Dust, sand and wind drive slope streaks on Mars

Domain: earth_science
Published: 2025-11-06
Editorial outcome: major_revision

Abstract
Mars’ slope streaks are dark albedo features created by the avalanching of surface dust. Recent work provided geostatistical evidence for the dry nature of streaks and their drivers, but presented no direct, quantitative measure of streak formation rates and seasonality. Here, I associate the global, spatio-temporal occurrence of more than 2.1 million slope streaks, identified between 2006 and 2024, with their hypothesized dry, (non)seasonal drivers: meteoroid impacts, marsquakes, and wind action. Streak formation rates vary across Mars and time, with an average of ~0.05 newly-formed streaks per existing streak per Mars Year. Only ~0.1% of the annually formed streak population can be directly attributed to non-seasonal processes like meteoroid impacts and quakes. The bulk of streak formation coincides with the seasonal delivery of atmospheric dust and peaks in the southern summer and autumn, when wind stress systematically exceeds the threshold required for the initiation of sand saltation and dust mobilization. The conditions most conducive to seasonal streak formation appear to occur at sunrise and sunset, explaining the lack of direct observations of streak forming events to date. This work underscores the dry nature of slope streaks and enumerates their potentially important role in the Martian dust cycle.

Introduction
Slope streaks are dark albedo features on Martian slopes that have been associated with the transient presence of liquid water or brines since their discovery in the 1970s 1–6. A recent study used a global, deep learning-enabled, geostatistical approach to suggest a purely dry nature of slope streaks on Mars 7, agreeing with the conclusions presented by preceding regional studies (e.g., refs. 8–12). Bickel and Valantinas 7 also reported statistical evidence for the decisive role of atmospheric dust delivery and dry, energetic triggers in driving the formation of slope streaks, specifically meteoroid impacts, marsquakes, and near-surface winds and gusts. Yet, Bickel and Valantinas 7 limited their analysis to Dickson et al.’s 13 MRO CTX (Mars Reconnaissance Orbiter Context Camera 14) global mosaic, which represents one snapshot in time, inhibiting any direct, sophisticated spatiotemporal correlation of slope streak distribution and formation with other relevant datasets, such as atmospheric models and catalogs of impact- and tectonic events.

Here, I use an improved, deep learning-driven approach to identify slope streaks in the full CTX image archive, enabling a holistic analysis of slope streak occurrence and formation in space and time, all across Mars. I use this dataset to (1) investigate the distribution, magnitude, rate, and seasonality of slope streak formation across the planet, (2) quantify the contribution of the three main drivers of streak formation to the overall streak production, and (3) estimate the potential contribution of slope streak formation to Mars’ overall atmospheric dust budget. This study represents a holistic, spatio-temporal investigation of one of Mars’ most enigmatic, transient surface features, with important implications for our understanding of modern Mars’ surface-to-atmosphere interactions and the dust cycle in general.

Results
Global streak formation rates and seasonality
I identify 2,169,234 individual slope streaks in 91,687 CTX images acquired between 2006 and 2024 (including MRO orbit 84,839, September 2024), covering Mars between 55°N and 35°S multiple times over. This census intentionally includes a substantial fraction of duplicate detections, as streaks persist over years and decades, appearing in several subsequent CTX images. All streak detections are binned into 0.25° × 0.25° ‘cells’; the highest density of streak-bearing cells (‘maximum count’) is located in five globally distinct streak ‘hotspots’: Amazonis, OMA (Olympus Mons Aureole), Tharsis, Arabia, and Elysium, agreeing with earlier global mapping efforts 7 (Fig. 1A). OMA features the highest density and number of streak-bearing cells, with a peak maximum count of 221 streaks/cell (Fig. 1A), while Arabia features the spatially most-extensive, coherent region of slope-bearing cells.

Integrating the maximum counts for all cells and adjusting them for the effect of spatially co-located (nested) streaks as well as for the recall of the used detector (~75%), the data suggest there are ~1.6 million CTX-scale slope streaks all across Mars, which is slightly lower than the ~1.9 million slope streaks estimated earlier by Bickel and Valantinas 7, but twice as high as previous estimates by Aharonson et al. 15.

Considering cells with image pairs that were acquired with a temporal difference of less than 6 months and that feature 100% spatial overlap with the respective cell, I derive differences in streak counts over time (‘difference counts’). Differences in streak counts per cell over time can be indicative of streak formation and fading, but – importantly – can also be caused by variations in imaging geometry and atmospheric conditions (false changes). Most prominently, changes in the atmospheric dust load and solar incidence angle, as well as spatial shifts caused by minor image geolocation inaccuracies can lead to the detection of false changes; for example, streaks that were shadowed in the first image of a pair are illuminated in the second image, which enables their detection – and thus mimics their formation (Fig. S1). I characterize the median ‘noise floor’ caused by false changes using a set of 100 ground-truth cells (Fig. 1C, D) and only consider difference counts that exceed the median noise floor, to minimize the impact of false changes. Here, the term noise floor describes the threshold that is used to distinguish between valid (i.e., used for the analysis) and invalid changes (i.e., not used for the analysis), in a statistical way.

My workflow identifies substantially (globally) enhanced streak count differences – more cells with higher count differences – during the southern summer and fall, with a peak between LS ~220° and ~360°, and a less pronounced tail that extends until LS ~60° (Fig. 1B). These peaks indicate distinct, seasonally enhanced streak formation rates, and agree with the results presented by earlier surveys conducted in specific regions on Mars 12,16. Most considered cells host low numbers of newly formed streaks (<10), but there are ‘bursts’ of streak formation with more than ~40 features that form between two images. As observed earlier by Bickel and Valantinas 7, the used streak detector tends to identify spatially co-located (nested) streaks as one single feature, suggesting that the detector-derived burst counts underestimate the actual (absolute) number of individual streaks that formed by a factor of ~2, on average.

I conduct a detailed, manual ground-truth time-series spot-check of two separate OMA slope streak monitoring cells centered at 24°N, 147°W and 28°N, 145°W using 77 images acquired between 2007 and 2023. The results of the spot-check (Fig. 1B) closely agree with both the number of the detector-observed newly formed streaks as well as their seasonal timing. The spot-check also confirms the observation that streak formation can happen steadily (≪10) and in bursts (>40) between consecutive images. Notably, the locations of highly active cells (derived by manual and detector counts) closely resemble the spatial distribution of cells with high to very high maximum streak counts, implying that the three independent measures of streak activity agree.

Using the first and most recent images taken of each analysis cell, I compute a global streak formation rate with the longest-possible temporal baseline (Δt, average of ~5.2 MY, and a minimum of 1 MY). Globally, all considered cells experience an average, normalized streak formation rate of ~5.5%/MY. This rate (5.5%/MY, for Δt > 1 MY) and the number of slope streaks estimated to be present across Mars (n=~1,600,000), along with two additional rates derived from image pairs with Δt > 2 MY (4.6%/MY) and Δt > 3 MY time difference (4.2%/MY) suggests that between ~88,000, ~73,600, and ~67,200 CTX-scale streaks should form per MY all over Mars. This implies that Mars’ entire streak population would be replenished within ~18 to ~24 MY, assuming that the overall population of streaks remains constant.

Sporadic, higher-than-average formation rates (up to ~88%/MY) can exclusively be attributed to streak bursts that happened at locations with few pre-existing streaks and between images with a relatively short temporal baseline (Δt ≪ 1 MY) and do not represent the long-term formation rate of the majority of the streak population. Image pairs with a difference of at least 3 MY suggest that the highest long-term formation rates (up to ~28%/MY) occur across all major hotspots, with the exception of Elysium (Fig. S2). Overall, these formation rates are in line with earlier, regional estimates of streak formation (~3 to ~7.9%/MY, with peaks up to ~30%/MY) 12,15–18 as well as previous estimates of streak lifetime (~11 to ~22 MY) 17–19, although the increased number of streaks that were mapped across Mars 7 leads to higher absolute formation rates of streaks than previously estimated (~factor ~1.3 to ~1.8) 15, further establishing slope streaks as a highly dynamic geologic process. It remains unclear whether formation rates and locations of peak activity change over decadal or centennial timescales.

Slope streak count data are openly available here: https://doi.org/10.48620/91828.

Seismicity as a non-seasonal driver of streak formation
Two of the twenty-four (~8%) InSight-recorded BB and LF events with back-azimuth-derived epicenter locations (‘Broadband’ and ‘Low-Frequency’, MQS Catalog v14) 20,21 feature – more or less distinct – bursts of newly formed streaks in post-quake images acquired of the reconstructed epicenter location: events S0105a, moment magnitude (Mw) 3.0 (2019-03-14, 10 new streaks), and S0173a, Mw 3.7 (2019-05-23, 15 new streaks), both located in Elysium. This implies that endogenic seismicity can trigger streak formation, although the current data imply that the contribution to the global formation budget might be small: assuming the 25 identified, newly formed streaks formed within one Mars Year, tectonic activity might be responsible for as little as ~0.04% of the streaks that form on Mars every MY. Importantly, the InSight data are spatiotemporally scarce, cover only ~2 MY and a small fraction of the planet, and thus do not allow for an extended comparison with the global-scale slope streak formation data. Further, the inaccuracies associated with the spatial localization of quake epicenters are substantial (e.g., ref. 22), and a direct relation between a quake and newly formed streaks is difficult to (dis)prove, particularly with image pairs that were acquired weeks and months apart. In reality, endogenic seismicity might be a substantially more important driver of streak formation than suggested by the results above.

An association of newly formed meteoroid impacts and streaks is more straightforward (Fig. 2A–E). Overall, 254 of the 1027 cells (~25%) with impact craters that formed during CTX operations 23 host slope streaks and are covered with at least 2 CTX images that meet the desired change detection requirements. Of those 254 cells, 14 cells (~6%) show bursts of newly formed streaks in post-impact images. In total, impact events appear to have triggered the formation of 255 (CTX-scale) slope streaks between 2006 and 2020, implying that impact events lead to the formation of an average of ~34 slope streaks per MY, or about ~0.05% of the annual formation population.

This analysis does not include two specific streak bursts that recently have been tied to seismicity by Bickel and Valantinas 7 and Lucas et al. 24: S1000a (impact) and S1222a (quake). S1000a is not part of Daubar et al.’s 23 global database, and the cells around S1222a’s current epicenter location (covering ~2000 km2) host a minimal number of slope streak detections (<10), which removes both events from my analysis.

Dust, wind action, and saltation as seasonal drivers of streak formation
The difference count data and modeled, low-resolution, regional-scale atmospheric data extracted from the Mars Climate Database (MCD v6.1) 25 suggest that ~20% of the cells with ongoing (directly observed) streak formation activity experience wind stress values beyond 0.02 Pa (maximum of the sol-at image pair mean LS, with a maximum temporal difference of 6 months or 90° LS between the images). Here, wind stress values are estimated from horizontal wind velocities (modeled at 4.5 m above the surface) and the spatiotemporally associated atmospheric density, assuming a simplified aerodynamic (surface) roughness of 2 centimeters. Throughout the year, ignoring streak formation timing, about ~60% of all streak-bearing cells experience peak wind stress values beyond 0.02 Pa (Fig. 3B, D). Several past studies suggested that wind stress in excess of 0.02 Pa is able to initiate and sustain the saltation of sand-sized particles (~100 µm) that are the most susceptible to wind shear (e.g., refs. 26–34). Upon re-impact on the surface, these particles can mobilize and eject smaller particles, including dust particles (<62 µm), making wind stress values beyond 0.02 Pa indicative of atmospheric conditions that are generally conducive to the mechanical triggering of streak formation. Generally, the average sol-maximum wind stress in streak-bearing cells tends to approach or exceed 0.02 Pa at around LS ~220°, in early southern summer (Fig. 4E, F), agreeing with the observed surge in streak formation activity (Fig. 1B). Notably, cells with faster near-surface winds and higher wind stresses tend to experience higher streak formation rates (Fig. 3), but that effect seems to vanish for wind velocities and wind stress values beyond ~12 to ~17 m/s (at an altitude of 4.5 m) and ~0.02 to ~0.03 Pa (Fig. 3). These spatiotemporal observations suggest that wind action, or more specifically, wind stress-induced saltation, is a major driver of streak formation. Importantly, MCD-predicted atmospheric parameters represent regional averages and are not expected to fully capture the dynamics of the atmosphere at the slope streak scale, which directly affects all estimates of wind stress. In addition, the assumed, simplified (constant) aerodynamic roughness cannot be expected to fully capture the variability of surface roughness across Mars.

The local times at which modeled maximum wind stresses occur (excluding wind stress values below 0.02 Pa) vary substantially across the five geographic streak hotspots: in Arabia, the conditions most conducive to streak formation appear to occur at sunrise; in Tharsis, these conditions appear in the late afternoon; in Amazonis, the most conducive wind stresses occur shortly after sunrise and shortly before sunset; and in OMA and Elysium, these conditions appear at and shortly after sunset (Fig. 3E). These observations might explain why direct observations of actively forming streaks – and potentially associated dust clouds or plumes – have not been made over the past decades of Mars imaging, as the vast majority of orbiters were constrained to imaging around noon and the early afternoon – and as only a few orbiters are capable of sunrise and sunset imaging, such as ESA’s Trace Gas Orbiter and Mars Express.

My data also suggest that 92% of all difference and maximum count cells are located in regions featuring very high TES albedo (>0.26, Fig. 5A, C), with ~99% of all streak-bearing cells exceeding the average surface albedo (across all analysis cells). Notably, higher surface albedo seems to lead to higher formation rates and maximum streak counts. Similarly, MCD-derived column-integrated dust optical depth values of cells with active (directly observed) streak formation (maximum of the sol-at image pair mean LS, with a maximum temporal difference of 6 months or 90° LS between the images, Fig. 1F) as well as the sol-maximum dust optical depth of streak-bearing cells in general (Fig. 4A, B), show an enhanced atmospheric dust load during the seasons that are associated with streak formation activity, with a distinct rise at LS ~220°, coinciding with streak formation activity (Fig. 1B). Notably, the sol-maximum dust optical depth at cells that experience streak formation and/or host slope streaks remain slightly above the background over the course of southern summer and fall, i.e., during streak formation season (Fig. 4A, B). Further, peak streak activity – and streak abundance in general – appears to be centered at elevations of ~-2000 to ~-2500 m (Fig. 5B, D), below the average background.

Figure 6 visualizes the interaction between the key drivers of streak occurrence, where all streak-bearing cells are located in high-albedo (dust-rich) terrain and at lower topographic elevations, with a distinct indication that higher albedo and lower elevations generally result in enhanced streak activity (for difference and maximum counts). Figure 6 also underlines the direct relation between streak activity, wind velocity, and wind stress, where regions of peak activity correspond to regions of enhanced wind action.

Methods
Few-shot learning-driven slope streak mapping
I build on the validated slope streak detector (based on YOLOv5, PyTorch 1.7, from https://github.com/ultralytics/yolov5) used in ref. 7 and substantially improve its overall detection performance by adding a total of 4737 and 389 slope streak labels located in a total of 575 and 41 CTX image patches (1000 × 1000 pixels each), for training and testing data, respectively, taken from 27 CTX images. The full, used training and testing datasets include 5744 and 424 streak labels taken from 68 CTX images. Each label represents one rectangular bounding box drawn around a slope streak. Data selection, labeling, and validation, as well as detector training, follow the identical procedures as documented by Bickel and Valantinas 7, including label augmentation (label rotation, up-down flipping, shearing, scaling, and contrast/brightness modifications), which in turn followed established procedures. All used CTX images are listed in Table S1.

The trained detector achieves a recall of ~75% (% streaks in test set identified) and a precision of ~94% (% correctly identified test set streaks) at a model confidence threshold of 0.5, with an AP (average precision) of ~80%. The detectors used by Bickel and Valantinas 7 and in this work cannot be directly compared due to their different test sets, but the performance of the new detector is expected to be substantially enhanced, due to the considerably larger and more diverse train set. The test results imply that the detector is able to identify about 75% of all streaks visible in a CTX image, while about 94% of its detections are correct, on average. For this study, I deploy the new detector with a confidence threshold of 0.5, i.e., I consider all detections with model confidence scores higher than 0.5. Due to the fact that individual streaks can be spatially co-located or overlap (nested streaks), I use a random set of 1000 streaks to estimate the average number of slope streaks per detection: ~2 (slightly lower than the ~2.33 estimated by Bickel and Valantinas 7, most likely due to the improved detector).

