Advanced Spaceborne Thermal Emission And Reflection Radiometer Research Articles

Assessment of the total lightning flash rate density (FRD) in northeast Brazil (NEB) based on TRMM orbital data from 1998 to 2013

The Northeast region of Brazil (NEB) concentrates on average 18 % of the total deaths associated with lightning strikes in Brazil. When considering population, the state of Piauí had the highest mortality rate in the region (1.8 deaths million−1), much higher than the national rate (0.8) and the NEB rate (0.5). This work aimed to evaluate the space-time distribution of total lightning (intracloud, cloud-to-cloud and cloud-to-ground) in NEB, covering the period from 1998 to 2013. For this purpose, we used data from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measuring Mission (TRMM) satellite, which provided information on the occurrence of total lightning, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor aboard on TERRA satellite, which provided the terrain elevation data to verify the influence of topography on the flash rate density (FRD). The full distribution was used to identify the hotspots (cities with the highest FRD), while the monthly distribution helped in the cluster analysis. NEB has great spatial and temporal variability of the recorded lightning rates, with average of 0–44.5 flash km-2 year−1. The regions with high total lightning rates are located in the states of Piauí, Maranhão and west of Bahia. The topography of the region seems to act as a facilitator of the convective process, leading to the formation of intense upward currents, essential for the generation of electric charges inside thunderstorms. CAPE values showed good relationship with lightning occurrence in the region. The cluster with the highest occurrence of lightning and hotspots is in the region of influence of the Intertropical Convergence Zone (ITCZ) and Mesoscale Convective Systems, suggesting an important relationship with large organized cloud systems.

ASTER and WorldView-3 satellite data for mapping lithology and alteration minerals associated with Pb-Zn mineralization

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (WV-3) satellite remote sensing data were used for mapping lithological units and hydrothermal alteration zones associated with Pb-Zn mineralization in the Kerman–Kashmar Tectonic Zone (KKTZ), Iran. The visible near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) bands of ASTER were used to map iron oxide/hydroxides, Al-OH minerals, Fe,Mg-OH minerals, quartz and carbonate minerals. The VNIR bands of WV-3 were used to discriminate Fe3+ and Fe2+ absorption intensities. Selective Principal Component analysis (SPCA), Spectral Angle Mapper (SAM), Linear Spectral Unmixing (LSU) and Automatic Lineament Extraction techniques were implemented. Lithological units were discriminated based on Al/Fe-OH, Fe2+/Fe3+ and Mg-Fe-OH/CO3 absorption properties. The spatial distribution of hematite, goethite, jarosite, gypsum, calcite, dolomite, kaolinite and muscovite were comprehensively detected. Some prospective zones were identified in the intersection of N-S, NW-SE and NE-SW trending fault systems, gossan, argillic/phyllic and dolomitic units.

Prospecting Fe-Skarn mineralization using ASTER satellite data: case study from Ravanj village, Markazi Province, Iran

The study area is located in Iran central zone and Urumieh-Dokhtar volcanoplutonic belt. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data was used to identify alteration zones associated with Fe-Skarn mineralization in the Ravanj village, Markazi Province, Iran. Argillic, phyllic and propylitic alteration zones are typically associated with Fe-Skarn mineralization in the study area. In this research, the Selective Principal Component Analysis (SPCA) method was applied to VNIR + SWIR bands of ASTER remote sensing data. Bands 1, 4, 6 and 8 were designated for identification clay minerals. Bands 4, 5, and 6 were selected for argillic alteration mapping. Bands 1, 2, and 4 were used to identification iron oxides/hydroxide minerals. Bands 5, 6, and 7 were chosen to map phyllic alteration zones. Bands 7, 8, and 9 were nominated to specify propylitic alteration mapping. According to the eigenvector statistics calculated using SPCA for ASTER, inverse SPC4 image identified clay minerals and SPC2 images detected argillic alteration, oxides/hydroxide minerals, phyllic alteration and propylitic alteration. In this paper, SPCA technique is an appropriate method because of the distinction between alteartion minerals, vegetation for Fe-Skarn mineralization exploration.

