Integrating Remote Sensing Datasets to Estimate Groundwater Pumping and Subsidence

Multiple satellite datasets are sensitive to different aspects of the water balance, but are traditionally not integrated because they are challenging to relate to each other. In this project, we develop a machine learning framework to integrate these datasets to predict groundwater pumping and land subsidence at large spatial scales.

Publications:

• Majumdar, S., Smith, R.G., A new hybrid water balance and machine learning approach for groundwater withdrawal prediction using integrated multi-temporal remote sensing datasets, Under Review in Water Resources Research. [link to preprint]

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