The deployment of the detector uses the identical methodology as detailed in ref. 7, utilizing one single NVIDIA RTX 3090 GPU (Graphical Processing Unit) and a processing pipeline developed over several years (e.g., refs. 48,49). The workflow retrieves map-projected and calibrated CTX images located between 55°N and 35°S (n=105,754, geographically constrained by the earlier global census conducted by Bickel and Valantinas 7) from https://image.mars.asu.edu/stream/, cuts them into 1000 × 1000 pixel patches, deploys the detector, and uses the standard YOLOv5 NMS (non-maximum suppression) to remove duplicate detections made in the same image patch, as is best practice for object detection. All detections are stored along with preview thumbnails and CTX-derived metadata, such as accurate geographic location, image solar longitude, image ID, image acquisition date, illumination conditions, and imaging geometry. Total processing of the entire (available) CTX image archive required ~1.5 months using one single GPU-enabled desktop computer. The detector identified a total of 2,169,234 individual slope streak detections, which are too many for a manual candidate review, as performed by Bickel and Valantinas 7; in this study, I rely on the thorough (statistical) assessment of the precision of the detector in the test set: this assessment suggests a precision of 94%, which means that a minimal number of false detections are included in the dataset, but strongly implies that those false detections will not have any substantial effect on the scientific results and interpretations. A purely statistical assessment of detector precision – without manual review – is common for datasets with extremely large numbers of detections, such as recently showcased in ref. 50, which mapped millions of boulders across the lunar surface.

Spatiotemporal data binning and statistics
I use a geographic grid consisting of 520,200 0.25° × 0.25° quadrangles that cover Mars between 55°N and 35°S. Each slope streak detection is associated with the unique ID of its ‘host cell’ or ‘analysis cell’. The spatiotemporal analysis is conducted on a cell-by-cell basis. Overall, I consider 91,687 CTX images. On average, each analysis cell intersects with 6.52 CTX images, with a minimum of 1 image and a maximum of 187 images. The used CTX images feature a homogeneous longitudinal distribution and a relatively homogeneous latitudinal distribution, with fewer images taken at very high northern latitudes, beyond ~45°N (Fig. S3). Past surveys have not identified any slope streaks at latitudes beyond ~40°N (e.g., ref. 7). The used CTX images feature a slightly heterogeneous seasonal distribution, with more images acquired over northern spring and summer (LS ~0° to ~170°) and fewer images over southern spring, summer, and autumn (LS ~220° to ~360°) (Fig. S3). The relative lack of images is particularly expressed at very high northern latitudes, beyond ~40°N, between ~200° and ~360° LS, where no slope streaks are present. It is unlikely that the seasonal imbalance of images has an effect on the results of this study, as the overabundance of images in northern spring and summers should affect the streak counts at those seasons, yet the peak streak formation season is associated with southern spring, summer, and autumn (Fig. 1B).

Maximum counts
This workflow uses the slope streak counts made by the detector in a given cell, in all CTX images that fully or partially overlap that cell. The workflow selects the overall maximum count made in each cell; in other words, the workflow derives the largest number of slope streaks ever recorded by CTX in a given cell. In addition, the workflow extracts the maximum (annual) climatology average solar MY horizontal wind velocity (Ux) at an altitude of 4.5 m above the surface (z), a daily column-integrated dust optical depth value (regularly kriged maps of 9.3 μm absorption column dust optical depth, 610 Pa 51), as well as the atmospheric density (ρa) at the respective time from the MCD 25,52. I use those parameters along with the law of the wall 33 to compute the wind shear velocity (u*) at surface level (with κ as the von Kármán constant, 0.4, and z0 as the aerodynamic surface roughness, here 2 cm):

Ux(z) = u* / κ * ln(z / z0) (1)

and then estimate the wind stress (τ) using 28,33:

τ = ρa * u*2 (2)

z0 is computed using (per, e.g., ref. 39):

z0 = 2 * D / 30 (3)

assuming a median grain size (D) of 30 cm, as recently used by Bickel et al. 34. I note that the utilization of one single D and z0 value as well as the usage of Eq. (3) largely oversimplifies the substantial variability of the aerodynamic roughness across Mars. Most notably, the assumed D and z0 values are relatively high and might not be fully representative for many slope streak-bearing regions. Wind stress values beyond ~0.02 Pa, either vortical (e.g., in dust devils or convective vortices) 53–55 or non-vortical (e.g., in near-surface wind gusts) 28,34 have been shown to be able to initiate and sustain the saltation of sand-sized particles (~100 µm), which can eject finer dust-sized particles upon re-impact on the surface. The ejection of dust through saltating sand-sized particles is suspected to be the main dust lifting mechanism on Mars (e.g., ref. 33, 56–60). Lastly, the workflow extracts the TES albedo 61 and MOLA topographic elevation 62,63 at each streak location.

Differential counts - seasonal
This workflow selects all CTX images that have a 100% overlap with a given cell, i.e., the process excludes images that might only partially cover a given cell, which likely lead to lower counts. Subsequently, the workflow identifies image pairs with a temporal acquisition difference of less than 6 months (one quarter MY, 90° LS), counts slope streaks in the before and after image, and computes a count difference, embodying an integration of the number of streaks that formed and faded between the two images. I identify 6 months as the most appropriate time difference (time window) for this analysis, as it represents the best compromise between data availability and temporal resolution, i.e., identified changes can be attributed to a season with reasonable accuracy – yet, the temporal window is wide enough to maximize the number of image pairs available, which in turn improves the overall spatiotemporal coverage of the analysis.

The slope streak counts can be substantially affected by the illumination and observation geometries of the available images, as well as their overall data quality and the state of the Martian atmosphere (e.g., atmospheric dust, Fig. 1C, D, S1). In fact, the conditions that mainly drive detection of false changes (solar incidence and atmospheric dust) are more prevalent during the southern summer and fall, which might affect the results of the presented change detection analysis. I carefully characterize the noise floor caused by false changes in a statistical manner by comparing detector- and human-derived (ground-truth) streak counts in 100 control cells across the five streak hotspots. The median absolute difference between detector- and human-derived change counts (i.e., counts of newly formed streaks) is 4 per cell per pair (Fig. 1C). As the total number of streaks per cell can vary drastically and as streaks can be nested, I define the noise floor as the median percentage of the difference between the human-derived count difference and the detector-derived count difference and the mean of the detector-derived before and after image counts, i.e., ~24% (Fig. 1D). This study only considers difference counts that exceed the median noise floor, to minimize the impact of false changes, acknowledging that the application of the noise floor reduces the overall sensitivity of the investigation to subtle, small-scale changes.

Importantly, even changes beyond the noise floor can be caused by real-world variability of the surface (e.g., Fig. 1C, D), which in turn represents the reality of the results. Notably, tightening the requirements on, for example, incidence and temporal differences between CTX images drastically reduces the overall number of cells with appropriate spatiotemporal coverage, impeding a representative, global-scale change detection analysis (Fig. 1G). My analysis does not consider the fading of slope streaks (negative formation rates), as the signature of fading streaks is less clearly defined (i.e., is not binary, as in the streak formation case, e.g., ref. 12) and is thus substantially less pronounced in the count data, and is also noticeably more affected by count noise.

As a last step, the workflow extracts the average LS of a given image pair to extract a daily column-integrated dust optical depth value (regularly kriged maps of 9.3 μm absorption column dust optical depth, 610 Pa) from the MCD 51. The workflow also extracts the maximum (regional, averaged) horizontal wind velocity value (at an altitude of 4.5 m above the surface) that is predicted by the MCD for the given sol, at the given LS and the given MY, as well as the corresponding atmospheric density value 25,52. As for the maximum count workflow, I use those parameters to compute the wind shear velocity at the surface level and to estimate the resulting wind stress.

As a last step, the workflow extracts the TES albedo 61 and MOLA topographic elevation 62 at all streak locations.

Differential counts – non-seasonal
This workflow focuses on all cells that contain (a) slope streaks and (b) a new impact that formed during CTX operations (n=1027, based on ref. 23) or an InSight epicenter location (n=24, BB ‘Broadband’ and LF ‘Low-Frequency’ events with back-azimuth, InSight MQS Catalog v14) 20,21. InSight BB and LF events are classified as tectonic events 41,64. If a cell contains a quake epicenter, all adjacent 8 cells are also included in the analysis, to broaden the spatial footprint of the overall analysis.

The workflow considers all CTX images that overlap a given cell at least by 90%, counts slope streaks in each image, and plots all counts as a function of time (Fig. 2E), along with the temporal brackets derived by Daubar et al. 23 for each impact event – or along with the exact timing of a given quake, as provided by the MQS Catalog v14 20. The utilization of a 90% overlap criterion slightly increases the count noise but substantially increases the number of images available for the analysis. I manually analyze each of the cell time-series, looking for subtle to stark increases in the streak counts that could be related to an impact or seismic event.

Manual ground-truth counts
In order to validate all automated maximum and difference counts, I conduct a detailed manual slope streak time-series analysis of two separate cells centered at 24°N, 147°W and 28°N, 145°W, in OMA. The analysis uses a total of 40 and 26 CTX image pairs, respectively, that feature 100% overlap with the cell and feature temporal differences of less than 6 months, acquired as part of a monitoring campaign between 2007 and 2023. This workflow resembles the differential count workflow, (manually) counts slope streaks in before and after images, per image pair, computes count differences, and records them along with the average LS of the respective CTX image pair (Fig. 1B).

Formation rates
I use an adapted workflow to derive slope streak formation rates on MY-scales. This workflow utilizes all CTX images that have a 100% overlap with a given cell, selects the two images with the largest temporal difference (with a maximum difference of ~17 years or ~8.5 MY, ~2007 to ~2024), counts slope streaks in the before and after image, and computes the count difference. The workflow uses two approaches to compute slope streak formation rates: (1) one approach relates the number of streaks that formed between the before and after image to their temporal difference, providing a measure of ‘newly formed slope streaks per MY’ (Nc):

Nc = Ndiff count / Δt (4)

Notably, the number of newly formed streaks is dependent on the overall abundance of slope streaks in a given cell (see, e.g., ref. 15), which is why approach (2) relates each difference count to the total number of slope streaks that are present in the after image (Ntotal), providing a measure of ‘percentage of population-increase per MY’ (Nn), directly following the methodology developed and used by 12,16–18:

Nn = (Ndiff count / Ntotal) * 100 (5)

