EnGeoMAP 2.0—Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission

Mielke, Christian; Rogaß, Christian; Boesche, Nina Kristine; Segl, Karl; Altenberger, Uwe

doi:10.3390/rs8020127

Cited by 39 publications

(29 citation statements)

References 45 publications

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“…However, S2 could not distinguish surface mineralogy correctly, unlike EnMAP. In another study, Mielke et al [116] showed that simulated EnMAP data provided better results than Hyperion in order to characterize mineral deposits and to highlight exploration anomalies (i.e., unexpected soil properties, which are indicators of valuable soil elements). They implemented an algorithm and expert system (EnGeoMAP, the EnMAP Geological Mapper ) to detect metal sulfide mineral deposit sites.…”

Section: Geology Applicationsmentioning

confidence: 99%

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

et al. 2018

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In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO) remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit time. Its recent launch suggests potential synergies between multi-and hyper-spectral data. This study, therefore, reviews 20 years of research and applications in satellite hyperspectral remote sensing through the analysis of Earth observation hyperspectral sensors' publications that cover the Sentinel-2 spectrum range: Hyperion, TianGong-1, PRISMA, HISUI, EnMAP, Shalom, HyspIRI and HypXIM. More specifically, this study (i) brings face to face past and future hyperspectral sensors' applications with Sentinel-2's and (ii) analyzes the applications' requirements in terms of spatial and temporal resolutions. Eight main application topics were analyzed including vegetation, agriculture, soil, geology, urban, land use, water resources and disaster. Medium spatial resolution, long revisit time and low signal-to-noise ratio in the short-wave infrared of some hyperspectral sensors were highlighted as major limitations for some applications compared to the Sentinel-2 system. However, these constraints mainly concerned past hyperspectral sensors, while they will probably be overcome by forthcoming instruments. Therefore, this study is putting forward the compatibility of hyperspectral sensors and Sentinel-2 systems for resolution enhancement techniques in order to increase the panel of hyperspectral uses.

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Section: Geology Applicationsmentioning

confidence: 99%

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

et al. 2018

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“…The geometrical transformation is applied at the end on the results to prevent resampling effects on the spectral analyses. The pre-processing chain was used previously for geological applications, e.g., by Mielke et al [85,86] proving that it can deal with the sensor limited quality and still delivers reflectance data that are adequate for mineralogical identification analyses.…”

Section: Eo-1 Hyperion Mineralogical Mappingmentioning

confidence: 99%

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

2017

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This study combines spaceborne multitemporal and hyperspectral data to analyze the spatial distribution of surface evaporite minerals and changes in a semi-arid depositional environment associated with episodic flooding events, the Omongwa salt pan (Kalahari, Namibia). The dynamic of the surface crust is evaluated by a change-detection approach using the Iterative-reweighted Multivariate Alteration Detection (IR-MAD) based on the Landsat archive imagery from 1984 to 2015. The results show that the salt pan is a highly dynamic and heterogeneous landform. A change gradient is observed from very stable pan border to a highly dynamic central pan. On the basis of hyperspectral EO-1 Hyperion images, the current distribution of surface evaporite minerals is characterized using Spectral Mixture Analysis (SMA). Assessment of field and image endmembers revealed that the pan surface can be categorized into three major crust types based on diagnostic absorption features and mineralogical ground truth data. The mineralogical crust types are related to different zones of surface change as well as pan morphology that influences brine flow during the pan inundation and desiccation cycles. These combined information are used to spatially map depositional environments where the more dynamic halite crust concentrates in lower areas although stable gypsum and calcite/sepiolite crusts appear in higher elevated areas.

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“…Field measured spectra have shown absorptions at 0.5070 μm, 0.6260 μm, 0.9310 μm, 1.4132 μm, 1.8080 μm. (Magendran and Sanjeevi, 2014, Murphy and Monteiro, 2013, Mielke et al, 2016, Roberto and Filho, 2000, Govil et al, 2018, Pour and Hashim, 2014, Pour and Hashim, 2015, Zhang et al, 2016, Boesche et al, 2015.…”

Section: Data and Methodolgymentioning

confidence: 99%

Identification of Iron Oxides Minerals in Western Jahajpur Region, India Using Aviris-Ng Hyperspectral Remote Sensing

Govil

Tripathi

Diwan

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Hyperspectral remote sensing is being considered as an advanced technique for mineral identification of surficial deposits. In this research different iron oxides minerals such as limonite, goethite has been identified using AVIRIS-NG airborne hyperspectral remote sensing covering the Omkarpura, Itwa, and Chhabadiya mines area in Jahajpur Bhilwara, Rajasthan, India. AVIRIS-NG has shown robust performance in iron oxide identification in the study area. Mineral spectral signatures of the AVIRIS-NG data were compared with spectra of USGS spectral library, and field investigated mineral spectra of iron oxides and found very promising. The results allow us to conclude that due the high signal to noise ratios of the AVIRIS-NG, it is capable to identify the different iron bearing minerals in the visible and infrared portion of the electromagnetic spectrum. * Corresponding author absorption at 0.43, 0.5 and 0.6 μm, and Fe 2+ have shown absorption features in 0.9 -1.2 μm(Magendran and Sanjeevi, This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-5-233-2018 | © Authors 2018. CC BY 4.0 License.

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EnGeoMAP 2.0—Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission

Cited by 39 publications

References 45 publications

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

Identification of Iron Oxides Minerals in Western Jahajpur Region, India Using Aviris-Ng Hyperspectral Remote Sensing

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