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

Martin Bachmann,Rudolf Richter,Aliaksei Makarau,Karl Segl

doi:10.3390/rs70810689

Abstract

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.

Highlights

With the advent of new satellite sensors such as the ESA Sentinels, there is a growing demand for highly reliable and well-documented Earth observation data to fulfil the data needs for COPERNICUS services
To underpin the importance of this topic, the Committee on Earth Observation Satellites (CEOS) initiated the Quality Assurance Framework for Earth Observation (QA4EO), which demands that all “Data and derived products shall have associated with them a fully traceable indicator of their quality” [1]
In the field of airborne hyperspectral remote sensing, the FP7 project EUropean Facility for Airborne Research (EUFAR) developed a set of harmonized Quality Indicators (QIs) for Level 1 and Level 2 data [4], which included the development of a full error propagation concept [5]

Summary

Introduction

With the advent of new satellite sensors such as the ESA Sentinels, there is a growing demand for highly reliable and well-documented Earth observation data to fulfil the data needs for COPERNICUS services. To underpin the importance of this topic, the Committee on Earth Observation Satellites (CEOS) initiated the Quality Assurance Framework for Earth Observation (QA4EO), which demands that all “Data and derived products shall have associated with them a fully traceable indicator of their quality” [1]. Quality Indicators (QIs), a concept of uncertainty of measurements is included in the QA4EO guidelines. In the field of airborne hyperspectral remote sensing, the FP7 project EUropean Facility for Airborne Research (EUFAR) developed a set of harmonized QIs for Level 1 (calibrated at-sensor radiance) and Level 2. Extending the EUFAR developments, an exhaustive uncertainty analysis was carried out for the Airborne Prism Experiment (APEX) sensor [6,7], and, on a smaller scale, for parts of the DLR hyperspectral pre-processing chain [8]

Objectives

Methods

Results

Conclusion