scholarly journals Spatial Autocorrelation of Martian Surface Temperature and Its Spatio-Temporal Relationships with Near-Surface Environmental Factors across China’s Tianwen-1 Landing Zone

2021 ◽  
Vol 13 (11) ◽  
pp. 2206
Author(s):  
Yaowen Luo ◽  
Jianguo Yan ◽  
Fei Li ◽  
Bo Li

Variations in the Martian surface temperature indicate patterns of surface energy exchange. The Martian surface temperature at a location is similar to those in adjacent locations; but, an understanding of temperature clusters in multiple locations will deepen our knowledge of planetary surface processes overall. The spatial coherence of the Martian surface temperature (ST) at different locations, the spatio-temporal variations in temperature clusters, and the relationships between ST and near-surface environmental factors, however, are not well understood. To fill this gap, we studied an area to the south of Utopia Planitia, the landing zone for the Tianwen-1 Mars Exploration mission. The spatial aggregation of three Martian ST indicators (STIs), including sol average temperature (SAT), sol temperature range (STR), and sol-to-sol temperature change (STC), were quantitatively evaluated using clustering analysis at the global and local scale. In addition, we also detected the spatio-temporal variations in relations between the STIs and seven potential driving factors, including thermal inertia, albedo, dust, elevation, slope, and zonal and meridional winds, across the study area during 81 to 111 sols in Martian years 29–32, based on a geographically and temporally weighted regression model (GTWR). We found that the SAT, STR, and STC were not randomly distributed over space but exhibited signs of significant spatial aggregation. Thermal inertia and dust made the greatest contribution to the fluctuation in STIs over time. The local surface temperature was likely affected by the slope, wind, and local circulation, especially in the area with a large slope and low thermal inertia. In addition, the sheltering effects of the mountains at the edge of the basin likely contributed to the spatial difference in SAT and STR. These results are a reminder that the spatio-temporal variation in the local driving factors associated with Martian surface temperature cannot be neglected. Our research contributes to the understanding of the surface environment that might compromise the survival and operations of the Tianwen-1 lander on the Martian surface.

2021 ◽  
Author(s):  
Rory Scarrott ◽  
Fiona Cawkwell ◽  
Mark Jessopp ◽  
Caroline Cusack

<p>The Ocean-surface Heterogeneity MApping (OHMA) algorithm is an objective, replicable approach to map the spatio-temporal heterogeneity of ocean surface waters. It is used to processes hypertemporal, satellite-derived data and produces a single-image surface heterogeneity (SH) dataset for the selected parameter of interest. The product separates regions of dissimilar temporal characteristics. Data validation is challenging because it requires In-situ observations at spatial and temporal resolutions comparable to the hyper-temporal inputs. Validating this spatio-temporal product highlighted the need to optimise existing vessel-based data collection efforts, to maximise exploitation of the rapidly-growing hyper-temporal data resource.</p><p>For this study, the SH was created using hyper-temporal 1km resolution satellite derived Sea Surface Temperature (SST) data acquired in 2011. Underway ship observations of near surface temperature collected on multiple scientific surveys off the Irish coast in 2011 were used to validate the results. The most suitable underway ship SST data were identified in ocean areas sampled multiple times and with representative measurements across all seasons.</p><p>A 3-stage bias reduction approach was applied to identify suitable ocean areas. The first bias reduction addressed temporal bias, i.e., the temporal spread of available In-situ ship transect data across each satellite image pixel. Only pixels for which In-situ data were obtained at least once in each season were selected; resulting in 14 SH image pixels deemed suitable out of a total of 3,677 SH image pixels with available In-situ data. The second bias reduction addressed spatial bias, to reduce the influence of over-sampled areas in an image pixel with a sub-pixel approach. Statistical dispersion measures and statistical shape measures were calculated for each of the sets of sub-pixel values. This gave heterogeneity estimates for each cruise transit of a pixel area. The third bias reduction addressed bias of temporally oversampled seasons. For each transit-derived heterogeneity measure, the values within each season were averaged, before the annual average value was derived across all four seasons in 2011.</p><p>Significant associations were identified between satellite SST-derived SH values, and In-situ heterogeneity measures related to shape; absolute skewness (Spearman’s Rank, n=14, ρ[12]= +0.5755, P<0.05), and kurtosis (Spearman’s Rank, n=14, ρ[12] = 0.5446, P < 0.05). These are a consequence of (i) locally-extreme measurements, and/or (ii) increased presence of sharp transitions detected spatially by In-situ data. However, the number and location of suitable In-situ validation sites precluded a robust validation of the SH dataset (14 pixels located in Irish waters, for a dataset spanning the North Atlantic). This requires more targeted data. The approach would have benefited from more samples over the winter season, which would have enabled more offshore validation sites to be incorporated into the analysis. This is a challenge that faces satellite product developers, who want to deliver spatio-temporal information to new markets. There is a significant opportunity for dedicated, transit-measured (e.g. Ferry box data), validation sites to be established. These could potentially synergise with key nodes in global shipping routes to maximise data collected by vessels of opportunity, and ensure consistent data are collected over the same area at regular intervals.</p>


2021 ◽  
Author(s):  
John B. Paustian

Karst environments are characterized by voids, i.e. sinkholes and conduits of varying size that arise from the active dissolution of carbonate rock by acidic groundwater. These voids, whether air-, water-, or soil-filled, can be difficult to image using near-surface geophysical methods due to the limited investigation depths of most active-source methods. In addition, due to the significant effort it takes to collect active-source data, investigators are often unable to monitor spatio-temporal variations in the subsurface. The ability to detect, image, and monitor subsurface voids improves the understanding of processes that create and transform voids, a vitally important insight across a variety of scientifc disciplines and engineering applications, including hydrogeology, geotechnical engineering, planetary science and even issues of national security. Using a 54-element nodal array (1C and 3C sensors), I image the subsurface of the USF GeoPark with ambient noise surface wave tomography. I also use complementary active-source geophysical datasets (e.g. 2D ERT) collected at the GeoPark to constrain and/or validate the tomography results. I address two research questions with this study: (1) How do ambient seismic methods complement active-source near-surface methods? (2) Can ambient noise tomography resolve voids in the karst environment? In this thesis, I discuss my answers to these questions and present the current state of surface wave methods in the karst environment, including the feasibility for utilizing ambient noise methods to monitor spatio-temporal changes in sinkhole and conduit formation. In addition to the ability to use seismic methods for temporal monitoring, ambient noise provides lower frequencies than what are achievable with active-source seismic methods. Using frequencies from 5-28 Hz, ambient noise tomography is able to image deeper into the subsurface (up to 100 m at 5 Hz) than previous active-source seismic studies at the GeoPark field site. This study yields a more robust and simple method to image voids in covered karst environments and a long-term installation of ambient seismic nodes enables future investigations of spatio-temporal variations in void structures.


2017 ◽  
Author(s):  
Iskhaq Iskandar ◽  
Wijaya Mardiansyah ◽  
Dedi Setiabudidaya ◽  
Muhammad Irfan ◽  
Pradanto Poerwono

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