Advanced Spaceborne Thermal Emission And Reflection Radiometer Research Articles

The use of ASTER and Landsat Remote Sensing data for exploring hydrothermal Mineral in Arabian-Nubian Shield

Multispectral remote sensing data provided valuable information for hydrothermal minerals exploration. In this article, Landsat-8 Operational Land Imager (OLI) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were processed and analyzed using band ratio and principal component techniques to demarcate areas of hydrothermal mineral resources by highlighting the most influenced zone of hydrothermal alteration (high-grade) in Egypt. The area is a part of the Arabian-Nubian Shield, covering 1068 km2, and comprises wide varieties of Pre- and Pan-African rock assemblages. Preparing and integrating thematic layers consisting of the grade of hydrothermal intensity in a GIS technique supported generating a hydrothermal mineral predictive map (HMPM) using a multi-criteria decision-making technique. The plausible zone of very high mineral resources covers about 6.57 %. Analysis of ASTER data revealed that this zone is reliable with phyllic and argillic minerals and iron-oxides. The results of OLI data helped in demarcating potential areas of hydrothermal alteration areas (Al-OH) mixed with iron oxides. Field observations and investigations enabled the validation of the HMPM. The spatial distribution of hydrothermal zones throughout the study was reliable to that observed in the mine areas. Field and ore microscopic examination verified the existence of Au-sulfide minerals connected with hydrothermal alteration zones (HAZs) in different areas e.g., Abu-Marawat, Gidami, Semna, Abu-Gaharish, and Hamama mines. Overall, integrating multi-spectral data through overlay analysis techniques can provide valuable information about hydrothermal mineral resources in an accurate and cost-effective approach.

Geological mapping and mineral exploration in Francistown region, north east Botswana from integration of ASTER TIR and aeromagnetic data

To map the region's geology and mineral exploration, this study looked at how remote sensing satellite imaging from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Thermal Infrared (TIR) sensor and aircraft geophysics may be used. The Francistown region forms part of the Zimbabwe craton, northeast of Botswana. It comprises felsic rock types, including granite and dry river sand, mafic-ultramafic rocks, and gneiss country rock. Based on their spectral emissivity responses, mafic-ultramafic units and quartz-rich units were discovered. These units are important targets for the discovery of mineral resources. The research region can be described as convoluted, fractured, intruded, metamorphosed, and ultimately worn from a geological and geophysical standpoint. Several remote sensing and aeromagnetic approaches were employed. The false color composite (FCC) and mineral indices using ASTER TIR data provided helpful information for delineating the area's geology and concentration of major-rock-forming minerals. The aeromagnetic data extracted lineaments well, and the Center for Exploration Target (CET) grid analysis system was very helpful. The CET Porphyry Analysis technique was applied in mapping near-circular porphyry features, which are good indicators of possible sites with high mineralization potential. The analysis of the lineament map shows that mineral deposits are mostly along the NE-SW direction. The results from the ASTER TIR and aeromagnetic enhancements indicate the occurrence of mafic-ultramafic rocks and felsic-rich units, which have been documented to host mineralization in the region. Porphyry structures are also mapped successfully along the general strike of the geological structure in the study area. The combination of these methods proved their success, and they can be used further in other regions of high mineral potential and lithological mapping for their cost efficiency and reliability.

Evaluating ASTER data and field spectrometry for lithological discrimination in semi-arid region, Northeast Kohat Plateau, Pakistan

