Hyperion Data Research Articles

The study of mineral distribution using hyperspectral Hyperion data along the shores of Lake Salda/Türkiye

Lake Salda is considered the most similar region on Earth to search for evidence of life on Mars, and its shoreline is home to microbialites containing some of the oldest known fossilized records of life on our planet. Understanding the historical process of Mars is also crucial for predicting the future of our planet, given its transition from a watery world to an arid one. In mineralogical studies to be carried out in sensitive areas such as private protected areas, it is a priority not to damage the area, and mineralogical research using remote sensing methods is frequently used in such studies. In this study, the mineralogical characteristics of the geological formations in and around Kocaadalar Burnu peninsula, located in the southwest of Lake Salda, were examined using remote sensing techniques. Mineral studies of Lake Salda were conducted using data from the Hyperion data of the Earth Observation-1 (EO-1) satellite, which can detect in 220 bands between 357 and 2576 nm wavelengths which is capable of hyperspectral sensing. The EO-1 satellite, the first spacecraft of NASA’s New Millennium Programme, which operated successfully from its launch in 2000 until the end of its mission in 2017, carried the multispectral Advanced Land Imager and LEISA Atmospheric Corrector sensors, in addition to the Hyperion hyperspectral sensor. The Hyperspectral Material Identification tool in the Tactical Hyperspectral Operations Resource module of the ENVI software, the United States Geological Survey spectral library, and the Adaptive Coherence Estimator algorithm were used in the study. According to the results of this study conducted on the shores of Lake Salda, in addition to the hydromagnesite mineral, the existence of which was previously detected by various researchers, trona, vermiculite, rivadavite and borate minerals such as ulexite, tincalconite and colemanite were also detected. The results of this study have shown that remote sensing methods can make significant contributions to research in special protected areas such as Lake Salda. It is thought that the presence of trona and boron minerals in the region will contribute positively to the understanding of the historical process of Salda Lake.

Development of Spectral Signature of Chir Pine Using Hyperion Data

Development of Spectral Signature of Chir Pine Using Hyperion Data

Lithological discrimination using ASTER and Hyperion data in Salem District, Tamil Nadu

— Lithological mapping is a crucial factor in identifying and mapping the spatial distribution of minerals. It aids in accurately defining the most promising primary prospects for local exploration. The differentiation of rock units across a wider region is likely to be attributed to remotely sensed satellite data. Therefore, the research focuses on utilizing remote sensing methods to create a geological map for a specific area in Salem district, Tamil Nadu, by employing HYPERION and ASTER satellite images. Various techniques, such as Band Ratio (BR), Spectral Angle Mapper (SAM), Minimum Noise Fraction (MNF), Mixture Tuned Mapped Filtering (MTMF), Spectral Feature Fitting (SFF), and Support Vector Machines (SVMs), are utilized to classify lithological units, which are crucial for data analysis. The outcomes of these methods will be compared to field-mapped geological boundaries to assess accuracy. In the final phase, a highly precise geological map is produced by combining remote sensing data with on-site investigations. The application of these approaches holds significant potential for enhancing geological mapping and mineral exploration in hard-to-reach areas.

Mapping Alteration Minerals Using ZY-1 02D Hyperspectral Remote Sensing Data in Coalbed Methane Enrichment Areas

As a clean energy resource, coalbed methane (CBM) is an important industry in China’s dual-carbon strategic planning. Despite the immense potential of CBM resources in China, the current exploration level remains low due to outdated survey technology, impeding large-scale exploration and development. This study investigates the application of hyperspectral data in CBM enrichment areas, specifically focusing on the extraction of alteration minerals in the Hudi coal mine area of the Qinshui Basin using ZY-1 02D and Hyperion hyperspectral data. The hyperspectral alteration mineral identification methods are summarized and analyzed. A method that combines spectral feature matching and diagnostic characteristic parameters is proposed for mineral extraction based on the spectral characteristics of different minerals. The extraction results are verified through field samples using X-ray diffraction analysis. Results show that (1) both ZY-1 02D and Hyperion hyperspectral data yield favorable extraction results for clay and carbonate minerals; (2) the overall accuracy of clay and carbonate minerals extraction is higher using ZY-1 02D data compared with Hyperion data, with accuracies of 81.67% and 79.03%, respectively; (3) the proposed method effectively extracts alteration minerals in CBM enrichment areas using hyperspectral data, thereby providing valuable technical support for the application of hyperspectral data.

