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At least 559 records · Page 31

Semi-Centennial of Landsat Observations & Pending Landsat 9 Launch

The first Landsat was placed in orbit on 23 July 1972, followed by a series of missions that have provided nearly continuous, two-satellite 8-day repeat image coverage of the Earth's land areas for the last half-century. These observations have substantially enhanced our understanding of the Earth's terrestrial dynamics, both as a major element of the Earth's physical system, the primary home of humans, and the major source of resources that support them. The history of Landsat is complex, reflective of the human systems that sustain it. Despite the conflicted perspectives surrounding the continuation of the program, Landsat has survived based on worldwide recognition of its critical contributions to understanding land dynamics, management of natural resources and Earth system science. Launch of Landsat 9 is anticipated in Fall 2021, and current planning for the next generation, Landsat Next is well underway. The community of Landsat data users is looking forward to another 50 years of the Landsat program.

Samuel N. Goward↗

An optimal sensing strategy of a proximity sensor system for recognition and localization of polyhedral objects

An algorithm is presented for the recognition and localization of thre-dimensional polyhedral objects based on an optical proximity sensor system capable of measuring the depth and orientation of a local area of an object surface. Emphasis is given to the determination of an optimal sensor trajectory or an optimal probing, for efficient discrimination among all the possible interpretations. The determination of an optimal sensor trajectory for the next probing consists of the selection of optimal beam orientations based on the surface normal vector distribution of the multiple interpretation image (MII) and the selection of an optimal probing plane by projecting the MII onto the projection plane perpendicular to a selected beam orientation and deriving the optimal path on the projection plane. The selection of optimal beam orientation and probing plane is based on the measure of discrimination power of a cluster of surfaces of an MII. Simulation results are shown.

Lee, Sukhan↗

Evaluation of SLAR and simulated thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

Kershaw County, South Carolina was selected as the study site for analyzing simulated thematic mapper MSS data and dual-polarized X-band synthetic aperture radar (SAR) data. The impact of the improved spatial and spectral characteristics of the LANDSAT D thematic mapper data on computer aided analysis for forest cover type mapping was examined as well as the value of synthetic aperture radar data for differentiating forest and other cover types. The utility of pattern recognition techniques for analyzing SAR data was assessed. Topics covered include: (1) collection and of TMS and reference data; (2) reformatting, geometric and radiometric rectification, and spatial resolution degradation of TMS data; (3) development of training statistics and test data sets; (4) evaluation of different numbers and combinations of wavelength bands on classification performance; (5) comparison among three classification algorithms; and (6) the effectiveness of the principal component transformation in data analysis. The collection, digitization, reformatting, and geometric adjustment of SAR data are also discussed. Image interpretation results and classification results are presented.

Hoffer, R. M.↗

Preliminary geological investigation of AIS data at Mary Kathleen, Queensland, Australia

The Airborne Imaging Spectrometer (AIS) was flown over granitic, volcanic, and calc-silicate terrain around the Mary Kathleen Uranium Mine in Queensland, in a test of its mineralocial mapping capabilities. An analysis strategy and restoration and enhancement techniques were developed to process the 128 band AIS data. A preliminary analysis of one of three AIS flight lines shows that the data contains considerable spectral variation but that it is also contaminated by second-order leakage of radiation from the near-infrared region. This makes the recognition of expected spectral absorption shapes very difficult. The effect appears worst in terrains containing considerable vegetation. Techniques that try to predict this supplementary radiation coupled with the log residual analytical technique show that expected mineral absorption spectra can be derived. The techniques suggest that with additional refinement correction procedures, the Australian AIS data may be revised. Application of the log residual analysis method has proved very successful on the cuprite, Nevada data set, and for highlighting the alunite, linite, and SiOH mineralogy.

Huntington, J. F.↗

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Preliminary work into this field was promising but given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation (CoCEI) and an execution crowdsourcing platform partner to solicit machine learning framework developments from external contenders. NASA provided contenders with images and video clips of spacesuits with simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, the top five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The weighted scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy. Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA environments such as the NASA Active Response Gravity Offload System (ARGOS). After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗

Europa Luminescence Microscope

The Europa Luminescence Microscope (ELM) is an automated fluorescence and dark-field mi-croscope designed to meet key objectives defined in the NASA Europa Lander Study Report, includ-ing the identification and characterization of morphological biosignatures. ELM’s heritage stems from a 2U cubesat fluorescence microscope, the Fluorescence Analysis for In-situ Research (FLAIR) im-ager, designed and built at NASA Ames Research Center, for the autonomous study of microbial bi-ology in low Earth orbit. For the ELM implementation, a sample is autonomously manipulated with a microfluidic system using in-line filter sets to capture successively smaller particles on 10, 1.0, and 0.1 µm pore-size filters for imaging. For darkfield imaging, ELM uses ultraviolet and visible light to image organic and inorganic structures with submicron resolution. The ability to detect structural and chemical biosignatures as small as 0.2 µm in size is achieved by imaging native fluorescence and us-ing fluorescence microscopy stains to identify key molecular and structural indicators of microbial life (proteins, lipids, nucleic acids). To excite fluorescence, ELM uses LEDs with wavelengths centered near 265, 370, 470, and 530 nm and five emission bands. The use of multiple excitation and emission wavelengths for native fluorescence imaging not only enables the detection of different molecular species, but also their rough classification. Excitation at 265 nm allows for the detection of smaller polyaromatic hydrocarbons (PAHs; 1-5 rings), aromatic amino acids, and proteins with little to no interference from mineral fluorescence, given proper emission band selection. 370 and 470 nm light excites increasingly larger PAH structures (e.g., coronene) and larger aromatic biomolecules that may be present (e.g., protective pigments). Similarly, inorganic fluorescence can be characterized and sep-arated from organic fluorescence, allowing the recognition and in some cases classification, of miner-als and other abiotic particles. ELM is based upon work supported by the NASA COLDTech and ICEE-2 programs.

Microscope↗

Neural network post-processing of grayscale optical correlator

In this paper we present the use of a radial basis function neural network (RBFNN) as a post-processor to assist the optical correlator to identify the objects and to reject false alarms. Image plane features near the correlation peaks are extracted and fed to the neural network for analysis. The approach is capable of handling large number of object variations and filter sets. Preliminary experimental results are presented and the performance is analyzed.

grayscale optical correlator↗

Learning and Prediction of Slip from Visual Information

This paper presents an approach for slip prediction from a distance for wheeled ground robots using visual information as input. Large amounts of slippage which can occur on certain surfaces, such as sandy slopes, will negatively affect rover mobility. Therefore, obtaining information about slip before entering such terrain can be very useful for better planning and avoiding these areas. To address this problem, terrain appearance and geometry information about map cells are correlated to the slip measured by the rover while traversing each cell. This relationship is learned from previous experience, so slip can be predicted remotely from visual information only. The proposed method consists of terrain type recognition and nonlinear regression modeling. The method has been implemented and tested offline on several off-road terrains including: soil, sand, gravel, and woodchips. The final slip prediction error is about 20%. The system is intended for improved navigation on steep slopes and rough terrain for Mars rovers.

rover↗

Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments

We consider estimation and statistical hypothesis testing on classification images obtained from the two-alternative forced-choice experimental paradigm. We begin with a probabilistic model of task performance for simple forced-choice detection and discrimination tasks. Particular attention is paid to general linear filter models because these models lead to a direct interpretation of the classification image as an estimate of the filter weights. We then describe an estimation procedure for obtaining classification images from observer data. A number of statistical tests are presented for testing various hypotheses from classification images based on some more compact set of features derived from them. As an example of how the methods we describe can be used, we present a case study investigating detection of a Gaussian bump profile.

Non-NASA Center↗

GeoAI advances in specific landform mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation. References: Arundel, Samantha T., Wenwen Li, and Sizhe Wang. 2020. “GeoNat v1.0: A Dataset for Natural Feature Mapping with Artificial Intelligence and Supervised Learning.” Transactions in GIS 24 (3): 556–72. https://doi.org/10.1111/tgis.12633. Arundel, Samantha T, and Gaurav Sinha. 2018. “Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms BT - Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017).” In Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017), Lecture Notes in Geoinformation and Cartography, edited by Paolo Fogliaroni, Andrea Ballatore, and Eliseo Clementini, 9–14. Cham: Springer International Publishing. Arundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan, and Philip T. Thiem. 2023. “Historical Maps Inform Landform Cognition in Machine Learning.” Abstracts of the ICA 6 (August): 1–2. https://doi.org/10.5194/ica-abs-6-10-2023. Evans, Ian S. 2012. “Geomorphometry and Landform Mapping: What Is a Landform?” Geomorphology 137 (1): 94–106. https://doi.org/10.1016/j.geomorph.2010.09.029.

machine learning↗

GeoAI Advances in Specific Landform Mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation.

machine learning↗

Photogrammetry and photo interpretation applied to analyses of cloud cover, cloud type, and cloud motion