Methodological and data limitations
The most important limitation of the accuracy of the presented results is the noise caused by (a) the slope streak counts and (b) the temporal bias introduced by the applied CTX image pair window (6 months), which in turn are rooted in the variability of the available CTX image data, as discussed above. In addition, there are other factors that affect the accuracy of the results presented throughout this work. The spatiotemporal analysis relies on various auxiliary datasets and model-derived products, specifically, TES surface albedo 61, MOLA topographic elevation 62,63, catalogs of new impact craters 23 and marsquakes 20,21, and the MCD 25,52. Each of those products is subject to different limitations that might affect the results of this study. For example, the used catalogs of new impacts and marsquakes are very likely incomplete, in space and time, as demonstrated by recent studies that identified additional impact events (see, e.g., ref. 65). InSight data, in particular, is extremely scarce in space and time, and only features minimal overlap with the other datasets used. TES and MOLA products feature spatial resolutions that are not representative of the local, meter-scale environment that is likely to play an important role in slope streak pre-conditioning and triggering. The MCD only provides approximate, low-resolution, region-scale (5.625° longitude by 3.75° latitude per cell), and thus heavily averaged insights into atmospheric processes, and cannot be expected to fully represent the dynamic, complex topographic environments slope streaks are located in. Recent work showed that the MCD appears to substantially underestimate the horizontal near-surface wind velocity 34, which implies that this work underestimates the wind stresses at slope streak locations. I underline the importance of local and regional studies – and the utilization of high-resolution products and meso-scale atmospheric models, in combination with the new dataset that is associated with this study – for the future verification and scrutiny of the results presented here, as previously highlighted by Bickel and Valantinas 7.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study reports results from a machine-learning-based survey for slope streaks using CTX data collected over nearly 20 years. The study explores relationships between the number of streaks observed from before and after images of a region and meteorological, insolation, and other ambient conditions to assess the drivers for streak activity.\nThe volume of detections alone makes the study interesting and potentially publishable, but I have numerous substantive technical concerns about the approach. Also, the study doesn’t provide any big-picture punchline or implications. Perhaps for a standard scientific article, a punchy conclusion isn’t necessary, but given the requirement that articles have broad impact, it’s hard to see that this article merits publication in Nature. Taking all these issues together, I feel compelled to recommend rejection for this study. I do think that the study (with some significant revisions) would probably merit publication, just not in Nature.\nIn what follows, I detail my concerns, starting with the most broad-sweeping ones and then followed by more narrow concerns that are listed below in the order they arise in the manuscript.\n-- Big Concerns --\n-The key results from this study rely on statistical correlations between ambient conditions and streak occurrence as a way to assess the drivers for streak activity. However, no attempt seems to be made to explore null hypotheses. For instance, the study makes the case (Lines 200-202) that dust streaks seem to occur mostly on surfaces with albedos exceeding 0.26, supposedly indicative of a dusty surface. However, the study never states what are the typical albedos across all studied regions. Is an albedo of 0.26 three standard deviations larger than the average? Without understanding the conditions and properties applicable to all studied areas, it’s difficult to determine how unusual (if at all) the places and times where streaks occur really are and whether the supposed drivers really are driving activity. Some quantitative assessment must be made of how unique are the places and times for streak activity.\n-There is also no assessment of any observational biases for detection. For example, I would expect, over regions and during times when atmospheric dust opacity is high, streak detection would be more difficult since the surface contrast necessary to identify streaks would be subdued. Are streaks easier to identify when the solar incidence angle is smaller (more direct sunlight allows for larger contrasts)? Perhaps biases have only a minor effect on the detection statistics, but without any assessment at all, it’s hard to say. Some assessment of potential biases should be included.\n-The driver favored by the study for streak activity seems to be high winds, which seems very plausible and consistent with previous work. However, estimates for wind stresses seem to come from the MCD Mars meteorological model. But the study never clarifies how specific these model-based wind stress estimates are to the time and place of streak occurrence. Given that the baselines for some of the observations span almost 20 years, it’s not at all clear how useful it is to estimate the maximum wind stress that occurs during that period to assess the influence of winds on streak formation. It should be clarified how the wind stress estimates were made (some similar concerns appear below). I’m not sure how, but there should next be made some attempt to narrow down the influence of the wind and exclude other possible drivers.\n-- Smaller Concerns --\n-Fig 1A – Why does the hand-count seem to differ so much from the automated count?\n-Fig 1G – Why are there so few points with large x value and small y value (bottom right corner)? Does this trend arise because there just aren’t observations in that quadrant, or are there lots of observations in that quadrant but no streak detections?\n-Fig 3 – It would be helpful to compare winds for cells with and without streaks. That would bolster the contention that winds are the key driver.\n-Line 57 - “Arabia features the largest number of streak-bearing cells with the most pronounced spatial extent” – What does “most pronounced spatial extent” mean?\n-Line 60 – “adjusting them for the effect of spatially co-located (‘nested’) streaks” – Meaning what?\n-It would be better to casting the detection density in terms of number per area, rather than number per 0.25x0.25 deg cell, given the wide range of latitudes sampled.\n-There REALLY needs to be more information about sampling statistics – How often were regions assessed and streaks were NOT found? Are trends with season indicative of the times you sampled, or do they reflect a real seasonal trend?\n-Lines 87-88 – “The results of the spot-check (Fig.1B) closely agree with both the number of the detector-observed newly-formed streaks as well as their seasonal timing.” – It is not at all obvious to me how or whether the two methods agree. For instance, the black dots appear to show a spike around Ls = 200. Such a peak is not at all apparent in the automated detections. How does Fig 1B demonstrate good agreement between the two methods?\n-Line 92 – “implying that the three independent measures of streak activity are in agreement” – What three independent measures? Mention is made of a hand-count and an automated approach. That sounds like two measures. What is the third measure?\n-Lines 95-96 = “Globally, all considered cells experience an average, normalized streak formation rate of ~5.5 %/MY” – 5.5% of what? I would have expected a streak formation rate to be measured in number of streaks per area per time.\n-Line 100 – “This implies that Mars’ entire streak population would be replenished within ~18 to ~24 MY.” – How long does it take for streaks to fade? Streaks would only be “replenished” if they went away, but so far, no information has been given about the rate of streak disappearance.\n-Fig 1 B – What is the right y-axis labeling? Presumably, the manual detections? The caption should clarify. The right y-axis title says, “difference OMA 2007 – 2023”. Does that title mean the number represents the difference between the number of streaks counted in 2007 and the number counted in 2023? Is the “count difference per 0.25x0.25 deg cell” also taken as the difference between 2007 and 2023?\n-Figure 3 – It appears Tharsis experiences the highest wind stresses at around the same time of day when MRO visits. Is there way to assess the un/likelihood that no streaks would be observed in the process of formation?\n-Lines 200-202 – “My data also suggest that 92 % of all difference and maximum count cells are located in regions featuring very high TES albedo (>0.26, Fig.4A,C)” – But what is the typical albedo of all regions surveyed? How does the “high” albedos compare to “low” albedos? The author suggests this high albedo indicates the presence of dust. Can the dust cover index (http://tes.asu.edu/~ruff/DCI/dci.html) also provide some indication of the amount of dust?\n-Lines 202-204 – “Similarly, MCD column-integrated dust optical depth measurements show an enhanced atmospheric dust load during the seasons that are associated with peak streak formation (Fig.1F).” – But there are streaks observed at a wide range of integrated dust opacities. Moreover, in Fig 1F, there seem to be no steaks observed around Ls = 250 for opacities below about 0.2. Is that because there are no periods with Ls = 250 and optical depth < 0.2, or are there observations made under those conditions during which no streaks were observed? The lack of explanation or consideration of this point highlights again one of the biggest issues for this study – there is no assessment or quantification of conditions during null detections, and so it is very hard to judge whether the trends that are suggested to promote streak formation are simply a product of observational bias.\n-Lines 270-271 – “The trained detector achieves a recall of ~0.75 (% streaks in testset identified) and a precision of ~0.94 (% correctly identified testset streaks)” – I presume the “%” is meant to indicate that 0.75 = 75% and 0.94 = 94%, but the way those statements are written, they almost read like 0.75% and 0.94%. Please clarify.\n-Line 297-300 – “This assessment suggests a precision of 94%, which means that a minimal number of false detections are included in the dataset, but strongly implies that those false detections will not have any substantial effect on the scientific results and interpretations.” – Even though the number of false detections might be small, there might be important trends or biases in the detections – certain observational conditions might be facilitate or hamper detection, but there doesn’t seem to be an assessment of such biases, making it difficult to judge what trends are real or which might arise from biases. Some assessment of these issues should be included (or, at least, alluded to from a previous study).\n-I struggled to understand how the detection limits were estimated and how they impact the bottom-line results. Lines 351-354 say “As the total number of streaks per cell can vary drastically and as streaks can be nested, I define the median noise floor as the percentage of the difference between the detector-derived and the mean of the human-derived before and after image counts, i.e., ~24 % (Fig.1D).” Does “24%” mean that, for example, if the before image shows 100 streaks and the after image shows 120 streaks, that change represents less than a 24% change and so the identification of streaks in the after image is discarded? I would have expected the “noise floor” for detections to be defined in terms of albedo contrast or image resolution, etc. Please clarify how the noise floor is defined, why it's defined that way, and how the threshold chosen impacts the results.", "reviewer_2": "Review of “Dust and wind drive slope streaks on Mars” by Bickel for Nature Communications\nSummary. Slope streaks are prevalent surface features on modern Mars. Found on dust-covered slopes, they are relatively superficial but persist for years, often without evidence of growth past their formation. Whereas mechanisms involving flows of liquid water were proposed relatively early (e.g., Morris, 1982) and as recently as 2017 (Bhardwaj et al., 2017), these features are now mostly accepted to consist of dry dust avalanches (e.g., Sullivan et al., 2001; Heyer et al., 2019; Dundas, 2020). Due to their often seasonal nature, slope streaks are thought to mainly be triggered by winds that are strong enough to initiate sand transport (e.g., Heyer et al., 2019; Dundas, 2020).\nIn his manuscript, Bickel seeks to further test and refine this hypothesis by harnessing the power of automated slope streak detection in the CTX archive through space and time. His results are consistent with previous (often more local or regional) findings that show a clear correlation between the occurrence of slope streaks and dust abundance, seasonal streak formation, and a correlation between when they form and the occurrence of stronger winds. He also shows that some streaks can be explained by marsquakes and meteor impacts, though these likely represent a small number of the total yearly inventory. Based on these observations, Bickel concludes that winds trigger the formation of streaks, where dust is available, and when wind stress exceeds the static threshold of motion of dust particles.\nOverall Evaluation. The manuscript tackles a popular topic in modern Mars processes. It is well-illustrated and well-written. The AI-driven automatic detection procedure is state of the art and robust. The global nature of the dataset allows the author to ask interesting questions. I have some reservations about the novelty of the results and whether they warrant publication in Nature Communications, but I will leave this assessment to the editor. Science-wise, I have one main concern, which relates to the author’s interpretation of the dataset (along with a couple of more minor comments, detailed below). Addressing it would amount to relatively minor textual changes but substantial revision of the message. Based on this evaluation, I’d recommend that the manuscript be returned to the author for revisions.\nMain Comments.\n1. The data indicates that aeolian saltation—not wind shear—is the main driver of slope streak formation. Whereas saltation itself is initiated by wind shear, this distinction is important because it points at the role of a third ingredient that is required to form wind streaks—the availability of saltatable sand.\n1.1. The manuscript reports a bed stress of 0.01 Pa as their preferred threshold for slope-streak initiation. It is inconsistently reported throughout the text as “the static threshold…to initiate the saltation of ~100 μm-sized grains” (e.g., l. 175-176) or “the static threshold required to initiate the motion of dust grains” (e.g., l. 18). However:\no Particles 100 μm in diameter are considered sand, whereas airborne dust on Mars is known to consist of much smaller particles (~ 1 μm; e.g., Tomasko et al., 1999; Fedorova et al., 2014). Sand and dust have drastically different static thresholds due to the outsized influence of electrostatic forces in finer particles. [Dust aggregates may form but they would break apart upon impact if entrained in saltation, e.g., Weitz et al., (2018).]\no The static (or fluid) thresholds for both dust and 100 μm sand on Mars are higher than 0.01 Pa, even as reported in the studies cited by Bickel to support that choice (Kok et al., 2012; Newman et al., 2022):\n• Newman et al. (2022) reports that winds need to exceed a threshold of 15 m/s (measured at 1.5 m above ground) “by some unknown amount” to initiate vortical dust transport. Assuming a typical atmospheric density of 0.015 kg/m3 and the z0 value of 0.01 m used in Newman et al. (2022), 15 m/s winds at 1.5 m translate to a shear velocity of 1.23 m/s, which in turn would imply a bed stress greater than 0.02 Pa. This value, as noted by Newman et al. (2022), is an absolute lower bound on the true threshold, and is only to be associated with vortical triggers, which lower the threshold of motion by enhancing pressure gradients locally.\n• I note that even the author himself chose to double this value in his related manuscript on dust devils, currently in review at Science Advances.\n• For non-vortical triggers, one should refer to the classic fluid threshold. Several formulations exist for the fluid threshold, like those reported in Kok et al. (2012) (e.g., their figure 21b). For example, Kok et al. (2012) shows the formulation of Iversen and White (1982) for the fluid threshold wind shear velocity, which reaches ~1.5 m/s for 100 μm grains, i.e., bed stresses greater than 0.03 Pa.\n• Other formulations exist, e.g., those of Swann et al. (2020), Andreotti et al. (2021), Gunn and Jerolmack (2022), etc., all inconsistent with the statement that 0.01 Pa coincides with the static threshold of either 100 μm sand or dust.\n• Although Kok et al. (2012) did not extend their plot to dust sizes, it shows that the threshold increases for finer grains as electrostatic forces become more dominant. Plugging in numbers in the classic formulation Shao and Lu (2000), one finds a threshold shear velocity of ~11-3.5 m/s for grains in the 1-10 μm range, which translates to bed stresses in the 0.18-1.8 Pa range, or greater than the quoted value of 0.01 Pa by one to two orders of magnitude. Such high stress values are not thought to be typically reached on Mars (neither from in situ measurements nor from GCMs), indicating that wind shear cannot typically be a direct trigger of dust lofting on Mars.\n• The inefficiency of winds at lifting dust has been well known for decades, and saltation is often thought of as the main driver of dust lofting on Mars, whereby winds drive saltation, and impacts of sand grains onto dusty surfaces eject dust particles from the bed (Greeley et al., 1981; Newman et al., 2004). Accordingly, the paths of dust storms closely coincide with aeolian sand transport pathways (Rubanenko et al., 2023).\no A bed stress value of 0.01 Pa—as chosen by Bickel— is likely closer to the dynamic (or impact) threshold of ~100 μm grains (e.g., Kok et al., 2012; Gunn and Jerolmack, 2022, etc.). This threshold corresponds to the minimum wind speed required to sustain saltation once it was initiated, and is lower than the fluid (or static threshold) on Mars due to the efficiency of grain splash upon grain-to-grain impacts. The fact that winds of this magnitude correlate with wind-streak formation suggests that saltation—not wind shear—triggers avalanching (whether by impact-driven destabilization of the slope, or by driving the migration of sandy bedforms like on dune slipfaces, e.g., Ewing et al., 2017).\n1.2. The reported periodicity of slope streak formation coincides with that of saltation as observed by orbiters and rovers:\no Aeolian saltation of sand is known to be highly seasonal on Mars, with active saltation occurring in southern summer and autumn (e.g., Ayoub et al., 2014; Bridges et al., 2017; Baker et al., 2018a,b; Lapotre and Rampe, 2018), coinciding with the seasonality of slope-streak formation observed in this study.\no Aeolian saltation was found to be largely concentrated at sunrise and sunset in Gale crater (Baker et al., 2022), coinciding with the diurnal nature of winds capable of transporting sand according to the MCD.\n1.3. Slope streaks form in proximity to loose sand. The spatial distribution of slope streaks coincides in Arabia, Elysium, and north of Valles Marineris with the presence of aeolian dune fields in the region, i.e., they tend to form in proximity to sand that is available for transport (see, e.g., the map of Rubanenko et al., 2021). Although dune fields are not typically found in the OMA region, sand ripples abound therein, also attesting to the availability of sand-sized particles (e.g., Vaz et al., 2023).\nIn summary, the observations reported by Bickel indicate that wind shear greater than 0.01 Pa correlates with slope streak formation, but not that wind shear is their trigger. Instead, this value is consistent with the dynamic threshold for saltation of sand—a much more efficient mechanism to lift dust on Mars. Furthermore, the spatial and temporal distributions of slope-streak formation are both consistent with known occurrences of active sand transport. Thus, unlike what the current title of the paper states (“Dust and Wind Drive Slope Streaks on Mars”), the presented data demonstrate that dust, sand, and saltation drive slope streaks on Mars.