Advanced Spaceborne Thermal Emission And Reflection Radiometer Research Articles

Elevation-Dependent Snow Cover Dynamics and Associated Topo-Climate Impacts in Upper Indus River Basin

In the present study, Improved Moderate Resolution Imaging Spectro-radiometer (MODIS) snow cover product (MOYDGL06*) has been used to evaluate the snow cover area (SCA) in Kabul, Jhelum, and Indus river basins for the time period of 2003 – 2020 with available MODIS land surface temperature (LST), and CHIRPS (precipitation) with objectives to evaluate the spatio-temporal SCA, and climate variables with respect to different elevations analyzed from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 3 (GDEM v3) and also to correlate the climatic variables with SCA. The results presented average annual SCA is around 50.7% – 64.7% in sub-basins of UIB, further it has been observed that SCA is decreasing on annual and seasonal timescale in all three basins. Elevation-dependent SCA, temperature, and precipitation presented a mix of trend on annual, seasonal, and monthly timescale at lower and higher altitude in all selected basins. Moreover, it was noticed that topography (slope, & aspect) also influences the SCA in the region. Furthermore, it has been examined that temperature has significant inverse relationship with SCA at middle and higher altitude in Indus, while in Kabul, and Jhelum no significant relationship observed at extreme lower and higher altitudes. It is also evident from relationship between SCA and climate variable that temperature is significantly responsible for decreasing trend of SCA rather than intense precipitation in all three river basins. Thus, all these elevation-dependent changes can improve our hydrological understanding which can have a considerable implication for hydrology, climate science, water resource management and socio-economic activities.

Morphometric analysis and watershed delineation of the Karnaphuli river basin: A comparative study using different DEMs in Chittagong, Bangladesh

AbstractThe present study aimed to delineate a robust watershed boundary and extract its morphometric parameters in the Karnaphuli watershed, Bangladesh, using different digital elevation models (DEMs). Two DEMs, the shuttle radar topographic mission (SRTM) and the terra advanced spaceborne thermal emission and reflection radiometer (ASTER), were employed to delineate watershed boundaries and evaluate various morphometric characteristics. Raster data such as fill sinks, flow direction, and flow accumulation were utilized to delineate the watershed. The morphometric analysis included the estimation of linear, areal, and relief parameters. The findings revealed a noticeable difference between the datasets; ASTER delineated a larger watershed area than SRTM. Regarding stream order, there were streams ranging from 1st to 6th order, encompassing numerous small, medium, and main channels of the river. Drainage density was calculated as 0.52 km/km² for SRTM and 0.51 km/km² for ASTER, both having a spatial resolution of 30 m. The bifurcation ratio ranged from 1.94 for SRTM to 2.45 for ASTER, indicating varying degrees of structural disturbance influenced by geological factors. The form factor indicated an elongated shape of the study area. The overall dimensions of the streams and the watershed extent suggest moderate mean annual rainfall discharge. The watershed exhibited relatively high denudation rates, attributed to the basin relief. A low drainage density indicated the importance of infiltration processes. The findings of this study provide valuable insights into the spatial variability of watershed characteristics, highlighting the need for tailored management strategies for the Karnaphuli River. Implementing targeted conservation measures and watershed management practices based on these morphometric parameters can enhance water resource sustainability and ecological health in the region.

Enhanced lithological mapping in arid crystalline regions using explainable AI and multi-spectral remote sensing data

Lithological classification is essential for understanding the spatial distribution of rocks, especially in arid crystalline areas. Artificial intelligence (AI) recent advancements with multi-spectral satellite imagery have been utilized to enhance lithological mapping in these areas. Here we employed different AI models namely, Support Vector Machine (SVM), Random Forest Classification (RFC), Logistic Regression, XGBoost, and K-nearest neighbors (KNN) for lithological mapping. This was followed by the application of explainable AI (XAI) for lithological discrimination (LD) which is still not widely explored. Based on the highest accuracy and F1 score of the previously mentioned models, RFC model outperformed all of them, and hence, it was integrated with XAI, using the SHapley Additive exPlanations (SHAP) method.This approach successfully identified critical multi-spectral features for LD in arid crystalline zones when applied on the Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and SRTM-DEM datasets covering the Hammash and the Wadi Fatimah areas in Egypt and the Kingdom of Saudi Arabia, respectively. Field validation in the Hammash area confirmed the RFC model's efficacy, achieving a satisfactory 94% overall accuracy for 18 features. SHAP was able to identify the top ten features for proper LD over the Hammash area with 90.3% accuracy despite the complex nature of the ophiolitic mélange. For validation purposes, RCF was then utilized in the Wadi Fatimah region, using only the top 10 critical features rendered from the SHAP analysis. It performed well and had 93% accuracy. Notably, XAI/SHAP results indicated that elevation data, Landsat-8's Green Band (B3), and the two ASTER SWIR bands (B5 and B6) were essential and significant for identifying island arc rocks. Moreover, the SHAP model effectively delineated complex mélange matrices, primarily using ASTER SWIR band (B8). Our findings highlight the successful combination of RFC with XAI for LD and its potential utilization in similar arid crystalline environments worldwide.

