Discriminatiοn of Irοn Deposits Using Feature Οriented Principal Cοmpοnent Selectiοn and Band Ratiο Methοds: Eastern Taurus /Turkey

Traoré, Mamadou; Çan, Tolga; Tekin, Senem

doi:10.30897/ijegeo.673143

Cited by 11 publications

(8 citation statements)

References 21 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Retrieval of soil surface parameters from SAR data normally can be realized using the backscattering model that presents the relation between the target parameters (soil moisture and International Journal of Environment and Geoinformatics 8(1): 065-077 (2021) Research Article How to cite: Sutariya et al, (2021 Soil Moisture Estimation using Sentinel-1 SAR data and Land Surface Temperature in Panchmahal district, Gujarat State, International Journal of Environment and Geoinformatics (IJEGEO), 8(1):065-077. doi: 10.30897/ijegeo.777434 roughness) and the SAR sensor configurations such as incidence angle, polarization, and frequency (Sahebi et al, 2002). The backscattered SAR signal is affected strongly from soil moisture and surface roughness on bare soil (Ulaby, Moore, and Fung, 1986;Zribi et al, 2007). For bare soils, different theoretical and empirical approaches have been developed and many approaches assumed that there is a linear behaviour between surface soil moisture and SAR backscattering coefficient (sigmanought:σ0) (Esetlili and Kurucu, 2016;Esetlili et al, 2018;Gao, Zribi, Escorihuela, and Baghdadi, 2017;Zribi, Baghdadi, Holah, and Fafin, 2005).…”

Section: Microwave Remote Sensing For Soil Moisture Estimationmentioning

confidence: 99%

“…Zeng et al, 2004 proposed a method to retrieve SMC based on the integration of LST and NDVI called Triangle method. LST is determined from thermal emission, and NDVI is estimated based on surface reflectance of red and the near infrared portions of the electromagnetic spectrum, so these methods sometimes are termed as optical, thermal infrared remote sensing (Amato et al, 2015;Rahimzadeh-Bajgiran et al, 2013;Traore et al, 2020).…”

Section: Multi-sensor Fusion For Soil Moisture Retrievalmentioning

confidence: 99%

See 1 more Smart Citation

Soil Moisture Estimation using Sentinel-1 SAR Data and Land Surface Temperature in Panchmahal District, Gujarat State

Sutariya

ANKUR

Meherbanali

et al. 2021

International Journal of Environment and Geoinformatics

View full text Add to dashboard Cite

This paper presents the potential for soil moisture (SM) retrieval using Sentinel-1 C-band Synthetic Aperture Radar (SAR) data acquired in Interferometric Wide Swath (IW) mode along with Land Surface Temperature (LST) estimated from analysis of LANDSAT-8 digital thermal data. In this study Sentinel-1 data acquired on 27 February 2020 was downloaded from Copernicus website and LANDSAT-8 OLI data acquired on 24 February 2020 from the website https://earthexplorer.usgs.gov/.The soil samples were collected from 70 test fields in different villages of three talukas for estimating soil moisture content using the gravimetric method. The Sentinel-1 SAR microwave data was analysed using open source tools of Sentinel Application Platform (SNAP) software for estimation of backscattering coefficient. Land surface temperature estimated using Landsat-8 thermal data. The Landsat-8, Thermal infrared sensor Band-10 data and operational land imager Band-4 and Band-5 data were used in estimating LST. The Soil Moisture Index (SMI) for all field test sites was computed using the LST values. The regression analysis using σ0VV and σ0VH polarization with soil moisture indicated that σ0VV polarization was more sensitive to soil moisture content as compared to σ0VH polarization. The multiple regression analysis using field measured soil moisture (MS %) as dependent variable, and σ0VV and SMI as independent variable was carried which resulted in the coefficient of determination (R2) of 0.788, 0.777 and 0.778 for Godhra, Goghamba and Kalol talukas, respectively. These linear regression equations were used to compute the predicted soil moisture in three talukas.

show abstract

Section: Microwave Remote Sensing For Soil Moisture Estimationmentioning

confidence: 99%

Section: Multi-sensor Fusion For Soil Moisture Retrievalmentioning

confidence: 99%

Soil Moisture Estimation using Sentinel-1 SAR Data and Land Surface Temperature in Panchmahal District, Gujarat State

Sutariya

ANKUR

Meherbanali

et al. 2021

International Journal of Environment and Geoinformatics

View full text Add to dashboard Cite

show abstract

“…At first, iron oxides BRs 3/1 and 4/2 for Landsat TM/ETM+ and OLI8, respectively, were applied in order to highlight iron oxides by the presence of bright pixels (Sabins, 1999). The iron oxides BR is useful for detecting ferric iron (Fe 3+ ) oxides such as hematite and goethite (Imbroane et al, 2007;Hung, 2013;Pour et al, 2019;Rockwell, 2013;Soe et al, 2005;Traore et al, 2020). At second, the BRs 5/7 and 6/7 for Landsat TM/ETM+ and OLI8, respectively, were applied in order to highlight hydroxyl-bearing minerals such as clay minerals (kaolinite, montmorillonite and alunite) by the presence of bright pixels (Clark, 1999;Hung, 2013;Imbroane et al, 2007;Sabins, 1999;Soe et al, 2005).…”

