Selected Research Papers
Ehsan Chatraei Azizabadi, Nasem Badreldin, Joint prediction of potato yield and nitrogen status at row closure using multi-task deep learning and UAV-derived LiDAR–multispectral data,Computers and Electronics in Agriculture, Volume 250,2026, 111893,ISSN 0168-1699, https://doi.org/10.1016/j.compag.2026.111893
Dastgheib, Z.A., Goharrokhi, M., Clark, S. et al. Prediction of total organic matter in marsh sediments: integrating reflectance clustering, spectral subranges, and color coefficients. J Soils Sediments 26, 211 (2026). https://doi.org/10.1007/s11368-026-04414-6
Gomroki, M., Gomroki, A., Gulden, R. H., Benaragama, D. I., Hasanlou, M., Badreldin, N., Kalantar, B., & Al-Najjar, H. (2026). 2D and 3D Urban Change Detection Methods Using Remote Sensing: A Review. Remote Sensing, 18(10), 1606. https://doi.org/10.3390/rs18101606
Ehsan Chatraei Azizabadi, Masoomeh Gomroki, Mohamed El-Shetehy, Keshav D. Singh, Abdul-Wahab Mossa, Nasem Badreldin, VNIR-SWIR hyperspectral spectroscopy for nitrogen assessment in potato crops: Deep learning and regression models across field and laboratory conditions,Smart Agricultural Technology,Volume 13,2026,101801,ISSN 2772-3755, https://doi.org/10.1016/j.atech.2026.101801
Youssef, A., & Badreldin, N. (2026). DigitalPedon: A novel digital twin framework for soil profile monitoring and global soil data interoperability. bioRxiv. https://doi.org/10.64898/2026.05.05.722891
Jingyi Wang, Haidong Wang, Zhenghao Li, Nasem Badreldin, Yuxuan Hu, Qilin Zhang, Min Sun, Xiong Yin, Mingshi Li,
An integrated land-sea framework for assessing coastal wetland ecological quality and the impact of Spartina alterniflora expansion: A case study from the eastern coast of China,Ecological Indicators,Volume 182,2026,114611,ISSN 1470-160X, https://doi.org/10.1016/j.ecolind.2026.114611
Jiahui Yang, Nasem Badreldin, Yanchen Gao, Changchao Yan, Yizhan Zhao, Miles Dyck, Hailong He,
Innovative methods for monitoring soil erosion: Utilizing InSAR technology effectively, CATENA,Volume 261,2025,109547,ISSN 0341-8162, https://doi.org/10.1016/j.catena.2025.109547.
Said El Goumi, Mustapha Namous, Abdenbi Elaloui, Samira Krimissa, Nasem Badreldin, Sakine Koohi, Nafia El Alaouy, El Houssaine Bouras, Assessing the SM2RAIN-ASCAT dataset in Morocco: Accuracy evaluation and drought monitoring application, Atmospheric Research,Volume 332,2026,108729,ISSN 0169 8095, https://doi.org/10.1016/j.atmosres.2025.108729.
Masoomeh Gomroki, Dilshan Benaragama, Christopher James Henry, Nasem Badreldin, Robert Gulden,
CWRepViT-Net: An encoder-decoder deep learning framework with RepViT blocks for crop weed semantic segmentation in soybean fields through their life journey,Smart Agricultural Technology,Volume 12,2025,101472,ISSN 2772-3755,https://doi.org/10.1016/j.atech.2025.101472.
Mohamed, S.A.; Metwaly, M.M.; Metwalli, M.R.; AbdelRahman, M.A.E.; Badreldin, N. (2023). Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions. M. Mousavi and N. Badreldin, "Enhanced Grassland Biomass Estimation Using Vegetation Indices and Biomass Proxy: A Comparative Study of Parametric and Non-parametric Models in Manitoba’s Prairie Ecozone," IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium, Brisbane, Australia, 2025, pp. 3459-3462, doi: 10.1109/IGARSS55030.2025.11243222. Sensing, 15, 1751.
James Kobina Mensah Biney, Jakub Houška, Olha Kachalova, Jiří Volánek, Prince Chapman Agyeman, David Kwesi Abebrese, Ehsan Chatraei Azizabadi, Nasem Badreldin, Significance of Planet SuperDove and refined Sentinel-2 imagery fusion for enhanced soil organic carbon prediction in croplands, CATENA,Volume 254,2025,108902,ISSN 0341-8162, https://doi.org/10.1016/j.catena.2025.108902.
