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MODIS and VIIRS Calibration History and Future Outlook

The MODIS is a key instrument for NASA’s EOS program, currently operated onboard the Terra and Aqua spacecraft launched in 1999 and 2002, respectively. The VIIRS is a MODIS follow-on instrument for the JPSS program. Adding to the ones operated onboard the S-NPP and NOAA-20 satellites launched in 2011 and 2017, respectively, three nearly identical VIIRS instruments will also be launched. This will enable the data records from MODIS and VIIRS to be extended beyond 2040. In addition to various applications and scientific studies of the Earth’s system, long-term data records from MODIS and VIIRS observations will greatly benefit the space-based climate observing system. This is attributed to the high-quality measurements and extensive calibration efforts, from pre-launch to post-launch. This paper provides an overview of MODIS and VIIRS calibration history and approaches applied to establish and maintain sensor calibration traceability and accuracy. It illustrates calibration and performance issues through different phases of the mission using examples derived from ground testing equipment, on-board calibrators, and other calibration targets. Moreover, discussed in this paper are outstanding challenges and future efforts to maintain and improve sensor calibration stability and long-term data quality, and to better support the space-based climate observing system.

MODIS↗

JPSS-2 VIIRS version 2 at-launch relative spectral response characterization

The JPSS-2 VIIRS sensor has completed its pre-launch test program and is now awaiting launch in the 2022 timeframe. The VIIRS spectral characterization, in the form of band averaged and supporting detector level relative spectral response (RSR) for each VIIRS band, was completed in 2019 and is based upon independent SpMA dual monochromator (all bands) and GSFC GLAMR laser system (reflectance bands only) spectral measurements, including first time measurements of the VIIRS SWIR bands by a laser system. The measurements and subsequent analysis effort by subject matter experts of the VIIRS DAWG has led to the July 2019 VIIRS Version 2 RSR release, the official at-launch RSR characterization for the JPSS-2 VIIRS mission. Version 2 replaces and improves upon the August 2018 Version 1 release by incorporating the GLAMR measurements into the analysis to produce an updated “fused” RSR for reflective solar bands (M1- M10, I1-I3, DNBLGS, DNBMGS) and by applying a CO2 absorption correction to the SpMA measurements for thermal band M13. For all other bands (M11, M12, M14-M16, I4, I5), the Version 1 characterization, based entirely upon the SpMA measurements, is carried forward into the Version 2 release. An assessment on compliance with spectral performance metrics finds that VIIRS is compliant on nearly all metrics, with a few minor exceptions. The version 2 RSR release includes band average (over all detectors and subsamples) RSR plus supporting RSR for each detector and subsample, and is available under EAR99 restrictions to the science community at a restricted access NASA eRoom site.

JPSS-2↗

Validation Assessment for the Soil Moisture Active Passive (SMAP) Level 4 Carbon (L4_C) Data Product Version 5

