Search Results (12)

The McMurdo Dry Valleys (DVs) of South Victoria Land, Antarctica, constitute the largest ice-free region on the continent and one of Earth’s most Mars-analog environments. Their hyper-arid polar desert conditions offer a unique setting for investigating surface weathering and mineralogical processes under extreme climates. This study presents the first regional-scale mapping of alteration and crystalline weathering minerals across the McMurdo DVs. It uses Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral data; visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands were analyzed through a Spectral Hourglass Workflow, endmember extraction, and spectral unmixing with Matched Filtering (MF) and Constrained Energy Minimization (CEM). Inter-algorithm consistency analysis between MF and CEM yielded 78.83% overall agreement with a Kappa coefficient of 0.75, indicating strong methodological consistency in mineral discrimination using ASTER VNIR+SWIR data. It should be noted that this agreement reflects internal algorithmic robustness rather than independent geological validation. Geological reliability is instead supported by documented field observations, lithological map comparisons, and spectral correspondence with the USGS spectral library. Validation employed documented field observations, lithological maps, and the USGS spectral library. Results reveal distinct spatial distributions of hematite-limonite/goethite, jarosite, kaolinite/smectite-illite-pyrophyllite-alunite, muscovite, hydrous silica/sericite/jarosite/hematite, epidote/chlorite, and calcite, closely associated with lithological units and unconsolidated deposits in Taylor, Wright, Victoria, and McKelvey Valleys. An inter-algorithm consistency check achieved 78.83% overall accuracy with a Kappa coefficient of 0.75, underscoring the robustness of ASTER VNIR+SWIR data for Antarctic mineral discrimination despite localized spectral mixing. Beyond refining the geological understanding of the McMurdo DVs, these results establish ASTER as an effective tool for regional mineralogical mapping in inaccessible polar terrains. The findings further strengthen the role of the Dry Valleys as a terrestrial analog for Mars, where similar mineralogical assemblages and spectral ambiguities have been observed, thereby contributing to both Antarctic geoscience and planetary exploration frameworks. Full article

Shaligou, located in the southwestern Altyn Mountains, is a newly discovered lithium-bearing pegmatite deposit that is driving increased exploration in the region. However, the challenging environmental conditions of the Altyn Mountains pose challenges for the exploration of lithium-bearing pegmatites. Consequently, remote sensing technology has emerged as an effective exploration tool. In this study, the spectral data of typical samples were collected to establish a spectral library of rocks and minerals in the study area capable of serving as a foundation for remote sensing analysis. Firstly, ASTER data were utilized successfully for lithological interpretation of the area. Secondly, high-resolution WorldView-3 data with a spatial resolution of 0.31 m were used to establish interpretation criteria for pegmatite dikes. Ground validation results were highly consistent with the remote sensing interpretations, confirming that the use of WorldView-3 data significantly enhances the accuracy of lithium-bearing pegmatite dike identification, providing valuable guidance for further exploration. Full article

Mineral mapping from satellite images provides valuable insights into subsurface mineral alteration for geothermal exploration. In previous studies, eight fundamental algorithms were used for mineral mapping utilizing USGS spectra, a collection of reflectance spectra containing samples of minerals, rocks, and soils created by the USGS. We used an ASD FieldSpec 4 Hi-RES NG portable spectrometer to collect spectra for analyzing ASTER images of the Coso Geothermal Field. Then, we established the ground-truth information and the spectral library by analyzing 97 samples. Samples collected from the field were analyzed using the CSIRO TSG (The Spectral Geologist of the Commonwealth Scientific and Industrial Research Organization). Based on the mineralogy study, multiple high-purity spectra of geothermal alteration minerals were selected from collected data, including alunite, chalcedony, hematite, kaolinite, and opal. Eight mineral spectral target detection algorithms were applied to the preprocessed satellite data with a proposed local spectral library. We measured the highest overall accuracy of 87% for alunite, 95% for opal, 83% for chalcedony, 60% for hematite, and 96% for kaolinite out of these eight algorithms. Three, four, five, and eight algorithms were fused to extract mineral alteration with the obtained target detection results. The results prove that the fusion of algorithms gives better results than using individual ones. In conclusion, this paper discusses the significance of evaluating different mapping algorithms. It proposes a robust fusion approach to extract mineral maps as an indicator for geothermal exploration. Full article