Development of narrow band index for accurate mapping of chlorophyll in senescent stage of teak (Tectona grandis L. f.) using Hyperion (EO1) data

Development of narrow band index for accurate mapping of chlorophyll in senescent stage of teak (Tectona grandis L. f.) using Hyperion (EO1) data

The Development of Dark Hyperspectral Absolute Calibration Model Using Extended Pseudo Invariant Calibration Sites at a Global Scale: Dark EPICS-Global

This research aimed to develop a novel dark hyperspectral absolute calibration (DAHAC) model using stable dark targets of “Global Cluster-36” (GC-36), one of the clusters from the “300 Class Global Classification”. The stable dark sites were identified from GC-36 called “Dark EPICS-Global” covering the surface types viz. dark rock, volcanic area, and dark sand. The Dark EPICS-Global shows a temporal variation of 0.02 unit reflectance. This work used the Landsat-8 (L8) Operational Land Imager (OLI), Sentinel-2A (S2A) Multispectral Instrument (MSI), and Earth Observing One (EO-1) Hyperion data for the DAHAC model development, where well-calibrated L8 and S2A were used as the reference sensors, while EO-1 Hyperion with a 10 nm spectral resolution was used as a hyperspectral library. The dark hyperspectral dataset (DaHD) was generated by combining the normalized hyperspectral profile of L8 and S2A for the DAHAC model development. The DAHAC model developed in this study takes into account the solar zenith and azimuth angles, as well as the view zenith and azimuth angles in Cartesian coordinates form. This model is capable of predicting TOA reflectance in all existing spectral bands of any sensor. The DAHAC model was then validated with the Landsat-7 (L7), Landsat-9 (L9), and Sentinel-2B (S2B) satellites from their launch dates to March 2022. These satellite sensors vary in terms of their spectral resolution, equatorial crossing time, spatial resolution, etc. The comparison between the DAHAC model and satellite measurements showed an accuracy within 0.01 unit reflectance across the overall spectral band. The proposed DAHAC model uncertainty level was determined using Monte Carlo simulation and found to be 0.04 and 0.05 unit reflectance for the VNIR and SWIR channels, respectively. The DAHAC model double ratio was used as a tool to perform the inter-comparison between two satellites. The sensor inter-comparison results for L8 and L9 showed a 2% difference and 1% for S2A and S2B across all spectral bands.

Spectral mapping of zeolite bearing paleolake deposits at Lake Tecopa, California and its implications for mapping zeolites on Mars

• Zeolitic tuff at paleolake Tecopa investigated using in-situ and orbital methods. • Reasons for limited orbital detections of zeolites in paleolake. • Importance of ground investigation to interpreting orbital data. While non-analcime zeolites are plausible mineral constituents of the Martian surface, their lack of prominent spectral characteristics in the visible to shortwave infrared wavelength region makes them difficult to identify using orbital data. This lack of detection has led to the assumption that they are limited or absent. Here we assess the capability of mapping non-analcime zeolites using different mapping techniques and present the possible reasons that could lead to limited detection of zeolites using an analog study from Paleolake Tecopa, California. We used spaceborne hyperspectral Hyperion and multispectral ASTER data to identify zeolites following X-ray Diffraction (XRD) and visible-near infrared and shortwave infrared (VNIR-SWIR) spectral analysis of collected samples containing zeolites and other associated minerals. We used four of the most common spectral mapping methods: (1) band ratios, (2) minimum noise fraction (MNF), (3) spectral angle mapper (SAM), and (4) linear spectral unmixing (LSU). We were able to map zeolite-rich tuff beds using carefully selected band indices, MNF band combinations, and the pixel spectra corresponding to a zeolite-rich abandoned quarry area. Hyperion results show less overall accuracy than the ASTER image products mostly due to the low signal-to-noise ratio of the Hyperion data. The spectral characteristics of zeolites in zeolite-rich pixels are masked by phyllosilicates (e.g., smectite) present in the ground resolution cell (GRC), making it difficult to identify zeolites in phyllosilicate-bearing deposits solely based on zeolite spectra. Therefore, the SAM product derived using the pixel spectrum of the well-exposed zeolite-rich quarry shows the highest accuracy in Hyperion image products. ASTER band ratio Band 2/Band 1, representing the ferric ion, was able to identify the zeolite-rich beds with the highest accuracies. However, most of the zeolite-rich paleolake beds were difficult to identify since they are buried by other beds, mixed with other beds due to physical weathering, or the areas of exposed beds are smaller than the GRC of the satellite. The results imply that the paucity of detected zeolites on Mars, as we see at Lake Tecopa, does not preclude their wider presence, either beneath other materials, obscured by surface dust, or mixed with more spectrally dominant phases. :

