A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data

Rasmus Houborg*, Matthew F. McCabe

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

176 Scopus citations


Satellite sensing in the visible to near-infrared (VNIR) domain has been the backbone of land surface monitoring and characterization for more than four decades. However, a limitation of conventional single-sensor satellite missions is their limited capacity to observe land surface dynamics at the very high spatial and temporal resolutions demanded by a wide range of applications. One solution to this spatio-temporal divide is an observation strategy based on the CubeSat standard, which facilitates constellations of small, inexpensive satellites. Repeatable near-daily image capture in RGB and near-infrared (NIR) bands at 3–4 m resolution has recently become available via a constellation of >130 CubeSats operated commercially by Planet. While the observing capacity afforded by this system is unprecedented, the relatively low radiometric quality and cross-sensor inconsistencies represent key challenges in the realization of their full potential as a game changer in Earth observation. To address this issue, we developed a Cubesat Enabled Spatio-Temporal Enhancement Method (CESTEM) that uses a multi-scale machine-learning technique to correct for radiometric inconsistencies between CubeSat acquisitions. The CESTEM produces Landsat 8 consistent atmospherically corrected surface reflectances in blue, green, red, and NIR bands, but at the spatial scale and temporal frequency of the CubeSat observations. An application of CESTEM over an agricultural dryland system in Saudi Arabia demonstrated CubeSat-based reproduction of Landsat 8 consistent VNIR data with an overall relative mean absolute deviation of 1.6% or better, even when the Landsat 8 and CubeSat acquisitions were temporally displaced by >32 days. The consistently high retrieval accuracies were achieved using a multi-scale target sampling scheme that draws Landsat 8 reference data from a series of scenes by using MODIS-consistent surface reflectance time series to quantify relative changes in Landsat-scale reflectances over given Landsat-CubeSat acquisition timespans. With the observing potential of Planet's CubeSats approaching daily nadir-pointing land surface imaging of the entire Earth, CESTEM offers the capacity to produce daily Landsat 8 consistent VNIR imagery with a factor of 10 increase in spatial resolution and with the radiometric quality of actual Landsat 8 observations. Realization of this unprecedented Earth observing capacity has far reaching implications for the monitoring and characterization of terrestrial systems at the precision scale.

Original languageEnglish (US)
Pages (from-to)211-226
Number of pages16
JournalRemote Sensing of Environment
StatePublished - May 2018

Bibliographical note

Funding Information:
Research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST). We acknowledge Planet's Ambassadors program for providing access to their imagery archive as well as the outreach efforts of Planet's Dr Joseph Mascaro. We greatly appreciate the logistical, equipment and scientific support offered to our team by Mr Jack King, Mr Alan King and employees of the Tawdeehiya Farm in Al Kharj, Saudi Arabia, without whom this research would not have been possible.

Publisher Copyright:
© 2018 Elsevier Inc.


  • CubeSat
  • Cubist
  • Landsat
  • Machine-learning
  • Planet
  • Spatio-temporal enhancement
  • VNIR

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Geology
  • Soil Science


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