Pedological maps in suitable scales are scarce in most countries due to the high costs involved in soil surveying. Therefore, methods for surveying and mapping must be developed to overpass the cartographic material obtention. In this sense, this work aims at assessing a digital soil map (DSM) built by multispectral data extrapolation from a source area to a target area using the ASTER time series modeling technique. For that process, eight representative toposequences were established in two contiguous micro-watersheds, with a total of 42 soil profiles for analyses and classification. We found Ferralsols, Plinthosols, Regosols, and a few Cambisols, Arenosols, Gleisols, and Histosols, typical of tropical regions. In the laboratory, surface soil samples were submitted to spectral readings from 0.40 µm to 2.50 µm. The soil spectra were morphologically interpreted, identifying shapes and main features typical of tropical soils. Soil texture grouped the curves by cluster analysis, forming a spectral library (SL). In parallel, an ASTER time series (2001, 2004, and 2006) was processed, generating a bare soil synthetic soil image (SySI) covering 39.7% of the target area. Multiple Endmember Spectral Mixture Analysis modeled the SL on the SySI generating DSM with 73% of Kappa index, in which identified about 77% is covered by rhodic Ferralsols. Besides the overestimation, the DSM represented the study area’s pedodiversity. Given the discussion raised, we consider including subsoil data and other features using other sensors in operations modeled by machine learning algorithms to improve results. Full article

Automatic cloud detection in remote sensing images is of great significance. Deep-learning-based methods can achieve cloud detection with high accuracy; however, network training heavily relies on a large number of labels. Manually labelling pixel-wise level cloud and non-cloud annotations for many remote sensing images is laborious and requires expert-level knowledge. Different types of satellite images cannot share a set of training data, due to the difference in spectral range and spatial resolution between them. Hence, labelled samples in each upcoming satellite image are required to train a new deep-learning-based model. In order to overcome such a limitation, a novel cloud detection algorithm based on a spectral library and convolutional neural network (CD-SLCNN) was proposed in this paper. In this method, the residual learning and one-dimensional CNN (Res-1D-CNN) was used to accurately capture the spectral information of the pixels based on the prior spectral library, effectively preventing errors due to the uncertainties in thin clouds, broken clouds, and clear-sky pixels during remote sensing interpretation. Benefiting from data simulation, the method is suitable for the cloud detection of different types of multispectral data. A total of 62 Landsat-8 Operational Land Imagers (OLI), 25 Moderate Resolution Imaging Spectroradiometers (MODIS), and 20 Sentinel-2 satellite images acquired at different times and over different types of underlying surfaces, such as a high vegetation coverage, urban area, bare soil, water, and mountains, were used for cloud detection validation and quantitative analysis, and the cloud detection results were compared with the results from the function of the mask, MODIS cloud mask, support vector machine, and random forest. The comparison revealed that the CD-SLCNN method achieved the best performance, with a higher overall accuracy (95.6%, 95.36%, 94.27%) and mean intersection over union (77.82%, 77.94%, 77.23%) on the Landsat-8 OLI, MODIS, and Sentinel-2 data, respectively. The CD-SLCNN algorithm produced consistent results with a more accurate cloud contour on thick, thin, and broken clouds over a diverse underlying surface, and had a stable performance regarding bright surfaces, such as buildings, ice, and snow. Full article

