Spatial Timeseries for Automated high-Resolution multi-Sensor data fusion (STARS)
Margaret C. Johnson (she/her)
[email protected]
Principal investigator: lead of data fusion methodological development and Julia code implementations.
NASA Jet Propulsion Laboratory 398L
Gregory H. Halverson (they/them)
[email protected]
Lead developer for data processing pipeline design and development, moving window implementation, and code organization and management.
NASA Jet Propulsion Laboratory 329G
Jouni I. Susiluoto
[email protected]
Technical contributor for methodology development, co- developer of Julia code for Kalman filtering recursion.
NASA Jet Propulsion Laboratory 398L
Kerry Cawse-Nicholson (she/her)
[email protected]
Concept development and project management. Advised on technical and scientific requirements for application and mission integration.
NASA Jet Propulsion Laboratory 329G
Joshua B. Fisher (he/him)
[email protected]
Concept development and project management
Chapman University
Glynn C. Hulley (he/him)
[email protected]
Advised on technical and scientific requirements for application and mission integration.
NASA Jet Propulsion Laboratory 329G
Nimrod Carmon (he/him)
[email protected]
Technical contributor for data processing, validation/verification, and hyperspectral resampling
NASA Jet Propulsion Laboratory 398L
STARS is a general data fusion methodology utilizing spatiotemporal statistical models to optimally combine high spatial resolution VSWIR measurements with high temporal resolution measurements from multiple instruments. The methods are highly-scalable, able to fuse <100 m spatial resolution products in near-real time (<24 hrs) on regional to global scales, to facilitate online data processing as well as large-scale reprocessing of mission datasets. The statistical spatiotemporal modeling framework provides with each fused surface reflectance product associated pixel-level uncertainties incorporating any known data source measurement uncertainties, bias characteristics, and degree of historical data missingness.
The specific capabilities offered by STARS are:
- automatic, high-resolution spatial and temporal gap-filling,
- a tunable fusion framework allowing the user to choose a level of accuracy vs computational complexity, and
- quantifiable uncertainties that can be used for downstream product sensitivity/uncertainty assessments and that can be incorporated into higher-order data product quality flags.
STARS is a significant advancement for surface reflectance data fusion and for quantifying (and potentially reducing) the uncertainty associated with satellite-derived inputs in retrievals of science quantities of interest.
The Julia implementation for the STARS data fusion algorithm is in STARS.jl.
There are several supporting sub-components in generalized Julia packages, including:
- SentinelTiles.jl for geo-referencing Sentinel UTM tiles
- MODLAND.jl for geo-referencing MODIS/VIIRS sinusoidal tiles
- CMR.jl for searching the Common Metadata Repository (CMR)
- HLS.jl for searching and downloading the Harmonized Landsat Sentinel (HLS) dataset
