Hyunglok Kim bio photo

Hyunglok Kim

Terrestrial Water Cycle
Remote Sensing
Bayesian Inference
Machine Learning
Deep Learning

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The second project I would like to discuss is called Assimilating satellite-based soil moisture data into land surface models based on parallel computing.

Various factors can increase errors in remotely-sensed soil moisture (SM) retrievals and land surface models (LSM) SM products1-3. SM in LSM will be affected by the model parameters and formulation, as well as by errors in the meteorological forcing variables. Specifically, anthropogenic effects such as irrigation activities cannot be integrated into LSMs without observational data. Recently, in order to remedy these uncertainties in both remotely sensed and LSM-based SM products, and to produce superior combined SM estimates, the Earth Science field has begun discussion of land data assimilation systems based on an Ensemble Kalman filter (or other filters)4,5. The LSM-based SM is revised using observational estimates contingent on the degree of correction regulated by the level of error associated with the model and observational products. These revised SM estimates affect the quality of antecedent wetness conditions, meaning that they can improve our knowledge of SM status and allow us to better forecast extreme climate events8. Assimilation systems and SM estimates from current sun-synchronous orbit satellites provide only limited surface wetness conditions because of the discontinuity in spatial and temporal coverage. However, I believe that integrating high temporal resolution SM data from space has the potential to efficiently complete the SM diurnal cycle and improve hydrologic simulations in LSMs. In a recent publication6, I proposed to assimilate sub-daily scale CYGNSS and SMAP SM data into land surface models; and I emphasized three main benefits of assimilating CYGNSS- and SMAP-derived SM data:

  • Estimate of soil moisture from SMAP and CYGNSS sensors can add significant value in improving performance of global-scale land surface models
  • By assimilating frequent CYGNSS observations, surface soil moisture estimates from land surface models can be further improved
  • The error characteristics of global-scale SMAP- and CYGNSS-assimilated soil moisture data are obtained using triple collocation analysis


  1. Reichle, R. H. (2008). Data assimilation methods in the Earth sciences. Advances in water resources, 31(11), 1411-1418.
  2. McColl, K. A., Vogelzang, J., Konings, A. G., Entekhabi, D., Piles, M., & Stoffelen, A. (2014). Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target. Geophysical research letters, 41(17), 6229-6236.
  3. Kim, H., Wigneron, J. P., Kumar, S., Dong, J., Wagner, W., Cosh, M. H., … & Lakshmi, V. (2020). Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions. Remote Sensing of Environment, 251, 112052.
  4. Kumar, S. V., Reichle, R. H., Koster, R. D., Crow, W. T., & Peters-Lidard, C. D. (2009). Role of subsurface physics in the assimilation of surface soil moisture observations. Journal of hydrometeorology, 10(6), 1534-1547.
  5. Crow, W. T., & Van Loon, E. (2006). Impact of incorrect model error assumptions on the sequential assimilation of remotely sensed surface soil moisture. Journal of Hydrometeorology, 7(3), 421-432.
  6. H. Kim, V. Lakshmi, Y. Kwon, and S. Kumar, First attempt of global-scale assimilation of subdaily scale soil moisture estimates from CYGNSS and SMAP into a land surface model [in press]