Glacier mapping and change analysis in Chandra basin, Western Himalaya, India during 1971–2016

ABSTRACT For carrying out glacier change analysis in Chandra basin, Western Himalaya, India during 1971–2016, glacier inventory is generated for year 2002 using Landsat Enhanced Thematic Mapper+ (ETM+) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 2 (GDEM V2) data. Total 395 glaciers are mapped covering 703.3 ± 20.4 km2 area with minimum glacier size being >0.02 km2. Out of these, 59 glaciers have debris-covered ice covering an area of 67.2 ± 1.9 km2. In this study, a subset of 169 glaciers is used for glacier area change analysis using Corona KH-4B (1971), Landsat ETM+ (2002) and Sentinel-2 (2016) images due to the availability of cloud free data sources. In 1971, 639.4 ± 5.8 km2 glacier area is estimated which decreased to 620.2 ± 18.0 km2 (–3.0 ± 3.0%) in 2002 and 608.1 ± 10.3 km2 (–4.9 ± 1.9%) in 2016. The change in glacier length of Samudra Tapu and Gepang Gath glaciers is showing increasing trend since last two decades as compared to Hamtah, Chhota Shigri and Bara Shigri glaciers due to presence of pro-glacial lake at their snout. Clean-ice glaciers, glaciers with low mean elevation, glaciers having steep surface, glaciers with west to south and east to south orientation, small size glaciers and glaciers having pro-glacial lake at the snout are responsible to higher loss in glacier area. Statistical trend analysis using Mann-Kendall test and Sen’s slope estimator test show that Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) based annual mean temperature have rising trend at a rate of 0.02°C per year during 1961–2015, and can be ascribed as one of the factors for glacier shrinking.

High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data

Generating a high-resolution whole-pixel geochemical contents map from a map with sparse distribution is a regression problem. Currently, multivariate prediction models like machine learning (ML) are constructed to raise the geoscience mapping resolution. Methods coupling the spatial autocorrelation into the ML model have been proposed for raising ML prediction accuracy. Previously proposed methods are needed for complicated modification in ML models. In this research, we propose a new algorithm called spatial autocorrelation-based mixture interpolation (SABAMIN), with which it is easier to merge spatial autocorrelation into a ML model only using a data augmentation strategy. To test the feasibility of this concept, remote sensing data including those from the advanced spaceborne thermal emission and reflection radiometer (ASTER), digital elevation model (DEM), and geophysics (geomagnetic) data were used for the feasibility study, along with copper geochemical and copper mine data from Arizona, USA. We explained why spatial information can be coupled into an ML model only by data augmentation, and introduced how to operate data augmentation in our case. Four tests—(i) cross-validation of measured data, (ii) the blind test, (iii) the temporal stability test, and (iv) the predictor importance test—were conducted to evaluate the model. As the results, the model’s accuracy was improved compared with a traditional ML model, and the reliability of the algorithm was confirmed. In summary, combining the univariate interpolation method with multivariate prediction with data augmentation proved effective for geological studies.

Multiscale and multitemporal surface temperature monitoring by satellite thermal infrared imagery at Mayon Volcano, Philippines

Mayon Volcano on eastern Luzon Island is the most active volcano in the Philippines. The high level of vulnerability to volcanic hazards is apparent since Mayon is surrounded by eight cities and municipalities with a total population of approximately one million inhabitants. Thus there is an urgent need to develop and expand reliable and affordable long-term methods in volcano monitoring. Despite abundant open access satellite dataset archives, satellite remote sensing has been underutilized in Mayon's hazard mapping and monitoring system. Here, we perform monitoring analysis on a nineteen-year time series of Land Surface Temperature (LST) obtained from satellite thermal infrared images. Both ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) thermal imagery (with 90 m spatial resolution) and MODIS (Moderate Resolution Imaging Spectroradiometer) LST products (1 km spatial resolution) are used for the analysis. The Ensemble Empirical Mode Decomposition (EEMD) is applied as the decomposition tool to extract components of different timescales from the LST time series. Physical interpretation of decomposed LST components at various periods are explored and compared with Mayon's eruption records. Specifically, the LST annual period component is compared to regular annual cycle air temperature. Results show that LST annual period component tends to lose its regularity following an eruption. The dissimilarity of the two time series has been taken as an indicator of volcanic restlessness. Trends of different timescales based on LST components have been constructed, which all indicate the rising trend. This means that Mayon volcano will remain restless in the future decades. This study presents the advantages and effectiveness of satellite remote sensing on volcanic monitoring.

