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Corrine Rojas

Publications and source records attributed to Corrine Rojas.

Geological, Multispectral, and Meteorological Imaging Results from the Mars 2020 Perseverance Rover in Jezero Crater

Perseverance’s Mastcam-Z instrument provides high-resolution stereo and multispectral images with a unique combination of spatial resolution, spatial coverage, and wavelength coverage along the rover’s traverse in Jezero crater, Mars. Images reveal rocks consistent with an igneous (including volcanic and/or volcaniclastic) and/or impactite origin and limited aqueous alteration, including polygonally fractured rocks with weathered coatings; massive boulder-forming bedrock consisting of mafic silicates, ferric oxides, and/or iron-bearing alteration minerals; and coarsely layered outcrops dominated by olivine. Pyroxene dominates the iron-bearing mineralogy in the fine-grained regolith, while olivine dominates the coarse-grained regolith. Solar and atmospheric imaging observations show significant intra- and intersol variations in dust optical depth and water ice clouds, as well as unique examples of boundary layer vortex action from both natural (dust devil) and Ingenuity helicopter–induced dust lifting. High-resolution stereo imaging also provides geologic context for rover operations, other instrument observations, and sample selection, characterization, and confirmation.

Mars 2020

OCI Geolocation Evaluation and Refinement Using Landsat Control Points

The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission is NASA’s next investment in ocean biology, clouds, and aerosol data records. A key feature of PACE is the inclusion of an advanced satellite radiometer, Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. The geolocation processing is performed for OCI using spacecraft navigation data and an instrument geometry model. To evaluate the geolocation accuracy for OCI and develop refinements to the processing methods, control point matching using Landsat data has been implemented as a step in the operational processing of OCI data at the Science Data Segment. This processing provides between 200 and 300 high-quality matchups per day with good global and geometric distribution, allowing rapid evaluation of the OCI geolocation accuracy. The results provided an early indication of the overall quality of the geolocation processing and of specific aspects needing improvement. A standard set of granules was identified to support rapid implementation and testing of geolocation refinements, and this approach has been highly successful in improving the geolocation processing accuracy to meet the science requirements. The evaluation will continue throughout the mission to ensure the ongoing accuracy of geolocation. This paper describes the control point matching methodology, the approach to development of the geolocation processing refinements, and the recent results.

PACE

OCI Geolocation Evaluation and Refinement Using Landsat Control Points

The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission is NASA’s next investment in ocean biology, clouds, and aerosol data records. A key feature of PACE is the inclusion of an advanced satellite radiometer, Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. The geolocation processing is performed for OCI using spacecraft navigation data and an instrument geometry model. To evaluate the geolocation accuracy for OCI and develop refinements to the processing methods, control point matching using Landsat data has been implemented as a step in the operational processing of OCI data at the Science Data Segment. This processing provides between 200 and 300 high-quality matchups per day with good global and geometric distribution, allowing rapid evaluation of the OCI geolocation accuracy. The results provided an early indication of the overall quality of the geolocation processing and of specific aspects needing improvement. A standard set of granules was identified to support rapid implementation and testing of geolocation refinements, and this approach has been highly successful in improving the geolocation processing accuracy to meet the science requirements. The evaluation will continue throughout the mission to ensure the ongoing accuracy of geolocation. This paper describes the control point matching methodology, the approach to development of the geolocation processing refinements, and the recent results.

PACE