A determination was made of the areal extent of terrain obscured by clouds and cloud shadows on a portion of an Apollo 9 photograph at the instant of exposure. This photogrammetrically determined area was then compared to the cloud coverage reported by surface weather observers at approximately the same time and location, as a check on result quality. Stereograms prepared from Apollo 9 vertical photographs, illustrating various percentages of cloud coverage, are presented to help provide a quantitative appreciation of the degradation of terrain photography by clouds and their attendant shadows. A scheme, developed for the U.S. Navy, utilizing pattern recognition techniques for determining cloud motion from sequences of satellite photographs, is summarized. Clouds, turbulence, haze, and solar altitude, four elements of our natural environment which affect aerial photographic missions, are each discussed in terms of their effects on imagery obtained by aerial photography. Data of a type useful to aerial photographic mission planners, expressing photographic ground coverage in terms of flying height above terrain and camera focal length, for a standard aerial photograph format, are provided. Two oblique orbital photographs taken during the Apollo 9 flight are shown, and photo-interpretations, discussing the cloud types imaged and certain visible geographical features, are provided.

Larsen, P. A.↗

The Spatial Vision Tree: A Generic Pattern Recognition Engine- Scientific Foundations, Design Principles, and Preliminary Tree Design

New foundational ideas are used to define a novel approach to generic visual pattern recognition. These ideas proceed from the starting point of the intrinsic equivalence of noise reduction and pattern recognition when noise reduction is taken to its theoretical limit of explicit matched filtering. This led us to think of the logical extension of sparse coding using basis function transforms for both de-noising and pattern recognition to the full pattern specificity of a lexicon of matched filter pattern templates. A key hypothesis is that such a lexicon can be constructed and is, in fact, a generic visual alphabet of spatial vision. Hence it provides a tractable solution for the design of a generic pattern recognition engine. Here we present the key scientific ideas, the basic design principles which emerge from these ideas, and a preliminary design of the Spatial Vision Tree (SVT). The latter is based upon a cryptographic approach whereby we measure a large aggregate estimate of the frequency of occurrence (FOO) for each pattern. These distributions are employed together with Hamming distance criteria to design a two-tier tree. Then using information theory, these same FOO distributions are used to define a precise method for pattern representation. Finally the experimental performance of the preliminary SVT on computer generated test images and complex natural images is assessed.

Rahman, Zia-ur↗

Recognition and characterization of hierarchical interstellar structure. I - Correlation function

The problem of the quantitative description of multiscale structure in interstellar cloud complexes and gravitational collapse calculations is considered, emphasizing the recognition and characterization of hierarchical fragmentation structure. The response of the two-point correlation function to a variety of analytical models for density structure is discussed for simple clustering of pointlike clouds to more complex models involving clouds with a distribution of sizes and densities and hierarchical substructure. By expressing the density distribution as the superposition of individual clouds, it is shown that the correlation function generates two types of terms: those involving each cloud's density convolved with itself and those involving pairs of different clouds. Major distortion of the correlation function are introduced by the presence of any image features with size scales a significant fraction of the image size.

Houlahan, Padraig↗

A multilevel approach to sequential detection of pictorial features

The problem of detecting the local similarity between templates in a given class and a given image using a hierarchically ordered sequential decision rule is examined. It is proposed that the set of templates be partitioned and a 'representative template' be defined for each of the partitions. Several levels of partitioning are defined. Elimination of mismatching locations and termination of computation can take place at each level of detection. Each level of testing is over a more restrictive subset of the template class than the previous level. Criteria are given for selecting representative templates, the ordering of components of a template vector for error evaluation, and the threshold sequences to be used in deciding about a 'match'. Suboptimal solutions are given satisfying these criteria. Examples showing recognition of linear features in test patterns and photographs obtained by aerial and spaceborne sensors are provided.