The post-launch Cal/Val phase of the SMAP mission is guided by two primary objectives for each science product team: 1) to calibrate, verify, and improve the performance of the science algorithms, and 2) validate accuracies of the science data products as specified in the SMAP Level-1 mission science requirements. Algorithm science and product maintenance activities during the SMAP extended mission phase have also involved periodic algorithm calibration and product refinements to maintain or enhance product consistency and performance as well as science utility. This report provides an assessment of the latest (Version 5) SMAP Level 4 Carbon (L4_C) product. The L4_C Version 5 (v5) global record now spans more than six years (March 2015 – present) of SMAP operations and has benefited from five major reprocessing updates to the operational product. These reprocessing events and L4_C product release updates have incorporated various algorithm refinements and calibration adjustments to account for similar refinements to the upstream GEOS land model assimilation system, SMAP brightness temperatures, and MODIS vegetation inputs used for L4_C processing. The SMAP L4_C algorithms utilize a terrestrial carbon flux model informed by daily surface and root zone soil moisture information contributed from the SMAP Level 4 Soil Moisture (L4_SM) product along with optical remote sensing-based (e.g. MODIS-based) land cover and canopy fractional photosynthetic active radiation (fPAR), and other ancillary biophysical data. The carbon flux model estimates global daily net ecosystem CO2 exchange (NEE) and the component carbon fluxes, namely, vegetation gross primary production (GPP) and soil heterotrophic respiration (Rh). Other L4_C product elements include surface (~0-5 cm depth) soil organic carbon (SOC) stocks and associated environmental constraints to these processes, including soil moisture-related controls on GPP and ecosystem respiration (Kimball et al. 2014, Jones et al. 2017). The L4_C product addresses SMAP carbon cycle science objectives by: 1) providing a direct link between terrestrial carbon fluxes and underlying freeze/thaw and soil moisture-related constraints to these processes, 2) documenting primary connections between terrestrial water, energy and carbon cycles, and 3) improving understanding of terrestrial carbon sink activity. The SMAP L4_C algorithms and operational product are mature and at a CEOS Validation Stage 4 level (Jackson et al. 2012) based on extensive validation of the multi-year record against a diverse array of independent benchmarks, well characterized global performance, and systematic refinements gained from five major reprocessing events. There are no Level-1 mission science requirements for the L4_C product; however, self-imposed requirements have been established focusing on NEE as the primary product field for validation, and on demonstrating L4_C accuracy and success in meeting product science requirements (Jackson et al. 2012). The other L4_C product fields also have strong utility for carbon science applications (e.g., Liu et al. 2019, Endsley et al. 2020); however, analysis of these other fields is considered secondary relative to primary validation activities focusing on NEE. The L4_C targeted accuracy requirements are to meet or exceed a mean unbiased root-mean-square error (ubRMSE, or standard deviation of the error) for NEE of 1.6 g C m-2 d-1 and 30 g C m-2 yr-1, emphasizing northern (≥45°N) boreal and arctic ecosystems; this accuracy is similar to that of tower eddy covariance measurement-based observations (Baldocchi 2008). Methods used for the latest v5 L4_C product performance and validation assessment have been established from the SMAP Cal/Val plan and previous studies (Jackson et al. 2012, Jones et al. 2017) and include: 1) consistency evaluations of the product fields against earlier product releases (version 4 or earlier); 2) comparisons of daily carbon flux estimates with independent tower eddy 4covariance measurement-based daily carbon (CO2) flux observations from core tower validation sites (CVS); and 3) consistency checks against other global carbon products, including soil carbon inventory records, global GPP records derived from tower observation upscaling methods, and satellite-based observations of canopy solar induced chlorophyll fluorescence (SIF) as a surrogate for GPP. Metrics used to evaluate relative agreement between L4_C product fields and observational benchmarks include correlation (r-value), RMSE differences, bias and model sensitivity diagnostics. Following these validation criteria, the present report provides a validation assessment of the latest L4_C product release (v5). Detailed descriptions of the L4_C algorithm and additional global product accuracy and performance results are given elsewhere (Jones et al. 2017, Endsley et al. 2020). The v5 L4_C product replaces earlier product versions and continues to show: (i) accuracy and performance levels meeting or exceeding SMAP L4_C science requirements; (ii) improvement over the previous product version (version 4); and (iii) suitability for a diversity of science applications. Example L4_C applications from the recent literature include clarifying environmental trends and controls on the northern terrestrial carbon sink (Liu et al. 2019), diagnosing drought-related impacts on ecosystem productivity (Li et al. 2020), and regional monitoring of cropland conditions for projecting annual yields (Wurster et al. 2020). 2EXPECTED L4_C ALGORITHM AND PRODUCTPERFORMANCE The L4_C algorithm performance, including variance and uncertainty estimates of model outputs, was determined during the mission pre-launch phase through spatially explicit model sensitivity studies using available model inputs similar to those currently being used for operational production and evaluating the resulting model simulations over the observed range of northern (≥45 °N) and global conditions (Kimball et al. 2012, Entekhabi et al. 2014). The L4_C algorithm options were also evaluated during the mission prelaunch phase, including deriving canopy fPAR from lower order NDVI (Normalized Difference Vegetation Index) inputs in lieu of using MODIS (MOD15) fPAR; and including an explicit model representation of boreal fire disturbance recovery impacts. These results indicated that the L4_C accuracy requirements (i.e., NEE ubRMSE ≤ 30 g C m-2 yr-1or ≤ 1.6 g C m-2 d-1) could be met from the baseline algorithms over more than 82% and 89% of global and northern vegetated land areas, respectively (Yi et al. 2013, Kimball et al. 2014). The global L4_C algorithm error budget for NEE derived during the mission prelaunch phase indicated that the estimated NEE ubRMSE uncertainty is proportional to GPP and is therefore larger in higher biomass productivity areas, including forests and croplands (Kimball et al. 2014). Likewise, NEE ubRMSE uncertainty is expected to be lower in less-productive areas, including grasslands and shrublands. Expected model NEE ubRMSE levels were also generally within targeted accuracy levels for characteristically less-productive boreal and Arctic biomes, even though relative model error as a proportion of total productivity (NEE RMSE / GPP) may be large in these areas. The estimated NEE uncertainty was lower than expected in some warmer tropical high biomass productivity areas (e.g. Amazon rainforest) because of reduced low temperature and moisture constraints to the L4_C respiration calculations so that the bulk of model uncertainty is contributed by GPP in these areas. Model NEE uncertainty in the African Congo was estimated to

