Sensor Fusion of Planet, Landsat and MODIS Data for Unprecedented Land Surface Monitoring

In the midst of a revolution Earth Observation, due to increasingly diverse and temporally dense data feeds enabled by cubesats and other sensors, there is a need to be interoperable across sensors.

In the journal Remote Sensing of Environment, Rasmus Houborg and Matt McCabe present the Cubesat-enabled Spatio-Temporal Enhancement Method (CESTEM), which uses multi-scale machine learning to stabilize radiometry across Planet’s flocks of cubesats.

By ingesting nearly-coincident images from Planet, Landsat and MODIS with this machine learning technique, Houborg and McCabe are able to produce data feeds consistent with Landsat 8 radiometry, but at the spatial and temporal resolution of Planet’s cubesats (i.e., 3.7m, daily).

The authors note that “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.”

 

Conceptual framework of the Cubesat-enabled Spatio-Temporal Enhancement Method (CESTEM) developed by Houborg and McCabe, which ingests near-coincident PlanetScope (PS), Landsat 8 (L8) and MODIS (MCD43) observations to model Landsat-consistent radiometry at the spatial and temporal resolution of Planet’s Dove cubesats.

Our websites use cookies.
We use cookies to improve our services and tailor content for you. Your browser settings control cookies. For more information about the use of cookies on our websites, please see our Privacy Policy.

Contact Sales