Application of hyperspectral and multispectral datasets for mineral mapping

In this study, hyperspectral datasets are simulated from multispectral data using a spectral reconstruction approach which is a sensor-independent technique. This technique makes use of information from atmospherically corrected multispectral Remote Sensing (MRS) data and normalized ground spectra for the simulation of HRS data. In this study EO-1, the ALI dataset was used for the simulation of hyperspectral Remote Sensing (HRS) data to discover the Udaipur region’s unique minerals. A total of 61 spectral bands with 10 nm bandwidth were simulated. The simulated HRS data were validated using visual interpretation, statistical and classification approaches. Simulated HRS data from EO-1 Advanced Land Imager (ALI) has shown a high correlation with EO-1 Hyperion data. Spectral Angle Mapper (SAM) classification was also performed on simulated hyperspectral data for mineral mapping. It was observed that simulated hyperspectral data have shown comparable results with Hyperion and are better than their corresponding multispectral datasets.

Evaluating automated endmember extraction for classifying hyperspectral data and deriving spectral parameters for monitoring forest vegetation health.

The hyperspectral remote sensing datasets possess high capability in differentiating the spectrally similar features, and thus, they are immensely important in various forestry activities, especially vegetation classifications. But extracting endmembers for data training is a challenging task. The present study is focused on the use of automated endmember extraction technique for deriving endmembers during the unavailability of ground spectra. We used the Sequential Maximum Angle Convex Cone (SMACC) method on EO-1 Hyperion data for endmember extraction in the Barkot forest range of Dehradun district, Uttarakhand which were used for classification of the study area using support vector machine (SVM). Further, we estimated the vegetation health of the region by assigning the threshold weights for various derived environmental variables such as NDVI (Normalised Difference Vegetation Index), CRI (Carotenoid Reflectance Index), Anthocyanin Reflectance Index (ARI), Modified Simple Ratio (MSR), Modified Chlorophyll Absorption Ratio Index (MCARI) and WBI (Water Band Index). Then, to further validate the health of the forest types, we correlated it with the Land Surface Temperature (LST) from LANDSAT 5 ETM + data. The results showed a high classification accuracy of 89.13%. The healthy vegetation area coverage of the area was about 78.6% with most healthy class as Tectona grandis and Shorea robusta and its correlation with LST showed lower temperature range in healthy vegetation areas and vice versa. The study was useful in determining the superiority of SMACC automated endmember extraction and estimating the vegetation health.

From months to minutes: Creating Hyperion, a novel data management system expediting data insights for oncology research and patient care

Here we describe the design and implementation of a novel data management platform for an academic cancer center which meets the needs of multiple stakeholders. A small, cross-functional technical team identified key challenges to creating a broad data management and access software solution: lowering the technical skill floor, reducing cost, enhancing user autonomy, optimizing data governance, and reimagining technical team structures in academia. The Hyperion data management platform was designed to meet these challenges in addition to usual considerations of data quality, security, access, stability, and scalability. Implemented between May 2019 and December 2020 at the Wilmot Cancer Institute, Hyperion includes a sophisticated custom validation and interface engine to process data from multiple sources, storing it in a database. Graphical user interfaces and custom wizards permit users to directly interact with data across operational, clinical, research, and administrative contexts. The use of multi-threaded processing, open-source programming languages, and automated system tasks (normally requiring technical expertise) minimizes costs. An integrated ticketing system and active stakeholder committee support data governance and project management. A co-directed, cross-functional team with flattened hierarchy and integration of industry software management practices enhances problem solving and responsiveness to user needs. Access to validated, organized, and current data is critical to the functioning of multiple domains in medicine. Although there are downsides to developing in-house customized software, we describe a successful implementation of custom data management software in an academic cancer center.