Integration of remote sensing, gravity and geochemical data for exploration of Cu-mineralization in Alwar basin, Rajasthan, India

In spite of the dominance of traditional mineral exploration methods that demand physical characterization of rocks and intense field work, remote sensing technologies have also evolved in the recent past to facilitate mineral exploration. In the present study, we have processed visible near infrared (VNIR) and shortwave infrared (SWIR) bands of Advanced space-borne thermal emission and reflection radiometer (ASTER) data to detect surface mineralization signatures in Mundiyawas - Khera area in Alwar basin, north-eastern Rajasthan, India using spectral angle mapper (SAM). The potential of SAM method to detect target under variable illumination condition was used to delineate galena, chalcopyrite, malachite etc. as surface signatures of mineralization. It was ensured that the identified surface anomalies were spectrally pure using pixel purity index. Spectral anomalies were validated in the field and also using X-Ray diffraction data. Spectral anomaly maps thus derived were integrated using weight of evidence method with the lineament density, geochemical anomaly, bouger anomaly maps to identify few additional potential areas of mineralization. This study thus establishes the importance of remote sensing in mineral exploration to zero in on potentially ore rich but unexplored zones.

Modeling outdoor thermal comfort using satellite imagery: A principle component analysis-based approach

Abstract A harmful effect of anthropogenic activities in urban environments is the increases of thermal discomfort and subsequently, a negative effect on humans’ mental and physical performance. Therefore, it is of high importance to detect, monitor, and predict thermal discomfort, especially its temporal and spatial patterns in cities. The objective of this study is to propose a new method for modeling outdoor thermal comfort based on remote sensing and climatic datasets. To do so, several datasets were utilized, including those from Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), Digital Elevation Model (DEM) from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and climatic datasets from local meteorological stations. The method was experimented in the city of Tehran, Iran. For modeling outdoor thermal comfort, the Least Squares Adjustment (LSA) model was presented based on the Principle Component Analysis (PCA). In this model, the Principle Components (PCs) of the environmental and surface biophysical parameters were considered as independent variables and Discomfort Index (DI) as dependent variable. Finally, by determining the optimal values of the adjustment coefficients for each independent variable, maps of outdoor thermal comfort at different timestamps were produced and analyzed. The results of the modeling showed that correlation coefficient and Root Mean Square Error (RMSE) between the modeled and observed outdoor thermal comfort values at the meteorological stations for the training data sets were 0.86 and 1.80, for the testing data set were 0.89 and 2.04, respectively, while it was 0.85 and 1.15 for the self-deployed devices. The average values of DI in warm season of year was 8.5 °C higher than the cold season of the year. Further, in both warm and cold seasons of year the mean value of DI for bare land was found higher than other land covers, whereas that of water bodies lower than others. Our findings suggest that efficiency can be achieved for modeling outdoor thermal comfort using LSA with remote sensing and climatic datasets.

Integration of remote sensing, GIS and 2D resistivity methods in groundwater development

An integration of remote sensing, geographical information system (GIS) and 2D resistivity techniques was used to evaluate the groundwater potential of Obafemi Owode local government area (LGA) in Ogun State, southwestern Nigeria. A composite Landsat 7 image was used to produce land use and lineament of the study area. Advanced space-borne thermal emission and reflection radiometer (ASTER) data were used to generate slope and drainage density. The study adopted modified drastic model for groundwater potential, integrating six parameters which are land use, lineament density, slope, drainage density, geology and soil map. The contribution of each theme to groundwater potential was weighted and ranked using ArcGIS 10.2 software. The groundwater potential zone of the study area delineated five distinct zones which include very high (196.39 km2), high (334.64 km2), moderate (481.76 km2), low (298.46 km2) and very low (95.73 km2). Eight zones were delineated for further study. A total of 4.8 line km of 2D resistivity profiles were investigated in one of the designated zones labeled “area D” based on the groundwater potential map. Pole–dipole and dipole–dipole arrays used reveal five geoelectric layers within the study area. The 2D inverse models of resistivity variation with depth suggest the occurrence of potential carbonate and silicate aquifers. To verify the interpreted results within the “high” groundwater potential zone, a well was drilled on traverse 1. A potential water-bearing aquifer was encountered at a depth of 75 m. The groundwater potential map of the study area was tested with five existing wells, and the result was impressive. The outcomes from this study show that the high potential zones would play a key role in future expansion of drinking water and irrigation development in the study area.