Section: Spectral Band Ratiomentioning

confidence: 99%

“…Based on this spectral library, the multispectral bands (1, 3, 4, and 5) and (2, 4, 5, and 6) were used for TM-ETM+ and OLI/ TIRS, respectively, to highlight the spectral response of iron oxides. The multispectral bands (1, 4, 5, and 7) and (2, 5, 6, and 7) were used for TM-ETM+ and OLI/TIRS, respectively, to enhance hydroxyl-bearing minerals (Osinowo et al, 2021;Pour et al, 2019;Traore et al, 2020). The resulting PCs images could then show targeted surface types such as rocks, waste, soils, villages/settlements by highlighting them as bright or dark pixels, depending on their respective (positive/negative) eigenvector magnitudes and signs (Boateng et al, 2018;Kujjo et al, 2018).…”

Section: Feature Oriented Principal Component Selectionmentioning

confidence: 99%

Evolution of the Koma Bangou Gold Panning Site (Niger) From 1984 to 2020 Using Landsat Imagery

Abdoulatif

Baratoux

et al. 2021

Earth and Space Science

View full text Add to dashboard Cite

Artisanal and small-scale mining (ASM) is mainly prevalent in developing countries (Abaidoo et al., 2019;S. Keita, 2001;Owusu et al., 2019). A few years ago, small-scale mining was estimated to account for 15%-20% of global non-fuel mineral production (Hentschel et al., 2002). Although the African continent is home to some of the world's most coveted mineral resources, this favorable geological context has largely failed to lead it toward an equitable and sustainable development trajectory (Hilson, 2002). In some African countries, extreme poverty (A. Keita, 2017;Owusu & Dwomoh, 2012) and crop failures due to climate change are driving rural populations, consisting mainly of farmers, to engage in artisanal mining to meet their daily needs. Artisanal mining is currently the most important rural non-farm activity in Africa (Hilson, 2020). It

show abstract

“…However, TIR satellite sensors have low temporal, spatial, and spectral resolutions, and the spectral responses in these bands may contain a mixture of features [11]. Vegetation or soil cover may cause high variation in granite spectral response, making it challenging to map the deposits [4,12]. High-spatial-resolution SWIR bands show spectral absorption for different granite compositions (e.g., muscovite and epidote) and calcite in 2.2 µm regions; a complete absorption is only observed for calcite near the 2.3 µm regions [4,10].…”

Section: Introductionmentioning

confidence: 99%

A Fusion of Feature-Oriented Principal Components of Multispectral Data to Map Granite Exposures of Pakistan

et al. 2021

View full text Add to dashboard Cite

Despite low spatial resolutions, thermal infrared bands (TIRs) are generally more suitable for mineral mapping due to fundamental tones and high penetration in vegetated areas compared to shortwave infrared (SWIR) bands. However, the weak overtone combinations of SWIR bands for minerals can be compensated by fusing SWIR-bearing data (Sentinel-2 and Landsat-8) with other multispectral data containing fundamental tones from TIR bands. In this paper, marble in a granitic complex in Mardan District (Khyber Pakhtunkhwa) in Pakistan is discriminated by fusing feature-oriented principal component selection (FPCS) obtained from the ASTER, Landsat-8 Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS) and Sentinel-2 MSI data. Cloud computing from Google Earth Engine (GEE) was used to apply FPCS before and after the decorrelation stretching of Landsat-8, ASTER, and Sentinel-2 MSI data containing five (5) bands in the Landsat-8 OLI and TIRS and six (6) bands each in the ASTER and Sentinel-2 MSI datasets, resulting in 34 components (i.e., 2 × 17 components). A weighted linear combination of selected three components was used to map granite and marble. The samples collected during field visits and petrographic analysis confirmed the remote sensing results by revealing the region’s precise contact and extent of marble and granite rock types. The experimental results reflected the theoretical advantages of the proposed approach compared with the conventional stacking of band data for PCA-based fusion. The proposed methodology was also applied to delineate granite deposits in Karoonjhar Mountains, Nagarparker (Sindh province) and the Kotah Dome, Malakand (Khyber Pakhtunkhwa Province) in Pakistan. The paper presents a cost-effective methodology by the fusion of FPCS components for granite/marble mapping during mineral resource estimation. The importance of SWIR-bearing components in fusion represents minor minerals present in granite that could be used to model the engineering properties of the rock mass.

show abstract

Discriminatiοn of Irοn Deposits Using Feature Οriented Principal Cοmpοnent Selectiοn and Band Ratiο Methοds: Eastern Taurus /Turkey

Abstract: International Symposium on Applied Geoinformatics (ISAG2019) was held in Istanbul on 7-9 November 2019. The symposium is organized with the aim of promoting the advancements to explore the latest scientific and technological developments and opportunities in the field of Geoinformatics.

Cited by 11 publications

References 21 publications

Soil Moisture Estimation using Sentinel-1 SAR Data and Land Surface Temperature in Panchmahal District, Gujarat State

Soil Moisture Estimation using Sentinel-1 SAR Data and Land Surface Temperature in Panchmahal District, Gujarat State

Evolution of the Koma Bangou Gold Panning Site (Niger) From 1984 to 2020 Using Landsat Imagery

A Fusion of Feature-Oriented Principal Components of Multispectral Data to Map Granite Exposures of Pakistan

Contact Info

Product

Resources

About