This study employs data of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor to delineate and map the distribution of sedimentary lithologies in the semi-arid region of Kohat Plateau, Pakistan. False color composites (FCC) and various image transformation and enhancement techniques including the principal component analysis (PCA), minimum noise fraction (MNF), and band rationing (BR) were used successfully to differentiate four lithological classes. These lithologies include chemically/biochemically formed beds of the marine environment and detrital sequences of marginal marine to the riverine environment. FCC from original reflectance data, PCA, and BR techniques displayed more prominent lithological variation. To map the lithology and show the potential of ASTER data, field spectrometry over the barren lithologies was carried out. The end-member spectra from field spectrometry shows strong agreement with the pixels spectra from ASTER scene. The Spectral Angle Mapper (SAM) mapping method was then used to produce a classified lithological map, where the image pixels spectra proved more suitable reference, in comparison to the end-member spectra. The accuracy of the classified lithological map was evaluated based on field-based point data, which resulted an overall accuracy of 70% and a Kappa coefficient value of 0.679. Carbonates and evaporites showed relatively higher user and producer accuracies which are attributed to their topographic behavior and weathered scree over the adjacent rock unit. The final lithological map provided a clearer picture of surface geology where the existing geological maps lacked lithological continuity.

Geochemical characteristics and mapping of Reşadiye (Tokat-Türkiye) bentonite deposits using machine learning and sub-pixel mixture algorithms

Reşadiye bentonite deposits, which play a significant role in Türkiye's bentonite production, are situated in Central Anatolia. Geochemical, mineralogical, and remote sensing data have been integrated to map the spatial distribution of clay minerals in the bentonite deposits and argillic areas. It is hypothesized that the bentonite samples occurred by the in-situ diagenetic alteration of rhyolite-dacite, trachyte, and andesite/basaltic andesitic composition pyroclastic rocks (ash-flow tuff). Biotite, clinoptilolite, calcite, dolomite, K-feldspar, opal-CT, quartz, and clay minerals are detected in most bentonite samples. The clay patterns determined in the bentonite samples in the X-ray diffraction (XRD) diagrams were 12.3–12.6 Å and were interpreted as being rich in Na-smectites. Mineral mapping in these deposits is essential for mining operations since the high-grade bentonite deposits can be affected by the other clay, gang, and ore minerals they contain in addition to the smectite. The sample spectra measurements matched montmorillonite and kaolin/smectite spectra. This study tests support vector machine (SVM) and artificial neural network (ANN) machine learning and MTMF subpixel algorithms in lithology and mineral mapping in Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data. It combines the power of subpixel unmixing algorithms to determine the distribution of clay and high-grade bentonites in argillic areas discriminated by machine learning. The results showed that the SVM algorithm can map better than ANN for argillic areas. Additionally, the distribution of high-grade bentonite and kaolin/smectite bearing sites in the study area is discriminated by the mixture-tuned matched filtered (MTMF) spectral classification method. As a result, this study shows that remote sensing studies can be utilized for the exploration and monitoring of high-grade bentonite sites during and/or post-mining operations.

Assessment of potential cobalt zones by modeling radiometric and ASTER data using the AHP method: A case of the Bou Azzer El Grara inlier, Central Anti-Atlas, Morocco

The Bou Azzer El Grara inlier belongs to the Central Anti-Atlas range, renowned for its mineral deposits. The study carried out on the inlier aims to identify potential areas of cobalt mineralization based on a multi-source methodology of radiometric and remote sensing data processing. The data used includes hydrothermal alteration minerals (argillic, phyllic, propylitic, iron oxides) mapped with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery using band ratio and principal component analysis (PCA) techniques, potassium enrichment zones through K/eTh and K/eU ratios calculated from radiometric data, as well as geological data (serpentinite outcrops and geological faults). The analytic hierarchy process (AHP) was used to combine the various thematic layers. Validation was carried out through the field studies in several anomalous zones. The mineral prospectivity map generated highlights nine major zones that appear the most favorable for mineralization, some of which coincide with zones in production and others requiring further exploration.