Chlorophyll-a concentrations in the Arabian Gulf waters of arid region: A case study from the northern coast of Qatar

Remote sensing of spectrally active Chlorophyll-a (Chl-a) in the gulf water of an arid region is important to understand the spatial and temporal variations of phytoplankton and biomass in the water. This study characterizes the spectral absorption of Chl-a and detects and maps the Chl-a of Al Arish–Al Ghariyah coastal region of northern Qatar using the data of Hyperion of EO-1, MultiSpectral Instrument (MSI) of Sentinel-2, and Operational Land Imager (OLI) of Landsat-8 satellites. The study of image spectra of Chl-a of coastal water of the Al Ghariyah region using the Hyperion image showed spectral band absorptions near 450 and 475 nm in the blue region and around 640, 730, 760, and 830 nm in the red region. The analysis of Hyperion data using image spectra by linear spectral unmixing (LSU) method showed the occurrence of Chl-a in very shallow and shallow water. The mapping of Chl-a of the Al Arish–Al​ Ghariyah coastal region was carried out using MSI and OLI data by NDCI, 2BDA, 3BDA, and FLH violet algorithms, which showed the concentrations and distributions of the Chl-a in the region. The performance of the algorithms was studied using WorldView-3 data, which provided the R2 values of 60% and the best suitability of the NDCI algorithm and MSI data to map the concentration of Chl-a. All the results were validated using field measurements, in-situ measurements, and laboratory analyses of field samples. The measurements of Chl-a showed the occurrence of poor occurrence of Chl-a and confirmed the imagery results. The remote sensing results with physical and chemical parameters of water samples allowed us to assess Chl-a concentrations in the region. This study evaluates the sensor’s capability and applications of satellite data and algorithms to map concentrations of Chl-a in the Gulf waters of the arid region.

Determining metamorphic grade from greenschist to amphibolite using Hyperion data: the case of an inverted metamorphic gradient of Almora nappe (lesser Himalaya)

The intricate nature of metamorphic terrain remains a complex problem for many remote sensing scientists. The metamorphic rock assemblages are composed by intrinsic mixtures of index minerals which are defined at different pressure and temperature zones. Metamorphic assemblage’s nature and geothermal gradient can be identified using reflectance spectroscopy. The study aims to determine the nature and gradient of metamorphism using spectral identification of the index mineral phases. A model for metamorphic assemblage was developed to predict the nature and grade of metamorphism in order to address the problem with the inadequate quantification of mineral spectral mixing. The study addresses the problem by incorporating semi-empirical method of classification of metamorphic rock assemblage based on reflectance characteristics. We use the simulated spectral library of Hyperion satellite data which is favourable for classifying geological outcrops in metamorphic terrain of the Almora nappe of lower Himalayas. The Hyperion data analysis reveals the nature and trend of change of the metamorphic grades within different zones based on mass proportion of index mineral assemblages. An inverse thermal grade of the metamorphic Almora group is observed along North Almora Thrust and South Almora Thrust, where reverse thrusting might have resulted in lowering of pressure and temperature condition. The developed model can be utilized for geological survey’s involving metamorphic terrain in inaccessible areas.

Mineralogical mapping using EO-1 Hyperion data for iron mine identification

The Tamera open pit mine is located in the Nefza mining district (north-west of Tunisia) where the reddish soil reveals its strong potential for heavy Fe mineralization, along with other metals (Pb, Zn, and Mn). This study explored Iron oxide mineralization in a particular nappe zone using multiscale field visible and near infrared/short waves infrared and EO-1 Hyperion hyperspectral data. Thus, spectral features fittings were conducted on Hyperion data for mineral mapping using scene-extracted endmembers. A first minimum noise fraction algorithm was used to reduce the hyperspectral data’s dimensionality, followed by the Pixel Purity Index and n-dimensional visualization to extract the pure pixels (endmembers) from the cluster of pixels in the data cube. Furthermore, 56 soil samples were collected from the test site and spectrally measured using Analytical Spectral Devices FieldSpecHiRes spectrometer, which covers the spectral range between 350 and 2500 nm. All samples were analyzed in the laboratory with x-ray diffraction. Endmember extracted spectra were compared with field measurements before EO-1 Hyperion processing. Mapping results revealed the occurrence of a mineralogy composition consisting particularly of iron oxide minerals (hematite and goethite) and bearing minerals, such as clays (kaolinite), within all samples.