SMAP↗

Independent Calibration Approach for the CLARREO Pathfinder Mission

A CLARREO (Climate Absolute Radiance and Refractivity Observatory) Pathfinder (CPF) mission has been funded to demonstrate retrieval of SI-traceable spectral reflectance with absolute uncertainty <0.3% (k=1). The mission consists of a Reflected Solar spectrometer that will be hosted on the International Space Station (ISS) in the 2023 timeframe and rely on a ratioing radiometer approach to retrieve the unprecedented accuracy. Demonstrating that the accuracy is achieved through an Independent Calibration effort similar in philosophy to the efforts in metrology laboratories relying on multiple, independent measurements to improve credibility for a sensor’s absolute and relative uncertainty error budgets. These measurements use different traceability paths and multiple instrument approaches and CPF’s Independent Calibration will be similar in this regard. The Independent Calibration relies on a pre-launch absolute radiometric calibration obtained from additional testing done after instrument thermal-vacuum (TVAC) testing. The added radiometric calibration is combined with a high fidelity instrument model to provide an on-orbit radiometric calibration independent from the ratioing radiometer approach. The current work describes the post-TVAC testing portion of the CPF Independent Calibration Plan and the Independent Model Development as well as planned on-orbit Evaluation of the Independent Calibration.

CLARREO Pathfinder↗

Mission Engagement Forum: Tools and Techniques

CIS’ Mission Engagement team is hosting its first-ever forum for government, academia, and industry audiences. This event will detail the Near Space Network’s service acquisition process and the services provided by the network for pre-launch planning.

near space network↗

Time-Resolved Observations of Precipitation Structure and Storm Intensity with a Constellation of Smallsats: Level-1 Radiance Algorithm Theoretical Basis Document

This Algorithm Theoretical Basis Document (ATBD) describes the theoretical background of the TROPICS native radiance calibration algorithms. It also includes TROPICS payload characteristics and the algorithm’s ancillary data (i.e., data coming from sources other than the TROPICS Space Vehicle). Details of the native radiance (i.e., Level-1a antenna temperatures and Level-1b brightness temperatures) data product format can be found in the TROPICS Data User’s Guide. This ATBD information on the pre-launch testing completed to verify the algorithm. The TROPICS Data User’s Guide will contain the post-launch radiance validation.