Vertical Accuracy Assessment of the ASTER, SRTM, GLO-30, and ATLAS in a Forested Environment

Understory topography serves as a crucial data source, playing an instrumental role in numerous forest ecosystem applications. However, the use of synthetic aperture radar interferometry and optical stereo for the acquisition of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), SRTM (Shuttle Radar Topography Mission), and GLO-30 (Copernicus Digital Elevation Model) DEM presents unique challenges, particularly in forested environments. These challenges are primarily due to limitations in penetration capability and the effects of foreshortening. ICESat-2/ATLAS, with its higher spatial sampling rate and strong penetrability, presents a new opportunity for estimating forest height parameters and understory terrain. We assessed the vertical accuracy of ASTER, SRTM, GLO-30, and ATLAS in the forest study areas of the United States compared to the reference dataset DTM provided by G-LiHT and we will further discuss the influence of different ground altitudes, forest types, slopes, and aspects on vertical accuracy. The study reveals that in a forested environment, ICESat-2 ATL03 exhibits the highest accuracy at the footprint scale, with a correlation coefficient (R2) close to 1 and Root Mean Square Error (RMSE) = 1.96 m. SRTM exhibits the highest accuracy at the regional scale, with an R2 close to 0.99, RMSE = 11.09 m. A significant decrease in accuracy was observed with increasing slope, especially for slopes above 15°. With a sudden increase in altitude, such as in mountainous situations, the accuracy of vertical estimation will significantly decrease. Aspect and forest cover indeed influence the accuracy of the four DEM products, but this influence lacks a clear pattern. Our results show that ICESat-2 and SRTM data might show sufficient and stable vertical accuracy in a forested environment.

Machine Learning-Based Lithological Mapping from ASTER Remote-Sensing Imagery

Accurately mapping lithological features is essential for geological surveys and the exploration of mineral resources. Remote-sensing images have been widely used to extract information about mineralized alteration zones due to their cost-effectiveness and potential for being widely applied. Automated methods, such as machine-learning algorithms, for lithological mapping using satellite imagery have also received attention. This study aims to map lithologies and minerals indirectly through machine-learning algorithms using advanced spaceborne thermal emission and reflection radiometer (ASTER) remote-sensing data. The capabilities of several machine-learning (ML) algorithms were evaluated for lithological mapping, including random forest (RF), support vector machine (SVM), gradient boosting (GB), extreme gradient boosting (XGB), and a deep-learning artificial neural network (ANN). These methods were applied to ASTER imagery of the Sar-Cheshmeh copper mining region of Kerman Province, in southern Iran. First, several spectral features that were extracted from ASTER bands were used as input data. Second, correlation coefficients between the original spectral bands and features were extracted. The importance of the random forest features (RF’s feature importance) was subsequently computed, and features with less importance were removed. Finally, the remained features were given to the models as input data in the second scenario. Accuracy assessments were performed for lithological classes in the study region, including Sar-Cheshmeh porphyry, quartz eye, late fine porphyry, hornblende dike, granodiorite, feldspar dike, biotite dike, andesite, and alluvium. The overall accuracy results of lithological mapping showed that ML-based algorithms without feature extraction have the highest accuracy. The overall accuracy percentages for ML-based algorithms without conducting feature extraction were 84%, 85%, 80%, 82%, and 80% for RF, SVM, GB, XGB, and ANN, respectively. The results of this study would be of great interest to geologists for lithological mapping and mineral exploration, particularly for selecting appropriate ML-based techniques to be implemented in similar regions.