Exploring beach placer minerals in the east coast of Tamil Nadu, India, using EO-1 Hyperion data

Recent advancements in remote sensing facilitate the mapping of mineral occurrences but lack quantitative estimation of mineral abundances. In the present study, EO-1 Hyperion data are used for mapping and estimating beach placer minerals in the Cuddalore coastal stretch of Tamil Nadu, India, using spectral angle mapper (SAM) algorithm, continuum removed band depth analysis, and random forest (RF) regression. Strategic beach placer minerals are exposed in the image due to their specific spectral reflection/absorption characteristics; these unique settings are used to explore their spatial distribution from associated features. EO-1 Hyperion images are analyzed for (i) atmospheric correction; (ii) selection and transformation of optimum bands; (iii) fixing the input pixels from selected bands; (iv) generating n-D angle—for the preparation of end-members with unique spectra of mineral classes; (v) comparative analysis between true end-member and reference spectra of minerals using SAM, spectral feature fitting, and binary encoding algorithms; and (vi) image classification using SAM. The result of SAM shows the occurrence of placer mineral deposits such as zircon with an overall accuracy of 81.06% and Kappa coefficient of 0.71. The zircon shows strong absorption in spectral geometric parameters of EO-1 Hyperion data, such as band depth and band area at the spectral range of 1075 to 1150 nm. The positive correlation between geometric parameters of mineral spectra in its absorption characteristics and the mineral concentration of in situ samplings is used for developing an RF prediction model for estimating the placer minerals from beach sand deposits. Significantly, the lower RMSE values estimated at 0.082 and 0.156 for reference spectra (in situ) and image spectra respectively proved the ability of EO-1 Hyperion data for quantifying mineral resources.

Detection of alteration zones using the Dirichlet process Stick-Breaking model-based clustering algorithm to hyperion data: the case study of Kuh-Panj porphyry copper deposits, Southern Iran

Detection of hydrothermal alteration zones (HAZs) associated with porphyry copper systems using remote sensing imagery is a crucial stage for discovering high potential zone of ore mineralization. Statistical model-based clustering methods have great potential for automatic and accurate detection of hydrothermal alteration minerals using hyperspectral remote sensing imagery. In this research, the Dirichlet Process based on Stick-Breaking (DPSB) model-based clustering algorithm was implemented to hyperion remote sensing imagery to discriminate HAZs associated with the Kuh-Panj porphyry copper deposit, south, Iran. The DPSB clustering algorithm was implemented and subsequently compared with the k-means algorithm, CLARA clustering, hierarchical clustering, Gaussian finite mixture model (GFMM), Gaussian model for high-dimensional (GMHD) and spectral clustering as well as spectral angle mapping (SAM). Results derived from the DPSB model-based clustering algorithm show 88.6% accuracy in distinguishing propylitic, argillic, advanced argillic, propylitic-argillic and phyllic alteration zones. The DPSB algorithm can be broadly implemented to hyperspectral remote sensing imagery for detecting alteration zones associated with porphyry systems.

Mapping of clay soils exposed to the shrinking - swelling phenomenon, with EO - 1 - Hyperion data in the region of Sidi - Chahmi, Algeria

Satellite images have a remarkable ability to help researchers map the distribution and detect the nature of the soil exposed on the earth's surface. The remote-sensing mapping of clayey soils makes it possible to determine their shrinking-swelling potentials. This approach is helpful in analyzing large areas where the traditional sampling approach with laboratory analysis is expensive. This high cost often leads to a limited number of samples and thus to a poor representation of a site with an underestimation of the presence of swelling soils. The Sidi - Chahmi area is representative of this type of soil. In addition, it is an area that undergoes the substantial urban sprawl of the city of Oran. The urbanization of this area is under the risk of clay-type soil movements, the identification of which by satellite imagery may help mitigate shrink-swell risk. EO-1-Hyperion hyperspectral images, whose effectiveness has already been proven for mapping clay minerals, were used to identify minerals such as illite, montmorillonite, and kaolinite in the Sidi - Chahmi area.

Improved minimum volume simplex analysis algorithm for unmixing of hyperspectral data

Some of the robust and high-ability processing methods like the improved Minimum Simplex Volume Analysis (MVSA) algorithm are generally time-consuming in hyperspectral data dealing especially the ground resolution is higher. In this research, MVSA was conducted for spectral analysis of HyMap and Hyperion data after a proposed data reduction to overcome the mentioned problem. The data cloud was therefore clustered based on the spectral angle mapper and Multi-Attribute Decision Making (MADM) based methods were used to determine the best threshold angle. Consequently, four indicators were applied as evaluation attributes for selecting the appropriate alternative, and “Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)” and “ELimination Et Choice Translating REality (ELECTRE)” were employed to finalize the decision. The methods resulted in 0.8 and 1.3 degrees as optimal spectral angles for HyMap and Hyperion, respectively. In this case, the image processing computational burden and the noise effect were considerably alleviated and the procedure led to accurate endmember identification.