Analysis of radiative heat flux using ASTER thermal images: Climatological and volcanological factors on Java Island, Indonesia

This study focuses on analysing natural Radiative Heat Flux (RHF) anomalies to map out the heat distribution across the Java Island. Leveraging remote sensing techniques, we calculated natural RHF anomalies using Land Surface Temperature (LST) and Land Surface Emissivity (LSE) data obtained from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. A key aspect of our approach was distinguishing between natural and anthropogenic heat sources by cross-referencing the LST Map with the Land Use Land Cover (LULC) map of Java Island. The study interprets natural RHF anomalies by examining regional trends in non-volcanic areas and local trends within volcanic regions, considering climatological and volcanological factors. Relation with climatological factors involves assessing soil moisture parameters from Soil Moisture Active Passive (SMAP) data, precipitation from monthly Global Precipitation Measurement (GPM) data, and classifications according to the Köppen-Geiger climate schema. Our regional analysis reveals high natural RHF anomalies in the northern regions of West Java, parts of Central Java, and most of East Java, attributed to low soil moisture and low precipitation in savanna and monsoon climates. On a more localised scale, RHF values are significantly high in volcanic areas, particularly around Central and East Java's volcanoes, such as Mt. Merapi, Mt. Slamet, Mt. Semeru, the Sidoarjo Mud Volcano, and Mt. Ijen. The Natural RHF anomalies at volcanoes in West Java were identified as not being high except at Mt Patuha. These areas exhibit average natural RHF anomalies ranging between 32.22 W/m2 and 115.13 W/m2, indicating strong and intense volcanic activity. The insights obtained from these findings explain the overall thermal characteristics of Java Island and highlight the presence of subsurface thermal zones associated with volcanic activity and geothermal potential.

Evaluation of urban drainage capacity using different satellite image sources and SWMM

ABSTRACT This research aims to cost-effectively identify inundation due to combined rainfall with tidal surges by evaluating the existing urban drainage system. The Storm Water Management Model (SWMM) applied two scenarios based on different Digital Elevation Model (DEM) sources. These are scenario A, i.e. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and scenario B, i.e. Shuttle Radar Topography Mission (SRTM). Then, catchment delineation and SWMM parameterization in the low-lying urban region of Chattogram City were executed. In contrast to scenario A, i.e. ASTER DEM, identified inundated nodes in the northwest part of the city, scenario B, i.e. SRTM DEM, showed more inundated nodes. In both scenarios, drainage capacity was observed in the field at two outlet points of Ispahani Khal (R2 = 0.91 and 0.85) and Chaktai Khal (R2 = 0.90 and 0.92), which reasonably mimics the model outcome. With an intensive field dataset, this model could provide relevant information for betterment.