\nThis point could be addressed by the author through:\no Rephrasing throughout where mentioning “wind action” or similar as the driver of slope streak formation, and adjusting the title accordingly.\no Clarifying which threshold is used and making it consistent throughout (“static” vs. “dynamic” and “100 μm” vs. “dust”). Based on the value of 0.01 Pa, the author should be reporting it as the dynamic threshold of ~100 μm sand.\no Adding a discussion of why a threshold of 0.01 Pa, the distribution of slope streaks, and their periodicity all point at aeolian saltation of sand as the trigger of avalanching.\n[As a side note: These comments are also very relevant to the related Bickel et al. manuscript currently in review at Science Advances. I hope the author will choose to take them into consideration when revising that other paper as well.]\n2. Previous work needs to be better represented in the reference list. Beyond studies being currently misrepresented (Kok et al., 2012; Newman et al., 2022), the current reference list is rather unbalanced, with 14 self-citations and many important references omitted.\nI cannot emphasize enough how much great work the author has been leading lately—and he should absolutely be credited for it—but several of the citations appear unnecessary, especially the whole series of papers on rock detection, chloride deposits, seismic events, or on the Mercury hollows. The author could minimize self-citations, for example, by removing a few of those. In parallel, I’d recommend that the author incorporate the following references (in addition to those already in there) in his new, strengthened discussion of observations:\no For saltation as a trigger of dust lofting: Greeley et al. (1981), Newman et al. (2004), Rubanenko et al. (2021; 2023).\no For thresholds of motion: Shao and Lu (2000), Swann et al. (2020), Andreotti et al. (2021), Gunn and Jerolmack (2022).\no For sand transport periodicity: Ayoub et al. (2014), Baker et al. (2018a,b), Lapotre and Rampe (2018), Baker et al. (2022).\nDetailed Comments:\nl. 18: Incorrect; wind stresses exceed the dynamic threshold required to initiate the motion of sand grains.\nl. 72-74: This is better explained in the methods but very opaque as described in the main text. I’d recommend defining “noise floor” (not everyone will be familiar with signal-processing terminology) and expanding a bit on how it was evaluated.\nl. 88-90: Is there any correlation between the topographic gradient of the slope on which streaks form and the continuous vs. bursty nature of streak formation?\nl. 113-114: Slope streaks are common, but I’d be cautious calling them “one of the most dynamic geologic processes on the planet”. They are quite thin/superficial, and once they appear, they most often remain inactive for years and disappear. In turn, many modern processes appear to be much more dynamic and involve greater volumes of geological material (such as ice or permafrost-related processes and dune or ripple migration; e.g., Diniega et al., 2021).\nFigure 1B: Seasonality is very similar to that of sand transport as observed from orbit by Ayoub et al. (2014).\nl. 167: “saltation” rather than “wind”\nl. 167-207: This whole section needs to be reworked (see main comment #1).\nl. 170-171: This won't mean much to the reader unless you specify at what height above the surface these are reported. (I realize it is mentioned in the methods but it should also be reported here, otherwise readers do not have the information they need to evaluate the significance of a 10 m/s wind.)\nl. 171: “surface-level wind stress”: more needs to be said here about how these were computed in the MCD. Presumably, the MCD assumes a value of aerodynamic roughness, z0, as its lower boundary condition. It is unclear whether this is the same value as that used by the author later when reconstructing shear velocity from MCD outputs (it technically should be, for consistency, and then corrected*).\n* This correction is needed because z0 is not scale independent, and MCD considers wind turbulence over much greater length scales than those relevant to sediment transport. [This is the same reason why, for example, the effective threshold of motion calibrated by Ayoub et al. (2014) over entire dune fields is much lower than the physical, threshold one would measure by blowing winds over individual sand grains.]\nL. 176: This is inconsistent with several prior and subsequent statements that winds exceed the threshold of motion of dust-sized particles (see main comment #1 above; dust and sand are not the same thing, have different static thresholds, and neither are close to the reported value of 0.01 Pa). A bed stress of 0.01 Pa is closer to the dynamic threshold for sand.\nFigure 3: “saltation” instead of “wind action”.\nl. 224: “saltation” instead of “wind action”. Note that this implies the availability of loose sand as an additional prerequisite.\nl. 320: A z0 value of 0.02 m is very high. Newman et al. (2022) use 0.01 m. A centimeter is also on the higher end of what’s expected in the presence of active saltation and meter-scale sand ripples (Alvarez et al., 2025). Over flat surfaces with little roughness (as might be expected of dusty surfaces) and under active saltation, z0 should be closer to 10-4-10-3 m (see figure 6 of Alvarez et al., 2025).\nl. 324-328: This is incorrect. Stresses of 0.01 Pa neither initiate sand nor dust transport; but they sustain sand transport, which in turn may drive dust lofting.\nl. 513: Only one “Dundas (2020)” reference is cited, so the “b” should be removed.\nReferences.\nAlvarez, Carlos A., et al. \"Aerodynamic roughness of rippled beds under active saltation at Earth-to-Mars atmospheric pressures.\" Nature Communications 16.1 (2025): 5113.\nAndreotti, Bruno, et al. \"A lower-than-expected saltation threshold at Martian pressure and below.\" Proceedings of the National Academy of Sciences 118.5 (2021): e2012386118.\nAyoub, F., et al. \"Threshold for sand mobility on Mars calibrated from seasonal variations of sand flux.\" Nature communications 5.1 (2014): 5096.\nBaker, M. M., et al. \"Coarse sediment transport in the modern Martian environment.\" Journal of Geophysical Research: Planets 123.6 (2018a): 1380-1394.\nBaker, Mariah M., et al. \"The Bagnold Dunes in southern summer: Active sediment transport on Mars observed by the Curiosity rover.\" Geophysical Research Letters 45.17 (2018b): 8853-8863.\nBaker, Mariah M., et al. \"Diurnal variability in aeolian sediment transport at Gale crater, Mars.\" Journal of Geophysical Research: Planets 127.2 (2022): e2020JE006734.\nBhardwaj, Anshuman, et al. \"Martian slope streaks as plausible indicators of transient water activity.\" Scientific reports 7.1 (2017): 7074.\nBridges, N. T., et al. \"Martian aeolian activity at the Bagnold Dunes, Gale Crater: The view from the surface and orbit.\" Journal of Geophysical Research: Planets 122.10 (2017): 2077-2110.\nDiniega, Serina, et al. \"Modern Mars' geomorphological activity, driven by wind, frost, and gravity.\" Geomorphology 380 (2021): 107627.\nDundas, Colin M. \"Geomorphological evidence for a dry dust avalanche origin of slope streaks on Mars.\" Nature Geoscience 13.7 (2020): 473-476.\nEwing, Ryan C., et al. \"Sedimentary processes of the Bagnold Dunes: Implications for the eolian rock record of Mars.\" Journal of Geophysical Research: Planets 122.12 (2017): 2544-2573.\nFedorova, Anna A., et al. \"Evidence for a bimodal size distribution for the suspended aerosol particles on Mars.\" Icarus 231 (2014): 239-260.\nGreeley, Ronald, et al. \"Dust storms on Mars: Considerations and simulations.\" (1981).\nGunn, Andrew, and Douglas J. Jerolmack. \"Conditions for aeolian transport in the solar system.\" Nature astronomy 6.8 (2022): 923-929.\nHeyer, T., et al. \"Seasonal formation rates of martian slope streaks.\" Icarus 323 (2019): 76-86.\nIversen, James D., and Bruce R. White. \"Saltation threshold on earth, mars and venus.\" Sedimentology 29.1 (1982): 111-119.\nKok, Jasper F., et al. \"The physics of wind-blown sand and dust.\" Reports on progress in Physics 75.10 (2012): 106901.\nLapotre, M. G. A., and E. B. Rampe. \"Curiosity's investigation of the Bagnold Dunes, Gale crater: Overview of the two‐phase scientific campaign and introduction to the special collection.\" Geophysical Research Letters 45.19 (2018): 10-200.\nMorris, Elliot C. \"Aureole deposits of the Martian volcano Olympus Mons.\" Journal of Geophysical Research: Solid Earth 87.B2 (1982): 1164-1178.\nNewman, C. E., P. L. Read, and S. R. Lewis. \"Investigating atmospheric predictability on Mars using breeding vectors in a general‐circulation model.\" Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography 130.603 (2004): 2971-2989.\nNewman, Claire E., et al. \"The dynamic atmospheric and aeolian environment of Jezero crater, Mars.\" Science Advances 8.21 (2022): eabn3783.\nRubanenko, L., et al. \"Automatic detection and segmentation of barchan dunes on Mars and Earth using a convolutional neural network.\" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 9364-9371.\nRubanenko, L., et al. \"Global surface winds and Aeolian sediment pathways on Mars from the morphology of barchan dunes.\" Geophysical Research Letters 50.18 (2023): e2022GL102610.\nShao, Yaping, and Hua Lu. \"A simple expression for wind erosion threshold friction velocity.\" Journal of Geophysical Research: Atmospheres 105.D17 (2000): 22437-22443.\nSullivan, Robert, et al. \"Mass movement slope streaks imaged by the Mars Orbiter Camera.\" Journal of Geophysical Research: Planets 106.E10 (2001): 23607-23633.\nSwann, C., D. J. Sherman, and R. C. Ewing. \"Experimentally derived thresholds for windblown sand on Mars.\" Geophysical Research Letters 47.3 (2020): e2019GL084484.\nTomasko, M. G., et al. \"Properties of dust in the Martian atmosphere from the Imager on Mars Pathfinder.\" Journal of Geophysical Research: Planets 104.E4 (1999): 8987-9007.\nVaz, David A., et al. \"Constraining the mechanisms of aeolian bedform formation on Mars through a global morphometric survey.\" Earth and Planetary Science Letters 614 (2023): 118196.\nWeitz, Catherine M., et al. \"The physical properties and geochemistry of grains on aeolian bedforms at Gale crater, Mars.\" Journal of Geophysical Research: Planets 127.11 (2022): e2021JE007061."}, "author_response": "I thank both reviewers for their detailed and constructive feedback. I carefully considered all\ncomments and revised the manuscript and supplementary information accordingly (text, figures, and\nsupplements). Changes in the manuscript and supplementary information are highlighted in green\ncolor.\nPlease find my replies (blue bold) to the reviewers’ comments (italic gray) below.\nPlease note that I integrated a few small, unrelated improvements and updates (not related to any of\nthe comments of the reviewers) throughout the manuscript, such as typo fixes and reference\nupdates.\nValentin Bickel\n1\n--- --- ---\nReviewer #1\nThe study reports results from a machine-learning-based survey for slope streaks using CTX data\ncollected over nearly 20 years. The study explores relationships between the number of streaks\nobserved from before and after images of a region and meteorological, insolation, and other ambient\nconditions to assess the drivers for streak activity.\nThe volume of detections alone makes the study interesting and potentially publishable, but I have\nnumerous substantive technical concerns about the approach. Also, the study doesn’t provide any\nbig-picture punchline or implications. Perhaps for a standard scientific article, a punchy conclusion\nisn’t necessary, but given the requirement that articles have broad impact, it’s hard to see that this\narticle merits publication in Nature. Taking all these issues together, I feel compelled to recommend\nrejection for this study. I do think that the study (with some significant revisions) would probably\nmerit publication, just not in Nature.\nIn what follows, I detail my concerns, starting with the most broad-sweeping ones and then followed\nby more narrow concerns that are listed below in the order they arise in the manuscript.\nMy reply: I thank reviewer #1 for their detailed feedback. I carefully addressed all of reviewer #1’s\nconcerns and improved the presentation of the overall conclusions and implications – further\nhighlighting their novelty and impact – as detailed in my point-by-point replies below.\nI would like to underline that I consider this a first-of-its-kind, trail-blazing study – the used data\nand methods might have limitations and the results might not be as ‘punchy’ as desired, but the\nutilization of deep learning-driven algorithms to enable the simultaneous analysis of numerous,\nextremely large geospatial datasets, and the investigation of dynamic surface processes in an\nabsolute, spatiotemporal manner, at a global-scale, are truly innovative and novel. Most\nimportantly, this study provides the first direct, global-scale, and quantitative evidence for the key\nrole of dust, sand, and wind in driving the formation of slope streaks on Mars – a subject that has\nbeen under intense debate for the past 50 years or so.\nI am convinced that the planetary science community will use similar approaches in the future to\naddress other fundamental questions and debates. This is why I believe that this study deserves\npublication in a Nature-tier journal, after all remaining technical and methodological concerns\nhave been addressed.\n-- Big Concerns --\n-The key results from this study rely on statistical correlations between ambient conditions and streak\noccurrence as a way to assess the drivers for streak activity. However, no attempt seems to be made\nto explore null hypotheses. For instance, the study makes the case (Lines 200-202) that dust streaks\nseem to occur mostly on surfaces with albedos exceeding 0.26, supposedly indicative of a dusty\nsurface. However, the study never states what are the typical albedos across all studied regions. Is an\nalbedo of 0.26 three standard deviations larger than the average? Without understanding the\nconditions and properties applicable to all studied areas, it’s difficult to determine how unusual (if at\nall) the places and times where streaks occur really are and whether the supposed drivers really are\ndriving activity. Some quantitative assessment must be made of how unique are the places and times\nfor streak activity.\nMy reply: Thank you for raising this concern. I agree with reviewer #1 that the analysis and\ninterpretations would benefit from the inclusion of ‘null hypotheses’ and ‘ambient conditions’,\n2\nwhere possible. In response to this comment, I integrated statistical information about the\nproperties of the ‘background’ (= all 520,200 analysis cells) into several figures, created new\nfigures, and added new text. Specifically, I added information about the ‘background’ distribution’s\nmean, 1-sigma, and 2-sigma to Figures 3 and 5 (previously Fig. 4) that enables a direct comparison\nof the properties of the streak-bearing cells and the background cells. In addition, I added\nexplanatory text whenever specific values, like a TES albedo value of 0.26, are used for a specific\npart of the analysis. Further, I added two new figures that enable a substantially more detailed\ncomparison of several key properties of the populations of streak-bearing and background cells,\nFigs. 4 and 6; Figure 4 compares the key atmospheric properties of streak-bearing vs background\ncells – including mean, 1-sigma, and 2-sigma – and Figure 6 provides a holistic comparison of the\nrelation between albedo, elevation, streak activity, and wind action, again, directly comparing\nstreak-bearing and background cells.\nThe direct comparison of streak-bearing and background cells further strengthens the results and\ninterpretations of this manuscript.\n-There is also no assessment of any observational biases for detection. For example, I would expect,\nover regions and during times when atmospheric dust opacity is high, streak detection would be more\ndifficult since the surface contrast necessary to identify streaks would be subdued. Are streaks easier\nto identify when the solar incidence angle is smaller (more direct sunlight allows for larger contrasts)?\nPerhaps biases have only a minor effect on the detection statistics, but without any assessment at all,\nit’s hard to say. Some assessment of potential biases should be included.\nMy reply: Observational biases that might be associated with the results of this study are intensely\ndescribed and discussed throughout the manuscript, as highlighted by the examples below. I\nrevised and augmented the existing text where possible and helpful, to further improve the\ncommunication of potential biases and limitations, for example:\nResults section: Differences in streak counts per cell over time can be indicative of streak formation\nand fading, but – importantly – can also be caused by variations in imaging geometry and atmospheric\nconditions (‘false changes’). Most prominently, changes in the atmospheric dust load and solar\nincidence angle, as well as spatial shifts caused by minor image geolocation inaccuracies can lead to the\ndetection of false changes; for example, streaks that were shadowed in the first image of a pair are\nilluminated in the second image, which enables their detection – and thus mimics their formation\n(Fig.S1). I characterize the median ‘noise floor’ caused by false changes using a set of 100 ground-truth\ncells (Fig.1C,D) and only consider difference counts that exceed the median noise floor, to minimize the\nimpact of false changes. Here, the term ‘noise floor’ describes the threshold that is used to distinguish\nbetween ‘valid’ (i.e., used for the analysis) and ‘invalid’ changes (i.e., not used for the analysis), in a\nstatistical way.\nFigure 1 Caption: […] The accuracy of the results presented in (B) is dependent on the ‘noise floor’ of\nthe detection method itself and is affected by the tradeoff between the allowed maximum image\nacquisition date delta (here, 6 months or ~90° LS) and data abundance, which reduces the ‘temporal\nresolution’ or ‘temporal accuracy’ of the detector-derived count differences; this inaccuracy is likely\ncausing the slight temporal offset between the detector-derived and manual counts. C) & D) quantify\nthe accuracy of the automated detection method using ground-truth data, as absolute counts and as\npercentage, respectively. Relation of the difference of solar incidence between image pairs and season\n(E), MCD column-integrated dust optical depth and season (F), and the difference of solar incidence\nbetween image pairs and the time between image pair acquisitions (G), per considered (slope streak-\nbearing) cell (as used for panel B).\nMethods section: The slope streak counts can be substantially affected by the illumination and\nobservation geometries of the available images, as well as their overall data quality and the state of the\nmartian atmosphere (e.g., atmospheric dust, Fig.1C,D,ED1). In fact, the conditions that mainly drive\ndetections of false changes (solar incidence and atmospheric dust) are more prevalent during southern\n3\nsummer and fall, which might affect the results of the presented change detection analysis. I carefully\ncharacterize the ‘noise floor’ caused by false changes in a statistical manner by comparing detector- and\nhuman-derived (‘ground-truth’) streak counts in 100 control cells across the five streak hotspots. The\nmedian absolute difference between detector- and human-derived change counts (i.e., counts of newly\nformed streaks) is 4 per cell per pair (Fig.1C). As the total number of streaks per cell can vary\ndrastically and as streaks can be nested, I define the ‘noise floor’ as the median percentage of the\ndifference between the human-derived count difference and the detector-derived count difference and\nthe mean of the detector-derived before and after image counts, i.e., ~24 % (Fig.1D). This study only\nconsiders difference counts that exceed the median noise floor, to minimize the impact of false changes,\nacknowledging that the application of the noise floor reduces the overall sensitivity of the investigation\nto subtle, small-scale changes. Importantly, even changes beyond the noise floor can be caused by data\nquality issues or differences (Fig.1C,D), which remains the most relevant limitation of the overall\naccuracy of the presented results. Notably, tightening the requirements on, for example, incidence and\ntemporal differences between CTX images drastically reduces the overall number of cells with\nappropriate spatio-temporal coverage, impeding a representative, global-scale change detection analysis\n(Fig.1G).