DE-striping hype spectral Remote Sensing Images using Deep Convolutional Neural Network

Hyperspectral far off detecting records reflectance or emittance information in a huge amount of bordering and tight unearthly groups, and accordingly has numerous data in distinguishing and planning the mineral zones. Then again, the science and natural information gives us some other productive data about the actual qualities of pictures and channels that have been recorded from the surface. In this work, we focus on de-striping the hyperspectral remote sensing images on Hyperion data by applying Deep Convolutional Neural Network (DCNN). What is clear is the high significance of applying the sufficient pre-preparing on Hyperion information as a result of low sign to-commotion proportion. By contrasting the known layers of DCNN model for de-striping hyperspectral pictures. The results obtained by applying the mentioned methods, it is revealed that all the higher stripes in an image as well as black color has been reduced and entirely associated with the Hyperion data alteration, and in contrast, the Hyperion imagery successfully corresponds to the de-striping of hyperspectral image with an accuracy of 91.89% using DCNN model. The proposed DCNN is capable of reaching high accuracy 150s after the start of the evaluation phase and never reaches low accuracy. The pre-trained DCNN model approach would be an adequate solution considering de-striping as its high inference time is lower compared existing available methods which are not as efficient for de-striping.

DE-striping Augmented Images of Blood Cells using Deep Convolutional Neural Network

The aim of this biomedical image processing-based research paper is to use augmented images of blood sample and Deep Convolutional Neural Network (DCNN) for the purpose of de-striping on Hyperion data to de-stripe on multicore platforms GPU. In order to reach our goal, we have started by analyzing the challenges associated with de-striping, Hyperion data and DCNN. A novel implementation pipeline of training, validating and evaluating stating from input an augmented blood image sample with the help analyzing the Hyperion data leading to the de-striping of augmented image blood sample by removing all the kernel black. What is clear is the high importance of applying the adequate pre-processing on Hyperion data because of low signal-to-noise ratio. By comparing the known layers of DCNN model for de-striping augmented images. The results obtained by applying the mentioned methods, it is revealed that all the higher stripes in an image as well as black color has been reduced and entirely associated with the Hyperion data alteration, and in contrast, the Hyperion imagery successfully corresponds to the de-striping of augmented image with an accuracy of 91.89% using DCNN model. The proposed DCNN is capable of reaching high accuracy within 150s after the launch of the evaluation phase and never reaches low accuracy. The pre-trained DCNN model approach would be an adequate solution considering de-striping as its high inference time is lower compared existing available methods which are not as efficient for de-striping.

Remote sensing of inland Sabkha and a study of the salinity and temporal stability for sustainable development: A case study from the West coast of Qatar

The inland sabkha of the Arabian Gulf is important to study for the occurrence of minerals, rocks, soil salinity, and stability of the sabkha due to the high demand for infrastructure and agriculture development region. This study describes the spectral absorptions of evaporite minerals, discriminates rocks, maps salt crusts, gypsiferous soil flats, and soil salinity, and studies the temporal stability of an inland sabkha of the Dukhan area, west coast of the State of Qatar. This was performed using satellite data of the Hyperion of EO1, ASTER of Terra, and multispectral instrument (MSI) of Sentinel-2. The occurrence of minerals in the area is detected using Hyperion data by the linear spectral unmixing (LSU) method and studied for their spatial distribution. The different geological formations of the sabkha were discriminated by using the VNIR (visible and near-infrared) and SWIR (shortwave infrared) spectral bands from ASTER by principal component analysis (PCA). The image developed by using the principal components (R:PC2, G:PC3, B:PC5) showed the formations in different tones. Salinity of the area was mapped using monthly data of MSI from 2018 to 2020 by normalized difference salinity index (NDSI) (band11-band12)/(band11 + band12). The results of the index displayed the distribution of salinity in the area. Besides, moisture of the area was studied by using the normalized difference moisture index (NDMI) (b8-b11)/(b8 + b11) and described the temporal stability of the sabkha. All the results of image analyses were validated through field and laboratory studies. The study of laboratory spectra of evaporite minerals namely gypsum, anhydrite, and halite present in the salt crusts and gypsiferous soil flats showed their unique spectral absorptions in between 1.4–1.5 μm and 1.9–2.0 μm whereas, the calcite and dolomite minerals of the carbonate formations exhibited deep absorptions near 2.345 and 2.495 μm respectively.