Assessment of Spatial Prediction Techniques Accuracy for Elevation Determination in Akure South Local Government, Ondo State

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a cooperative effort between NASA and Japan's Ministry of Economy Trade and Industry (METI), with the collaboration of scientific and industry organizations in both countries. The ASTER instrument provides a more robust remote sensing imaging capability when compared to the older Landsat Thematic Mapper. This paper deals with the accuracy assessment of elevation data obtained using ASTER from each of the eleven (11) selected extrapolation/interpolation algorithms: Inverse Distance Weighting, Natural Neighbor, Spline Regular, Spline Tension, Universal Kriging, Empirical Bayesian Kriging, Topo to Raster, global (trend surface), local polynomial, kernel interpolation with barriers and radial basis functions in Digital Elevation Model (DEM) surface creation. The data were compared with reference to ground control points of differential GPS measurements in the study area. The error statistics were generated between DGPS measurements and Extracted elevation data from each selected interpolation method. It was observed that Spline Regular Interpolation shown the best overall accuracy of ±11.520m when elevation data extracted from Inverse distance weighting, Natural Neighbour, Spline T, Topo to Raster, Universal Kriging, Empirical Bayesian kriging, Global polynomial interpolation (GPI), local polynomial interpolation (LPI), Radial basis function and Kernel interpolation of ±15.170, ±14.340, ±12.336, ±13.551, ±14.707, ±13.711, ±15.363, ±13.964, ±13.590 and ±15.376 respectively when compared with elevation values from GPS method. The study recommends capacity building in the form of workshop, training, and flexible integration of point elevation data to DEM.

Assessment of Spatial Prediction Techniques Accuracy for Elevation Determination in Akure South Local Government, Ondo State

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a cooperative effort between NASA and Japan's Ministry of Economy Trade and Industry (METI), with the collaboration of scientific and industry organizations in both countries. The ASTER instrument provides a more robust remote sensing imaging capability when compared to the older Landsat Thematic Mapper. This paper deals with the accuracy assessment of elevation data obtained using ASTER from each of the eleven (11) selected extrapolation/interpolation algorithms: Inverse Distance Weighting, Natural Neighbor, Spline Regular, Spline Tension, Universal Kriging, Empirical Bayesian Kriging, Topo to Raster, global (trend surface), local polynomial, kernel interpolation with barriers and radial basis functions in Digital Elevation Model (DEM) surface creation. The data were compared with reference to ground control points of differential GPS measurements in the study area. The error statistics were generated between DGPS measurements and Extracted elevation data from each selected interpolation method. It was observed that Spline Regular Interpolation shown the best overall accuracy of ±11.520m when elevation data extracted from Inverse distance weighting, Natural Neighbour, Spline T, Topo to Raster, Universal Kriging, Empirical Bayesian kriging, Global polynomial interpolation (GPI), local polynomial interpolation (LPI), Radial basis function and Kernel interpolation of ±15.170, ±14.340, ±12.336, ±13.551, ±14.707, ±13.711, ±15.363, ±13.964, ±13.590 and ±15.376 respectively when compared with elevation values from GPS method. The study recommends capacity building in the form of workshop, training, and flexible integration of point elevation data to DEM.

Ensemble Extreme Learning Machine Approach to Thermal Infrared Subpixel Temperature Estimation

The disadvantage of low resolution and low signal-to-noise ratio (SNR) of thermal infrared (TIR) data makes it difficult to be applied in practice, although it has been studied for decades. In contrast, visible and near infrared (VNIR) and shortwave infrared (SWIR) data have higher resolution and SNR. In this letter, an ensemble extreme learning machine (EELM) is proposed, which is based on the extreme learning machine (ELM), to fuse together VNIR, SWIR, and TIR to improve TIR resolution and SNR. The associated ELM nets are trained with correlated training data which are selected from the random forest method. The temperature estimation cube can be obtained after putting SWIR data and VNIR data into these networks. Finally, the cube structure is adjusted by an ensemble rule to obtain the final temperature estimation results. We test the EELM algorithm on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) L1B data set and compare it with other scholars’ algorithms. The experimental results show that the EELM algorithm can not only improve the resolution of TIR data, but also estimate the subpixel temperature of TIR more accurately.