Spectral mixture analysis of intimate mixtures for lithological mapping

Spectral mixing is frequently encountered in remotely sensed images while imaging heterogeneous surfaces. Spectral mixing occurs at the macroscopic level due to the sensor’s low spatial resolution or multiple reflections from materials received by the sensor. Due to the mixing effect, retrieving the information of spectral endmembers becomes complicated. In this study, two different unmixing methods: (i) linear mixing models (LMM) and (ii) the Hapke nonlinear mixing model, were used to quantitatively estimate the fractional abundance of various rock types that will be helpful in remotely mapping the mineral prospects. Three types of constraints using the least square approach were applied independently to LMM and Hapke models, namely FCLS (sum to unity and nonnegativity constrained), NNLS (nonnegativity constrained), and UCLS (unconstrained). AVIRIS-NG (Airborne Visible and InfraRed Imaging Spectrometer-Next Generation) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) images from the Sittampundi anorthosite complex (SAC) were utilised for the spectral mixture analysis. Few studies are available on the anorthosite rocks of Sittampundi as an analogue to the Lunar anorthosite. The study area is known for its chromite deposits occurring in pyroxenite rocks. Therefore, in the present study, endmembers considered are anorthosite and pyroxenite/metagabbro.The moderately dense vegetation cover of the study area obscures the detection of rock outcrops on the surface. The study area exhibits nonlinear spectral mixing effects due to the vegetation cover. Hence apart from the two rock endmembers, vegetation is also considered as an endmembers in spectral mixture analysis. The results show a high fractional abundance of vegetation and anorthosite throughout the study region, with the pyroxenite/metagabbro mainly concentrated over the western and eastern regions. The Hapke model shows more accurate results than LMM on the AVIRIS-NG image. The Hapke model outperforms LMM in accurately retrieving the fractional abundance of endmembers due to the nonlinear spectral mixing effects caused by vegetation. Additionally, the constrained least squares methods, i.e., FCLS and NNLS, performed better than UCLS in linear and nonlinear mixing models, resulting in accurate and reliable abundance maps. AVIRIS-NG outperformed the ASTER images in fractional abundance mapping of endmembers, highlighting the importance of utilising higher spatial and spectral resolution data for more reliable land cover and lithology mapping. Overall, this study demonstrates the potential of spectral mixture analysis in identifying geologic outcrops in vegetation-covered regions. Hence, the methodology presented in this study benefits the lithological mapping of vegetation-covered regions and has wide applications in quantitatively assessing the lithology of mineral prospects.

Intercomparison of Landsat Operational Land Imager and Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer Radiometric Calibrations Using Radiometric Calibration Network Data

This paper presents a comprehensive intercomparison study investigating the radiometric performance of and concurrence among the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat 8 Operational Land Imager (L8 OLI), and Landsat 9 OLI (L9 OLI) instruments. This study leverages data sourced from the Radiometric Calibration Network (RadCalNet) and focuses on spectral bands relevant for vegetation analysis and land cover classification, encompassing a thorough assessment of data quality, uncertainties, and underlying influencing factors. This study’s outcomes underscore the efficacy of RadCalNet in evaluating the precision and reliability of remote sensing data, offering valuable insights into the strengths and limitations of ASTER, L8 OLI, and L9 OLI. These insights serve as a foundation for informed decision making in environmental monitoring and resource management, highlighting the pivotal role of RadCalNet in gauging the radiometric performance of remote sensing sensors. Results from RadCalNet sites, namely Railroad Valley Playa and Gobabeb, show their possible suitability for sensors with spatial resolutions down to 15 m. The results indicate that the measurements from both ASTER and OLI closely align with the data from RadCalNet, and the observed agreement falls comfortably within the total range of potential errors associated with the sensors and the test site information.

Co-registration and residual correction of digital elevation models: a comparative study

Abstract. Digital elevation models (DEMs) are currently one of the most widely used data sources in glacier thickness change research, due to the high spatial resolution and continuous coverage. However, raw DEM data are often misaligned with each other, due to georeferencing errors, and a co-registration procedure is required before DEM differencing. In this paper, we present a comparative analysis of the two classical co-registration methods proposed by Nuth and Kääb (2011) and Rosenholm and Torlegard (1988). The former is currently the most commonly used method in glacial studies, while the latter is a seminal work in the photogrammetric field that has not been extensively investigated by the cryosphere community. Furthermore, we also present a new residual correction method using a generalized additive model (GAM) to eliminate the remaining systematic errors in DEM co-registration results. The performance of the two DEM co-registration methods and three residual correction algorithms (the GAM-based method together with two parametric-model-based methods) was evaluated using multiple DEM pairs from the Greenland Ice Sheet and mountain glaciers, including Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEMs, ZiYuan-3 (ZY-3) DEMs, the Shuttle Radar Topography Mission (SRTM) DEM, and the Copernicus DEM. The experimental results confirm our theoretical analysis of the two co-registration methods. The method of Rosenholm and Torlegard has a greater ability to remove DEM misalignments (an average of 4.6 % and 13.7 % for the test datasets from Greenland Ice Sheet and High Mountain Asia, respectively) because it models the translation, scale, and rotation-induced biases, while the method of Nuth and Kääb considers translation only. The proposed GAM-based method performs statistically better than the two residual correction methods based on parametric regression models (high-order polynomials and the sum of the sinusoidal functions). A visual inspection reveals that the GAM-based method, as a non-parametric regression technique, can capture complex systematic errors in the DEM co-registration residuals.

