellite sensing technologies, we are transforming traditional soil surveys into dynamic and scalable digital systems.
These efforts support precision agriculture, carbon accounting, sustainable land management, and climate-smart decision-making. Ultimately, our goal is to improve our understanding of soils and provide the information needed to manage this critical natural resource more effectively.
GeoEye satellite images of District 22, Tehran, acquired in (a) 2003 and (b) 2020, showing urban development.
https://www.mdpi.com/remotesensing/remotesensing-18-01606/article_deploy/html/images/remotesensing-18-01606-g004.png
Solver chain output for a loamy topsoil across a drying–wetting cycle (θ = 0.08–0.43 cm3 cm−3, T = 15°C). All solvers ran automatically on each update() call: (a) matric potential |ψ| (Van Genuchten); (b) unsaturated hydraulic conductivity K(θ).
https://www.biorxiv.org/content/biorxiv/early/2026/05/08/2026.05.05.722891/F2.large.jpg
Team Members/ Collaborators
Publications
DigitalPedon: A novel digital twin framework for soil profile monitoring and global soil data interoperability Youssef, A., & Badreldin, N. (2026). bioRxiv. https://doi.org/10.64898/2026.05.05.722891
Systematic review and bibliometric analysis of innovative approaches to soil fertility assessment and mapping: trends and techniques Fatimazahra, T., Krimissa, S., Ismaili, M. et al., Appl Geomat 17, 177–215 (2025). https://doi.org/10.1007/s12518-025-00611-z
Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions Mohamed, S.A.; Metwaly, M.M.; Metwalli, M.R.; AbdelRahman, M.A.E.; Badreldin, N. (2023). Remote Sensing, 15, 1751. https://doi.org/10.3390/rs15071751
Estimating the spatial distribution of soil volumetric water content in an agricultural field employing remote sensing and other auxiliary data under different tillage management practices Abebrese, D. K., Biney, J. K. M., Kara, R. S., Báťková, K., Houška, J., Matula, S., Badreldin, N., Truneh, L. A., & Shawula, T. A. (2024). Soil Use and Management, 40, e12981. https://doi.org/10.1111/sum.12981
An Overview of Software Sensor Applications in Biosystem Monitoring and Control Badreldin, N.; Cheng, X.; Youssef, A. (2024). Sensors, 24, 6738. https://doi.org/10.3390/s24206738
2D and 3D Urban Change Detection Methods Using Remote Sensing: A Review Gomroki, M., Gomroki, A., Gulden, R. H., Benaragama, D. I., Hasanlou, M., Badreldin, N., Kalantar, B., & Al-Najjar, H. (2026). Remote Sensing, 18(10), 1606. https://doi.org/10.3390/rs18101606