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Ph.D. in Geospatial Science and Engineering, South Dakota State University
Research interest:
Water resources
Selected publications:
Salas, E. A. L.; Suburbayalu, S. K.; Partee, E. B.; Willis, L. P.; Mitchell, K. Potential of mapping dissolved oxygen in the Little Miami River using Sentinel-2 images and machine learning algorithms. Remote Sensing Applications: Society and Environment2022, 26, 100759. doi: 10.1016/j.rsase.2022.100759
Salas, E. A. L.; Suburbayalu, S. K. Hyperspectral Bare Soil Index (HBSI): Mapping soil using an ensemble of spectral indices in machine learning environment. Land2023, 12(7), 1375. doi: 10.3390/land12071375
Salas, E. A. L.; Suburbayalu, S. K. Perimeter-Area Soil Carbon Index (PASCI): Modeling and estimating soil organic carbon using relevant explicatory waveband variables in machine learning environment. Geo-Spatial Information Science2024, 27(6), 1739-1746. doi: 10.1080/10095020.2023.2211612
Salas, E. A. L.; Suburbayalu, S. K.; Bennett, R.; Partee, E. B.; Brownknight, J. et al. Integration of Google Earth Engine, Sentinel-2 images, and machine learning for temporal mapping of total dissolved solids in river systems. Scientific Reports2025, 15(1), 27555. doi: 10.1038/s41598-025-12548-9
Salas, E. A. L.; Schrack, K.; Suburbayalu, S. K.; Bennett, R. Hybrid spatiotemporal modeling of nutrient cycling in wetland ecosystems using advanced mapping techniques and machine learning approaches. Scientific Reports2026, 16(1), 9954. doi: 10.1038/s41598-026-40585-5