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

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.

[1]  R. Richter,et al.  Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction , 2002 .

[2]  Andreas Hueni,et al.  Towards agreed data quality layers for airbornehyperspectral imagery , 2011 .

[3]  K. Itten,et al.  Advanced radiometry measurements and Earth science applications with the Airborne Prism Experiment (APEX) , 2015 .

[4]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[5]  Stefan Kaiser,et al.  Simulation of Spatial Sensor Characteristics in the Context of the EnMAP Hyperspectral Mission , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Daniel Schläpfer,et al.  Correction of cirrus effects in Sentinel-2 type of imagery , 2011 .

[7]  Daniel Schläpfer,et al.  Considerations on Water Vapor and Surface Reflectance Retrievals for a Spaceborne Imaging Spectrometer , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Luis Guanter,et al.  EeteS—The EnMAP End-to-End Simulation Tool , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  Luis Guanter,et al.  Simulation of Optical Remote-Sensing Scenes With Application to the EnMAP Hyperspectral Mission , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[10]  E. Iso,et al.  Measurement Uncertainty and Probability: Guide to the Expression of Uncertainty in Measurement , 1995 .

[11]  D. C. Robertson,et al.  MODTRAN cloud and multiple scattering upgrades with application to AVIRIS , 1998 .

[12]  Josep Peñuelas,et al.  The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis , 2011 .

[13]  Daniel Schläpfer,et al.  Atmospheric Precorrected Differential Absorption Technique to Retrieve Columnar Water Vapor , 1998 .

[14]  U. Benz,et al.  The EnMAP hyperspectral imager—An advanced optical payload for future applications in Earth observation programmes , 2006 .

[15]  Daniel Schläpfer,et al.  Operational Atmospheric Correction for Imaging Spectrometers Accounting for the Smile Effect , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Tobias Storch,et al.  EnMAP Ground Segment Design: An Overview and its Hyperspectral Image Processing Chain , 2013 .

[17]  Kattathu Joseph Mathew,et al.  Guide to the expression of uncertainty in measurements , 2017 .

[18]  Pablo J. Zarco-Tejada,et al.  Assessing Canopy PRI for Water Stress detection with Diurnal Airborne Imagery , 2008 .

[19]  R. Richter,et al.  Correction of satellite imagery over mountainous terrain. , 1998, Applied optics.

[20]  Martin Bachmann,et al.  Including Quality Measures in an Automated Processing Chain for Airborne Hyperspectral Data , 2007 .

[21]  Robert O. Green,et al.  On-orbit radiometric and spectral calibration characteristics of EO-1 Hyperion derived with an underflight of AVIRIS and in situ measurements at Salar de Arizaro, Argentina , 2003, IEEE Trans. Geosci. Remote. Sens..

[22]  William H. Press,et al.  Numerical recipes in C , 2002 .

[23]  L. Guanter,et al.  Spectral calibration of hyperspectral imagery using atmospheric absorption features. , 2006, Applied optics.

[24]  Ruth C. Carter,et al.  Principles , 2003, Law’s Reality.

[25]  Hermann Kaufmann,et al.  ENMAP data product standards , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[26]  Tiit Nilson,et al.  Diffuse sky radiation influences the relationship between canopy PRI and shadow fraction , 2015 .

[27]  R. Jenssen,et al.  1 THE HYMAP TM AIRBORNE HYPERSPECTRAL SENSOR : THE SYSTEM , CALIBRATION AND PERFORMANCE , 1998 .

[28]  W. Verhoef,et al.  Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data , 2007 .

[29]  Lorraine Remer,et al.  The MODIS 2.1-μm channel-correlation with visible reflectance for use in remote sensing of aerosol , 1997, IEEE Trans. Geosci. Remote. Sens..