Lithological Discrimination And Mapping Using Aster Swir Data In The Tundi Area, Jharkhand

ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) multispectral data is utilized in mineralogical and lithological mapping along with alteration zone mapping. The present study is carried out using ASTER data, which has shown the effectiveness SWIR spectral bands of ASTER data in lithological mapping. Field validation is carried out in the areas where lithological updation was expected from the sspectral analysis. From the band ratios, argillic, ferric, phyllic and propylytic alteration zones were demarcated. Thus, ASTER SWIR bands coupled with the advanced image enhancement techniques are recommended as a rapid and cost effective tool for lithological discrimination and mapping in the hostile terrain of Dhanbad and other geological areas.

Snow Avalanche Hazard Mapping Using a GIS-Based AHP Approach: A Case of Glaciers in Northern Pakistan from 2012 to 2022

Snow avalanches are a type of serious natural disaster that commonly occur in snow-covered mountains with steep terrain characteristics. Susceptibility analysis of avalanches is a pressing issue today and helps decision makers to implement appropriate avalanche risk reduction strategies. Avalanche susceptibility maps provide a preliminary method for evaluating places that are likely to be vulnerable to avalanches to stop or reduce the risks of such disasters. The current study aims to identify areas that are vulnerable to avalanches (ranging from extremely high and low danger) by considering geo-morphological and geological variables and employing an Analytical Hierarchy Approach (AHP) in the GIS platform to identify potential snow avalanche zones in the Karakoram region in Northern Pakistan. The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) was used to extract the elevation, slope, aspect, terrain roughness, and curvature of the study area. This study includes the risk identification variable of land cover (LC), which was obtained from the Landsat 8 Operational Land Imager (OLI) satellite. The obtained result showed that the approach established in this study provided a quick and reliable tool to map avalanches in the study area, and it might also work with other glacier sites in other parts of the world for snow avalanche susceptibility and risk assessments.

Site Suitability Analysis Using GIS for a Renal Hospital in Urban Canacona, Goa, India

Site suitability analysis is a strategic approach for identifying an optimal location for a specific activity through an examination of the current site. To conduct a comprehensive site analysis, various factors, including administrative data, topographical characteristics, and thorough research on land use and land cover, may be taken into account. This study aims to propose the most fitting site for establishing a renal hospital in Canacona, where a notable increase in renal disorders has been observed in recent years. The study is prompted by the local population’s demand for healthcare services and the absence of adequate existing healthcare infrastructure. To compile the primary dataset, GPS survey and on-site observations were conducted to gather the geographical coordinates of existing hospitals and pharmacies. The secondary dataset includes satellite images obtained from Landsat 8 (OLI) to create a Land Use and Land Cover (LULC) map, as well as data from the Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) for generating a Digital Elevation Model (DEM). These datasets encompass various parameters such as DEM, LULC, road networks, existing hospitals, and pharmacies, all of which were utilized in the development of the site suitability model. The process involves assigning values to these parameters, and subsequently, a weighted overlay analysis was employed to determine suitable areas for the proposed renal hospital. The results were categorized into four classes: undesirable, less suitable, moderately suitable, and most suitable sites. After careful analysis, the optimal location for the renal hospital in Canacona was identified, spanning an area of approximately 1.55527 hectares.