Several regions in the High Arctic still lingered poorly explored for a variety of mineralization types because of harsh climate environments and remoteness. Inglefield Land is an ice-free region in northwest Greenland that contains copper-gold mineralization associated with hydrothermal alteration mineral assemblages. In this study, Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and WorldView-3 multispectral remote sensing data were used for hydrothermal alteration mapping and mineral prospecting in the Inglefield Land at regional, local, and district scales. Directed principal components analysis (DPCA) technique was applied to map iron oxide/hydroxide, Al/Fe-OH, Mg-Fe-OH minerals, silicification (Si-OH), and SiO2 mineral groups using specialized band ratios of the multispectral datasets. For extracting reference spectra directly from the Landsat-8, ASTER, and WorldView-3 (WV-3) images to generate fraction images of end-member minerals, the automated spectral hourglass (ASH) approach was implemented. Linear spectral unmixing (LSU) algorithm was thereafter used to produce a mineral map of fractional images. Furthermore, adaptive coherence estimator (ACE) algorithm was applied to visible and near-infrared and shortwave infrared (VINR + SWIR) bands of ASTER using laboratory reflectance spectra extracted from the USGS spectral library for verifying the presence of mineral spectral signatures. Results indicate that the boundaries between the Franklinian sedimentary successions and the Etah metamorphic and meta-igneous complex, the orthogneiss in the northeastern part of the Cu-Au mineralization belt adjacent to Dallas Bugt, and the southern part of the Cu-Au mineralization belt nearby Marshall Bugt show high content of iron oxides/hydroxides and Si-OH/SiO2 mineral groups, which warrant high potential for Cu-Au prospecting. A high spatial distribution of hematite/jarosite, chalcedony/opal, and chlorite/epidote/biotite were identified with the documented Cu-Au occurrences in central and southwestern sectors of the Cu-Au mineralization belt. The calculation of confusion matrix and Kappa Coefficient proved appropriate overall accuracy and good rate of agreement for alteration mineral mapping. This investigation accomplished the application of multispectral/multi-sensor satellite imagery as a valuable and economical tool for reconnaissance stages of systematic mineral exploration projects in remote and inaccessible metallogenic provinces around the world, particularly in the High Arctic regions. Full article

The ASTER Volcanic Ash Library (AVAL) is presented, developed using quantitative laboratory thermal infrared (TIR) emission spectroscopic methods, spanning the 2000–400 cm⁻¹ (5–25 μm wavelength) range, including the Earth’s TIR atmospheric window (8–12 μm). Each spectral suite is unique owing to the chemical composition and proportion of glass to crystals per sample and is divided into six size fractions. AVAL, used with an appropriate spectral mixture model applied to orbital multispectral TIR data, provides a unique ability to study active volcanic ash plumes. We present the first example of this application to an ash plume produced by the Sakurajima Volcano in Japan. The emissivity variations measured in ash plumes using an ever-expanding ash spectral library will provide future quantitative inputs for both atmospheric models, where the ash composition is unknown or estimated, as well as compositional probes into ongoing eruptions. Full article

The distribution and dynamic changes in impervious surface areas (ISAs) are crucial to understanding urbanization and its impact on urban heat islands, earth surface energy balance, hydrological cycles, and biodiversity. Remotely sensed data play an essential role in ISA mapping, and numerous methods have been developed and successfully applied for ISA extraction. However, the heterogeneity of ISA spectra and the high similarity of the spectra between ISA and soil have not been effectively addressed. In this study, we selected data from the US Geological Survey (USGS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral libraries as samples and used blue and near-infrared bands as characteristic bands based on spectral analysis to propose a novel index, the perpendicular impervious surface index (PISI). Landsat 8 operational land imager data in four provincial capital cities of China (Wuhan, Shenyang, Guangzhou, and Xining) were selected as test data to examine the performance of the proposed PISI in four different environments. Threshold analysis results show that there is a significant positive correlation between PISI and the proportion of ISA, and threshold can be adjusted according to different needs with different accuracy. Furthermore, comparative analyses, which involved separability analysis and extraction precision analysis, were conducted among PISI, biophysical composition index (BCI), and normalized difference built-up index (NDBI). Results indicate that PISI is more accurate and has better separability for ISA and soil as well as ISA and vegetation in the ISA extraction than the BCI and NDBI under different conditions. The accuracy of PISI in the four cities is 94.13%, 96.50%, 89.51%, and 93.46% respectively, while BCI and NDBI showed accuracy of 77.53%, 93.49%, 78.02%, and 84.03% and 58.25%, 57.53%, 77.77%, and 64.83%, respectively. In general, the proposed PISI is a convenient index to extract ISA with higher accuracy and better separability for ISA and soil as well as ISA and vegetation. Meanwhile, as PISI only uses blue and near-infrared bands, it can be used in a wider variety of remote sensing images. Full article