TROPICS↗

Experimental Results and Interfacial Lift-off Model Predictions of Critical Heat Flux for Flow Boiling with Subcooled Inlet Conditions – In Preparation for Experiments Onboard the International Space Station

This study investigates critical heat flux (CHF) for subcooled flow boiling of n-Perfluorohexane based on results of pre-launch Earth-gravity Mission Sequence Tests (MSTs) of the Flow Boiling and Condensation Experiment (FBCE), which was launched to the International Space Station (ISS) in August 2021. CHF measurements were made in a rectangular channel having a 2.5 mm by 5 mm cross-section and a 114.6-mm long heated segment. Both single-sided and double-sided heating were tested in vertical upflow in Earth gravity for a variety of inlet conditions. The inlet subcooling was varied in the range of 0.4 – 32.0°C and encompassed both near-saturated and highly subcooled conditions. Experimental trends and high-speed video records were investigated to better understand the mechanism of CHF. Overall trends show CHF increases as flow rate and/or inlet subcooling are increased. Flow features from the events around CHF justify the applicability of the Interfacial Lift-off Model and the determination of limiting criteria for its application. The present experimental data are combined with prior databases for various flow orientations with respect to Earth gravity and microgravity data collected on parabolic flights. Predictions are made using the Interfacial Lift-off Model for this consolidated subcooled-inlet FBCE-CHF database. A heat utility ratio was included in the model to capture the effects of subcooling and corresponding thermodynamic non-equilibrium. An overall mean absolute error of 19.04% indicates good predictive capability of the model for both heating configurations, different gravity environments, and a wide range of inlet subcooling.

flow boiling↗

Navigation Prediction Performance During OSIRIS-REx Proximity Operations at (101955) Bennu

The OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification and Security–Regolith Explorer) Orbit Determination team performed covariance analyses prior to the commencement of proximity operations (ProxOps) at (101955) Bennu to determine the expected predicted trajectory performance in order to meet trajectory knowledge requirements throughout each phase of the mission. One of the primary requirements placed on the predicted trajectory performance was based on the performance during orbital phases leading up to the maneuver to initiate the Touch-and-Go (TAG) trajectory descent. Throughout ProxOps the nominal force models being used to predict the spacecraft trajectory were updated in an effort to improve the prediction performance. The most significant models that contributed to prediction performance were of solar radiation pressure, thermal reradiation of the spacecraft, predicted attitude errors, and desaturation maneuvers. Efforts were made throughout all of ProxOps to monitor, trend, predict, and update spacecraft modeling to improve the prediction performance. These efforts were vital to reduce the spacecraft knowledge errors necessary to achieve a TAG target smaller than pre-launch analysis allowed due to the rough terrain of Bennu. Increased precision in predicted trajectory errors allowed for refined uncertainties to be used for future phase planning throughout the mission. The navigation team successfully predicted the spacecraft trajectory throughout all of ProxOps achieving predicted trajectories errors less than originally analyzed.

Jason M. Leonard↗

A Modern Load Relief Guidance Scheme for Space Launch Vehicles

Launch vehicle load relief algorithms are concerned with realizing a reduction of transient bending moments near maximum dynamic pressure. Traditional approaches to load relief typically use inner-loop acceleration feedback to reduce the wind-induced angle of attack. When implemented in the inner loop, load relief bandwidth is necessarily limited by the achievable stability margins, and when acceleration feedback is employed, by the uncertainty associated with structural modes that couple with the body-mounted accelerometer. The structure of inner loop load relief increases the dimensionality of the flight control gain and filter optimization problem. Most importantly, classical load relief laws do not take advantage of high-rate and high-accuracy GPS-aided inertial velocity data that is readily available from modern strap down IMUs. In this paper, a novel load relief guidance scheme is described that uses direct angle-of-attack feedback in a clever mechanization. The steering commands are determined by examining the wind-perturbed dynamics of a launch vehicle with respect to a gravity turn ascent trajectory. An angle of attack estimate is derived from GPS-aided inertial data and pre-launch range wind measurements, and it is shown that a reduction worst-case rigid-body loads can be realized without requiring air data. The algorithm also includes a high-rate navigation data preprocessing scheme that operates directly on the IMU delta-theta and delta-velocity measurements in order to produce a filtered acceleration estimate at the vehicle center of mass. The outer-loop guidance scheme simplifies the design process for the classical inner-loop autopilot. Algorithm performance is demonstrated using Monte Carlo analysis of a representative liquid booster in a production high fidelity launch vehicle simulation.