Land Surface Longwave Radiation Retrieval from ASTER Clear-Sky Observations

Surface longwave radiation (SLR) plays a pivotal role in the Earth’s energy balance, influencing a range of environmental processes and climate dynamics. As the demand for high spatial resolution remote sensing products grows, there is an increasing need for accurate SLR retrieval with enhanced spatial detail. This study focuses on the development and validation of models to estimate SLR using measurements from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Given the limitations posed by fewer spectral bands and data products in ASTER compared to moderate-resolution sensors, the proposed approach combines an atmospheric radiative transfer model MODerate resolution atmospheric TRANsmission (MODTRAN) with the Light Gradient Boosting Machine algorithm to estimate SLR. The MODTRAN simulations were performed to construct a representative training dataset based on comprehensive global atmospheric profiles and surface emissivity spectra data. Global sensitivity analyses reveal that key inputs influencing the accuracy of SLR retrievals should reflect surface thermal radiative signals and near-surface atmospheric conditions. Validated against ground-based measurements, surface upward longwave radiation (SULR) and surface downward longwave radiation (SDLR) using ASTER thermal infrared bands and surface elevation estimations resulted in root mean square errors of 17.76 W/m2 and 25.36 W/m2, with biases of 3.42 W/m2 and 3.92 W/m2, respectively. Retrievals show systematic biases related to extreme temperature and moisture conditions, e.g., causing overestimation of SULR in hot humid conditions and underestimation of SDLR in arid conditions. While challenges persist, particularly in addressing atmospheric variables and cloud masking, this work lays a foundation for accurate SLR retrieval from high spatial resolution sensors like ASTER. The potential applications extend to upcoming satellite missions, such as the Landsat Next, and contribute to advancing high-resolution remote sensing capabilities for an improved understanding of Earth’s energy dynamics.

Observations of the macrophysical properties of cumulus cloud fields over the tropical western Pacific and their connection to meteorological variables

Abstract. The poor representation of the macrophysical properties of shallow oceanic cumuli in climate models contributes to the large uncertainty in cloud feedback. These properties are also difficult to measure because it requires high-resolution satellite imagery that is seldomly collected over ocean. Here, we examine cumulus cloud macrophysical properties, their size, shape, and spatial distributions, over the tropical western Pacific using 170 15 m resolution scenes from Terra's Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) collected during the 2019 Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) mission. The average cloud fraction (CF) was 0.12, half of which was contributed by clouds less than 1.6 km in area-equivalent diameter. This compared well to Terra's Multi-angle Imaging SpectroRadiometer (MISR) resolution-corrected CF of 0.14 but less than the 0.19 measured by Terra's Moderate Resolution Imaging Spectroradiometer (MODIS). The cloud size distribution exhibited a power law form with an exponent of 2.93 and an area–perimeter power law with a dimension of 1.25. ASTER, MISR, and CAMP2Ex aircraft lidar showed excellent agreement in the cloud top height (CTH) distribution peak altitude of ∼ 750 m. We examined cumulus properties in relation to meteorological variables and found that the variation in mean CTH is controlled most by the total column water vapor, lower-tropospheric stability (LTS), and estimated inversion strength (EIS). The variation in CF is most controlled by surface wind speed and near-cloud relative humidity instead of LTS/EIS, suggesting the need to improve low-cloud parameterizations in climate models that use LTS/EIS based on stratocumulus studies.

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.

Characterizing SO2 Emission Rate, Thermal Anomalies, from Opened and Closed Vent System at Agung, Bromo, and Sinabung Volcanoes in Indonesia

Agung, Bromo, and Sinabung Volcanoes have high volcanic activity over the last decade, and have different eruption characteristics. Hence, it would be fascinating to study the characteristics of their volcanic activity patterns based on SO2 emission rates and thermal anomaly correlated with the seismicity data. The SO2 emission rate measurement was carried out using the Differential Optical Absorption Spectroscopy (DOAS), and calculated based on SO 2 column density, distance of measurement, wind speed, and wind direction. In addition, SO2 emission was detected using Ozone Monitoring Instrument (OMI) images with daily global coverage. Thermal anomaly detection was performed using Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) of Thermal Infrared (TIR) subsystem with high spatial resolution (90x90 m). ASTER TIR images were corrected for radiometric and thermal atmospheric. The emissivity and brightness temperature separation algorithm was applied to obtain surface temperature of Agung, Bromo, and Sinabung Volcanoes. All the data were correlated with the seismicity of each volcano. The SO2 emission rates correlate with the magma ascent to the shallow depth in an open system volcano (Bromo Volcano). In the closed-system volcanoes (early phase of Agung and Sinabung), SO2 emission was detected after the transition of closed to open system. Magmatic injection from the reservoir to the shallow depth was detected as thermal anomalies, such as in Agung Volcano. Whereas in Bromo Volcano, the thermal anomaly was insignificant since Bromo Volcano has an explosive eruption at a short period, so the ASTER image could not observe the thermal anomaly on the eruption time. Thermal anomaly pattern in Sinabung Volcano was the manifestation of new magmatic injection to the shallow depth. Therefore, their increase serves as indicators for the increasing magmatic activity prior to the eruptions. Keywords: SO2 emission rate, thermal anomaly, DOAS, OMI, ASTER, Open Vent, Closed Vent