Hyunglok Kim bio photo

Hyunglok Kim

Terrestrial Water Cycle
Remote Sensing
Bayesian Inference
Machine Learning
Deep Learning

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The first project is titled Developing a method for a satellite-based soil moisture retrieval algorithm using GPS signals.

I was fortunate enough to secure the NASA research grant (FINNEST - expand) for the period 2019-2022 for this project, and this scholarship is now supporting me in pursuit of my master’s degree in Data Science. In December 2016, NASA successfully launched the Cyclone Global Navigation Satellite System (CYGNSS) constellation of eight micro-satellites into low Earth orbit1. CYGNSS was designed to measure the ocean’s surface wind field using a bistatic scatterometer technique with Global Positioning System (GPS) reflectometry receivers.

In contrast to other well-known microwave-based soil moisture (SM) retrieval satellites in sun-synchronous orbit (SSO), such as SMOS and SMAP, which have revisit times of 1 to 3 days, the CYGNSS micro-satellites randomly receive surface-reflected GPS signals at several revisits per day. As the temporal repeat of the existing satellites in SSO ranges from about 1 to 3 days depending on overpass latitude, the higher temporal repeat of the eight micro-satellites would add value to existing microwave-based satellite SM retrieval systems.


Figure: (up) Satellite-based soil moisture observation systems and (bottom) Water cycle over land.

In a recent publication2, I proposed a method of using CYGNSS observations in SM estimates; and I emphasized three main benefits of using CYGNSS-derived SM data:

  • Filling the gap in SSO satellite observations to allow temporally continuous observations
  • Improving the accuracy of current data assimilation systems
  • Allowing analysis of observational-based diurnal SM variability in areas that have not been studied before, using existing space-borne microwave and bistatic radar techniques.


  1. Ruf, C. S., Gleason, S., Jelenak, Z., Katzberg, S., Ridley, A., Rose, R., … & Zavorotny, V. (2012, July). The CYGNSS nanosatellite constellation hurricane mission. In 2012 IEEE International Geoscience and Remote Sensing Symposium (pp. 214-216). IEEE.
  2. Kim, H., & Lakshmi, V. (2018). Use of Cyclone Global Navigation Satellite System (CYGNSS) observations for estimation of soil moisture. Geophysical Research Letters, 45(16), 8272-8282.