NESC↗

Establishing Launch Readiness of NASA ISS Payload OCO-3

The Orbiting Carbon Observatory – 3 (OCO-3) is a NASA Earth-monitoring payload, designed and built by the Jet Propulsion Laboratory (JPL) to perform space-based observation of variations of global carbon dioxide (CO2) by sampling its emission sources and gradients from the unique vantage point of the International Space Station (ISS). This paper describes the development and results of the end-to-end verification, validation and calibration activities that were performed to meet OCO-3's science measurement, functional and performance requirements prior to launch. The pre-launch activities encompassed a suite of tests and analyses that were performed after payload integration and during launch vehicle integration that helped the OCO-3 project demonstrate its launch and mission readiness in terms of ISS compatibility, ground-based instrument calibrations, flight dynamics and environmental integrity and pointing knowledge and accuracy.

Srivastava, Priyanka↗

Osiris-Rex Shape Model Performance During the Navigation Campaign

The Navigation Campaign of the OSIRIS-REx mission began when the first image of Bennu was recorded by the PolyCam high-resolution imager on Au-gust 17, 2018. In the ensuing months, two teams began building shape models based on imagery taken during the Approach and Preliminary survey phases to be used for the transition to landmark navigation in the Orbital A phase. The orbit determination team began analyzing and characterizing the performance and errors associated with each shape model delivery working closely to iterate on the next shape model delivery. By the end of Orbital A, shape models produced by the Altimetry Working Group and JPL exceeded pre-launch performance re-quirements. This paper provides a summary of the analysis performed during operations.

Leonard, Jason M.↗

Satellite Conjunction Assessment, Risk Analysis and Collision Avoidance Best Practices: The History, Evolution and Opportunities

Since Explorer 1 was launched on January 31, 1958, the United States (U.S.) has reaped the benefits of space exploration. New markets and new technologies have spurred the economy and changed lives in many ways across the national security, civil, and commercial sectors. Space technologies and space-based capabilities now provide global communications, navigation and timing, weather forecasting, and more. Space exploration also presents challenges that impact not only the U.S. but also its allies and other partners. A significant increase in the volume and diversity of activity in space means that it is becoming increasingly congested. Emerging commercial ventures such as satellite servicing, in-space manufacturing, and tourism as well as new technologies enabling small satellites and large constellations of satellites present serious challenges for safely and responsibly using space in a stable, sustainable manner. To meet these challenges, the U.S. seeks to improve global awareness of activity in space by publicly sharing flight safety-related information and by coordinating its own on-orbit activity in a safer, more responsible manner. It seeks to bolster stability and reduce current and future operational on-orbit risks so that space is sustained for future generations. To this end, new and better Space Situational Awareness (SSA) capabilities are needed to keep pace with the increased congestion, and the U.S. seeks to create a dynamic environment that encourages and rewards commercial providers who improve these capabilities. The National Aeronautics and Space Administration (NASA) Spacecraft Conjunction Assessment and Collision Avoidance Best Practices Handbook reflects how NASA currently operates, which has evolved over time. Consideration is given to important topics such as spacecraft and constellation design; spacecraft “trackability;” pre-launch preparation and early launch activities; on-orbit collision avoidance; and automated trajectory guidance and maneuvering. This talk aims to present examples of responsible practices for spacecraft Owners/Operators (O/O) to consider for lowering collision risks and operating safely in space (from LEO and beyond) in a stable and sustainable manner. As technology and innovation continues to improve upon existing capabilities, what kind of new challenges are presented in the SSA community? Can space exploration and commercial ventures thrive while keeping paramount the safety and protection of the space environment? What are the challenges and opportunities that need to be addressed and considered for space situational awareness in Cis-Lunar Space? It may prove useful for entities offering, or intending to offer, SSA or Conjunction Assessment (CA) services to consider such examples of responsible practices to protect the space environment for future use by all.