\n-The driver favored by the study for streak activity seems to be high winds, which seems very plausible\nand consistent with previous work. However, estimates for wind stresses seem to come from the MCD\nMars meteorological model. But the study never clarifies how specific these model-based wind stress\nestimates are to the time and place of streak occurrence. Given that the baselines for some of the\nobservations span almost 20 years, it’s not at all clear how useful it is to estimate the maximum wind\nstress that occurs during that period to assess the influence of winds on streak formation. It should be\nclarified how the wind stress estimates were made (some similar concerns appear below). I’m not\nsure how, but there should next be made some attempt to narrow down the influence of the wind and\nexclude other possible drivers.\nMy reply: The (substantial) limitations of the MCD are described in the Methods section - however,\nI agree that the mention of these critical caveats is too short, appears too late, and might be\nentirely overlooked. I added several sentences about the limitations of the MCD as well as the\nassociated methodologies and assumptions throughout the manuscript, wherever MCD outputs\nare utilized and showcased, for example:\nResults: The difference count data and modelled, low-resolution, regional-scale atmospheric data\nextracted from the Mars Climate Database (MCD v6.1, Forget et al., 1999) suggest that ~20 % of the\ncells with ongoing (directly observed) streak formation activity experience wind stress values beyond\n0.02 Pa (maximum of the sol at image pair mean L , with a maximum temporal difference of 6 months\nS\nor 90° L between the images). Wind stress values are computed from horizontal wind velocities\nS\n(modelled at 4.5 m above the surface) and the spatio-temporally associated atmospheric density,\nassuming an aerodynamic (surface) roughness of 2 centimeters. […]\nand\nResults: Importantly, MCD-predicted atmospheric parameters represent regional averages and are not\nexpected to fully capture the dynamics of the atmosphere at the slope streak scale.\nand\nDiscussion: Importantly, all estimates of wind stress are based on several assumptions, such as the\nutilization of one average aerodynamic roughness for the entire surface of Mars. These assumptions\nfollow several preceding studies (e.g., Charalambous et al., 2021; Bickel et al., 2025a) but are likely to\naffect the overall accuracy of the derived wind stress estimates, also because of the low spatial\nresolution of the MCD. Notably, a recent study that used the movements of dust devils to measure near-\nsurface wind speeds across Mars (Bickel et al., 2025a) suggests that the global-scale MCD – not\nsurprisingly – systematically and substantially underestimates local-scale wind velocities, suggesting\nthat the wind stresses at streak locations could be larger than estimated here, further underscoring the\nability of near-surface winds to initiate sand saltation and trigger streak formation. As highlighted by\nBickel and Valantinas (2025), future studies of slope streak formation and activity should utilize\natmospheric meso-scale models to address some of the shortcomings of this study.\nand\nMethods: The MCD only provides approximate, low-resolution, region-scale (5.625° longitude by\n3.75° latitude per cell), and thus heavily averaged insights into atmospheric processes and cannot be\nexpected to fully represent the dynamic, complex topographic environments slope streaks are located in.\nI underline the importance of local or regional studies – and the utilization of high-resolution products\nand meso-scale atmospheric models – for the future verification and scrutinization of the results\npresented here, as previously highlighted by Bickel and Valantinas (2025).\nDespite its limitations, the MCD (along with other GCM-based databases) currently remain(s) the\nonly viable source of information about atmospheric parameters on a global scale.\n-- Smaller Concerns --\n-Fig 1A – Why does the hand-count seem to differ so much from the automated count?\nMy reply: The machine learning-driven detector tends to group streaks that are spatially close in\none single detection (‘nested streaks’), as is evident in e.g. Figure 2A. This is a fundamental\nlimitation of convolutional neural networks – if features are too close to each other, they cannot\nbe detected separately anymore (usually called ‘occlusion’). A human operator is able to\ndisentangle most of those nested streaks/occlusions, which results in higher streak counts. This is\nspecifically called out in line 81 (including an additional clarification):\nAs observed earlier by Bickel and Valantinas (2025), the used streak detector tends to identify spatially\nco-located (‘nested’) streaks as one single feature, suggesting that the detector-derived burst counts\nunderestimate the actual (absolute) number of individual streaks that formed by a factor of ~2, on\naverage.\n-Fig 1G – Why are there so few points with large x value and small y value (bottom right corner)?\nDoes this trend arise because there just aren’t observations in that quadrant, or are there lots of\nobservations in that quadrant but no streak detections?\nMy reply: The lack of CTX images with large solar incidence angle differences and small image\nacquisition date differences is expected, because short acquisition date differences – coupled with\nthe sun-synchronous orbit of MRO – directly results in very similar illumination conditions. In other\nwords, for MRO and CTX, illumination geometry differences are controlled by seasonal changes,\nover long temporal periods (as shown in Figure 1G).\n-Fig 3 – It would be helpful to compare winds for cells with and without streaks. That would bolster\nthe contention that winds are the key driver.\nMy reply: Thank you for raising this point. I agree – and addressed this concern in my reply and\nassociated revisions above.\n-Line 57 - “Arabia features the largest number of streak-bearing cells with the most pronounced\nspatial extent” – What does “most pronounced spatial extent” mean?\nMy reply: Thank you for raising this issue. I re-worded that sentence in the following way:\n[…] OMA features the highest density and number of streak-bearing cells, with a peak maximum count\nof 221 streaks/cell (Fig.1A), while Arabia features the spatially most-extensive, coherent region of\nslope-bearing cells. […]\nbut is the most wide-spread spatially; in other words, Arabia has the highest number of connected\nstreak-bearing cells (= the largest coherent region of slope-bearing cells).\n-Line 60 – “adjusting them for the effect of spatially co-located (‘nested’) streaks” – Meaning what?\nMy reply: This statement refers to the fact that the neural network tends to group individual, co-\nlocated streaks, which leads to an underestimate of the actual, absolute number of present streaks\n– see my reply to a similar comment above.\n-It would be better to casting the detection density in terms of number per area, rather than number\nper 0.25x0.25 deg cell, given the wide range of latitudes sampled.\nMy reply: Thank you for the suggestion. I understand the concern, however, I prefer using a ‘cell-\nbased’ metric because the entire analysis is based on ‘cells’ (such as the maximum and difference\ncounts); in this way, a cell-based metric is more intuitive and also helps to implicitly communicate\nsome of the limitations of my analysis method, such as related to the ‘ML noise floor’.\n-There REALLY needs to be more information about sampling statistics – How often were regions\nassessed and streaks were NOT found? Are trends with season indicative of the times you sampled,\nor do they reflect a real seasonal trend?\nMy reply: On average, each of the 520,200 analysis cells is covered by 6.52 images, with a\nminimum of 1 and a maximum of 187 images (both extreme cases are very rare). This strongly\nimplies that cells without slope streak detections (as e.g. shown in Figure 1A) indeed do not host\nany slope streaks.\nHowever, I agree that additional statistics about the used CTX dataset would be helpful to\ncontextualize the study and potential biases. I added new information to the Methods section,\nalong with a new supplementary figure:\nData binning and statistics\nI use a geographic grid consisting of 520,200 0.25°x0.25° quadrangles that cover Mars between 55°N\nand 35°S. Each slope streak detection is associated with the unique ID of its ‘host cell’ or ‘analysis\ncell’. The spatiotemporal analysis is conducted on a cell-by-cell basis. Overall, I consider 91,687 CTX\nimages. On average, each analysis cell intersects with 6.52 CTX images, with a minimum of 1 image\nand a maximum of 187 images. The used CTX images feature a homogeneous longitudinal distribution\nand a relatively homogeneous latitudinal distribution, with fewer images taken at very high northern\nlatitudes, beyond ~45°N (Fig.S3). Past surveys have not identified any slope streaks at latitudes beyond\n~40°N (e.g., Bickel & Valantinas, 2025). The used CTX images feature a slightly heterogeneous\nseasonal distribution, with more images acquired over northern spring and summer (L ~0° to ~170°)\nS\nand fewer images over southern spring, summer, and autumn (L ~220° to ~360°) (Fig.S3). The relative\nS\nlack of images is particularly expressed at very high northern latitudes, beyond ~40°N, between ~200°\nand ~360° L , where no slope streaks are present. It is unlikely that the seasonal imbalance of images\nS\nhas an effect on the results of this study, as the overabundance of images in northern spring and summer\nshould affect the streak counts at those seasons, yet, the peak streak formation season is associated with\nsouthern spring, summer, and autumn (Fig.1B).\nand in the supplementary information:\nLST for all 91,687 used CTX images. (E) histogram of the distribution of the number of CTX images intersecting analysis\ncells. (F) relation of CTX image center latitude and image acquisition LS.\n-Lines 87-88 – “The results of the spot-check (Fig.1B) closely agree with both the number of the\ndetector-observed newly-formed streaks as well as their seasonal timing.” – It is not at all obvious to\nme how or whether the two methods agree. For instance, the black dots appear to show a spike\naround Ls = 200. Such a peak is not at all apparent in the automated detections. How does Fig 1B\ndemonstrate good agreement between the two methods?\nMy reply: Thank you for raising this concern. It is important to consider the ‘temporal resolution’ of\nthe ML-derived count difference dataset, i.e., ~6 months or ~90° L . This ‘temporal window’ needs\nS\nto be so wide to be able to include enough image data to run a representative, global-scale\ndifference count analysis. In other words, I trade temporal resolution for statistical robustness. The\nL offset between the manual count peak and the first surge in the ML-derived counts is only ~10°\nS\nor less than ~1 month (note that I use the ‘mean’ L of the before and after image to plot the data).\nS\nMy interpretation of the data – and the underlying limitation of the methodology – is that the ML-\nderived surge and the manual count surge are identical, but that the ML-detected surge appears\n7\nslightly delayed, due to the inaccuracy introduced by the image selection tradeoff. In addition, the\nML-derived surge is less pronounced due to the fact that neural networks tend to merge co-located\nstreaks, i.e., underestimate the actual number of streaks present (see comment above).\nI specifically address these points in the revised manuscript, for example in the caption of Figure 1:\n[…] The accuracy of the results presented in (B) is dependent on the ‘noise floor’ of the detection\nmethod itself and is affected by the tradeoff between the allowed maximum image acquisition date delta\n(here, 6 months or ~90° L ) and data abundance, which reduces the ‘temporal resolution’ or ‘temporal\nS\naccuracy’ of the detector-derived count differences; this inaccuracy is likely causing the slight temporal\noffset between the detector-derived and manual counts. […]\nand throughout the main text, where appropriate/helpful:\nAs observed earlier by Bickel and Valantinas (2025), the used streak detector tends to identify spatially\nco-located (‘nested’) streaks as one single feature, suggesting that the detector-derived burst counts\nunderestimate the actual (absolute) number of individual streaks that formed by a factor of ~2, on\naverage.\n[…]\nSimilarly, MCD-derived column-integrated dust optical depth values of cells with active (directly\nobserved) streak formation (maximum of the sol at image pair mean L , with a maximum temporal\nS\ndifference of 6 months or 30° L between the images, Fig.1F) as well as the sol-maximum dust optical\nS\ndepth of streak-bearing cells in general (Fig.4A,B), show an enhanced atmospheric dust load during the\nseasons that are associated with streak formation activity, with a distinct rise at L ~220°, coinciding\nS\nwith streak formation activity (Fig.1B).\n-Line 92 – “implying that the three independent measures of streak activity are in agreement” – What\nthree independent measures? Mention is made of a hand-count and an automated approach. That\nsound like two measures. What is the third measure?\nMy reply: The three independent measures are 1) the manual count, 2) the ML-derived maximum\ncounts, and 3) the ML-derived difference counts. I improved the wording of the sentence to make\nthe three elements more obvious:\n[…] Notably, the locations of highly active cells (derived by manual and detector counts) closely\nresemble the spatial distribution of cells with high to very high maximum streak counts, implying that\nthe three independent measures of streak activity agree.\n-Lines 95-96 = “Globally, all considered cells experience an average, normalized streak formation rate\nof ~5.5 %/MY” – 5.5% of what? I would have expected a streak formation rate to be measured in\nnumber of streaks per area per time.\nMy reply: I agree, number of streaks/area/time or number of streaks/time/cell seem to be the\nmost intuitive metrics. However, the community has largely agreed on expressing streak formation\nrates by relating the number of newly formed streaks to the number of previously visible\nstreaks/time, as a percentage. I follow this community standard throughout the manuscript. The\nexact method is detailed in the Methods section:\nFormation rates\nI use an adapted workflow to derive slope streak formation rates on MY-scales. This workflow utilizes\nall CTX images that have a 100 % overlap with a given cell, selects the two images with the largest\ntemporal difference (with a maximum difference of ~17 years or ~8.5 MY, ~2007 to ~2024), counts\nslope streaks in the before and after image, and computes the count difference. The workflow uses two\napproaches to compute slope streak formation rates: 1) one approach relates the number of streaks that\nformed between the before and after image to their temporal difference, providing a measure of ‘newly-\nformed slope streaks per MY’ (𝑁 ):\n𝑐\n8\n𝑁\n𝑑𝑖𝑓𝑓_𝑐𝑜𝑢𝑛𝑡\n𝑁 = (4)\n𝑐 ∆\n𝑡\nNotably, the number of newly-formed streaks is dependent on the overall abundance of slope streaks in\na given cell (see e.g. Aharonson et al., 2003), which is why approach 2) relates each difference count to\nthe total number of slope streaks that are present in the after image (𝑁 ), providing a measure of\n𝑡𝑜𝑡𝑎𝑙\n‘percentage of population-increase per MY’ (𝑁 ), directly following the methodology developed and\n𝑛\nused by Bergonio et al. (2013), Heyer et al. (2019), Stillman et al. (2024), and Schorghofer et al. (2007):\n𝑁\n𝑑𝑖𝑓𝑓_𝑐𝑜𝑢𝑛𝑡\n𝑁 = ∗100 (5)\n𝑛 ∆ ∗ 𝑁\n𝑡 𝑡𝑜𝑡𝑎𝑙\n-Line 100 – “This implies that Mars’ entire streak population would be replenished within ~18 to ~24\nMY.” – How long does it take for streaks to fade? Streaks would only be “replenished” if they went\naway, but so far, no information has been given about the rate of streak disappearance.\nMy reply: As described in line ~366 this study does not consider fading streaks due to the\nsubstantially larger technical challenges and methodological uncertainties:\nMy analysis does not consider the fading of slope streaks (negative formation rates), as the signature of\nfading streaks is less clearly defined (i.e., is not binary, as in the streak formation case, e.g. Stillman et\nal., 2024) and is thus substantially less pronounced in the count data, and is also noticeably more\naffected by count noise.\nThe above-quoted statement regarding the replenishment of the global slope streak populations is\npurely based on the global census of surface-visible, CTX-scale slope streaks, as derived by this\nwork and the preceding Bickel and Valantinas (2025), assuming a constant population of streaks\n(i.e., the population does not grow larger or bigger). I improved the wording of the respective\nsentence to help communicate those assumptions:\n[…] This implies that Mars’ entire streak population would be replenished within ~18 to ~24 MY,\nassuming that the overall population of streaks remains constant.\n-Fig 1 B – What is the right y-axis labeling? Presumably, the manual detections? The caption should\nclarify. The right y-axis title says, “difference OMA 2007 – 2023”. Does that title mean the number\nrepresents the difference between the number of streaks counted in 2007 and the number counted in\n2023? Is the “count difference per 0.25x0.25 deg cell” also taken as the difference between 2007 and\n2023?\nMy reply: Thank you for raising this concern. I improved the caption of Figure 1 to better\ncommunicate the meaning of Fig. 1B:\n[…] B) heatmap of the relation between streak count difference (left y axis) and seasonality per cell\n(number of cells considered is 596); manual ground-truth measurements overlain (black dots, right x\naxis); white ellipses indicate (manually-scanned) image pairs without any change. Manual counts were\nderived using images acquired between 2007 and 2023. […]\n-Figure 3 – It appears Tharsis experiences the highest wind stresses at around the same time of day\nwhen MRO visits. Is there way to assess the un/likelihood that no streaks would be observed in the\nprocess of formation?\nMy reply: Unfortunately, I cannot think of a way to quantify the ‘un/likelihood’ of detecting active\nstreak formation in Tharsis (or elsewhere), mostly because CTX (and most other ‘black &\nwhite’/’single-frame’ cameras) are completely insensitive to the (proposedly) second- to minute-\nscale streak formation. I have been targeting Tharsis and other regions with TGO’s CaSSIS in an\nattempt to use CaSSIS’ color and/or single-overflight stereo capabilities to unambiguously identify\n9\nstreak displacements (e.g. expressed through color fringe artefacts), which would be indicative of\nformation – but without success, so far. However, CaSSIS imaging activities are ongoing, as\nmentioned in the manuscript:\nCurrently, TGO’s CaSSIS instrument is attempting to capture the active formation of slope streaks and\nverify the existence of associated dust plumes.\nPlease note that I updated Figure 3E to only consider cells with wind stress > 0.02 Pa for the LST\nanalysis (with a few minor changes to the figure and text).\n-Lines 200-202 – “My data also suggest that 92 % of all difference and maximum count cells are located in regions featuring very high TES albedo (>0.26, Fig.4A,C)” – But what is the typical albedo of all regions surveyed? How does the “high” albedos compare to “low” albedos? The author suggests this high albedo indicates the presence of dust. Can the dust cover index\n(http://tes.asu.edu/~ruff/DCI/dci.html) also provide some indication of the amount of dust?