USGS Science⌕ Search

USGS · 70190518

The Mars Science Laboratory Curiosity rover Mastcam instruments: Preflight and in-flight calibration, validation, and data archiving

Abstract

The NASA Curiosity rover Mast Camera (Mastcam) system is a pair of fixed-focal length, multispectral, color CCD imagers mounted ~2 m above the surface on the rover's remote sensing mast, along with associated electronics and an onboard calibration target. The left Mastcam (M-34) has a 34 mm focal length, an instantaneous field of view (IFOV) of 0.22 mrad, and a FOV of 20° × 15° over the full 1648 × 1200 pixel span of its Kodak KAI-2020 CCD. The right Mastcam (M-100) has a 100 mm focal length, an IFOV of 0.074 mrad, and a FOV of 6.8° × 5.1° using the same detector. The cameras are separated by 24.2 cm on the mast, allowing stereo images to be obtained at the resolution of the M-34 camera. Each camera has an eight-position filter wheel, enabling it to take Bayer pattern red, green, and blue (RGB) “true color” images, multispectral images in nine additional bands spanning ~400–1100 nm, and images of the Sun in two colors through neutral density-coated filters. An associated Digital Electronics Assembly provides command and data interfaces to the rover, 8 Gb of image storage per camera, 11 bit to 8 bit companding, JPEG compression, and acquisition of high-definition video. Here we describe the preflight and in-flight calibration of Mastcam images, the ways that they are being archived in the NASA Planetary Data System, and the ways that calibration refinements are being developed as the investigation progresses on Mars. We also provide some examples of data sets and analyses that help to validate the accuracy and precision of the calibration.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

James F. Bell, A. Godber, S. McNair, M.A. Caplinger, J.N. Maki, M. T. Lemmon, J. Van Beek, M.C. Malin, D. Wellington, K.M. Kinch, M.B. Madsen, C. Hardgrove, M.A. Ravine, E. Jensen, D. Harker, Ryan B. Anderson, Kenneth E. Herkenhoff, R.V. Morris, E. Cisneros, R. G. Deen. 2017-07-30. The Mars Science Laboratory Curiosity rover Mastcam instruments: Preflight and in-flight calibration, validation, and data archiving. https://doi.org/10.1002/2016ea000219

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

A new amphibious earthquake catalog for the Alaska Peninsula: Training, testing, and applying machine learning earthquake picking in offshore and amphibious environments

The dual seismological developments of large-scale offshore observational networks and high-quality machine-learning earthquake detection and picking allow unprecedented seismicity detection in high hazard subduction zones. We develop a high resolution machine learning enhanced seismicity catalog for the Alaska Peninsula stretch of the Alaska-Aleutian Subduction Zone, which was instrumented with both land and ocean bottom seismometers during the Alaska Amphibious Community Seismic Experiment (AACSE) in 2018–2019. We first use an analyst-checked AACSE catalog to develop new local land and ocean-bottom earthquake waveform training data sets, which we then use to fine-tune the EQTransformer machine learning picking model. In the process, we test multiple training strategies for best picking performance on ocean-bottom waveform data. We evaluate the effect of choice of pre-trained starting model, addition of waveform bandpass filtering, and choice of random metadata split. We find no training strategy to be substantially better than the others. Instead, simply having a local waveform data set to fine-tune the picking model substantially improves performance on both the ocean-bottom and land seismic data from AACSE. We finally apply the separately trained land and ocean-bottom models to continuous seismic data for AACSE and use an iterative catalog refinement method to detect new small-magnitude seismicity. The machine learning enhanced catalog has roughly 3x more earthquakes than the analyst-checked catalog, and the magnitude of completeness is regionally variable but decreases 0.1–0.4 magnitude units. With more small seismicity detected, seismicity patterns mirror prior observations in the region. Detection is especially improved far offshore and in the outer rise.

Alaska↗

Widespread landslide activity in an extreme wet season and implications for regional sediment management, eastern San Francisco Bay area, California

Watershed sediment production is expected to increase in a warmer future with more extreme rain, with cascading effects throughout drainage and sediment-transport networks. This study investigated landscape-scale sediment movement in the eastern San Francisco Bay area, California, USA, during the extreme 2016–2017 wet season that brought major rainfall, landslides, and flooding. Mapping 8,928 landslides across a 1,050-km 2 study area revealed new sediment yield of 510–956 t/km 2 , equivalent to denudation of 193–361 mm/ky. These results correspond closely to long-term denudation rates in the northern and central California Coast Ranges, indicating that mass wasting in very wet years dominates long-term sediment mobilization. However, due to long residence times in drainage networks, the 2017 landslides contributed at most ∼1%–2% of the estimated locally derived fluvial sediment transport to San Francisco Bay. Although the amount of sediment mobilized did not threaten municipal water supplies, small rangeland impoundments in this mixed-use landscape lost storage capacity to new sedimentation. Considering regional sediment supply and demand, even the exceptionally large sediment delivery in an extreme wet year cannot meet the need for sediment to accrete tidal wetlands in the bay. To keep pace with rising sea levels, this abnormally high terrestrial sediment input would need to occur in 50 of the next 75 years, an unlikely occurrence due to the prevalence of recent drought years. Shoreline protection and restoration in the bay would need additional sources of sediment, such as through management of dredged sediment through beneficial-reuse programs.

California↗

Patterns and drivers of cliff erosion in Big Sur, California, USA using repeat photogrammetry, 2017–2023

Seacliff erosion in steep terrain poses major risks to transportation and critical infrastructure. In Big Sur, California, USA, seacliff erosion threatens the sustainability of the central coast stretch of California State Route 1, a transportation corridor that is critical to the region's economy. Published cliff retreat rates for the region range from 1 to 40 cm yr −1 , highlighting that high-resolution, process-based studies could enhance understanding of the causes of spatial and temporal variability. We quantified cliff erosion and investigated its drivers along ∼13 km of the Big Sur coastline at week–month timescales during the late fall to early spring wet seasons between January 2017 and June 2023 by analyzing 3D point clouds developed from aerial imagery using four-dimensional structure-from-motion (4D SfM) photogrammetry techniques. We calculated cliff face retreat rates of 2.23 ± 3.06 cm yr −1 (mean ±1 σ ), an order of magnitude lower than long-term estimated rates for the region (which included large deep-seated landslides), but in line with short-term rates reported across California. Change detection imagery comparison, cliff profiles through time, and statistical analysis reveal a cyclical cliff evolution process in which erosion by wave action at the cliff base destabilizes the cliff and primes it for subsequent failure during precipitation events. Although more erosion by volume could be attributed to precipitation-induced increases in soil moisture (784 m 3 km −1 yr −1 ) compared with erosion attributed to wave power (282 m 3 km −1 yr −1 ), our observations underscore the coupled nature of these processes in driving cliff evolution, consistent with established theory and observations.

California↗