Earth observations

Remote sensing Earth observations, which are widely used to assess surface water quality, also have groundwater applications. For groundwater applications, satellite-based gravity measurements have been widely used to evaluate changes in groundwater storage, highlighting global regions vulnerable to unsustainable groundwater depletion. However, the coarse spatial resolution of current satellite-based groundwater assessments is insufficient for local-scale management. For example, satellites have helped highlight the unsustainable groundwater depletion in northern India and Pakistan, but higher-resolution in situ groundwater quality data reveal that contamination is an even larger problem, with more than 60% of the aquifer restricted by excessive salinity or arsenic. 

Thus, there is a significant need for global understanding of groundwater quality data to complement our increasing ability to measure global groundwater storage. Although Earth observing satellites do not provide direct measurements of groundwater quality, recent research shows that they can produce proxies related to groundwater contamination processes and thereby provide indirect insights. Poulin et al. (2020) showed that Earth observations about population density, road density, precipitation, temperature, and landcover in Uganda and Bangladesh were strongly correlated with microbial contamination levels in shallow groundwater. The authors produced country-level maps of a "microbial groundwater contamination index" derived from Earth observations.

More generally, Earth observations can support predictive modelling efforts as they can provide additional variables to include in predictive models. For example, predictions of nitrate and herbicide concentrations in groundwater rely on information about anthropogenic activities (e.g., landcover, population density), which can be derived from Earth observations. Similarly, information on soil salinity can be retrieved from Earth Observations and input to predictive models of groundwater salinity. Vulnerability mapping can be derived from Earth observations and available spatial datasets. The Cape Town Aquifer Use Case provides a local scale example.

Furthermore, several studies have employed Earth observations, or products derived from them, to develop continental- and global-scale models of geogenic groundwater contamination by arsenic and fluoride.


  • Amini, M., Abbaspour, K. C., Berg, M., Winkel, L., Hug, S. J., Hoehn, E., Yang, H., & Johnson, C. A. (2008). Statistical Modeling of Global Geogenic Arsenic Contamination in Groundwater. Environmental Science & Technology, 42(10), 3669–3675.
  • Anning, D. W., Paul, A. P., McKinney, T. S., Huntington, J. M., Bexfield, L. M., & Thiros, S. A. (2012). Predicted nitrate and arsenic concentrations in basin-fill aquifers of the southwestern United States.
  • Ayotte, J. D., Medalie, L., Qi, S. L., Backer, L. C., & Nolan, B. T. (2017). Estimating the High-Arsenic Domestic-Well Population in the Conterminous United States. Environmental Science & Technology, 51(21), 12443–12454.
  • MacDonald, A. M., Bonsor, H. C., Ahmed, K. M., Burgess, W. G., Basharat, M., Calow, R. C., Dixit, A., Foster, S., Gopal, K., Lapworth, D. J., Lark, R. M., Moench, M., Mukherjee, A., Rao, M. S., Shamsudduha, M., Smith, L., Taylor, R. G., Tucker, J., van Steenbergen, F., & Yadav, S. K. (2016). Groundwater quality and depletion in the Indo-Gangetic Basin mapped from in situ observations. Nature Geoscience, 9(10), 762–766.
  • Podgorski, J., Eqani, S., Khanam, T., Ullah, R., Shen, H., & Berg, M. (2017). Extensive arsenic contamination in high-pH unconfined aquifers in the Indus Valley. Science Advances, 3(8), e1700935.
  • Podgorski, J., Labhasetwar, P., Saha, D., & Berg, M. (2018). Prediction Modeling and Mapping of Groundwater Fluoride Contamination throughout India. Environmental Science & Technology, 52(17), 9889–9898.
  • Podgorski, J., & Berg, M. (2020). Global threat of arsenic in groundwater. Science, 368(6493), 845 LP – 850.
  • Podgorski, J., Wu, R., Chakravorty, B., & Polya, D. A. (2020). Groundwater Arsenic Distribution in India by Machine Learning Geospatial Modeling. International Journal of Environmental Research and Public Health, 17(19), 7119;
  • Poulin, C., Peletz, R., Ercumen, A., Pickering, A. J., Marshall, K., Boehm, A. B., Khush, R., & Delaire, C. (2020). What Environmental Factors Influence the Concentration of Fecal Indicator Bacteria in Groundwater? Insights from Explanatory Modeling in Uganda and Bangladesh. Environmental Science & Technology, 54(21), 13566–13578.;
  • Rodell, M., Famiglietti, J. S., Wiese, D. N., Reager, J. T., Beaudoing, H. K., Landerer, F. W., & Lo, M.-H. (2018). Emerging trends in global freshwater availability. Nature, 557(7707), 651–659.
  • Rodríguez-Lado, L., Sun, G., Berg, M., Zhang, Q., Xue, H., Zheng, Q., & Johnson, C. A. (2013). Groundwater Arsenic Contamination Throughout China. Science, 341(6148), 866 LP – 868.;
  • Scanlon, B. R., Zhang, Z., Reedy, R. C., Pool, D. R., Save, H., Long, D., Chen, J., Wolock, D. M., Conway, B. D., & Winester, D. (2015). Hydrologic implications of GRACE satellite data in the Colorado River Basin. Water Resources Research, 51(12), 9891–9903.
  • Stackelberg, P. E., Barbash, J. E., Gilliom, R. J., Stone, W. W., & Wolock, D. M. (2012). Regression models for estimating concentrations of atrazine plus deethylatrazine in  shallow groundwater in agricultural areas of the United States. Journal of Environmental Quality, 41(2), 479–494.;
  • Taghadosi, M. M., Hasanlou, M., & Eftekhari, K. (2019). Retrieval of soil salinity from Sentinel-2 multispectral imagery. European Journal of Remote Sensing, 52(1), 138–154.;
  • Wu, R., Podgorski, J., Berg, M., & Polya, A. (2020). Geostatistical model of the spatial distribution of arsenic in groundwaters in Gujarat State, India. Environmental Geochemistry and Health.