Ramapriyan, H. K.↗

NASA Tech Briefs, June 2014

Topics include: Real-Time Minimization of Tracking Error for Aircraft Systems; Detecting an Extreme Minority Class in Hyperspectral Data Using Machine Learning; KSC Spaceport Weather Data Archive; Visualizing Acquisition, Processing, and Network Statistics Through Database Queries; Simulating Data Flow via Multiple Secure Connections; Systems and Services for Near-Real-Time Web Access to NPP Data; CCSDS Telemetry Decoder VHDL Core; Thermal Response of a High-Power Switch to Short Pulses; Solar Panel and System Design to Reduce Heating and Optimize Corridors for Lower-Risk Planetary Aerobraking; Low-Cost, Very Large Diamond-Turned Metal Mirror; Very-High-Load-Capacity Air Bearing Spindle for Large Diamond Turning Machines; Elevated-Temperature, Highly Emissive Coating for Energy Dissipation of Large Surfaces; Catalyst for Treatment and Control of Post-Combustion Emissions; Thermally Activated Crack Healing Mechanism for Metallic Materials; Subsurface Imaging of Nanocomposites; Self-Healing Glass Sealants for Solid Oxide Fuel Cells and Electrolyzer Cells; Micromachined Thermopile Arrays with Novel Thermo - electric Materials; Low-Cost, High-Performance MMOD Shielding; Head-Mounted Display Latency Measurement Rig; Workspace-Safe Operation of a Force- or Impedance-Controlled Robot; Cryogenic Mixing Pump with No Moving Parts; Seal Design Feature for Redundancy Verification; Dexterous Humanoid Robot; Tethered Vehicle Control and Tracking System; Lunar Organic Waste Reformer; Digital Laser Frequency Stabilization via Cavity Locking Employing Low-Frequency Direct Modulation; Deep UV Discharge Lamps in Capillary Quartz Tubes with Light Output Coupled to an Optical Fiber; Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems, Version II; Advanced Sensor Technology for Algal Biotechnology; High-Speed Spectral Mapper; "Ascent - Commemorating Shuttle" - A NASA Film and Multimedia Project DVD; High-Pressure, Reduced-Kinetics Mechanism for N-Hexadecane Oxidation; Method of Error Floor Mitigation in Low-Density Parity-Check Codes; X-Ray Flaw Size Parameter for POD Studies; Large Eddy Simulation Composition Equations for Two-Phase Fully Multicomponent Turbulent Flows; Scheduling Targeted and Mapping Observations with State, Resource, and Timing Constraints;

Source record↗

Computer-implemented land use classification with pattern recognition software and ERTS digital data

Significant progress has been made in the classification of surface conditions (land uses) with computer-implemented techniques based on the use of ERTS digital data and pattern recognition software. The supervised technique presently used at the NASA Earth Resources Laboratory is based on maximum likelihood ratioing with a digital table look-up approach to classification. After classification, colors are assigned to the various surface conditions (land uses) classified, and the color-coded classification is film recorded on either positive or negative 9 1/2 in. film at the scale desired. Prints of the film strips are then mosaicked and photographed to produce a land use map in the format desired. Computer extraction of statistical information is performed to show the extent of each surface condition (land use) within any given land unit that can be identified in the image. Evaluations of the product indicate that classification accuracy is well within the limits for use by land resource managers and administrators. Classifications performed with digital data acquired during different seasons indicate that the combination of two or more classifications offer even better accuracy.

Joyce, A. T.↗

First-results from the Perseverance SHERLOC Investigation: Aqueous Alteration Processes and Implications for Organic Geochemistry in Jezero Crater, Mars