Improving HJ-1B/IRS LST Retrieval of the Generalized Single-Channel Algorithm with Refined ERA5 Atmospheric Profile Database

Land surface temperature (LST) is a fundamental variable of environmental monitoring and surface equilibrium. Although the HJ-1B infrared scanner (IRS) has accumulated many observations, further application of HJ-1B/IRS is limited by the lack of LST products. This study refined the ERA5 atmospheric profile database, instead of the widely used traditional TIGR atmospheric profile database, and simulated the coefficients of the generalized single-channel (GSCs) algorithms to improve LST retrieval. GSCs can be divided into the GSCw and GSCwT algorithms, depending on whether the input is atmospheric water vapor content (WVC) or in situ near-surface air temperature and WVC. Land surface emissivity (LSE) was obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) and vegetation/snow cover products. Then, the retrieved LSTs were evaluated using the LSTs from the RTE algorithm, TIGRw/TIGRwT profiles, and in situ near-surface air temperature from the HiWATER experiment in China from 2012 to 2014. The bias (root mean square error (RMSE)) values are displayed as ERA5wT < RTE < ERA5w < TIGRwT < TIGRw. The accuracy of ERA5wT, with a bias (RMSE) of 0.02 K (2.30 K), is higher than that of RTE, with a bias (RMSE) of 0.74 K (2.47 K). The accuracy of RTE is preferable to that of ERA5w, with a bias (RMSE) of 0.89 K (2.48 K), followed by TIGRwT, with a bias (RMSE) of −1.18 K (2.50 K), and then, TIGRw, with a bias (RMSE) of 1.60 K (2.77 K). In summary, the accuracy of LST obtained by GSC from the refined ERA5 atmospheric profiles is higher than that obtained from the TIGR profiles. The accuracy of LST obtained by GSCwT is greater than that obtained by GSCw. The accuracy of LST obtained using in situ near-surface air temperature is higher than that obtained using ERA5 air temperature. The accuracy of LSEASTER is slightly better than that of LSEMOD21. The aforementioned conclusions can provide scientific support to generate HJ-1B/IRS LST products.

Analysis of the Controlling Effect of Excess Topography on the Distribution of Coseismic Landslides during the Iburi Earthquake, Japan, on 6 September 2018

Coseismic landslides cause changes in the hillside material, and this erosion process plays an important role in the evolution of the topography. Previous studies seldom involved research on the influence of excess topography on the occurrences of coseismic landslides. The Iburi earthquake, which occurred in Japan on 6 September 2018 and triggered a large number of landslides, provided a research example to explore the relationship between coseismic landslides and excess topography. We used the average slope of the lithology as the threshold slope of the corresponding stratum to calculate the excess topography of the different lithological units. Based on the advanced spaceborne thermal emission and reflection radiometer (ASTER) digital elevation model (DEM) with a resolution of 30 m, a quantitative analysis was conducted on the excess topography in the study area. The results indicate that the excess topography in the study area was mainly distributed in the valleys on both sides of the river, and the thickness of the excess topography on the high and steep ridges was generally greater than that at the foot of the slope, which has a relatively flat topography or a low elevation. In the area affected by the earthquake, approximately 94.66% of the coseismic landslides (with an area of approximately 28.23 m2) developed in the excess topography area, indicating that the distribution of the excess topography had a strong controlling influence on the spatial distribution of the coseismic landslides. The Iburi earthquake mainly induced shallow landslides, but the thickness of the landslide body was much smaller than the excess topography height in the landslides-affected area. This may imply that the excess topography was not completely removed by the coseismic landslides, and the areas where the earthquake landslides occurred still have the possibility of producing landslides in the future.