Integration of Selective Dimensionality Reduction Techniques for Mineral Exploration Using ASTER Satellite Data

There are a significant number of image processing methods that have been developed during the past decades for detecting anomalous areas, such as hydrothermal alteration zones, using satellite images. Among these methods, dimensionality reduction or transformation techniques are known to be a robust type of methods, which are helpful, as they reduce the extent of a study area at the initial stage of mineral exploration. Principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) are the dimensionality reduction techniques known as multivariate statistical methods that convert a set of observed and correlated input variables into uncorrelated or independent components. In this study, these techniques were comprehensively compared and integrated, to show how they could be jointly applied in remote sensing data analysis for mapping hydrothermal alteration zones associated with epithermal Cu–Au deposits in the Toroud-Chahshirin range, Central Iran. These techniques were applied on specific subsets of the advanced spaceborne thermal emission and reflection radiometer (ASTER) spectral bands for mapping gossans and hydrothermal alteration zones, such as argillic, propylitic, and phyllic zones. The fuzzy logic model was used for integrating the most rational thematic layers derived from the transformation techniques, which led to an efficient remote sensing evidential layer for mineral prospectivity mapping. The results showed that ICA was a more robust technique for generating hydrothermal alteration thematic layers, compared to the other dimensionality reduction techniques. The capabilities of this technique in separating source signals from noise led to improved enhancement of geological features, such as specific alteration zones. In this investigation, several previously unmapped prospective zones were detected using the integrated hydrothermal alteration map and most of the known hydrothermal mineral occurrences showed a high prospectivity value. Fieldwork and laboratory analysis were conducted to validate the results and to verify new prospective zones in the study area, which indicated a good consistency with the remote sensing output. This study demonstrated that the integration of remote sensing-based alteration thematic layers derived from the transformation techniques is a reliable and low-cost approach for mineral prospectivity mapping in metallogenic provinces, at the reconnaissance stage of mineral exploration.

New logical operator algorithms for mapping of hydrothermally altered rocks using ASTER data: A case study from central Turkey

This study uses logical operator algorithms along with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral reflectance and emissivity data to map hydrothermally altered igneous rocks associated with mineralization around the towns of Hamurcu and Başdere in central Anatolia, Turkey. In addition to band ratios used in previous studies, the logical operator algorithms developed in this work perform multiple mineral indices and directed principal components (DPC) analysis, thereby providing better constraints on the spatial extent of hydrothermally altered rocks around the Hamurcu and Başdere areas. We designed the unique mineral indices and DPC analysis based on the effective use of ASTER bands corresponding to diagnostic spectral absorption features of alteration minerals. The calculation of threshold values of logical operator algorithms is based on a two-stepped statistical approach. The novel logical operator algorithms successfully mapped hydrothermal alteration zones in the study area that include mineral assemblages, such as kaolinite, alunite, sericite, smectite, and ferric iron oxides together with various forms of silica. The Hamurcu area contains altered rocks that display a characteristic alteration pattern of a high-sulfidation epithermal system. On the other hand, alteration minerals are primarily located along the faults in the southern part of the Başdere area. The results of field investigations in conjunction with laboratory spectral reflectance measurements, microscopic examination of thin sections and X-ray diffraction analysis of the samples are quite consistent with ASTER mineral maps derived from the logical operator algorithms from this work. As a result, the new logical operator algorithms implemented to ASTER data of the study area prove to be a powerful tool for generating preliminary, quick, and reliable alteration mineral maps at low cost in similar terrains of different geographic locations.

Application of Landsat-8, Sentinel-2, ASTER and WorldView-3 Spectral Imagery for Exploration of Carbonate-Hosted Pb-Zn Deposits in the Central Iranian Terrane (CIT)

The exploration of carbonate-hosted Pb-Zn mineralization is challenging due to the complex structural-geological settings and costly using geophysical and geochemical techniques. Hydrothermal alteration minerals and structural features are typically associated with this type of mineralization. Application of multi-sensor remote sensing satellite imagery as a fast and inexpensive tool for mapping alteration zones and lithological units associated with carbonate-hosted Pb-Zn deposits is worthwhile. Multiple sources of spectral data derived from different remote sensing sensors can be utilized for detailed mapping a variety of hydrothermal alteration minerals in the visible near infrared (VNIR) and the shortwave infrared (SWIR) regions. In this research, Landsat-8, Sentinel-2, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (WV-3) satellite remote sensing sensors were used for prospecting Zn-Pb mineralization in the central part of the Kashmar–Kerman Tectonic Zone (KKTZ), the Central Iranian Terrane (CIT). The KKTZ has high potential for hosting Pb-Zn mineralization due to its specific geodynamic conditions (folded and thrust belt) and the occurrence of large carbonate platforms. For the processing of the satellite remote sensing datasets, band ratios and principal component analysis (PCA) techniques were adopted and implemented. Fuzzy logic modeling was applied to integrate the thematic layers produced by image processing techniques for generating mineral prospectivity maps of the study area. The spatial distribution of iron oxide/hydroxides, hydroxyl-bearing and carbonate minerals and dolomite were mapped using specialized band ratios and analyzing eigenvector loadings of the PC images. Subsequently, mineral prospectivity maps of the study area were generated by fusing the selected PC thematic layers using fuzzy logic modeling. The most favorable/prospective zones for hydrothermal ore mineralizations and carbonate-hosted Pb-Zn mineralization in the study region were particularly mapped and indicated. Confusion matrix, field reconnaissance and laboratory analysis were carried out to verify the occurrence of alteration zones and highly prospective locations of carbonate-hosted Pb-Zn mineralization in the study area. Results indicate that the spectral data derived from multi-sensor remote sensing satellite datasets can be broadly used for generating remote sensing-based prospectivity maps for exploration of carbonate-hosted Pb-Zn mineralization in many metallogenic provinces around the world.