\n-Lines 202-204 – “Similarly, MCD column-integrated dust optical depth measurements show an\nenhanced atmospheric dust load during the seasons that are associated with peak streak formation\n(Fig.1F).” – But there are streaks observed at a wide range of integrated dust opacities. Moreover, in\nFig 1F, there seem to be no steaks observed around Ls = 250 for opacities below about 0.2. Is that\nbecause there are no periods with Ls = 250 and optical depth < 0.2, or are there observations made\nunder those conditions during which no streaks were observed? The lack of explanation or\nconsideration of this point highlights again one of the biggest issues for this study – there is no\nassessment or quantification of conditions during null detections, and so it is very hard to judge\nwhether the trends that are suggested to promote streak formation are simply a product of\nobservational bias.\nMy reply: Thank you for raising this concern; I addressed both points in my reply to a similar\nconcern above (including text, caption, and figure revisions).\n-Lines 270-271 – “The trained detector achieves a recall of ~0.75 (% streaks in testset identified) and a\nprecision of ~0.94 (% correctly identified testset streaks)” – I presume the “%” is meant to indicate\nthat 0.75 = 75% and 0.94 = 94%, but the way those statements are written, they almost read like\n0.75% and 0.94%. Please clarify.\nMy reply: Thank you for raising this issue; I re-worked that section to avoid any confusion:\n[…] The trained detector achieves a recall of ~75 % (% streaks in testset identified) and a precision of\n~94 % (% correctly identified testset streaks) at a model confidence threshold of 0.5, with an AP\n(average precision) of ~80 %. […]\n-Line 297-300 – “This assessment suggests a precision of 94%, which means that a minimal number of\nfalse detections are included in the dataset, but strongly implies that those false detections will not\nhave any substantial effect on the scientific results and interpretations.” – Even though the number of\nfalse detections might be small, there might be important trends or biases in the detections – certain\nobservational conditions might be facilitate or hamper detection, but there doesn’t seem to be an\nassessment of such biases, making it difficult to judge what trends are real or which might arise from\nbiases. Some assessment of these issues should be included (or, at least, alluded to from a previous\nstudy).\nMy reply: Thank you for raising this concern. Unfortunately, the overwhelming number of streak\ndetections makes it impossible to properly address any (highly unlikely) negative effects of false\ndetections on the observations and interpretations of this work, at least beyond the state-of-the-\nart assessment I am conducting using a dedicated model performance ‘test set’. I would like to\n10\npoint out that a precision of ~94 % is unusually high for an object detection architecture, strongly\nsuggesting that the performance of the detector is outstanding. I am convinced that a precision of\n~94 % is robust enough to derive statistical interpretations from the results showcased throughout\nthis work.\n-I struggled to understand how the detection limits were estimated and how they impact the bottom-\nline results. Lines 351-354 say “As the total number of streaks per cell can vary drastically and as\nstreaks can be nested, I define the median noise floor as the percentage of the difference between the\ndetector-derived and the mean of the human-derived before and after image counts, i.e., ~24 %\n(Fig.1D).” Does “24%” mean that, for example, if the before image shows 100 streaks and the after\nimage shows 120 streaks, that change represents less than a 24% change and so the identification of\nstreaks in the after image is discarded? I would have expected the “noise floor” for detections to be\ndefined in terms of albedo contrast or image resolution, etc. Please clarify how the noise floor is\ndefined, why it's defined that way, and how the threshold chosen impacts the results.\nMy reply: The definition of a ‘noise floor’ is challenging for various reasons. On the one hand, the\ndetection performance of the ML detector is nearly, but not entirely, constant. More importantly,\nthe quality and properties of the available CTX image data are extremely heterogeneous – and\nmany of the properties most relevant to streak detection, such as SNR, atmospheric dust, or image\ngeolocation accuracy (see Fig. S1) are not captured in the associated meta-data at all.\nThis means that the noise floor must rely on streak counts alone, as its only ‘source of\ninformation’. Also, an absolute count noise floor is not particularly helpful, as any threshold (e.g.\nthe median of a calibration population) would not properly apply to cells with very low or very\nlarge numbers of streaks (the differences in streak counts can be drastic, see Fig. 1A). This is why I\n(imperfectly) defined the noise floor as a percentage of the pre-existing streak population, drawing\nfrom how the community computes streak formation rates in general.\nUltimately, I used manual and detector-derived counts to compute the differences between the\nhuman-derived and the detector-derived counts, then related those to the mean of the detector-\nderived before- and after-image-counts, as a percentage, to account for the pre-existing streak\npopulation (where cells with numerous streaks would experience substantially larger numbers of\nnewly formed streaks). I then picked the median of the distribution as the ‘median noise floor’, i.e.,\n24 % (see Fig. 1D).\nIn your example, the used noise floor criterion would not admit the count increase as valid, as the\nrelative increase is not significant enough. If the after count would be 128 or larger, it would be\nconsidered a valid count increase. Unfortunately, the limitations of the CTX data make such a\ndrastic noise floor criterion necessary. Ultimately, the criterion will reduce the sensitivity of the\noverall analysis, making it somewhat insensitive to small-scale changes – but, on the other hand, it\nhelps ensure the overall robustness of the detection of larger-scale changes.\nI re-worked the respective sentences in the Methods section, to improve the wording of the\noriginal manuscript, for example:\nAs the total number of streaks per cell can vary drastically and as streaks can be nested, I define the\n‘noise floor’ as the median percentage of the difference between the human-derived count difference\nand the detector-derived count difference and the mean of the detector-derived before and after image\ncounts, i.e., ~24 % (Fig.1D). This study only considers difference counts that exceed the median noise\nfloor, to minimize the impact of false changes, acknowledging that the application of the noise floor\nreduces the overall sensitivity of the investigation to subtle, small-scale changes.\n11\n--- --- ---\nReviewer #2\nReview of “Dust and wind drive slope streaks on Mars” by Bickel for Nature Communications\nSummary. Slope streaks are prevalent surface features on modern Mars. Found on dust-covered\nslopes, they are relatively superficial but persist for years, often without evidence of growth past their\nformation. Whereas mechanisms involving flows of liquid water were proposed relatively early (e.g.,\nMorris, 1982) and as recently as 2017 (Bhardwaj et al., 2017), these features are now mostly\naccepted to consist of dry dust avalanches (e.g., Sullivan et al., 2001; Heyer et al., 2019; Dundas,\n2020). Due to their often seasonal nature, slope streaks are thought to mainly be triggered by winds\nthat are strong enough to initiate sand transport (e.g., Heyer et al., 2019; Dundas, 2020).\nIn his manuscript, Bickel seeks to further test and refine this hypothesis by harnessing the power of\nautomated slope streak detection in the CTX archive through space and time. His results are\nconsistent with previous (often more local or regional) findings that show a clear correlation between\nthe occurrence of slope streaks and dust abundance, seasonal streak formation, and a correlation\nbetween when they form and the occurrence of stronger winds. He also shows that some streaks can\nbe explained by marsquakes and meteor impacts, though these likely represent a small number of the\ntotal yearly inventory. Based on these observations, Bickel concludes that winds trigger the formation\nof streaks, where dust is available, and when wind stress exceeds the static threshold of motion of\ndust particles.\nOverall Evaluation. The manuscript tackles a popular topic in modern Mars processes. It is well-\nillustrated and well-written. The AI-driven automatic detection procedure is state of the art and\nrobust. The global nature of the dataset allows the author to ask interesting questions. I have some\nreservations about the novelty of the results and whether they warrant publication in Nature\nCommunications, but I will leave this assessment to the editor. Science-wise, I have one main concern,\nwhich relates to the author’s interpretation of the dataset (along with a couple of more minor\ncomments, detailed below). Addressing it would amount to relatively minor textual changes but\nsubstantial revision of the message. Based on this evaluation, I’d recommend that the manuscript be\nreturned to the author for revisions.\nMy reply: I thank reviewer #2 for their positive, detailed, and constructive feedback. I carefully\naddressed all of reviewer #2’s comments, concerns, and suggestions, as detailed in my point-by-\npoint replies below.\nI would like to underline that I consider this a first-of-its-kind, trail-blazing study – the used data\nand methods might have limitations and the results might not be as ‘punchy’ as desired, but the\nutilization of deep learning-driven algorithms to enable the simultaneous analysis of numerous,\nextremely large geospatial datasets, and the investigation of dynamic surface processes in an\nabsolute, spatiotemporal manner, at a global-scale, are truly innovative and novel. Most\nimportantly, this study provides the first direct, global-scale, and quantitative evidence for the key\nrole of dust, sand, and wind in driving the formation of slope streaks on Mars – a subject that has\nbeen under intense debate for the past 50 years or so.\nI am convinced that the planetary science community will use similar approaches in the future to\naddress other fundamental questions and debates. This is why I believe that this study deserves\npublication in a Nature-tier journal, after all remaining technical and methodological concerns\nhave been addressed.\n1. The data indicates that aeolian saltation—not wind shear—is the main driver of slope streak\nformation. Whereas saltation itself is initiated by wind shear, this distinction is important because it\npoints at the role of a third ingredient that is required to form wind streaks—the availability of\nsaltatable sand.\n1.1. The manuscript reports a bed stress of 0.01 Pa as their preferred threshold for slope-streak\ninitiation. It is inconsistently reported throughout the text as “the static threshold…to initiate the\nsaltation of ~100 μm-sized grains” (e.g., l. 175-176) or “the static threshold required to initiate the\nmotion of dust grains” (e.g., l. 18). However:\no Particles 100 μm in diameter are considered sand, whereas airborne dust on Mars is known to\nconsist of much smaller particles (~ 1 μm; e.g., Tomasko et al., 1999; Fedorova et al., 2014). Sand and\ndust have drastically different static thresholds due to the outsized influence of electrostatic forces in\nfiner particles. [Dust aggregates may form but they would break apart upon impact if entrained in\nsaltation, e.g., Weitz et al., (2018).]\no The static (or fluid) thresholds for both dust and 100 μm sand on Mars are higher than 0.01 Pa,\neven as reported in the studies cited by Bickel to support that choice (Kok et al., 2012; Newman et al.,\n2022):\n• Newman et al. (2022) reports that winds need to exceed a threshold of 15 m/s (measured at 1.5 m\nabove ground) “by some unknown amount” to initiate vortical dust transport. Assuming a typical\natmospheric density of 0.015 kg/m3 and the z0 value of 0.01 m used in Newman et al. (2022), 15 m/s\nwinds at 1.5 m translate to a shear velocity of 1.23 m/s, which in turn would imply a bed stress\ngreater than 0.02 Pa. This value, as noted by Newman et al. (2022), is an absolute lower bound on the\ntrue threshold, and is only to be associated with vortical triggers, which lower the threshold of motion\nby enhancing pressure gradients locally.\n• I note that even the author himself chose to double this value in his related manuscript on dust\ndevils, currently in review at Science Advances.\n• For non-vortical triggers, one should refer to the classic fluid threshold. Several formulations exist\nfor the fluid threshold, like those reported in Kok et al. (2012) (e.g., their figure 21b). For example, Kok\net al. (2012) shows the formulation of Iversen and White (1982) for the fluid threshold wind shear\nvelocity, which reaches ~1.5 m/s for 100 μm grains, i.e., bed stresses greater than 0.03 Pa.\n• Other formulations exist, e.g., those of Swann et al. (2020), Andreotti et al. (2021), Gunn and\nJerolmack (2022), etc., all inconsistent with the statement that 0.01 Pa coincides with the static\nthreshold of either 100 μm sand or dust.\n• Although Kok et al. (2012) did not extend their plot to dust sizes, it shows that the threshold\nincreases for finer grains as electrostatic forces become more dominant. Plugging in numbers in the\nclassic formulation Shao and Lu (2000), one finds a threshold shear velocity of ~11-3.5 m/s for grains\nin the 1-10 μm range, which translates to bed stresses in the 0.18-1.8 Pa range, or greater than the\nquoted value of 0.01 Pa by one to two orders of magnitude. Such high stress values are not thought\nto be typically reached on Mars (neither from in situ measurements nor from GCMs), indicating that\nwind shear cannot typically be a direct trigger of dust lofting on Mars.\n• The inefficiency of winds at lifting dust has been well known for decades, and saltation is often\nthought of as the main driver of dust lofting on Mars, whereby winds drive saltation, and impacts of\nsand grains onto dusty surfaces eject dust particles from the bed (Greeley et al., 1981; Newman et al.,\n2004). Accordingly, the paths of dust storms closely coincide with aeolian sand transport pathways\n(Rubanenko et al., 2023).\no A bed stress value of 0.01 Pa—as chosen by Bickel— is likely closer to the dynamic (or impact)\nthreshold of ~100 μm grains (e.g., Kok et al., 2012; Gunn and Jerolmack, 2022, etc.). This threshold\ncorresponds to the minimum wind speed required to sustain saltation once it was initiated, and is\nlower than the fluid (or static threshold) on Mars due to the efficiency of grain splash upon grain-to-\ngrain impacts. The fact that winds of this magnitude correlate with wind-streak formation suggests\nthat saltation—not wind shear—triggers avalanching (whether by impact-driven destabilization of\nthe slope, or by driving the migration of sandy bedforms like on dune slipfaces, e.g., Ewing et al.,\n2017).\n1.2. The reported periodicity of slope streak formation coincides with that of saltation as observed by\norbiters and rovers:\no Aeolian saltation of sand is known to be highly seasonal on Mars, with active saltation occurring in\nsouthern summer and autumn (e.g., Ayoub et al., 2014; Bridges et al., 2017; Baker et al., 2018a,b;\nLapotre and Rampe, 2018), coinciding with the seasonality of slope-streak formation observed in this\nstudy.\no Aeolian saltation was found to be largely concentrated at sunrise and sunset in Gale crater (Baker\net al., 2022), coinciding with the diurnal nature of winds capable of transporting sand according to\nthe MCD.\n1.3. Slope streaks form in proximity to loose sand. The spatial distribution of slope streaks coincides in\nArabia, Elysium, and north of Valles Marineris with the presence of aeolian dune fields in the region,\ni.e., they tend to form in proximity to sand that is available for transport (see, e.g., the map of\nRubanenko et al., 2021). Although dune fields are not typically found in the OMA region, sand ripples\nabound therein, also attesting to the availability of sand-sized particles (e.g., Vaz et al., 2023).\nIn summary, the observations reported by Bickel indicate that wind shear greater than 0.01 Pa\ncorrelates with slope streak formation, but not that wind shear is their trigger. Instead, this value is\nconsistent with the dynamic threshold for saltation of sand—a much more efficient mechanism to lift\ndust on Mars. Furthermore, the spatial and temporal distributions of slope-streak formation are both\nconsistent with known occurrences of active sand transport. Thus, unlike what the current title of\nthe paper states (“Dust and Wind Drive Slope Streaks on Mars”), the presented data demonstrate that\ndust, sand, and saltation drive slope streaks on Mars.\nThis point could be addressed by the author through:\no Rephrasing throughout where mentioning “wind action” or similar as the driver of slope streak\nformation, and adjusting the title accordingly.\no Clarifying which threshold is used and making it consistent throughout (“static” vs. “dynamic” and\n“100 μm” vs. “dust”). Based on the value of 0.01 Pa, the author should be reporting it as the dynamic\nthreshold of ~100 μm sand.\no Adding a discussion of why a threshold of 0.01 Pa, the distribution of slope streaks, and their\nperiodicity all point at aeolian saltation of sand as the trigger of avalanching.\n[As a side note: These comments are also very relevant to the related Bickel et al. manuscript\ncurrently in review at Science Advances. I hope the author will choose to take them into consideration\nwhen revising that other paper as well.]\nall of those comments and thoughts, also in context of the new/revised figures I created in\nresponse to some of reviewer #1’s comments. I will reply to the key items below:\n- the role of sand and saltation: thank you for highlighting the important role of sand and\nsaltation for the initiation/mobilization of dust (and streaks). I agree with the reviewer that the\nimportant role of saltation and sand should be emphasized – and that the abundance of sand-sized\nparticles and the timing of sand migration largely coincide with streak activity, strengthening the\nobservations and interpretations of the manuscript. In response to reviewer #2’s comment, I\nintegrated a series of changes throughout the manuscript and added a new section to the\nDiscussion, focused on the interaction between the three key ingredients of streak activity (slopes,\ndust, wind) and the important role of sand – see text snippet attached to my reply below.\nIn addition, the manuscript is now consistently referring to ‘wind action’ as the driver of activity,\nas suggested by the reviewer.\nI carefully considered changing the title of the manuscript, as suggested, and ultimately converged\non the following title:\nDust, Sand and Wind Drive Slope Streaks on Mars\n- usage of the term “static threshold”: I agree that the term was used inaccurately and did not add\nany substantial value. I removed the term from the manuscript entirely.\n- usage of 0.01 Pa as a minimum wind stress value indicative of saltation & streak initiation\nconditions: I agree that the utilization of 0.01 Pa as a minimum ‘threshold’ for dust mobilization\nwas an inappropriate choice. The revised manuscript now applies a ‘threshold’ of 0.02 Pa, as\nidentified/used by various studies throughout the literature. I recognize that this ‘threshold’ only\nrepresents a lower bound or minimal stress value that needs to be exceeded to initiate the\nsaltation of sand-sized particles – and with that the mobilization of dust. I also recognize the\nsubstantial uncertainties associated with the atmospheric variables predicted by the MCD,\nalong\nwith other limitations of the available data, models, and tools.