Locating Potential Run-of-River Hydropower Sites by Developing Novel Parsimonious Multi-Dimensional Moving Window (PMMW) Algorithm with Digital Elevation Models

We developed a Parsimonious Multi-dimensional Moving Window (PMMW) algorithm that only requires Digital Elevation Model (DEM) data of a watershed to efficiently locate potentially optimal hydropower sites. The methodology requires only open source DEM data; therefore, it can be used even in remotest watersheds of the world where in situ measurements are scarce or not available at all. We used three parameters in this algorithm, and tested the method using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Shuttle Radar Topography Mission (SRTM) derived DEMs. Our case study on the Morony Watershed, Montana, USA shows that (1) along with 6 out of the 7 existing hydropower plants being successfully located, 12 new potential hydropower sites were also identified, using a clearance of 1 km, diversion of 90 m, and Hydropower Index (HI) threshold of 109 m as the criteria. For the 12 new potential hydropower sites, 737.86 Megawatts (MW) ± 84.56 MW untapped hydropower potential in the Morony Watershed was also derived; (2) SRTM DEM is more suitable for determining the potential hydropower sites; (3) although the ASTER and SRTM DEMs provide elevation data with high accuracy, micro-scale elevation differences between them at some locations may have a profound impact on the HI.

Lithological mapping of northern Kohat Plateau’s limestone outcrops using integrated remote sensing and reflectance spectroscopy techniques

ABSTRACT Remote sensing (RS) has emerged as a valuable technology for precisely demarcating the extent of lithological units in rugged terrains. It offers valuable insights into the spatial distribution and structural dynamics of complex topographies. In this study, RS technology manifested by Sentinel-2A and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery is used to discriminate carbonaceous strata in various parts of the Kohat Plateau region, northwest Pakistan. Various spectral image transformation techniques, including principal component analysis (PCA), minimum noise fraction (MNF), band ratio (BR), and carbonate index (CI), were utilized after atmospheric correction modules. The resultant red-green-blue (RGB) color composites obtained from Sentinel-2A, incorporating PCA (PC1, PC2, PC4) and MNF (MNF1, MNF2, MNF3), demonstrated its applicability to accurately discriminate the target features. The derived RGB color composites from the BR on ASTER data (BR5/1, BR5/7, BR3/4 × 5/4) and CI B13/B14 effectively identified the limestone bodies, providing a better approach to differentiate limestone from other lithological units. Also, the integration of spectroscopic field data with satellite-based resultant images represented a meaningful contribution to rectifying previous geological maps. The derived maps from aforementioned techniques exhibited a notable attribute to record the target units and enhance limestone differentiation from other lithologies.

Space based Earth observation approach for exploring hydrocarbon prospects in an intracratonic rift basin – A study in north Cambay basin, Gujarat

In the present study, we carried out a conjugate analysis of Landsat-8 Operational Land Imager (OLI), Sentinel-2B Multi-Spectral Instrument (MSI), Advanced Land Observation Satellite Phased Array L-band SAR (ALOS PALSAR), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over a study area of northern Cambay Basin to delineate prospective areas for hydrocarbon exploration. In this regard, we utilized feature-oriented principal component (PC) images of Landsat OLI data, PC images of Sentinel-2B visible and near-infrared (VNIR)-short-wave infrared (SWIR) bands, thresholded Cartosat-1 Digital Elevation Model (DEM), and polarization image composite of ALOS PALSAR data to delineate geomorphic and lineament features in the study area. Most of the geomorphic features, such as scroll plain, gullies, elevated pediment, circular drainage anomalies, etc., indicate localized structural uplift. Geomorphic anomalies and lineaments, essential for identifying structural controls of near-surface hydrocarbon accumulation, were identified using enhanced satellite data. Geophysical anomalies derived from gravity datasets supplemented the presence and down-depth continuity of geological structures. Spectral profiles of calcrete-bearing soil samples were used to identify calcrete-rich zones in the optical remote sensing image data as a proxy owing to field evidence of close association of calcrete-rich soils with known subsurface hydrocarbon accumulations. Sub-pixel mapping algorithm Constrained Energy Minimization (CEM) applied to SWIR bands of ASTER data was used to delineate surface proxies of near-surface hydrocarbon seepage. Isopach maps of shallowest reservoir formations (Mid-/ Late Eocene) were used as indicators of favorable near-surface hydrocarbon accumulation.Earth observation-based geomorphic, lineament, and spectral anomalies were integrated with supplementary datasets, such as reservoir isopach and absorbed gas anomalies, as controls using the weighted summation method for quantitative deduction of the hydrocarbon potential map of the study area. The weights for input variables or parameters (geomorphic, spectral, geochemical, etc.) were inferred based on spatial associations of these anomalous units/variables with the existing oil & gas well locations. The identified potential map is in broad synergy with the field observations on localized uplift, geo-botanical anomalies, and calcrete-rich zones within the extent of high & moderate hydrocarbon potential areas.