Collision Avoidance↗

Vicarious Calibration of the Long Near Infrared Band: Cross-Sensor Differences in Sensitivity

Numerous assumptions and approximations are employed when translating satellite-derived radiance to surface remote sensing reflectance (RRS) for ocean color applications. Among these is the vicarious calibration coefficient (g) of the ‘long’ near infrared band (NIRL) used for atmospheric correction. For this band, the pre-launch calibration has always been deemed sufficient [thus g(NIRL) = 1.00] as long as other bands are vicariously calibrated. Recent research, however, suggests that MODIS/Aqua RRS time series are quite sensitive to g(NIRL) (and associated vicarious gains in other bands). In this work, we assessed the sensitivity of VIIRS/SNPP RRS to NIRL calibration, and compared our results to previous MODIS/Aqua and SeaWiFS/OrbView2 analysis. In doing so, we note g(NIRL) sensitivities of mission-averaged RRS timeseries are lower for VIIRS and SeaWiFS, relative to MODIS. At the scale of monthly climatologies, however, all sensors show prominent g(NIRL) sensitivity, with that of SeaWiFS being the most substantial. These findings informed simulation analyses, whereby we identified signal-to-noise ratio (SNR) and radiant path geometry, as well as their interaction, as having notable impacts on g(NIRL) sensitivity. As such, g(NIRL) sensitivity is a necessary consideration for reflectance uncertainty budgets, especially for sensors with higher NIR SNR or particular prevailing radiant path geometries. Given the geometry components embedded within g(NIRL) sensitivity, such studies should be coupled with cross-sensor intercalibrations (e.g., using simultaneous same view measurements) toward minimizing NIRL errors between satellite instruments, but such efforts will not completely remediate remaining cross-sensor biases in RRS.

Brian B Barnes↗

Planetary Protection Requirements and Aimpoint Biasing for Mars 2020 Mission

The Mars 2020 mission was launched from Cape Canaveral, Florida on July 30, 2020, and landed at Mars Jezero Crater on February 18, 2021. Throughout the 7-month interplanetary trajectory, there were various requirements and constraints the navigation team had to observe. Particularly pertinent to trajectory control and maneuver design were the planetary protection requirements for preventing microbial contamination of Mars. Consequently, the aimpoints for injection and early TCMs were biased away to reduce the probability of unintended Mars impact, thus satisfying these requirements. In addition, extensive pre-launch analyses were done to ensure other requirements could be met with high probabilities.

Kruizinga, Gerhard↗

On-Orbit Contingency Plan for the Mars 2020 Mission

NASA’s Mars 2020 mission successfully launched to Mars July 30, 2020. An on-orbit contingency plan was developed to accomplish a controlled re-entry of the spacecraft in the event of a launch vehicle anomaly. The entire launch period was assessed, and satisfactory de-orbit burns were designed for every launch opportunity. An operational implementation was created for rapid adoption if needed, and validated using information from the Gravity Recovery and Climate Experiment (GRACE) satellites. This paper presents the orbit determination models and filter strategies used to conduct the pre-launch analyses, as well as the operational implementation validation methodologies and results.

Kruizinga, Gerhard↗

Reconstructed Performance of the Mars InSight Lander’s Supersonic Parachute & Comparison with the Phoenix Lander

