In‐flight calibration and performance of the Mars Exploration Rover Panoramic Camera (Pancam) instruments

Abstract

[1] The Mars Exploration Rover Panoramic Camera (MER/Pancam) instruments have acquired more than 60,000 high-resolution, multispectral, stereoscopic images of soil, rocks, and sky at the Gusev crater and Meridiani Planum landing sites since January 2004. These images, combined with other MER data sets, have enabled new discoveries about the composition, mineralogy, and geologic/geochemical evolution of both sites. One key to the success of Pancam in contributing to the overall success of MER has been the development of a calibration pipeline that can quickly remove instrumental artifacts and generate both absolute radiance and relative reflectance images with high accuracy and precision in order to influence tactical rover driving and in situ sampling decisions. This paper describes in detail the methods, assumptions, and models/algorithms in the calibration pipeline developed for Pancam images, based on new measurements and refinements performed primarily from flight data acquired on Mars. Major calibration steps include modeling and removal of detector bias signal, active and readout region dark current, electronic ''shutter smear,'' and pixel-to-pixel responsivity (flatfield) variations. Pancam images are calibrated to radiance (W/m 2 /nm/sr) using refined preflight-derived calibration coefficients, or radiance factor (I/F) using near-in-time measurements of the Pancam calibration target and a model of aeolian dust deposition on the target as a function of time. We are able to verify that the absolute radiance calibration of most Pancam images is accurate to within about 10% or less and that the filter-to-filter and pixel-to-pixel precision of the calibrated relative reflectance data (both based on measurements of the Pancam calibration target) are typically about 3% and 1% or less, respectively. Examples are also presented of scientific applications made possible by the high fidelity of the calibrated Pancam data. These include 11-color visible to near-IR spectral analysis, calculation of ''true color'' and chromaticity values, and generation of ''super resolution'' image data products. This work represents a follow-on and enhancement to the Pancam preflight calibration process described by Bell et al. (2003).

The Solar Spectrum on the Martian Surface and its Effect on Photovoltaic Performance

“…The radiatively calibrated ("RAD") dataset of the pancam allows each pixel to be converted into an absolute intensity to produce a value for the radiance in W/m 2 -nm-sr. The absolute radiance calibration of the Pancam is within about 10%, and the filter-to-filter precision of the reflectance measurements is about 3% or less [7]. Given the known reflectance of the calibration target [6] and the measured intensity of reflection, the intensity of sunlight on the calibration target can be used to calculate the actual incident intensity of the sunlight on Mars.…”

The persistence of a chlorophyll spectral biosignature from Martian evaporite and spring analogues under Mars-like conditions

“…This method provides 11 wavelength spectra that can be used for direct comparison with Pancam relative reflectance values (Bell et al 2006). The same procedure was performed for the Mastcam 'science filter' and Bayer filter spectral bands (Table 1; Ghaemi 2009;Malin et al 2010;Bell et al 2012) in order to address the implications of our results for observations by the MSL rover.…”

Mining remote-image repositories with application to Mars Rover stereoscopic image datasets

“…Other related works on computing geological features in from MER rover images include . The methods proposed therein often make use of color or stereoscopic data which is time consuming to generate (see Figure 1(b-d) for examples of such images and [12,13] for details on how these images are generated) if these images are not already available and, in cases where the data is available, the additional information can significantly increase the processing time required to generate results for general queries over a very large number of images.…”

“…Each EDR image may be processed to generate up to 12 different types of RDR images but typically between 4-8 RDRs may be generated for a new EDR. Reduced data records are numerous and include color images formed from multiple images of a surface using different optical filters [12,13], e.g., Figure 1(b-d), 3D stereoscopic images reconstructed from the Pancam stereo system, e.g., Figure 5(b), and image mosaics, e.g., Figure 3. Of particular interest for geological research is the 3D stereoscopic data produced by the Pancam imaging system.…”