Atmospheric Precorrected Differential Absorption Technique to Retrieve Columnar Water Vapor

Schläpfer, Daniel; Borel, Christoph C.; Keller, Johannes; Itten, K.I.

doi:10.1016/s0034-4257(98)00044-3

Cited by 176 publications

(107 citation statements)

References 11 publications

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“…The Water Vapour content is retrieved from Level-1C image using a Sentinel-2 adapted APDA (Atmospheric Pre-corrected Differential Absorption) algorithm [11] which uses a ratio between band B8A and band B09. The quantification value to convert Digital Numbers to Water Vapour column in cm is equal to 1000.…”

Section: Level-2a Products Descriptionmentioning

confidence: 99%

Copernicus Sentinel-2A Calibration and Products Validation Status

et al. 2017

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As part of the Copernicus programme of the European Commission (EC), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission that is acquiring high spatial resolution optical imagery. This article provides a description of the calibration activities and the status of the mission products validation activities after one year in orbit. Measured performances, from the validation activities, cover both Top-Of-Atmosphere (TOA) and Bottom-Of-Atmosphere (BOA) products. The presented results show the good quality of the mission products both in terms of radiometry and geometry and provide an overview on next mission steps related to data quality aspects.

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Section: Level-2a Products Descriptionmentioning

confidence: 99%

Copernicus Sentinel-2A Calibration and Products Validation Status

et al. 2017

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“…Other Sen2Cor outputs comprise (i) an estimation of the aerosol optical thickness (AOT) using the dense dark vegetation (DDV) algorithm [19] and the (ii) retrieval of water vapor (WV) using the pre-corrected differential absorption algorithm (APDA, [20]) analyzing Sentinel-2 bands B8a and B9. Example outputs for AOT and WV are shown in Figure 2.…”

Section: Sentinel-2 Level2-a Data and Value-added Productsmentioning

confidence: 99%

“…Sen2Cor includes a scene classification module (example in Figure 1b) to map no data or defective pixels (pixel value = 0-1), four different cloud clover class probabilities (7)(8)(9)(10), and six different classes including shadows (2), cloud shadows (3), vegetation (4), soils and deserts (5), water (6), and snow (11). Other Sen2Cor outputs comprise (i) an estimation of the aerosol optical thickness (AOT) using the dense dark vegetation (DDV) algorithm [19] and the (ii) retrieval of water vapor (WV) using the pre-corrected differential absorption algorithm (APDA, [20]) analyzing Sentinel-2 bands B8a and B9. Example outputs for AOT and WV are shown in Figure 2.…”

Section: Sentinel-2 Level2-a Data and Value-added Productsmentioning

confidence: 99%

Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples

et al. 2016

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This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth. Using the European Space Agency's (ESA) Sen2Cor algorithm, the platform processes ESA's Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data . Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R 2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R 2 = 0.83) and a RMSE of 0.32 m 2 /m 2 (12% of mean value).

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“…Therefore the accuracy of the AOT retrieval depends on the availability and the correct identification of DDV pixels, and the validity of the assumption on the spectral correlation between SWIR and VNIR for these pixels. 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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Atmospheric Precorrected Differential Absorption Technique to Retrieve Columnar Water Vapor

Cited by 176 publications

References 11 publications

Copernicus Sentinel-2A Calibration and Products Validation Status

Copernicus Sentinel-2A Calibration and Products Validation Status

Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples

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

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