On November 26th, 2018 the Mars InSight landersuccessfully touched down at Elysium Planitia. InSight’s Entry,Descent, and Landing sequence included an 11.8-m supersonicallydeployed Disk-Gap-Band parachute that was a largelybuild-to-print version of the parachute used to successfully landthe Phoenix lander a decade earlier. This paper describes thereconstructed performance of InSight’s supersonic parachuteat Mars, highlighting the differences and similarities with theperformance of Phoenix’s parachute. Measurements from theonboard inertial measurement unit along with pre-launch measurementsof the parachute system and spacecraft, assumptionsabout the vehicle’s aerodynamics, and models for the Martianatmosphere were used to reconstruct the spacecraft’s trajectoryand the parachute system’s performance. The reconstruction resultswere compared against pre-flight predictions. Reconstructionof the InSight trajectory leading up to parachute deploymentshowed that the vehicle trimmed in a lift-down orientationduring entry and thus experienced greater deceleration thanexpected by most pre-flight simulations. This led to parachutedeployment conditions that diverged from the nominal preflightpredictions. The parachute was mortar-deployed at aMach number of approximately 1.5, below the nominal preflightexpectation of 1.66. The approximate dynamic pressureat mortar fire was between 518 Pa and 546 Pa, which agreedwell with pre-flight expectations. The mortar system performednominally, and the system’s deployment (0.755 s) and inflation(1.1 s) times were in line with pre-flight modeling. The peakinflation load was 45 kN, well below the parachute’s 67 kNdesign limit load. Following deployment of the parachute,the vehicle’s rotational rates and the dynamics of the systemwere in excellent agreement with pre-flight expectations. Theperformance of the InSight parachute system was also foundto agree well with that of the Phoenix parachute system, asexpected given the similarities between the two systems.

Karlgaard, Chris↗

Assessment of M2020 Terrain Relative Landing Accuracy: Flight Performance vs Predicts

Terrain Relative Navigation (TRN) was a critical enabling Entry, Descent, and Landing (EDL) technology that enabled Mars 2020 mission Perseverance rover to land at Jezero crater. TRN pro-vides real-time, autonomous, map-relative position determination and generates a landing target based on a priori knowledge of hazards. The required performance for TRN was to land within 60m of the selected target. The required 60m was sub-allocated to various error sources in three major categories: targeting error, knowledge error, and control error. The targeting error is the error in selecting an appropriate landing target and the knowledge of the target on the surface. It includes the Lander Vision System (LVS) position localization with respect the ground, the synchronization between the Lander Vision System measurement and the main Navigation filter, and errors associated with the LVS Reference Map and Safe Target Selec-tion (STS). The knowledge error is the contribution of knowledge growth from the synchronization with LVS to touchdown. The control error encompasses how accurately the system could stay on the desired reference trajectory. The TRN error budget uses a combination of analysis, simulation, and hardware test-ing results to bound the various error contributions obtained during the verification and validation process. This paper first presents a description the TRN system, focusing on the architecture of LVS and STS. The paper then gives detailed overview of the TRN error budget, with a description of the major error contribu-tions in each of the three categories. Next, the paper gives the results for three versions of the error budget, pre-launch, in-flight pre-landing, and post-landing. The paper compares the pre-flight analysis, the pre-landing analysis using in-flight data during cruise, to the post-landing analysis of the TRN performance. Pre-landing analysis best estimate of the landing performance was 33m, compared to the 60m require-ment. Post-landing analysis estimated a landing accuracy of 8.53m or better, much better than the 33m pre-landing estimate. The actual post-landing imagery calculated the distance of the rover to the targeted location to be 5m. The post-landing analysis closely bounds the image-based assessment of landing accu-racy, indicating the success of the error budget architecture in bounding the landing accuracy, as well as the fidelity of the simulations used to model and predict performance.

Chen, Allen↗

An Overview of Experiments and Modeling of Polysiloxane-Coated Thermal Protection Systems for Missions to Mars, Titan, and Beyond.