\nIn response to your concern, the manuscript now relies on a 0.02 Pa wind stress criterion as a way\nto indicate atmospheric conditions that are likely to be conducive to the initiation of sand saltation\nand dust mobilization. At the same time, the manuscript more clearly communicates the\nlimitations of the data and all involved assumptions, both in the main text and the Methods\nsection. For example, the key assumptions associated with a wind stress value of 0.02 Pa and its\nimplications are specifically called out at the first opportunity in the main text:\nDust and wind action as seasonal drivers of streak formation\nThe difference count data and modelled, low-resolution, regional-scale atmospheric data extracted from\nthe Mars Climate Database (MCD v6.1, Forget et al., 1999) suggest that ~20 % of the cells with\nongoing (directly observed) streak formation activity experience wind stress values beyond 0.02 Pa\n(maximum of the sol at image pair mean L , with a maximum temporal difference of 6 months or 90°\nS\nL between the images). Wind stress values are computed from horizontal wind velocities (modelled at\nS\n4.5 m above the surface) and the spatio-temporally associated atmospheric density, assuming an\naerodynamic (surface) roughness of 2 centimeters. Throughout the year, ignoring streak formation\ntiming, about ~60 % of all streak-bearing cells experience peak wind stress values beyond 0.02 Pa\n(Fig.3B,D). Several past studies suggested that wind stress in excess of 0.02 Pa is able to initiate the\nsaltation of sand-sized particles (~100 µm) that are the most susceptible to wind shear (e.g., Greeley et\nal., 1981; Ayoub et al., 2014; Newman et al., 2004,2022; Rubanenko et al., 2023; Gunn & Jerolmack,\n2022; Swann et al., 2022; Kok et al., 2012; Bickel et al., 2025a). Upon re-impact on the surface, these\nparticles can mobilize and eject smaller particles, including dust particles (<62 µm), making wind stress\n15\nvalues beyond 0.02 Pa indicative of atmospheric conditions that are generally conducive to the\nmechanical triggering of streak formation. […]\n- periodicity of sand migration/saltation and abundance of sand-sized particles: thank you for\npointing this out! I added a discussion of these important observations – and their implications – to\nthe Discussion section:\nDiscussion\nDust and wind as dominant drivers of slope streak activity on Mars – and the important role of sand\nsaltation\nMy observations highlight how the interaction of three key pre-conditioning factors and drivers lead to\nseasonal slope streak activity: 1) the presence of topographic inclines, 2) a combination of surface dust\nand the seasonal delivery of atmospheric dust, as well as 3) the mechanical mobilization of dust through\nwind action, which in turn is driven by near-surface winds and the atmospheric density. All three pre-\nconditions and drivers are required to form slope streaks. The data suggest that the wind stress at slope\nstreak-bearing locations is not necessarily higher than elsewhere on Mars but seems to be high enough\n(> ~0.02 Pa) to initiate the saltation of sand-sized particles (~100 µm) and – with that – the mobilization\nof dust particles. On inclined terrain, the local mobilization of sand and dust can lead to a cascading\neffect and the formation of slope streaks.\nThis mechanism highlights the seemingly important role of another key factor, the availability of sand-\nsized particles (in the ~100 µm range), which are the most susceptible to wind stress and, thus, are the\nmost likely to trigger downslope dust displacement through saltation. Notably, past surveys established\nthat aeolian features, which imply the presence of loose sand, such as (barchan) dunes and ripples,\nare widespread across Mars (Rubanenko et al., 2021; Vaz et al., 2023), including all the geographic slope\nstreak hotspots identified in this work. Other surveys established that sand migration is strongly\nseasonally controlled as well and surges in early southern summer, remaining high throughout southern\nautumn (e.g., Ayoub et al., 2014; Baker et al., 2018; Lapotre & Rampe, 2018). This implies that the\nspatial distribution of sand as well as the periodicity and magnitude of sand migration closely agree\nwith the distribution and activity of slope streaks, directly supporting the hypothesis that dust, wind,\nand (saltating) sand jointly drive slope streak formation on Mars.\nImportantly, all estimates of wind stress are based on several assumptions, such as the utilization of one\naverage aerodynamic roughness for the entire surface of Mars. These assumptions follow several\npreceding studies (e.g., Charalambous et al., 2021; Bickel et al., 2025a) but are likely to affect the\noverall accuracy of the derived wind stress estimates, also because of the low spatial resolution of the\nMCD. Notably, a recent study that used the movements of dust devils to measure near-surface wind\nspeeds across Mars (Bickel et al., 2025a) suggests that the global-scale MCD – not surprisingly –\nsystematically and substantially underestimates local-scale wind velocities, suggesting that the wind\nstresses at streak locations could be larger than estimated here, further underscoring the ability of near-\nsurface winds to initiate sand saltation and trigger streak formation. As highlighted by Bickel and\nValantinas (2025), future studies of slope streak formation and activity should utilize atmospheric\nmeso-scale models to address some of the shortcomings of this study.\n[…]\n2. Previous work needs to be better represented in the reference list. Beyond studies being currently\nmisrepresented (Kok et al., 2012; Newman et al., 2022), the current reference list is rather\nunbalanced, with 14 self-citations and many important references omitted.\nI cannot emphasize enough how much great work the author has been leading lately—and he should\nabsolutely be credited for it—but several of the citations appear unnecessary, especially the whole\nseries of papers on rock detection, chloride deposits, seismic events, or on the Mercury hollows. The\nauthor could minimize self-citations, for example, by removing a few of those. In parallel, I’d\nrecommend that the author incorporate the following references (in addition to those already in\nthere) in his new, strengthened discussion of observations:\no For saltation as a trigger of dust lofting: Greeley et al. (1981), Newman et al. (2004), Rubanenko et\nal. (2021; 2023).\no For thresholds of motion: Shao and Lu (2000), Swann et al. (2020), Andreotti et al. (2021), Gunn and\nJerolmack (2022).\no For sand transport periodicity: Ayoub et al. (2014), Baker et al. (2018a,b), Lapotre and Rampe\n(2018), Baker et al. (2022).\nMy reply: Thank you for raising this issue. I agree with the reviewer, of course. For context, the\nexcessive number of self-citations in the Methods section originates from (predominantly\nnegative) past experiences with the peer review of manuscripts that involved machine learning\nmethods. In most cases, the reviewers expected or specifically requested an excessive number of\nexample studies to consider a machine learning-driven methodology “robust” and “credible”. I am\ndelighted to see that reviewer #2 (and reviewer #1 as well) consider(s) the used machine learning\nmethodology “state-of-the-art” and “robust” – and I am more than happy to substantially reduce\nthe references of other machine learning studies.\nIn total, I removed the following references:\nBickel et al. (2020a), Bickel et al. (2021), Ruesch & Bickel (2023), Mills et al. (2024), Conway,\nBickel et al. (2025), Bickel et al. (2024b), Bickel et al. (2025a), Bickel et al. (2025b)\nIn return, I added 10 of the 13 suggested references.\nDetailed Comments:\nl. 18: Incorrect; wind stresses exceed the dynamic threshold required to initiate the motion of sand\ngrains.\nMy reply: Thank you for pointing this out, I fixed this particular sentence in the abstract as follows:\n[…] The bulk of streak formation coincides with the seasonal delivery of atmospheric dust and peaks in\nthe southern summer and autumn, when wind stress systematically exceeds the minimum pressure\nrequired for the initiation of sand saltation and dust mobilization. […]\nl. 72-74: This is better explained in the methods but very opaque as described in the main text. I’d\nrecommend defining “noise floor” (not everyone will be familiar with signal-processing terminology)\nand expanding a bit on how it was evaluated.\nMy reply: Thank you for raising this issue; I re-worked the respective section:\n[…] I characterize the median ‘noise floor’ caused by false changes using a set of 100 ground-truth cells\n(Fig.1C,D) and only consider difference counts that exceed the median noise floor, to minimize the\nimpact of false changes. Here, the term ‘noise floor’ describes the threshold that is used to distinguish\nbetween ‘valid’ (i.e., used for the analysis) and ‘invalid’ changes (i.e., not used for the analysis), in a\nstatistical way.\nl. 88-90: Is there any correlation between the topographic gradient of the slope on which streaks form\nand the continuous vs. bursty nature of streak formation?\nMy reply: That would be a very interesting relation to consider – unfortunately, the spatial\nresolution of the available topographic data is too low to properly capture the slope angles of the\nterrain at (CTX-scale) slope streak source regions (on a global scale). Bickel and Valantinas (2025)\nattempted to use low resolution data to look at the general relation between streaks and slope,\nbut without any distinct results, see below. The exploration of a potential relation between slope\nangle and streak formation rates/bursts remains an intriguing subject for future work though.\nFigure, from Bickel and Valantinas (2025). Black solid histogram represents dark slope streaks, as considered\nin this study; black outline represents the slope angle ‘background’. Mean values are indicated. Red/orange\nand beige/tan histograms show other, unrelated features. This figure shows that streaks are located on\nsteeper-than-average slopes, but the extracted slope values do not properly capture the actual streak-scale\nslope angles.\nl. 113-114: Slope streaks are common, but I’d be cautious calling them “one of the most dynamic\ngeologic processes on the planet”. They are quite thin/superficial, and once they appear, they most\noften remain inactive for years and disappear. In turn, many modern processes appear to be much\nmore dynamic and involve greater volumes of geological material (such as ice or permafrost-related\nprocesses and dune or ripple migration; e.g., Diniega et al., 2021).\nMy reply: Thank you; I re-purposed that phrase from Aharonson et al. (2003) but agree that it\nseems to be somewhat misleading. I rephrased the respective sentence to:\n[…] leads to higher absolute formation rates of streaks than previously estimated (~factor ~1.3 to ~1.8,\nAharonson et al., 2003), further establishing slope streaks as a highly dynamic geologic process.\nFigure 1B: Seasonality is very similar to that of sand transport as observed from orbit by Ayoub et al.\n(2014).\nl. 167: “saltation” rather than “wind”\nl. 167-207: This whole section needs to be reworked (see main comment #1).\nMy reply: Thank you – see my reply/replies above.\nl. 170-171: This won't mean much to the reader unless you specify at what height above the surface\nthese are reported. (I realize it is mentioned in the methods but it should also be reported here,\notherwise readers do not have the information they need to evaluate the significance of a 10 m/s\nwind.)\nMy reply: Yes, I agree, of course. I added the altitude to each mention of wind velocity, for\nexample:\n[…] Wind stress values are computed from horizontal wind velocities (modelled at 4.5 m above the\nsurface) and the spatio-temporally associated atmospheric density, assuming an aerodynamic (surface)\nroughness of 2 centimeters. […]\n18\n[…] but that effect seems to vanish for wind velocities and wind stress values beyond ~12 to ~17 m/s\n(at an altitude of 4.5 m) and ~0.02 to ~0.03 Pa (Fig.3). […]\nl. 171: “surface-level wind stress”: more needs to be said here about how these were computed in the\nMCD. Presumably, the MCD assumes a value of aerodynamic roughness, z0, as its lower boundary\ncondition. It is unclear whether this is the same value as that used by the author later when\nreconstructing shear velocity from MCD outputs (it technically should be, for consistency, and then\ncorrected*).\n* This correction is needed because z0 is not scale independent, and MCD considers wind turbulence\nover much greater length scales than those relevant to sediment transport. [This is the same reason\nwhy, for example, the effective threshold of motion calibrated by Ayoub et al. (2014) over entire dune\nfields is much lower than the physical, threshold one would measure by blowing winds over individual\nsand grains.]\nMy reply: All wind stress values mentioned and used throughout the manuscript are computed by\nmyself, using the equations and parameters listed and discussed in the Methods section. I do not\nextract any wind stress values from the MCD directly.\nL. 176: This is inconsistent with several prior and subsequent statements that winds exceed the\nthreshold of motion of dust-sized particles (see main comment #1 above; dust and sand are not the\nsame thing, have different static thresholds, and neither are close to the reported value of 0.01 Pa). A\nbed stress of 0.01 Pa is closer to the dynamic threshold for sand.\nFigure 3: “saltation” instead of “wind action”.\nl. 224: “saltation” instead of “wind action”. Note that this implies the availability of loose sand as an\nadditional prerequisite.\nMy reply: Thank you for pointing this out; I fixed those issues along with the revisions made in\nresponse to your major concern above.\nl. 320: A z0 value of 0.02 m is very high. Newman et al. (2022) use 0.01 m. A centimeter is also on the\nhigher end of what’s expected in the presence of active saltation and meter-scale sand ripples\n(Alvarez et al., 2025). Over flat surfaces with little roughness (as might be expected of dusty surfaces)\nand under active saltation, z0 should be closer to 10-4-10-3 m (see figure 6 of Alvarez et al., 2025).\nMy reply: Thank you for raising this point. I agree that the selection and utilization of one single\naerodynamic roughness value for all of Mars is likely to affect the accuracy of the analysis.\nUnfortunately, we do not have any information on the grain size distribution at slope streak\nlocations, which makes it very challenging to select an appropriate aerodynamic\nroughness/median grain size. For this reason, I closely follow a workflow I recently developed for\nBickel et al (2025a) that is using a median grain size derived from several landed missions,\nultimately leading to 𝒛 of 2 cm.\nextended the discussion of this matter in the Methods section and added a note to the main text\nas well:\nResults: […] Wind stress values are computed from horizontal wind velocities (modelled at 4.5 m\nabove the surface) and the spatio-temporally associated atmospheric density, assuming an aerodynamic\n(surface) roughness of 2 centimeters. […]\nDiscussion: […] Importantly, all estimates of wind stress are based on several assumptions, such as the\nutilization of one average aerodynamic roughness for the entire surface of Mars. […]\nMethods: […] I note that the utilization of one single 𝑧 value oversimplifies the substantial variability\n0\nof the aerodynamic roughness across Mars. […]\n19\nl. 324-328: This is incorrect. Stresses of 0.01 Pa neither initiate sand nor dust transport; but they\nsustain sand transport, which in turn may drive dust lofting.\nMy reply: Thank you for pointing this out; I fixed this issue along with the revisions made in\nresponse to your major concern above.\nl. 513: Only one “Dundas (2020)” reference is cited, so the “b” should be removed.\nMy reply: Fixed, thank you.\nReferences.\nAlvarez, Carlos A., et al. \"Aerodynamic roughness of rippled beds under active saltation at Earth-to-\nMars atmospheric pressures.\" Nature Communications 16.1 (2025): 5113.\nAndreotti, Bruno, et al. \"A lower-than-expected saltation threshold at Martian pressure and below.\"\nProceedings of the National Academy of Sciences 118.5 (2021): e2012386118.\nAyoub, F., et al. \"Threshold for sand mobility on Mars calibrated from seasonal variations of sand\nflux.\" Nature communications 5.1 (2014): 5096.\nBaker, M. M., et al. \"Coarse sediment transport in the modern Martian environment.\" Journal of\nGeophysical Research: Planets 123.6 (2018a): 1380-1394.\nBaker, Mariah M., et al. \"The Bagnold Dunes in southern summer: Active sediment transport on Mars\nobserved by the Curiosity rover.\" Geophysical Research Letters 45.17 (2018b): 8853-8863.\nBaker, Mariah M., et al. \"Diurnal variability in aeolian sediment transport at Gale crater, Mars.\"\nJournal of Geophysical Research: Planets 127.2 (2022): e2020JE006734.\nBhardwaj, Anshuman, et al. \"Martian slope streaks as plausible indicators of transient water activity.\"\nScientific reports 7.1 (2017): 7074.\nBridges, N. T., et al. \"Martian aeolian activity at the Bagnold Dunes, Gale Crater: The view from the\nsurface and orbit.\" Journal of Geophysical Research: Planets 122.10 (2017): 2077-2110.\nDiniega, Serina, et al. \"Modern Mars' geomorphological activity, driven by wind, frost, and gravity.\"\nGeomorphology 380 (2021): 107627.\nDundas, Colin M. \"Geomorphological evidence for a dry dust avalanche origin of slope streaks on\nMars.\" Nature Geoscience 13.7 (2020): 473-476.\nEwing, Ryan C., et al. \"Sedimentary processes of the Bagnold Dunes: Implications for the eolian rock\nrecord of Mars.\" Journal of Geophysical Research: Planets 122.12 (2017): 2544-2573.\nFedorova, Anna A., et al. \"Evidence for a bimodal size distribution for the suspended aerosol particles\non Mars.\" Icarus 231 (2014): 239-260.\nGreeley, Ronald, et al. \"Dust storms on Mars: Considerations and simulations.\" (1981).\nGunn, Andrew, and Douglas J. Jerolmack. \"Conditions for aeolian transport in the solar system.\"\nNature astronomy 6.8 (2022): 923-929.\nHeyer, T., et al. \"Seasonal formation rates of martian slope streaks.\" Icarus 323 (2019): 76-86.\n29.1 (1982): 111-119.\nKok, Jasper F., et al. \"The physics of wind-blown sand and dust.\" Reports on progress in Physics 75.10\n(2012): 106901.\nLapotre, M. G. A., and E. B. Rampe. \"Curiosity's investigation of the Bagnold Dunes, Gale crater:\nOverview of the two‐phase scientific campaign and introduction to the special collection.\"\nGeophysical Research Letters 45.19 (2018): 10-200.\nMorris, Elliot C. \"Aureole deposits of the Martian volcano Olympus Mons.\" Journal of Geophysical\nResearch: Solid Earth 87.B2 (1982): 1164-1178.\nNewman, C. E., P. L. Read, and S. R. Lewis. \"Investigating atmospheric predictability on Mars using\nbreeding vectors in a general‐circulation model.\" Quarterly Journal of the Royal Meteorological\nSociety: A journal of the atmospheric sciences, applied meteorology and physical oceanography\n130.603 (2004): 2971-2989.\nNewman, Claire E., et al. \"The dynamic atmospheric and aeolian environment of Jezero crater, Mars.\"\nScience Advances 8.21 (2022): eabn3783.\nRubanenko, L., et al. \"Automatic detection and segmentation of barchan dunes on Mars and Earth\nusing a convolutional neural network.\" IEEE Journal of Selected Topics in Applied Earth Observations\nand Remote Sensing 14 (2021): 9364-9371.\nRubanenko, L., et al. \"Global surface winds and Aeolian sediment pathways on Mars from the\nmorphology of barchan dunes.\" Geophysical Research Letters 50.18 (2023): e2022GL102610.\nShao, Yaping, and Hua Lu. \"A simple expression for wind erosion threshold friction velocity.\" Journal\nof Geophysical Research: Atmospheres 105.D17 (2000): 22437-22443.\nSullivan, Robert, et al. \"Mass movement slope streaks imaged by the Mars Orbiter Camera.\" Journal\nof Geophysical Research: Planets 106.E10 (2001): 23607-23633.\nSwann, C., D. J. Sherman, and R. C. Ewing. \"Experimentally derived thresholds for windblown sand on\nMars.\" Geophysical Research Letters 47.3 (2020): e2019GL084484.\nTomasko, M. G., et al. \"Properties of dust in the Martian atmosphere from the Imager on Mars\nPathfinder.\" Journal of Geophysical Research: Planets 104.E4 (1999): 8987-9007.\nVaz, David A., et al. \"Constraining the mechanisms of aeolian bedform formation on Mars through a\nglobal morphometric survey.\" Earth and Planetary Science Letters 614 (2023): 118196.\nWeitz, Catherine M., et al. \"The physical properties and geochemistry of grains on aeolian bedforms\nat Gale crater, Mars.\" Journal of Geophysical Research: Planets 127.11 (2022): e2021JE007061.\nMy reply: Thank you for making the effort to create and include such a detailed list of references!\nI thank both reviewers for their additional feedback. I carefully considered all comments and revised\nthe manuscript and supplementary information accordingly (text and supplements). Changes in the\nmanuscript and supplementary information are highlighted in pink color; changes made during the\nfirst revision of the manuscript remain highlighted in green color.\nPlease find my replies (blue bold) to the reviewers’ comments (italic gray) below.\nValentin Bickel\n1\n--- --- ---\nReviewer #1\nI thank the author for addressing many of my previous concerns, and I think the addition of several\nfigures and much clarifying text has improved the paper considerably.