ASTER Global Digital Elevation Model (GDEM) and ASTER Global Water Body Dataset (ASTWBD)

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a 14-channel imaging instrument operating on NASA’s Terra satellite since 1999. ASTER’s visible–near infrared (VNIR) instrument, with three bands and a 15 m Instantaneous field of view (IFOV), is accompanied by an additional band using a second, backward-looking telescope. Collecting along-track stereo pairs, the geometry produces a base-to-height ratio of 0.6. In August 2019, the ASTER Science Team released Version 3 of the global DEM (GDEM) based on stereo correlation of 1.8 million ASTER scenes. The DEM has 1 arc-second latitude and longitude postings (~30 m) and employed cloud masking to avoid cloud-contaminated pixels. Custom software was developed to reduce or eliminate artifacts found in earlier GDEM versions, and to fill holes due to the masking. Each 1×1 degree GDEM tile was manually inspected to verify the completeness of the anomaly removal, which was generally excellent except across some large ice sheets. The GDEM covers all of the Earth’s land surface from 83 degrees north to 83 degrees south latitude. This is a unique, global high spatial resolution digital elevation dataset available to all users at no cost. In addition, a second unique dataset was produced and released. The raster-based ASTER Global Water Body Dataset (ASTWBD) identifies the presence of permanent water bodies, and marks them as ocean, lake, or river. An accompanying DEM file indicates the elevation for each water pixel. To date, over 100 million 1×1 degree GDEM tiles have been distributed.

Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging

Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolution retrieved from satellite-based thermal infrared imagery have limited use in the detailed study of surface energy balance, evapotranspiration, and climatic change at the urban spatial scale. Downscaling LST is a practicable approach to obtain high accuracy and high-resolution LST. In this study, a machine learning-based geostatistical downscaling method (RFATPK) is proposed for downscaling LST which integrates the advantages of random forests and area-to-point Kriging methods. The RFATPK was performed to downscale the 90 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST 10 m over two representative areas in Guangzhou, China. The 10 m multi-type independent variables derived from the Sentinel-2A imagery on 1 November 2017, were incorporated into the RFATPK, which considered the nonlinear relationship between LST and independent variables and the scale effect of the regression residual LST. The downscaled results were further compared with the results obtained from the normalized difference vegetation index (NDVI) based thermal sharpening method (TsHARP). The experimental results showed that the RFATPK produced 10 m LST with higher accuracy than the TsHARP; the TsHARP showed poor performance when downscaling LST in the built-up and water regions because NDVI is a poor indicator for impervious surfaces and water bodies; the RFATPK captured LST difference over different land coverage patterns and produced the spatial details of downscaled LST on heterogeneous regions. More accurate LST data has wide applications in meteorological, hydrological, and ecological research and urban heat island monitoring.