Iron is an indicator for soil fertility and the usability of an area for cultivating crops. Remote sensing is the only suitable tool for surveying large areas at a high temporal and spatial interval, yet a relative high spectral resolution is needed for mapping iron contents with reflectance data. Sentinel-2 has several bands that cover the 0.9 μm iron absorption feature, while space-borne sensors traditionally used for geologic remote sensing, like ASTER and Landsat, had only one band in this feature. In this paper, we introduce a curve-fitting technique for Sentinel-2 that approximates the iron absorption feature at a hyperspectral resolution. We test our technique on library spectra of different iron bearing minerals and we apply it to a Sentinel-2 image synthesized from an airborne hyperspectral dataset. Our method finds the wavelength position of maximum absorption and absolute absorption depth for minerals Beryl, Bronzite, Goethite, Jarosite and Hematite. Sentinel-2 offers information on the 0.9 μm absorption feature that until now was reserved for hyperspectral instruments. Being a satellite mission, this information comes at a lower spatial resolution than airborne hyperspectral data, but with a large spatial coverage and frequent revisit time. Full article

This paper uses the refined Generalized Split-Window (GSW) algorithm to derive the land surface temperature (LST) from the data acquired by the Visible and Infrared Radiometer on FengYun 3B (FY-3B/VIRR). The coefficients in the GSW algorithm corresponding to a series of overlapping ranges for the mean emissivity, the atmospheric Water Vapor Content (WVC), and the LST are derived using a statistical regression method from the numerical values simulated with an accurate atmospheric radiative transfer model MODTRAN 4 over a wide range of atmospheric and surface conditions. The GSW algorithm is applied to retrieve LST from FY-3B/VIRR data in an arid area in northwestern China. Three emissivity databases are used to evaluate the accuracy of different emissivity databases for LST retrieval, including the ASTER Global Emissivity Database (ASTER_GED) at a 1-km spatial resolution (AG1km), an average of twelve ASTER emissivity data in the 2012 summer and emissivity spectra extracted from spectral libraries. The LSTs retrieved from the three emissivity databases are evaluated with ground-measured LST at four barren surface sites from June 2012 to December 2013 collected during the HiWATER field campaign. The results indicate that using emissivity extracted from ASTER_GED can achieve the highest accuracy with an average bias of 1.26 and −0.04 K and an average root mean square error (RMSE) of 2.69 and 1.38 K for the four sites during daytime and nighttime, respectively. This result indicates that ASTER_GED is a useful emissivity database for generating global LST products from different thermal infrared data and that using FY-3B/VIRR data can produce reliable LST products for other research areas. Full article

This study presents preliminary results of the validation of the Moderate Resolution Imaging Spectroradiometer (MODIS) daily LST products (MOD/MYD11A1, Version 5) using longwave radiation ground measurements obtained at 12 stations in the North Arid and Semi-Arid Area Cooperative Experimental Observation Integrated Research program. In this evaluation process, the broadband emissivity at each station was obtained from the ASTER Spectral Library or estimated from the MODIS narrowband emissivity Collection 5. A comparison of the validation results based on those two methods shows that no significant differences occur in the short-term validation, and a sensitivity analysis of the broadband emissivity demonstrates that it has a limited effect on the evaluation results. In general, the results at the 12 stations indicate that the LST products have a lower accuracy in China’s arid and semi-arid areas than in other areas, with a mean absolute error of 2–3 K. Compared with the temporal mismatch, the spatial mismatch has a stronger effect on the validation results in this study, and the stations with homogeneous land cover have more comparable MODIS LST accuracies. Comparisons between the stations indicate that the spatial mismatch can increase the influence of the temporal mismatch. Full article

The potentials of the forthcoming new European Space Agency’s (ESA) satellite sensor, Sentinel-2, for archaeological studies was examined in this paper. For this reason, an extensive spectral library of crop marks, acquired through numerous spectroradiometric campaigns, which are related with buried archaeological remains, has been resampled to the spectral characteristics of Sentinel-2. In addition, other existing satellite sensors have been also evaluated (Landsat 5 Thematic Mapper (TM); Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER); IKONOS; Landsat 4 TM; Landsat 7 Enhance Thematic Mapper Plus (ETM+); QuickBird; Satellite Pour l’Observation de la Terre (SPOT); and WorldView-2). The simulated data have been compared with the optimum spectral regions for the detection of crop marks (700 nm and 800 nm). In addition, several existing vegetation indices have been also assessed for all sensors. As it was found, the spectral characteristics of Sentinel-2 are able to better distinguish crop marks compared to other existing satellite sensors. Indeed, as it was found, using a simulated Sentinel-2 image, not only known buried archaeological sites were able to be detected, but also other still unknown sites were able to be revealed. Full article