A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015

Abstract. The spatio-temporal variation of surface water storage (SWS) in the Congo River basin (CRB), the second-largest watershed in the world, remains widely unknown. In this study, satellite-derived observations are combined to estimate SWS dynamics at the CRB and sub-basin scales over 1992–2015. Two methods are employed. The first one combines surface water extent (SWE) from the Global Inundation Extent from Multi-Satellite (GIEMS-2) dataset and the long-term satellite-derived surface water height from multi-mission radar altimetry. The second one, based on the hypsometric curve approach, combines SWE from GIEMS-2 with topographic data from four global digital elevation models (DEMs), namely the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing Satellite (ALOS), Multi-Error-Removed Improved Terrain (MERIT), and Forest And Buildings removed Copernicus DEM (FABDEM). The results provide SWS variations at monthly time steps from 1992 to 2015 characterized by a strong seasonal and interannual variability with an annual mean amplitude of ∼101±23 km3. The Middle Congo sub-basin shows a higher mean annual amplitude (∼71±15 km3). The comparison of SWS derived from the two methods and four DEMs shows an overall fair agreement. The SWS estimates are assessed against satellite precipitation data and in situ river discharge and, in general, a relatively fair agreement is found between the three hydrological variables at the basin and sub-basin scales (linear correlation coefficient >0.5). We further characterize the spatial distribution of the major drought that occurred across the basin at the end of 2005 and in early 2006. The SWS estimates clearly reveal the widespread spatial distribution of this severe event (∼40 % deficit as compared to their long-term average), in accordance with the large negative anomaly observed in precipitation over that period. This new SWS long-term dataset over the Congo River basin is an unprecedented new source of information for improving our comprehension of hydrological and biogeochemical cycles in the basin. As the datasets used in our study are available globally, our study opens opportunities to further develop satellite-derived SWS estimates at the global scale. The dataset of the CRB's SWS and the related Python code to run the reproducibility of the hypsometric curve approach dataset of SWS are respectively available for download at https://doi.org/10.5281/zenodo.7299823 and https://doi.org/10.5281/zenodo.8011607 (Kitambo et al., 2022b, 2023).

Statistical retrieval of volcanic activity in long time series orbital data: Implications for forecasting future activity

Several high spatial resolution thermal infrared (TIR) missions are planned for the coming decade and their data will be crucial to constrain volcanic activity patterns throughout pre- and post-eruption phases. Foundational to these patterns is the subtle (1−2 K) thermal behavior, which is easily overlooked using lower spatial resolution data. In preparation for these new data, we conducted the first study using the entire twenty-two-year archive of higher spatial, lower temporal resolution TIR data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. This archive presents a unique opportunity to quantify low-magnitude temperature anomalies and small plumes over long time periods. We developed a new statistical algorithm to automatically detect the full range of thermal activity and applied it to >5000 ASTER scenes of five volcanoes with well-documented eruptions. Unique to this algorithm is its ability to use both day and night data, account for clouds, quantify accurate background temperatures, and dynamically scale depending on the anomaly size. Results improve upon those from the more commonly used lower spatial resolution data, despite the less frequent temporal coverage of ASTER, and show that high spatial resolution TIR data are equally as effective. Significantly, the smaller, subtle thermal detections served as precursory signals in ∼81% of eruptions, and the algorithm's results create a framework for classifying future eruptive styles.