Considerations on Water Vapor and Surface Reflectance Retrievals for a Spaceborne Imaging Spectrometer

Richter, Rudolf; Schläpfer, Daniel

doi:10.1109/tgrs.2008.916470

Cited by 14 publications

(7 citation statements)

References 30 publications

(36 reference statements)

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“…The validation results of the WV retrieval algorithm, using the water absorption region of 820 nm, are consistent with the APDA algorithm validation performed by Richter and Schlapfer [128], who estimated an RMSE of 10% for Sentinel-2 data, using the 940 nm bands.…”

Section: Product Quality and Validationsupporting

confidence: 85%

Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS)

Alonso¹,

Bachmann

Burch

et al. 2019

Sensors

116

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Imaging spectrometry from aerial or spaceborne platforms, also known as hyperspectral remote sensing, provides dense sampled and fine structured spectral information for each image pixel, allowing the user to identify and characterize Earth surface materials such as minerals in rocks and soils, vegetation types and stress indicators, and water constituents. The recently launched DLR Earth Sensing Imaging Spectrometer (DESIS) installed on the International Space Station (ISS) closes the long-term gap of sparsely available spaceborne imaging spectrometry data and will be part of the upcoming fleet of such new instruments in orbit. DESIS measures in the spectral range from 400 and 1000 nm with a spectral sampling distance of 2.55 nm and a Full Width Half Maximum (FWHM) of about 3.5 nm. The ground sample distance is 30 m with 1024 pixels across track. In this article, a detailed review is given on the applicability of DESIS data based on the specifics of the instrument, the characteristics of the ISS orbit, and the methods applied to generate products. The various DESIS data products available for users are described with the focus on specific processing steps. The results of the data quality and product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements. The limitations of the DESIS data products are also subject to a critical examination.

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Section: Product Quality and Validationsupporting

confidence: 85%

Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS)

Alonso¹,

Bachmann

Burch

et al. 2019

Sensors

116

View full text Add to dashboard Cite

show abstract

“…For scene-based water vapour retrieval, the atmospheric pre-corrected differential absorption technique (APDA) is used (see [23]). The considerations when applied to spaceborne imaging spectrometer data are discussed in detail in [24].…”

Section: Enmap Atmospheric Correction Over Landmentioning

confidence: 99%

Estimating the Influence of Spectral and Radiometric Calibration Uncertainties on EnMAP Data Products—Examples for Ground Reflectance Retrieval and Vegetation Indices

et al. 2015

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As part of the EnMAP preparation activities this study aims at estimating the uncertainty in the EnMAP L2A ground reflectance product using the simulated scene of Barrax, Spain. This dataset is generated using the EnMAP End-to-End Simulation tool, providing a realistic scene for a well-known test area. Focus is set on the influence of the expected radiometric calibration stability and the spectral calibration stability. Using a Monte-Carlo approach for uncertainty analysis, a larger number of realisations for the radiometric and spectral calibration are generated. Next, the ATCOR atmospheric correction is conducted for the test scene for each realisation. The subsequent analysis of the generated ground reflectance products is carried out independently for the radiometric and the spectral case. Findings are that the uncertainty in the L2A product is wavelength-dependent, and, due to the coupling with the estimation of atmospheric parameters, also spatially variable over the scene. To further illustrate the impact on subsequent data analysis, the influence on two vegetation indices is briefly analysed. Results show that the radiometric and spectral stability both have a high impact on the uncertainty of the narrow-band Photochemical Reflectance Index (PRI), and also the broad-band Normalized Difference Vegetation Index (NDVI) is affected.

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“…• Calculation of the Water Vapor (WV) column map using the Atmospheric Pre-corrected Differential Absorption algorithm (Schläpfer et al, 1998) (Richter and Schlapfer, 2008) using the 820 nm absorption region. The WV is calculated only for land pixels.…”

Section: Desis L2a Processor and Productsmentioning

confidence: 99%

The Desis L2a Processor and Validation of L2a Products Using Aeronet and Radcalnet Data

Reyes

Alonso

Bachmann

et al. 2022

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

Self Cite

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Abstract. The hyperspectral instrument ”DLR Earth Sensing Imaging Spectrometer” (DESIS) is a VNIR sensor on-board of the International Space Station (ISS) and operational since October 2019. DESIS acquires images of Earth on user request with a swath of about 30 km width and 235 bands with a Full Width at Half Maximum (FWHM) of 3.5 nm in the spectral range between 400 to 1000 nm.In this article we will present the basis of the atmospheric correction by PACO software, implemented inside the DESIS Ground Segment as L2A processor. The resulting L2A products will be validated against independent in-situ measurements.The aerosol optical thickness and water vapor will be compared with the Aerosol Robotic Network (AERONET) measurements and the surface reflectance will be validated with the Radiometric Calibration Network (RadCalNet) data.

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Considerations on Water Vapor and Surface Reflectance Retrievals for a Spaceborne Imaging Spectrometer

Cited by 14 publications

References 30 publications

Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS)

Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS)

Estimating the Influence of Spectral and Radiometric Calibration Uncertainties on EnMAP Data Products—Examples for Ground Reflectance Retrieval and Vegetation Indices

The Desis L2a Processor and Validation of L2a Products Using Aeronet and Radcalnet Data

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