Data Processing for the Space-Based Desis Hyperspectral Sensor

Carmona, E.; Avbelj, Janja; Alonso, Kevin; Bachmann, Martin; Cerra, Daniele; Eckardt, Andreas; Gerasch, Birgit; Graham, L.; Günther, Burghardt; Heiden, Uta; Kerr, Grégoire; Knodt, Uwe; Krutz, David; Krawcyk, H.; Makarau, Aliaksei; Miller, R.L.; Müller, Rupert; Perkins, R.; Walter, Ingo

doi:10.5194/isprs-archives-xlii-1-w1-271-2017

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…The DESIS level 2A product is available in its original form, as well as three postprocessing variants. The original variant provides a totality of 235 bands, each covering a wavelength range of 2.55 nm [27]. The other variants are binning products, for which several bands are combined.…”

Section: Methodsmentioning

confidence: 99%

Utilization of Hyperspectral Remote Sensing Imagery for Improving Burnt Area Mapping Accuracy

2021

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Wildfires pose a direct threat when occurring close to populated areas. Additionally, their significant carbon and climate feedbacks represent an indirect threat on a global, long-term scale. Monitoring and analyzing wildfires is therefore a crucial task to increase the understanding of interconnections between fire and ecosystems, in order to improve wildfire management activities. This study investigates the suitability of 232 different red/near-infrared band combinations based on hyperspectral imagery of the DESIS sensor with regard to burnt area detection accuracy. It is shown that the selection of wavelengths greatly influences the detection quality, and that especially the utilization of lower near-infrared wavelengths increases the yielded accuracy. For burnt area analysis based on the Normalized Difference Vegetation Index (NDVI), the optimal wavelength range has been found to be 660–670 nm and 810–835 nm for the red band and near-infrared band, respectively.

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Section: Methodsmentioning

confidence: 99%

Utilization of Hyperspectral Remote Sensing Imagery for Improving Burnt Area Mapping Accuracy

2021

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“…PRISMA is the Italian hyperspectral mission launched on 27 March 2019 on board the VEGA (Vettore Europeo di Generazione Avanzata) rocket by ASI (Italian Space Agency). PRISMA represents the first mission with a dedicated hyperspectral (HS) camera, though other similar missions have been recently launched (Guanter et al., 2015) or are planned in the future years (Carmona et al., 2017; Cawse‐Nicholson et al., 2021; Nieke & Rast, 2018).…”

Section: Datamentioning

confidence: 99%

Wildfires Temperature Estimation by Complementary Use of Hyperspectral PRISMA and Thermal (ECOSTRESS & L8)

et al. 2022

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This paper deals with detection and temperature analysis and of wildfires using PRISMA imagery. Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by ASI (Agenzia Spaziale Italiana, Italian Space Agency) launched in 2019. This mission provides hyperspectral images with a spectral range of 400–2,500 nm and an average spectral resolution less than 12 nm and a spatial resolution of 30 m/pixel. This study focuses on the wildfire temperature estimation over the Bootleg Fire, US 2021. The analysis starts by considering the Hyperspectral Fire Detection Index (HFDI) which is used to analyze the informative content of the images, along with the analysis of some specific visible, near‐infrared and shortwave‐infrared bands. This first analysis is used as input to perform a temperature estimation of the areas with active wildfire. Surface temperature is retrieved using PRISMA radiance and a linear mixing model based on two background components (vegetation and burn scar) and two active fire components. PRISMA temperatures are compared with LST (Land Surface Temperature) products from NASA's ECOSTRESS and Landsat 8 which imaged the Bootleg Fire before and after PRISMA. A critical discussion of the results obtained with PRISMA is presented, followed by the advantages and limitation of the proposed approach.

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“…orbit and attitude measurements, geometric calibration values, spectral channel information). Rolling shutter, smile and keystone correction is performed by the L1B processor [5]. The L1B product comprises all information for further processing.…”

Section: Product Generationmentioning

confidence: 99%

Processing, Validation And Quality Control Of Spaceborne Imaging Spectroscopy Data From Desis Mission on the Iss

Müller

Bachmann

Alonso

et al. 2018

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

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The German Aerospace Center (DLR) and Teledyne Brown Engineering (TBE), located in Huntsville, Alabama, USA, cooperate to develop and operate the new space-based hyperspectral sensor DLR Earth Sensing Imaging Spectrometer (DESIS). While TBE provides the MultiUser platform MUSES and infrastructure for operation of the DESIS instrument on the ISS, DLR is responsible for providing the instrument and the processing software as well as instrument in-flight calibration and product quality operations. MUSES has been already launched and installed on the International Space Station ISS in early 2017 and DESIS will follow mid of 2018. We present here an overview of the DESIS instrument, the on-ground data processing, the in-flight calibration and product quality investigations.

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Data Processing for the Space-Based Desis Hyperspectral Sensor

Cited by 6 publications

References 9 publications

Utilization of Hyperspectral Remote Sensing Imagery for Improving Burnt Area Mapping Accuracy

Utilization of Hyperspectral Remote Sensing Imagery for Improving Burnt Area Mapping Accuracy

Wildfires Temperature Estimation by Complementary Use of Hyperspectral PRISMA and Thermal (ECOSTRESS & L8)

Processing, Validation And Quality Control Of Spaceborne Imaging Spectroscopy Data From Desis Mission on the Iss

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