Quantitative assessment of water content and mineral abundances at Gale crater on Mars with orbital observations

2020 ◽  
Vol 637 ◽  
pp. A79
Author(s):  
Yang Liu ◽  
Federico Stachurski ◽  
Zhenghao Liu ◽  
Yongliao Zou
Keyword(s):  
Water Content ◽  
Near Infrared ◽  
Climate Models ◽  
Ground Truth ◽  
Quantitative Model ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Transfer Model ◽  
Ground Truth Data ◽  
Gale Crater

Context. The information of water content can help to improve atmospheric and climate models, and thus provide a better understanding of the past and present role of water and aqueous alteration on Mars. Mineral abundances can provide unique constraints on their formation environments and thus also on the geological and climate evolution of Mars. Aims. In this study, we used a state-of-the-art approach to derive the hydration state and mineral abundances over Gale crater on Mars, analysing hyperspectral visible/near-infrared data from the Observatoire pour la Minéralogie, l’Eau, les Glaces et l’Activité (OMEGA) instrument onboard Mars Express and from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) instrument onboard the Mars Reconnaissance Orbiter. Methods. The Discrete Ordinate Transfer model was used to perform atmospheric and thermal correction of the OMEGA and CRISM hyperspectral data in order to derive the surface single scattering albedos (SSAs) at Gale crater, Mars. Water content was estimated using a linear relationship between the derived effective single-particle absorption thickness at 2.9 μm from SSAs and the water weight percentage. Mineral abundances were retrieved by performing the linear spectral unmixing of SSAs from CRISM data. The results were compared with the ground-truth results returned from Curiosity rover. Results. The water content for most areas at Gale crater derived using the OMEGA data is around 2–3 water weight percent (water wt % hereafter), which is in agreement with that derived from the in situ measurements by Curiosity’s Sample Analysis at Mars instrument. However, the sensitivity tests show that uncertainties exist due to the combination of several factors including modelling bias, instrumental issue, and different sensing techniques. The derived mineral abundances using the orbital data are not fully consistent with that derived by Curiosity, and the discrepancy may be due to a combination of dust cover, texture, and particle size effects, as well as the effectiveness of the quantitative model. Conclusions. The ground-truth data from Curiosity provide a critical calibration point for the quantitative method used in the orbital remote-sensing observations. Our analysis indicates that the method presented here has great potential for mapping the water content and mineral abundances on Mars, but caution must be taken when using these abundance results for geological interpretations.

scholarly journals MAPPING OF FOLIAR CONTENT USING RADIATIVE TRANSFER MODELING AND VIS-NIR HYPERSPECTRAL CLOSE-RANGE REMOTE-SENSING

2015 ◽  
Vol XL-3/W3 ◽  
pp. 467-472 ◽  
Author(s):  
S. Jay ◽  
R. Bendoula ◽  
X. Hadoux ◽  
N. Gorretta
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Spatial Resolution ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Radiative Transfer Model ◽  
Integrating Sphere ◽  
Leaf Angle ◽  
Transfer Model

Most methods for retrieving foliar content from hyperspectral data are well adapted either to remote-sensing scale, for which each spectral measurement has a spatial resolution ranging from a few dozen centimeters to a few hundred meters, or to leaf scale, for which an integrating sphere is required to collect the spectral data. In this study, we present a method for estimating leaf optical properties from hyperspectral images having a spatial resolution of a few millimeters or centimeters. In presence of a single light source assumed to be directional, it is shown that leaf hyperspectral measurements can be related to the directional hemispherical reflectance simulated by the PROSPECT radiative transfer model using two other parameters. The first one is a multiplicative term that is related to local leaf angle and illumination zenith angle. The second parameter is an additive specular-related term that models BRDF effects. <br><br> Our model was tested on visible and near infrared hyperspectral images of leaves of various species, that were acquired under laboratory conditions. Introducing these two additional parameters into the inversion scheme leads to improved estimation results of PROSPECT parameters when compared to original PROSPECT. In particular, the RMSE for local chlorophyll content estimation was reduced by 21% (resp. 32%) when tested on leaves placed in horizontal (resp. sloping) position. Furthermore, inverting this model provides interesting information on local leaf angle, which is a crucial parameter in classical remote-sensing.