Fatimazahra, T., Krimissa, S., Ismaili, M. et al. Systematic review and bibliometric analysis of innovative approaches to soil fertility assessment and mapping: trends and techniques. Appl Geomat 17, 177–215 (2025). https://doi.org/10.1007/s12518-025-00611-z
Azizabadi, E.C., Badreldin, N. A Review on Potato Crop Yield and Nitrogen Management Utilizing Remote/Proximal Sensing Technologies and Machine Learning Models in Canada. Potato Res. 68, 1659–1679 (2025). https://doi.org/10.1007/s11540-024-09803-3
Chatraei Azizabadi, E., El-Shetehy, M., Cheng, X., Youssef, A., & Badreldin, N. (2025). In-Season Potato Nitrogen Prediction Using Multispectral Drone Data and Machine Learning. Remote Sensing, 17(11), 1860. https://doi.org/10.3390/rs17111860
Baiddah, A., Krimissa, S., Namous, M., Eeloudi, H., Ismaili, M., Hajji, S., Aboutaib, F. & Badreldin, N. (2025). Estimating erosion, sediment yield, and dam lifetime using revised universal soil loss equation and erosion potential model in the Chichaoua watershed and Boulaouane Dam, High Atlas, Morocco. Ecological Engineering & Environmental Technology, 26(3), 132–158. https://doi.org/10.12912/27197050/199824
Mousavi, M.; Biney, J.K.M.; Kishchuk, B.; Youssef, A.; Cordeiro, M.R.C.; Friesen, G.; Cattani, D.; Namous, M.; Badreldin, N. (2024). A Hierarchical Machine Learning-Based Strategy for Mapping Grassland in Manitoba’s Diverse Ecoregions. Remote Sensing, 16, 4730. https://doi.org/10.3390/rs16244730
Badreldin, N.; Cheng, X.; Youssef, A. (2024). An Overview of Software Sensor Applications in Biosystem Monitoring and Control. Sensors, 24, 6738. https://doi.org/10.3390/s24206738
Azizabadi, E.C., Badreldin, N. (2024). A Review on Potato Crop Yield and Nitrogen Management Utilizing Remote/Proximal Sensing Technologies and Machine Learning Models in Canada. Potato Research. https://doi.org/10.1007/s11540-024-09803-3
Biney, J. K. M., Houška, J., & Badreldin, N. (2024). Is the estimation of soil organic carbon using the colour space model, based on visible spectroscopy range, a reliable approach? Soil Use and Management, 40, e13147. https://doi.org/10.1111/sum.13147
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). 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. Soil Use and Management, 40, e12981. https://doi.org/10.1111/sum.12981
Encabo, J. B. M., Cordeiro, M. R. C., Badreldin, N., McGeough, E. J., & Walker, D. (2023). Assessment of remotely sensed inventories for land cover classification of public grasslands in Manitoba, Canada. Grass and Forage Science, 78(4), 590–601. https://doi.org/10.1111/gfs.12631
Hu, Y., Zhang, F., Luo, Z., Badreldin, N., Benoy, G., & Xing, Z. (2023). Soil and water conservation effects of different types of vegetation cover on runoff and erosion driven by climate and underlying surface conditions. CATENA. https://doi.org/10.1016/j.catena.2023.107347
Mohamed, S.A.; Metwaly, M.M.; Metwalli, M.R.; AbdelRahman, M.A.E.; Badreldin, N. (2023). Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions. Remote Sensing, 15, 1751. https://doi.org/10.3390/rs15071751
Badreldin, N.; Lobb, D.A. (2023). The Costs of Soil Erosion to Crop Production in Canada between 1971 and 2015. Sustainability, 15, 4489. https://doi.org/10.3390/su15054489
Baiddah A, Krimissa S, Hajji S, Ismaili M, Abdelrahman K, El Bouzekraoui M, Eloudi H, Elaloui A, Khouz A, Badreldin N. and Namous M. (2023). Head-cut gully erosion susceptibility mapping in semi-arid region using machine learning methods: insight from the high Atlas, Morocco. Frontiers in Earth Science, 11:1184038.
doi: 10.3389/feart.2023.1184038
Hu, Y., Xing, Z., Zhang, F. et al. (2022). Analysis and estimation of nonpoint source pollution under different land use in Anjiagou watershed, Gansu, China. Environmental Science and Pollution Research, 29, 77428–77447. https://doi.org/10.1007/s11356-022-20687-z
Hu Y, Tian Q, Zhang J, Benoy G, Badreldin N, Xing Z, et al. (2022). Effectiveness of Chinese pine (Pinus tabulaeformis) plantation at reducing runoff and erosion rates in Anjiagou Watershed in Semi-arid Region of Gansu, China. PLoS ONE,17(7): e0271200. https://doi.org/10.1371/journal.pone.0271200
Badreldin, N.; Prieto, B.; Fisher, R. (2021). Mapping Grasslands in Mixed Grassland Ecoregion of Saskatchewan Using Big Remote Sensing Data and Machine Learning. Remote Sensing, 13, 4972. https://doi.org/10.3390/rs13244972