\nMy reply: I thank reviewer #1 for their additional feedback.\nHowever, I remain unconvinced of the key assertions of the paper, and I’m still not sure the study has\na sufficiently robust and broad impact to merit publication in Nature. I do think the study should be\npublished, though, just not in Nature.\nMy key concern is still that the data do not convincingly demonstrate that slope formation\ncorresponds to wind stresses that exceed previously suggested thresholds, and this assertion appears\nto be central to the paper. From the abstract, “The bulk of streak formation coincides with the\nseasonal delivery of atmospheric dust and peaks in the southern summer and autumn, ** when wind\nstress systematically exceeds the minimum pressure required for the initiation of sand saltation and\ndust mobilization. **”\nBut then lines 182-184: “The difference count data and modelled, low-resolution, regional-scale\natmospheric data extracted from the Mars Climate Database (MCD v6.1, Forget et al., 1999) suggest\nthat ~20 % of the cells with ongoing (directly observed) streak formation activity experience wind\nstress values beyond 0.02 Pa...”\nFigure 3 seems to illustrate these relationships. Panel b, for instance, suggests streak formation often\noccurs for wind stresses below 0.02 Pa. Likewise, panel d suggests nearly half (40%) form with the\nsame low wind stresses.\nI struggle to understand how the data presented clearly indicate a correlation between wind stress\nand streak formation. Perhaps I’ve misunderstood the data, in which case, I apologize, but the case\nfor this correlation does not seem very clear cut.\nWithout a clear and compelling punchline like this, I have trouble see why this study should be\npublished in Nature. So I must continue to recommend against its publication.\nMy reply: Thank you for raising your concern again. This study is limited by several factors, most\nprominently, by a) the very low spatial resolution of the MCD and b) the extremely heterogeneous\nCTX orbital image coverage. To name a few specific examples, these limitations lead to inaccurate\nmeasurements of the exact timing of streak formation, as image pairs can be up to 6 months apart\n(and the analysis is using the median date for the retrieval of atmospheric data, although the\nstreaks could have formed at any time between the two images); the large pixels (several\nkilometers across) of the global-scale MCD do not properly capture the wind speeds at surface\nlevel and the atmospheric density for all slope streak locations at all times; and the lack of\nknowledge about the grain size distribution at slope streak locations requires the usage of a\nsimplified, single aerodynamic roughness value across Mars to estimate wind stress. Overall, these\nlimitations affect the accuracy and precision of all observations in this study, which in turn affects\nthe relations shown throughout the manuscript.\nHowever, despite the various limitations, the combination of all observations presented\nthroughout the manuscript still makes a very compelling case for the key role of dust, wind, and\nsand in driving slope streak formation. Most importantly, the data strongly suggest a distinct surge\nin streak activity at around Ls 220° that coincides with the timing of the delivery of atmospheric\n2\ndust in slope streak-bearing locations (Fig. 1); at the exact same time of the year, the wind stresses\nin slope-bearing locations are surging as well, largely beyond the 0.02 Pa mark, which is commonly\naccepted as the threshold that marks atmospheric conditions that can initiate the saltation of\nsand-sized particles, which in turn can trigger slope streak formation (Fig. 4). I would like to note\nthat the actual wind stress values are likely substantially higher than reported by the MCD, due to\nits low spatial resolution – as recently observed by an unrelated study that used dust devil\nmigration to measure wind speed values across Mars (Bickel et al., 2025, DOI:\nhttps://doi.org/10.1126/sciadv.adw5170, available online after October 8th).\nWhen specifically looking at Fig. 3: Fig. 3, and Fig. 3B in particular, is affected by the low temporal\nresolution of the streak activity measurements, as atmospheric properties (such as wind speed and\natmospheric density) might not be taken from the times streaks actually formed (in a worst case\nthere might be a 6 month offset). I would like to emphasize that the usage of the 6-month time\nwindow is absolutely essential to assemble a sufficient number of CTX image pairs (a shorter time\nwindow drastically reduces the number of available image pairs). Fig. 4 tackles the shortcomings of\nFig. 3 by reporting the maximum wind stress value that is encountered over the course of a full day\nover all seasons, around the year. And this maximum daily wind stress value starts to largely\nexceed the 0.02 Pa threshold right at Ls 220°, directly coinciding with peak dust delivery and streak\nformation. Please note that Fig. 3 is specifically labeled as “evidence for […] streak formation\nmagnitude” and Fig. 4 is specifically labeled as “evidence for […] streak formation seasonality”.\nOverall, the presented data makes a strong, statistically sound case for the decisive role of dust,\nwind, and sand in slope streak formation – while clearly emphasizing the limitations, assumptions,\nand shortcomings of the used data and applied methods. I scrutinized the manuscript one more\ntime and further highlighted the key assumptions and limitations of the study, to make it more\nobvious why and how the observations of this study are limited – and what could be done in future\nstudies to address the shortcomings and to refine these results. For example:\nHere, wind stress values are estimated from horizontal wind velocities (modelled at 4.5 m above the\nsurface) and the spatio-temporally associated atmospheric density, assuming a simplified aerodynamic\n(surface) roughness of 2 centimeters.\n[…]\nImportantly, MCD-predicted atmospheric parameters represent regional averages and are not\nexpected to fully capture the dynamics of the atmosphere at the slope streak scale, which directly\naffects all estimates of wind stress. In addition, the assumed, simplified (constant) aerodynamic\nroughness cannot be expected to fully capture the variability of surface roughness across Mars.\n[…]\nImportantly, all estimates of wind stress are based on several assumptions, such as the utilization of\none average, simplified (and potentially too high) aerodynamic roughness for the entire surface of\nMars. These assumptions follow several preceding studies (e.g., Charalambous et al., 2021; Bickel et\nal., 2025a) but are likely to affect the overall accuracy of the derived wind stress estimates, also\nbecause of the low spatial resolution of the MCD. Notably, a recent study that used the movements of\ndust devils to measure near-surface wind velocities across Mars (Bickel et al., 2025a) suggests that the\nglobal-scale MCD – not surprisingly – systematically and substantially underestimates local-scale wind\nvelocities, suggesting that the wind stresses at streak locations could be larger than estimated here,\nfurther underscoring the ability of near-surface winds to initiate sand saltation and trigger streak\nformation. As highlighted by Bickel and Valantinas (2025), future studies of slope streak formation and\nactivity should utilize atmospheric meso-scale models and a wider range of aerodynamic roughness\nvalues – in combination with the new slope streak dataset that is associated with this study – to\naddress some of the key shortcomings of this study.\n[…]\n3\n[…] assuming a global median grain size (D) of 30 cm, as recently used by Bickel et al. (2025a). I note\nthat the utilization of one single D and 𝑧 value as well as the usage of equation (3) largely\n0\noversimplifies the substantial variability of the aerodynamic roughness across Mars. Most notably, the\nassumed D and 𝑧 values are relatively high and might not be fully representative for many slope\n0\nstreak-bearing regions.\n[…]\nMethodological and data limitations\nThe most important limitation of the accuracy of the presented results is the noise caused by a) the\nslope streak counts and b) the temporal bias introduced by the applied CTX image pair window (6\nmonths), which in turn is rooted in the variability of the available CTX image data, as discussed above.\nIn addition, there are other factors that affect the accuracy of the results presented throughout this\nwork. The spatio-temporal analysis relies on various auxiliary datasets and model-derived products,\nspecifically, TES surface albedo (Christensen et al., 2001), MOLA topographic elevation (USGS, 2020),\ncatalogs of new impact craters (Daubar et al., 2022) and marsquakes (MQS Catalog v14, 2019;\nClinton et al., 2021), and the MCD (Forget et al., 1999, 2017). Each of those products is subject to\ndifferent limitations that might affect the results of this study. For example, the used catalogs of new\nimpacts and marsquakes are very likely incomplete, in space and time, as demonstrated by recent\nstudies that identified additional impact events (see e.g., Bickel et al., 2025b). InSight data, in particular,\nis extremely scarce in space and time, and only features minimal overlap with the other datasets used.\nTES and MOLA products feature spatial resolutions that are not representative of the local, meter-\nscale environment that is likely to play an important role in slope streak pre-conditioning and\ntriggering. The MCD only provides approximate, low-resolution, region-scale (5.625° longitude by 3.75°\nlatitude per cell), and thus heavily averaged insights into atmospheric processes and cannot be\nexpected to fully represent the dynamic, complex topographic environments slope streaks are located\nin. Recent work showed that the MCD appears to substantially underestimate the horizontal near-\nsurface wind velocity (Bickel et al., 2025), which implies that this work underestimates the wind\nstresses at slope streak locations. I underline the importance of local and regional studies – and the\nutilization of high-resolution products and meso-scale atmospheric models, in combination with the\nnew dataset that is associated with this study – for the future verification and scrutinization of the\nresults presented here, as previously highlighted by Bickel and Valantinas (2025).\n4\n--- --- ---\nReviewer #2\nReview of revised manuscript, “Dust, Sand and Wind Drive Slope Streaks on Mars” by Bickel for\nNature Communications\nSummary. Slope streaks are prevalent surface features on modern Mars. Found on dust-covered\nslopes, they are relatively superficial but persist for years, often without evidence of growth past their\nformation. These features are now mostly accepted to consist of dry dust avalanches. Due to their\noften seasonal nature, slope streaks are thought to mainly be triggered by winds that are strong\nenough to initiate sand transport. In his manuscript, Bickel seeks to further test and refine this\nhypothesis by harnessing the power of automated slope streak detection in the CTX archive through\nspace and time. His results are consistent with previous (often more local or regional) findings that\nshow a clear correlation between the occurrence of slope streaks and dust abundance, seasonal\nstreak formation, and a correlation between when they form and the occurrence of stronger winds,\nwhen saltation of sand-sized particles can initiate dust lofting. He also shows that some streaks can\nbe explained by marsquakes and meteor impacts, though these likely represent a small number of the\ntotal yearly inventory. Based on these observations, Bickel concludes that winds trigger the formation\nof streaks, where dust is available, and when wind stress exceeds the static threshold of motion of\ndust particles.\nOverall Evaluation. The manuscript tackles a popular topic in modern Mars processes. It is well-\nillustrated and well-written. The AI-driven automatic detection procedure is state of the art and\nrobust. The global nature of the dataset allows the author to ask interesting questions. While I agree\nthat the study is one of the first applications of machine learning (ML) in a global, spatiotemporal\ncontext on Mars, the ML tools themselves are not new, and the scientific findings are for the most\npart very much in line with previously published results. I thus still have some reservations about\nwhether the results warrant publication in Nature Communications, but I leave this assessment to the\neditor. Science-wise, the author mostly addressed the points I raised in my initial review (all but a\ncouple, see below). Once a few final touchups are made (see below), it is my opinion that the\nmanuscript won't need further review.\nMy reply: I thank reviewer #2 for their additional review and feedback. I am still convinced that\nthis study represents a substantial leap forward – lifting studies of slope streak formation from a\nlocal to a global level, providing robust, spatiotemporal evidence – which, in my opinion, warrants\npublication in a high-profile journal, also to ensure quick and efficient dissemination with the\nscientific community.\nI addressed all your remaining concerns, as described in my point-by-point replies below.\nThank you again for your interest in this work and for your constructive feedback, which triggered and\nsupported several key improvements.\nDetailed Comments:\n- Throughout the manuscript: the author seems to have misinterpreted a comment of mine regarding\nthe use of \"wind action.\" Whereas he opted to use \"wind action\" as a generic term for the trigger of\nslope streaks, my intent was to convey the exact opposite, i.e., that it should be avoided (as it is most\nlikely not the direct trigger for dust transport — saltation most likely is). I'd urge the author to go\nthrough the manuscript systematically, and make sure terminology is used precisely. It's ok to use\n\"wind action\" when discussing its correlation with the distribution of slope streaks (i.e., wind strength\ncorrelates with their occurrence), but \"saltation\" should be used when discussing the physical trigger.\nMy reply: Thank you for pointing this out again – I revised the manuscript accordingly and added\n“saltation” in all appropriate places, e.g. in the revised sub-chapter title:\nl. 18: the use of \"minimum pressure\" is odd; please replace with \"dynamic threshold\" (or just\n\"threshold\" if you prefer not to distinguish between static and dynamic thresholds).\nMy reply: I replaced “minimum pressure” with “threshold”, thank you.\nl. 188: A z0 of 2 centimeters is very high, and the only reference for it is a paper by the same author\nthat is not published yet. While it was made available during the initial review stage, I do not have\naccess to the preprint anymore. I cannot check how this value was obtained at this time, but based on\nthe author's response, it seems to be based on grain sizes as observed at multiple landing sites on\nMars. However:\n- z0 only is a direct function of grain size (d) under aerodynamically rough conditions and when there\nis no saltation. When that is the case, z0 is equal to about 2 to 3 times d divided by 30. For z0 to be\nequal to 2 cm, one would need grains ~20-30 cm in diameter, which is unrealistic.\n- Even assuming there is no active saltation, winds over fine sediment beds on Mars are likely\naerodynamically smooth (e.g., Duran Vinent et al., 2019). In that case, z0 scales with the thickness of\nthe viscous sublayer - not grain size - and should be of the order of 10^(-4)-10^(-3) m.\n- When saltation is active - as may be expected to be the case during slope streak formation based on\nthe present study - z0 is controlled by the thickness of the transport layer (not grain size directly; e.g.,\nAndreotti, 2004). On Mars, that corresponds to a z0 that varies with shear velocity but that is < 1 cm\n(see e.g. fig. 6 in Alvarez et al., 2025).\nMy reply: Thank you for highlighting the importance of the aerodynamic roughness again. As\nstated before and as acknowledged in several sections throughout the manuscript, all of my\nassumptions made about the aerodynamic roughness are subject to substantial uncertainty,\nparticularly the assumption that z0 remains constant across the planet and the assumption that z0\n= 2 cm. We just do not have any accurate information on the grain size distribution at any slope\nstreak location. I recognize that 2 cm are at the higher end of aerodynamic roughness values, but it\nrepresents a compromise between the very rocky (e.g. Viking 2, e.g., Moore et al., 1977; Golombek\n& Rapp, 1997; Mutch et al., 1977) and smoother (e.g. InSight) landing sites spacecraft have visited\nin the past. That is also the reason why the same z0 value was used in Bickel et al. (2025), which\nwill be published on October 8th. I would like to note that slope streak source regions often feature\nHiRISE-scale (~30 cm) roughness and boulders, despite being located in very dusty terrain (see\nHiRISE examples below). For those reasons I would like to hold on to z0 = 2 cm for this particular\nmanuscript, following Bickel et al. (2025).\nHowever, I acknowledge the substantial limitations of the assumptions made, of course. I\nscrutinized the manuscript again and inserted further notes that emphasize the assumptions made\nas well as their limitations, for example:\n[…]\n𝑧 is computed using (per, e.g., Charalambous et al., 2022):\n0\n𝐷\n𝑧 =2∗ (3)\n0 30\nassuming a global median grain size (D) of 30 cm, as recently used by Bickel et al. (2025a). I note that\nthe utilization of one single D and 𝑧 value as well as the usage of equation (3) largely oversimplifies\n0\nthe substantial variability of the aerodynamic roughness across Mars. Most notably, the assumed D\n6\nand 𝑧 values are relatively high and might not be fully representative for many slope streak-bearing\n0\nregions.\nTwo snips of a randomly picked HiRISE image in Scamander Vallis (ESP_011289_1950). Downslope direction to the\nbottom left. Note the roughness of the surface along the streak source regions.\nReferences:\nGolombek & Rapp (1997). Size-frequency distributions of rocks on Mars and Earth analog sites: Implications for future\nlanded missions. Journal of Geophysical Research 102 (E2).\nMoore et al. (1977). Surface Materials of the Viking Landing Site. Journal of Geophysical Research 82 (28).\nMutch et al. (1977). The geology of the Viking Lander 2 site. Journal of Geophysical Research 82 (28).\n- l. 191, 284, 443: such weak winds are unlikely to be able to \"initiate\" saltation, but they can\n\"sustain\" it. Please replace \"initiate\" with \"sustain\". It might seem like a semantic distinction, but it is\nan important one that is at the crux of all sand transport studies on Mars.\nMy reply: Thank you for pointing this out. As discussed in the manuscript, wind stress values of\n~0.02 Pa are generally considered to be representative of conditions that lead to the initiation of\n7\nthe saltation of sand-sized (~100 um) particles, e.g. Greeley et al. (1981), Ayoub et al. (2014),\nNewman et al. (2022), Kok et al. (2012). Some authors claim that this value is substantially lower,\ne.g. Swann et al. (2022). For this reason, I think that usage of the term “initiate” is appropriate –\nhowever, I agree that the term “sustain” is helpful to communicate another important\nconsequence of high wind stress conditions. I added “and sustain” to all mentioned lines in the\nmanuscript, e.g.:\nSeveral past studies suggested that wind stress in excess of 0.02 Pa is able to initiate and sustain the\nsaltation of sand-sized particles (~100 µm)\n- l. 280: should be \"the mechanical mobilization of dust through *saltation*\" (not \"wind action\").\nMy reply: Fixed, thank you.\n- l. 606-608: erroneous mention to a \"2025c\" Bickel et al. reference.\nMy reply: Thank you, fixed.\nReferences:\nAlvarez, Carlos A., et al. \"Aerodynamic roughness of rippled beds under active saltation at Earth-to-\nMars atmospheric pressures.\" Nature Communications 16.1 (2025): 5113.\nAndreotti, Bruno. \"A two-species model of aeolian sand transport.\" Journal of Fluid Mechanics 510\n(2004): 47-70.\nDuran Vinent, Orencio, et al. \"A unified model of ripples and dunes in water and planetary\nenvironments.\" Nature Geoscience 12.5 (2019): 345-350.\nMy reply: Thank you for including those additional references!\n8"}]

Decision letter