scholarly journals Retrieval of Sediment Filling Factor in a Salt Panne from Multi-View Hyperspectral Imagery

2020 ◽  
Vol 12 (3) ◽  
pp. 422 ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann ◽  
Christopher S. Lapszynski ◽  
Anna Christina Tyler ◽  
Sarah Goldsmith
Keyword(s):  
Moisture Content ◽  
Bulk Density ◽  
Filling Factor ◽  
Imaging System ◽  
Hyperspectral Imagery ◽  
Ground Truth ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Ground Truth Data ◽  
The Common

This work describes a study using multi-view hyperspectral imagery to retrieve sediment filling factor through inversion of a modified version of the Hapke radiative transfer model. We collected multi-view hyperspectral imagery from a hyperspectral imaging system mounted atop a telescopic mast from multiple locations and viewing angles of a salt panne on a barrier island at the Virginia Coast Reserve Long-Term Ecological Research site. We also collected ground truth data, including sediment bulk density and moisture content, within the common field of view of the collected hyperspectral imagery. For samples below a density threshold for coherent effects, originally predicted by Hapke, the retrieved sediment filling factor correlates well with directly measured sediment bulk density ( R 2 = 0.85 ). The majority of collected samples satisfied this condition. The onset of the threshold occurs at significantly higher filling factors than Hapke’s predictions for dry sediments because the salt panne sediment has significant moisture content. We applied our validated inversion model to successfully map sediment filling factor across the common region of overlap of the multi-view hyperspectral imagery of the salt panne.

scholarly journals Minimax Bridgeness-Based Clustering for Hyperspectral Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1162 ◽  
Author(s):  
Steven Le Moan ◽  
Claude Cariou
Keyword(s):  
Similarity Measures ◽  
Feature Space ◽  
Ground Truth ◽  
Parameter Tuning ◽  
Hyperspectral Data ◽  
Ground Truth Data ◽  
Sensing Applications ◽  
Nearest Neighbours ◽  
Silhouette Index ◽  
Clustering Approach

Hyperspectral (HS) imaging has been used extensively in remote sensing applications like agriculture, forestry, geology and marine science. HS pixel classification is an important task to help identify different classes of materials within a scene, such as different types of crops on a farm. However, this task is significantly hindered by the fact that HS pixels typically form high-dimensional clusters of arbitrary sizes and shapes in the feature space spanned by all spectral channels. This is even more of a challenge when ground truth data is difficult to obtain and when there is no reliable prior information about these clusters (e.g., number, typical shape, intrinsic dimensionality). In this letter, we present a new graph-based clustering approach for hyperspectral data mining that does not require ground truth data nor parameter tuning. It is based on the minimax distance, a measure of similarity between vertices on a graph. Using the silhouette index, we demonstrate that the minimax distance is more suitable to identify clusters in raw hyperspectral data than two other graph-based similarity measures: mutual proximity and shared nearest neighbours. We then introduce the minimax bridgeness-based clustering approach, and we demonstrate that it can discover clusters of interest in hyperspectral data better than comparable approaches.

scholarly journals Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat

2021 ◽  
Vol 13 (9) ◽  
pp. 1697
Author(s):  
Alexander Jenal ◽  
Hubert Hüging ◽  
Hella Ellen Ahrends ◽  
Andreas Bolten ◽  
Jens Bongartz ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Near Infrared ◽  
Imaging System ◽  
N Uptake ◽  
Ground Truth ◽  
Short Wave ◽  
Camera System ◽  
Ground Truth Data ◽  
Single Observation ◽  
Wheat Field

UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R2: 0.75 to 0.84). These results indicate the imaging system’s potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed.

Characterization of Alpine peatlands based on remote sensing of vegetation and water content

2021 ◽  
Author(s):  
Sonia Silvestri ◽  
Alessandra Borgia
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Regional Scale ◽  
Ground Truth ◽  
Organic Carbon Content ◽  
Ground Truth Data ◽  
The World ◽  
The Eu

