Publications

* Indicates student author. For a full list of publications, see my Google Scholar Profile

[15.] Majumdar, S.*, Smith, R., Conway, B., Lakshmi, V., Advancing Remote Sensing and Machine Learning-Driven Frameworks for Groundwater Withdrawal Estimation in Arizona: Linking Land Subsidence to Groundwater Withdrawals. In Press in Hydrological Processes.

[14.] Adams, K. H., Reager, J. T., Rosen, P., Wiese, D. N., Farr, T. G., Rao, S., Smith, R. et al. (2022). Remote sensing of groundwater: Current capabilities and future directions. Water Resources Research, 58, e2022WR032219. https://doi.org/10.1029/2022WR032219

[13.] Li, J.*, Smith, R., Grote, K., 2022, Analyzing Spatio-Temporal Mechanisms of Land Subsidence in the Parowan Valley, Utah, In Press in Hydrogeology Journal.

[12.] Lees, M., Knight, R., Smith, R., 2022, Development and Application of a 1D Compaction Model to Understand 65 Years of Subsidence in the San Joaquin Valley, Water Resources Research. [link]

[11.] Smith, R. G., Hashemi, H., Chen, J., & Knight, R. (2021). Apportioning deformation among depth intervals in an aquifer system using InSAR and head data. Hydrogeology Journal, 29(7), 2475-2486. [link]

[10.] Smith, R., Li, J.*, 2021. Modeling elastic and inelastic pumping-induced deformation with incomplete water level records in Parowan Valley, Utah. Journal of Hydrology. [link]

[9.] Smith, R.G., Oyler, L.*, Campbell, C., Woolley, E., Hopkins, B., Kerry, R., Hansen, N., 2021, A new approach for estimating and delineating within-field crop water stress zones with satellite imagery. International Journal of Remote Sensing. [link] [preprint]

[8.] Majumdar, S.*, Smith, R.G., Butler, J.J., Lakshmi, V., 2020, Groundwater Withdrawal Prediction Using Integrated Multi-Temporal Remote Sensing Datasets and Machine Learning. Water Resources Research. [link] [preprint]

[7.] Smith, R.G., Majumdar, S.*, 2020, Groundwater Storage Loss Associated with Land Subsidence in Western US Mapped Using Machine Learning. Water Resources Research. [link] [preprint]

[6. ] Smith, R.G., R. Knight, 2019, Modelling land subsidence using InSAR and Airborne Electromagnetic Data. Water Resources Research. [pdf]

[5. ] Smith, R.G., T. Mukerji, 2019, Correlating Geological and Seismic Data with Unconventional Resource Production Curves Using Machine Learning. Geophysics. [pdf]

[4. ] Smith, R.G., R. Knight, S. Fendorf, 2018, Over-Pumping Leads to California Groundwater Arsenic Threat. Nature Communications. [pdf]

[3. ] Knight, R., Smith, R.G., Asch, T., Abraham, J., Cannia, J., Viezzoli, A., Fogg, G., 2018, Mapping Aquifer Systems with Airborne Electromagnetics in the Central Valley of California. Groundwater. [link]

[2. ] Smith, R.G., R. Knight, J. Chen, J.A. Reeves, H.A. Zebker, T. Farr, and Z. Liu, 2017, Estimating the permanent loss of groundwater storage in the southern San Joaquin Valley, California. Water Resources Research [link].

[1. ] Nordin, M., Smith, R.G., Knight, R., 2016, The use of color wheels to communicate uncertainty in the interpretation of geophysical data. The Leading Edge.