The Perseverance rover landed in Jezero crater, a site selected to fulfill the Mars-2020 mission goals of characterizing the geology of habitable environments and searching for signs of life while collecting samples for return to Earth [1]. Jezero hosted an open-basin lake during the late Noachian/early Hesperian (~3.7 Ga) [1-2], has units associated with the largest carbonate deposit identified on Mars [3-4], and has a well-preserved delta with clay and carbonate-bearing sediments, well-suited to preservation of organics [1,3-4]. Investigating the nature of organics and aqueous environments within their geologic con-text allows us to understand important aqueous processes and determine habitability within Jezero crater. Previous in situ landed measurements of organics could not resolve their spatial and mineralogical con-text [5-6]. Although Martian meteorites lack geological context, the spatial distribution of organic compounds in Martian meteorites have allowed recognition of an association between aqueous processes and organics [7-8]. Here, we show for the first time in-situ associations between carbonate-forming ultramafic alteration process, later stage aqueous sulfate and perchlorate formation, and organics on the Martian surface. Methodology and geological context: We use the Perseverance rover’s SHERLOC instrument (Scanning Habitable Environments with Raman and Lumines-ence of Organics and Chemicals), a deep-ultraviolet fluorescence and Raman scattering spectrometer capable of mapping the organic and mineral composition with a spatial resolution of 100 μm resolution to report the presence of organics and aqueously formed minerals at Jezero crater [9]. These spectral detections were compared with co-located images obtained with the autofocus context imager (ACI) and the WATSON camera for textural analysis [9]. As of writing, the Per-severance rover has abraded five targets that were measured with the SHERLOC instrument. The five targets are located in two different orbitally-identified geological units within the floor of Jezero crater; the Crater Floor Fractured Rough unit (CF-Fr) and the Séítah region within the Crater Floor Fractured 1 unit (CF-F1) [10]. In orbital infrared spectroscopic data, the CF-Fr unit is associated with pyroxene spectral signatures and minor alteration, while the Séítah region is associated with olivine and minor Mg-rich carbonates and clays [3-4,10]. Carbonation of ultramafic protolith recorded within Jezero crater: All scans of abraded targets within the Séítah region reveal strong peaks at 1080–1090 cm−1 consistent with carbonate and peak singlets or doublets at 820–840 cm−1 attributed to olivine (Fig. 1), consistent with orbital infrared observations. Our detailed micron-scale petrographic and spectroscopic evidence shows that these carbonates formed through carbonation of an ultramafic protolith. The supporting observations include: (1) Carbonate cation compositions match those of olivine, suggesting mixed Fe- and Mg-olivine gave rise to mixed Fe- and Mg-carbonates, similar to observations of ultramafic systems on Earth and within Martian meteorites [3-4,7-8]. (2) The ob-served carbonates co-occur with hydrated materials, gypsum, and potentially aqueously-formed phases, amorphous silicates and phosphate. (3) The spectral and textural variation of olivine and carbonate dominated zones and olivine-carbonate mixtures within both primary grains and interstitial zones are expected for carbonated ultramafic protoliths. (4) These mineral associations and textures closely resemble those observed within the ALH84001 and Nakhlite meteorites attributed to olivine carbonation on Mars [6-7]. Taken together, micron-scale SHERLOC documentation of these phenomena bridge previous orbital and meteorite observations and demonstrate in-situ regionally extensive (~106 km2) ultramafic alteration resulting in geo-logical deposition of carbonates. Furthermore, we observe that olivine carbonation was involved in preserving and possibly synthesizing organics, which makes this environment potentially habitable, as previously suggested in [1,3-4] (Fig. 1). Late-stage aqueous perchlorate and sulfate in Jezero crater: An abrasion target within the CF-Fr unit contains combinations of high intensity 950-955 cm−1 peaks and minor 1090-1095 cm−1 and 1150-1155 cm−1 peaks that are spectral fits to anhydrous perchlorate (Fig. 1). Some spectra show a combination of 950-955 cm−1 peaks with equally strong 1010-1020 cm−1 peaks, low intensity broad features at 1120 cm−1, and occasional broad 3450 cm−1 hydration (-OH) features, indicating a mixture of Ca-sulfate and perchlorate that is minimally hydrated (Fig. 1). The detections of per-chlorates within Jezero crater differ from previous measurements (e.g. Phoenix lander, Curiosity rover, Tissint meteorite [7,11]) because they are observed to be intimately related to aqueous processes including sulfate formation, they present as a secondary white void-fill occurring within the interior of the rock, and they are found to likely be Na-perchlorate. implications for their formation: Three different types of organics embedded within three different lithologies were observed within the abraded targets. Organics associated with low intensity ~340 nm fluorescence were widespread within targets with no apparent association to particular minerals (Fig. 1). Organics associated with ~305 nm and ~275 nm fluorescence correlated with sulfates within the Bellegarde target in the CF-Fr unit, while organics associated with high intensity ~340 nm fluorescence correlated with carbonate, phosphate, and amorphous silicate mixtures within the Garde target in the Séítah region (Fig. 1). Although assignment of fluorescence signatures to specific organic compounds is not conclusive, ~340 nm fluorescence is generally more consistent with 2-ring aromatic organics, ~275 nm fluorescence is more consistent with 1-ring aromatic organics, and ~305 nm fluorescence can be created by either 2- or 1-ring aromatics [12]. These observations indicate that the strongest fluorescence signatures interpreted as organics were found in materials associated with aqueous processes, i.e. sulfate- and carbonate-bearing materials, suggesting both brines and ultramafic carbonation aqueous environments were capable of preserving organics on ancient Mars. In Martian meteorites, simple aromatic organics proposed to have been synthesized through aqueous processes can be found within minerals associated with olivine carbonation and in spatial association with perchlorate and sulfate materials [7-8], similar to SHERLOC observations. Hence, we advance an abiotic aqueous synthesis origin for the organics although we cannot rule out the presence of organics from meteoritic in-fall or putative organic biosignatures. Detailed analyses will be required upon return of these materials to Earth.

E L Scheller↗