Phenolic Impregnated Carbon Ablator (PICA) gained heritage during the historic Stardust mission, where it successfully returned samples from a comet’s tail and has since been instrumental in delivering payloads to the surface of Mars [1-3]. Most recently, PICA enabled the safe return of samples collected from asteroid Bennu as part of the OSIRIS-REx mission. This rich legacy underscores PICA’s critical role in allowing NASA’s most ambitious exploration missions. However, the friable nature of its phenolic phase presents challenges during handling and pre-launch activities. To mitigate this issue, PICA is coated with a polysiloxane resin system, which serves to suppress particulate dispersion and thereby safeguard spacecraft components. A comprehensive understanding of the polysiloxane resin’s behavior is imperative, as it profoundly shapes the material response of PICA during atmospheric entry by influencing its thermal and oxidative stability. This influence extends to thermocouple plugs embedded within thermal protection systems. These plugs have demonstrated their significance in missions such as Mars Science Laboratory (MSL) and Mars 2020, where the MEDLI and MEDLI2 instrumentation suites delivered in-valuable insights into the performance of thermal protection systems during entry into the Martian atmosphere [4]. Looking ahead, missions such as Dragonfly, set to descend into Titan’s dense atmosphere, aim to leverage advanced sensor technologies to further refine our understanding of thermal protection response [5]. Moreover, thermocouple plugs play an essential role in validating cutting-edge material response models, such as those pioneered under NASA’s Entry Systems Modeling Project (ESM), designed, in-part, to predict the operational integrity of thermal protection systems under the extreme stresses of atmospheric entry. To achieve these modeling goals, ground-based experiments are crucial to provide the foundational data necessary for developing and refining these predictive tools. To this end, an extensive test campaign was conducted at the Hypersonic Materials Environmental Test System (HyMETS) to investigate the high-temperature behavior of the polysiloxane resin in an air environment [6]. These experiments revealed critical phenomena, including the formation of a silicon oxycarbide layer that enhances oxidation resistance, moderates surface temperatures, and alters in-depth thermal response. Building on these findings, subsequent tests were designed to simulate atmospheric entry conditions in reactive gases, such as CO2 and N2, to mimic the environments of Mars and Titan, respectively, as well as non-reactive gases representing the atmospheres of the Ice Giants (Neptune and Uranus). A heating rate dependent decomposition mechanism has been identified for the polysiloxane resin under oxidizing conditions (Fig. 1). In the initial stage, the resin and the underlying thermal protection system undergo pyrolysis, rapidly generating a thin amorphous silicon oxycarbide interwoven with carbonaceous char and residual fibers from PICA. During the second stage, the nascent oxide layer establishes a robust, oxidation-resistant thermal barrier coating, which significantly impedes heat transfer to the underlying carbonaceous char, resulting in a stagnation of the surface temperature. A key factor contributing to this thermal resistance is the low recombination efficiency of atomic oxygen (γ), which further diminishes the heat load on the material’s interior layers [7]. Moreover, as the surface temperature stagnates, the silicon oxycarbide phase separates into distinct regions of silica and free graphite. Ultimately, when the heat flux reaches a critical threshold, a third stage is triggered, leading to the breakdown of the coating through carbothermal reduction, exposing the underlying char layer. This exposure leads to a dramatic surface temperature spike, driven by highly exothermic reactions between atomic oxygen and the char layer, further accelerating material degradation. A detailed mass and heat transfer model of PICA coated with polysiloxane resin was implemented in the Porous material Analysis Toolbox based on OpenFOAM, PATO [8]. The initial stage was considered negligible in this model because the resin decomposition occurs rapidly within a thin surface layer. Instead, the coating was directly considered as an oxygen-resistant thermal barrier coating. For the second stage, the thin amorphous silicon oxycarbide was treated as a pure silica surface to simplify the thermochemical behavior. The model ac-counts for surface equilibrium processes using representative elements of the coating-environment system. For the third stage, specific boundary conditions were developed to estimate the onset and progression of the coating removal. Two-dimensional material response simulations were conducted to compare uncoated and coated PICA using boundary conditions calibrated with HyMETS data. Fig. 2 illustrates that the simulations closely align with experimental data, successfully reproducing measured temperature profiles. This work will include the latest advancements in the coating model, including the calibration of recombination of atomic oxygen at the surface during the second phase. These simulated results will be further validated against additional CO2 data points from HyMETS, reinforcing the models’ predictive capabilities. These mechanisms and their effects on thermal protection systems, including thermochemical behavior and thermocouple probe performance in extreme environments, provide crucial insights for optimizing spacecraft designs that safeguard scientific payload and ensure mission success in future planetary exploration endeavors.

Active Oxidation↗