&lt;p&gt;Storing up to 70 kg of carbon per cubic meter, peatlands are among the most carbon-dense environments in the world. If in pristine conditions, peatlands support a number of ecosystem services as for example water retention and mitigation of droughts and floods, water purification, water availability to wildlife. Their preservation is one of the main goals of the EU policy and of other initiatives around the world.&lt;/p&gt;&lt;p&gt;Despite their importance, Alpine peatlands have been rarely studied and their presence is not even included in the EU maps, as for example the JRC Relative Cover of Peat Soils map, and only some sites are included in the Corine Land Cover map. The precise localization of peatland sites and the assessment of their extent is the first fundamental step for the implementation of adequate conservation policies. To this end, satellite remote sensing is the ideal instrument to provide adequate spatial resolution to detect and characterize Alpine peatlands at the regional scale. In this study, we use Sentinal-2 satellite data combined with 2m spatial resolution digital elevation model (from LiDAR data) to detect and quantify the extent of peatlands in the Trentino - Alto Adige region, an area of about 12,000 sq km located in the heart of the Italian Alpine region. Ground truth data include 71 peatlands that cover a total surface of more than 2,000 sq m. Field campaigns and lab analyses on some selected sites show that, on average, the sampled peatlands have depth of about 1m, Bulk Density of 0.128 g cm&lt;sup&gt;-3&lt;/sup&gt; and LOI of 63%, hence indicating that the organic carbon content by soil volume is high, being on average 0.04 g cm&lt;sup&gt;-3&lt;/sup&gt;. Satellite data analysis allowed us to detect a large number of peatland sites with high accuracy, thus confirming the importance of Alpine peatlands as carbon stock sites for the region. Moreover, thanks to the correlation between two indices (NDVI and NDWI) we could characterize the water content of these sites, hence analyzing its seasonal variation and inferring possible future scenarios linked to climate change effects.&lt;/p&gt;

Mineral mapping at the Ikh Shankhai porphyry Cu deposits, Mongolia using WorldView-3 data

2020 ◽  
Author(s):  
Son Youngsun ◽  
Kim Kwang-Eun
Keyword(s):  
Spatial Resolution ◽  
Near Infrared ◽  
Matched Filter ◽  
Extreme Environments ◽  
Ground Truth ◽  
Spectral Signature ◽  
Ground Truth Data ◽  
Mineral Mapping ◽  
Porphyry Cu ◽  
Porphyry Cu Deposit

&lt;p&gt;Southeastern Mongolia has limited access due to its extreme environments (long and harsh winter) and lack of infrastructure (e.g., road). Satellite remote sensing technique is one of the most effective methods to get geological information in areas where field survey is difficult. WorldView-3 (WV3), launched in August 2014, is high-spatial resolution commercial multispectral sensor developed by DigitalGlobe. WV3 measures reflected radiation in eight visible near infrared (VNIR) bands between 0.42 and 1.04 &amp;#13211; and in eight short-wave infrared (SWIR) bands between 1.20 and 2.33, which have 1.24- and 7.5-m spatial resolution, respectively. In this study, WV3 VNIR and SWIR data were used to identify and map the various minerals in the Ikh Shankhai porphyry Cu deposit district, Mongolia.&lt;/p&gt;&lt;p&gt;The Ikh-Shankhai porphyry Cu deposit is located within Gurvansayhan island arc terrane in southeastern (SE) Gobi mineral belt, Mongolia. The Ikh-Shankhai district include the porphyry system containing Cu-Au with primary chalcopyrite, which is classified into disseminated type and stockwork quartz type. This district consists of Late Devonian-Early Carboniferous andesite, tuff and siltstone intruded by Carboniferous-Permian granite, granodiorite and granodiorite porphyry.&lt;/p&gt;&lt;p&gt;The WV 3 data were analyzed using mixture-tuned-matched filter (MTMF) which locates a known spectral signature in the presence of a mixed or unknown background. MTMF does not require knowledge of all of the spectral endmembers and is suited for used where materials with distinct spectral signatures occur within a single pixel. From the WV3 analysis result using mixture-tuned-matched filter (MTMF), we identified the location and abundance of alteration minerals. Advanced argillic minerals (alunite, kaolinite (or dickite), and pyrophyllite) were dominant in the lithocaps of the Budgat and Gashuun Khudag prospects; whereas, phyllic (illite) and propylitic (calcite and epidote) minerals were dominant in the areas surrounding the lithocaps. In addition, the distribution of ferric minerals (hematite and goethite) was mapped because of the oxidation of pyrite. Field work at the Ikh-Shankhai porphyry Cu district to evaluate the accuracy of the mineral mapping results was carried out in August, 2018. Reflectance spectra acquisition using a portable ASD TerraSpec Halo mineral identifier (the attached GPS covered a spectral range of 0.35 &amp;#8211; 2.5 &amp;#181;m) was conducted in the altered outcrops of the Ikh-Shankhai porphyry Cu district. Mineral mapping results compared well with the field spectral measurements collected for the ground truth and demonstrated WV3 capability for identifying and mapping minerals associated with hydrothermal alteration. Evaluation of the WV3 mineral mapping results using ground truth data indicates, however, a difficulty in mapping spectrally similar minerals (e.g., kaolinite and dickite) due to spectral resolution limitation.&lt;/p&gt;