Reliability of using ASTER data in lithologic mapping and alteration mineral detection of the basement complex of West Berenice, Southeastern Desert, Egypt

The Neoproterozoic basement complex of Egypt outcrops in the Eastern Desert (ED) and southern Sinai, and is regarded to represent the northwestern continuation of the Arabian–Nubian Shield (ANS). The area west of Berenice has been given little attention, although it represents the key to understanding the geologic history of the South Eastern Desert and the entire ED. The present work is an integrated study using remote sensing (Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data imagery) and field-structural data to assess their utility in lithologic mapping and in detecting alteration mineral zones with considerable accuracy. A false-color-composite image, color-ratio composite image, decorrelation stretch, mafic index (MI), and quartz index (QI) were used to discriminate and map the various rock units. Correlation of False-color images of the ASTER band combination (7, 3, 1) with previous lithological mapping studies of the study area allow preliminary discrimination of different lithologies and drawing a base map. Subsequently, ASTER band ratios tested (4/1, 3/1, 12/14) and (4/7, 4/6, 4/10) with the ground truth data were used to refine the base map and construct the digital detailed geologic map. Petrographically, the investigated rock samples show kaolinite, chlorite, and epidote in alteration zones. The spectral angle mapper (SAM)-supervised classification using reference spectra of the USGS spectral library was used for detecting alteration zones. For verification, total accuracy assessment was carried out to evaluate the band combination and band ratios used in the lithologic mapping. That reached to 85.01% for band ratio 4/1, 3/1, 12/14 in RGB.

Validation of ASTER Emissivity Retrieval Using the Mako Airborne TIR Imaging Spectrometer at the Algodones Dune Field in Southern California, USA

Validation of emissivity (ε) retrievals from spaceborne thermal infrared (TIR) sensors typically requires spatial extrapolations over several orders of magnitude for a comparison between centimeter-scale laboratory ε measurements and the common decameter and lower resolution of spaceborne TIR data. In the case of NASA’s Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) temperature and ε separation algorithm (TES), this extrapolation becomes especially challenging because TES was originally designed for the geologic surface of Earth, which is typically heterogeneous even at centimeter and decameter scales. Here, we used the airborne TIR hyperspectral Mako sensor with its 2.2 m/pixel resolution, to bridge this scaling issue and robustly link between ASTER TES 90 m/pixel emissivity retrievals and laboratory ε measurements from the Algodones dune field in southern California, USA. The experimental setup included: (i) Laboratory XRD, grain size, and TIR spectral measurements; (ii) radiosonde launches at the time of the two Mako overpasses for atmospheric corrections; (iii) ground-based thermal measurements for calibration, and (iv) analyses of ASTER day and night ε retrievals from 21 different acquisitions. We show that while cavity radiation leads to a 2% to 4% decrease in the effective emissivity contrast of fully resolved scene elements (e.g., slipface slopes and interdune flats), spectral variability of the site when imaged at 90 m/pixel is below 1%, because at this scale the dune field becomes an effectively homogeneous mixture of the different dune elements. We also found that adsorption of atmospheric moisture to grain surfaces during the predawn hours increased the effective ε of the dune surface by up to 0.04. The accuracy of ASTER’s daytime emissivity retrievals using each of the three available atmospheric correction protocols was better than 0.01 and within the target performance of ASTER’s standard emissivity product. Nighttime emissivity retrievals had lower precision (<0.03) likely due to residual atmospheric effects. The water vapor scaling (WVS) atmospheric correction protocol was required to obtain accurate (<0.01) nighttime ASTER emissivity retrievals.

A New Band Ratio Approach for Discriminating Calcite and Dolomite by ASTER Imagery in Arid and Semiarid Regions

In this article, a new approach is developed for discrimination of calcite (limestone) and dolomite (dolostone) by utilizing data of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The study area is located at the Zagros Mountains, SW Iran, in which outcrops of sedimentary carbonate rocks are abundant. The approach exploits short-wave infrared spectral features of dolomite and calcite and is based on the spectral variations of these minerals in bands 7 and 8 of ASTER imagery. In order to evaluate the efficacy of the method, spectral analysis was carried out on the spectra of dolomite, dolostone, calcite, and limestone, available in ASTER spectral library. The statistical analysis of the spectra showed that dolomite and calcite have distinguishable b8/b7 band ratios. Spectral measurements of rock samples and knowledge of the mineral composition of rock samples attained by staining carbonate rocks in Alizarin red-S solution were used to evaluate the method used in this study. The applied imagery was normalized by the internal average relative reflectance (IARR) method due to the arid and semiarid climate of the region. The results showed that combined with spectral measurements of rock samples, the ((b5 + b9)/b7)/((b6 + b9)/b8), and b8/b7 band ratios can be used to effectively discriminate limestone and dolostone. This approach is completely independent of variations in illumination conditions of image pixels; therefore, topographic correction of the image is not needed. The approach performs better in areas where weathering of rock surfaces and vegetation cover are minimal.