Linear spectral unmixing of near-infrared hyperspectral data from Juventae Chasma, Mars

2009 ◽  
Author(s):  
Lorenz Wendt ◽  
Jean-Philippe Combe ◽  
Patrick C. McGuire ◽  
Janice L. Bishop ◽  
Gerhard Neukum
Keyword(s):  
Near Infrared ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Linear Spectral Unmixing

VNIR-SWIR Superspectral Mineral Mapping: An Example from Cuprite, Nevada

2020 ◽  
Vol 86 (11) ◽  
pp. 695-700
Author(s):  
Kathleen E. Johnson ◽  
Krzysztof Koperski
Keyword(s):  
Near Infrared ◽  
Thermal Emission ◽  
High Spatial Resolution ◽  
Ground Truth ◽  
Random Forest Classifier ◽  
Hyperspectral Data ◽  
United States Geological Survey ◽  
Mineral Mapping ◽  
Mapping Techniques ◽  
Shortwave Infrared

Cuprite, Nevada, is a location well known for numerous studies of its hydrothermal mineralogy. This region has been used to validate geological interpretations of airborne hyperspectral imagery (AVIRIS HSI ), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER ) imagery, and most recently eight-band WorldView-3 shortwave infrared (SWIR ) imagery. WorldView-3 is a high-spatial-resolution commercial multispectral satellite sensor with eight visible-to-near-infrared (VNIR ) bands (0.42–1.04 μm) and eight SWIR bands (1.2–2.33 μm). We have applied mineral mapping techniques to all 16 bands to perform a geological analysis of the Cuprite, Nevada, location. Ground truth for the training and validation was derived from AVIRIS hyperspectral data and United States Geological Survey mineral spectral data for this location. We present the results of a supervised mineral-mapping classification applying a random-forest classifier. Our results show that with good ground truth, WorldView-3 SWIR + VNIR imagery produces an accurate geological assessment.

scholarly journals Preliminary estimation of fat depth in the lamb short loin using a hyperspectral camera

2018 ◽  
Vol 58 (8) ◽  
pp. 1488 ◽  
Author(s):  
S. Rahman ◽  
P. Quin ◽  
T. Walsh ◽  
T. Vidal-Calleja ◽  
M. J. McPhee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Subcutaneous Fat ◽  
Ground Truth ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Total Fat ◽  
Hyperspectral Camera

The objectives of the present study were to describe the approach used for classifying surface tissue, and for estimating fat depth in lamb short loins and validating the approach. Fat versus non-fat pixels were classified and then used to estimate the fat depth for each pixel in the hyperspectral image. Estimated reflectance, instead of image intensity or radiance, was used as the input feature for classification. The relationship between reflectance and the fat/non-fat classification label was learnt using support vector machines. Gaussian processes were used to learn regression for fat depth as a function of reflectance. Data to train and test the machine learning algorithms was collected by scanning 16 short loins. The near-infrared hyperspectral camera captured lines of data of the side of the short loin (i.e. with the subcutaneous fat facing the camera). Advanced single-lens reflex camera took photos of the same cuts from above, such that a ground truth of fat depth could be semi-automatically extracted and associated with the hyperspectral data. A subset of the data was used to train the machine learning model, and to test it. The results of classifying pixels as either fat or non-fat achieved a 96% accuracy. Fat depths of up to 12 mm were estimated, with an R2 of 0.59, a mean absolute bias of 1.72 mm and root mean square error of 2.34 mm. The techniques developed and validated in the present study will be used to estimate fat coverage to predict total fat, and, subsequently, lean meat yield in the carcass.

Sign in / Sign up

Export Citation Format

Share Document