Omongwa Pan, Namibia (June 2015) - an EnMAP Preparatory Flight Campaign

DOI

The dataset is composed of Neo HySpex (VNIR/SWIR) hyperspectral imagery acquired during airplane overflights on June 6th, 2015 covering the Omongwa Pan located in the South-West Kalahari, Namibia. The dataset includes three cloud-free flight lines with 408 spectral bands ranging from VNIR to SWIR wavelength regions (0.4-2.5 µm). The dataset also includes Level 2A EnMAP-like imagery simulated using the end-to-end Simulation tool (EeteS). The overall goal of the campaign was to acquire imagery over the Omongwa Pan and use the spectral reflectance for the analyses of surface sediments, specifically the mineralogical composition of exposed surface evaporites / salts on the airborne and spaceborne scale. The data are highly novel and can be used to test estimation of surface sediment properties in a highly saline and dynamic environment.

The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that aims at monitoring and characterizing the Earth’s environment on a global scale. EnMAP serves to measure and model key dynamic processes of the Earth’s ecosystems by extract-ing geochemical, biochemical and biophysical parameters, which provide information on the status and evolution of various terrestrial and aquatic ecosystems. In the frame of the EnMAP preparatory phase, pre-flight campaigns including airborne and in-situ measurements in different environments and for several application fields are being conducted. The main purpose of these campaigns is to support the development of scientific applications for EnMAP. In addition, the acquired data are input in the EnMAP end-to-end simulation tool (EeteS) and are employed to test data pre-processing and calibration-validation methods. The campaign data are made freely available to the scientific community under a Creative Commons Attribution 4.0 International License. An overview of all available data is provided in in the EnMAP Flight Campaigns Metadata Portal (https://www.enmap.org/data_tools/flights/).

The airborne datasets were obtained at an altitude of 2850 m above ground level over the Omongwa pan on the 6th UTC 2015 during a GFZ/ DIMAP airborne campaign. The hyperspectral data have been acquired using two Neo HySpex cameras in nine flight stripes with alternating SE/ NW heading under blue sky conditions at 10:30-12:00 UTC with a sun elevation angle of 40-45° and sun azimuth angle of -10° to 20°. The NEO HySpex system consists of two push-broom hyperspectral cameras (VNIR-1600 operating over the 0.4 1.0 µm and SWIR 320m e operating over 1.0 2.5 µm range) with a total of 408 wavebands and a spectral resolution of 3.7 nm (VNIR-1600) and 6.0 nm (SWIR-320m e) (Norsk Elektro Optikk 2017). The acquired data were radiometrically corrected using sensor specific software of the instrument manufacturer. Further processing from at sensor radiance to orthorectified surface reflectance was realized with the GFZ in-house processing chain HyPrepAir based on the sensor models and the simultaneously measured IMU/GPS data stream (Brell et al. 2016).

In a first step, physically based atmospheric correction of the HySpex data was carried out in sensor geometry for the separated VNIR and SWIR sensors with the ATCOR-4 software (Richter and Schläpfer 2016) based on the radiative transfer model MODTRAN 5 (Richter and Schläpfer 2002). A desert aerosol model, water vapour column of 1.0 g m^-2, and a visibility of 60 km, were selected as atmospheric parameters. Spectral smile could be detected and removed using the ATCOR-4 smile detection routine. In a second step, a direct geometric correction was realized. The VNIR sensor was used as a reference to co-register the SWIR sensors automatically based on a ray tracing procedure (Brell et al. 2016).

Thus, the SWIR spectra are implemented and adapted to the overlapping VNIR spectra wavelength. Then the flight stripes were composed into a single mosaic with a spatial resolution of 2.3 m without image feathering or colour balancing to keep the original data values. To further remove atmospheric attenuation and spectral artefacts an Empirical Line Calibration (ELC) (Aspinall et al, 2002) implemented in ENVI 5.3 (Harris Geospatial Solutions 2015) was performed using field-measurements of several reflectance targets with different albedo.

Identifier
DOI https://doi.org/10.5880/enmap.2020.005
Related Identifier https://doi.org/10.1007/s101090100071
Related Identifier https://doi.org/10.1109/TGRS.2016.2518930
Related Identifier https://doi.org/10.3390/rs12030474
Related Identifier https://doi.org/10.1016/j.jag.2018.12.012
Related Identifier https://doi.org/10.3390/rs9020170
Related Identifier https://doi.org/10.3390/rs70708830
Related Identifier http://www.rese.ch/pdf/atcor3_manual.pdf
Related Identifier https://www.rese-apps.com/pdf/atcor4_manual.pdf
Related Identifier https://doi.org/10.1109/JSTARS.2012.2188994
Related Identifier https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.566.5481&rep=rep1&type=pdf
Related Identifier https://doi.org/10.2312/enmap.2020.005
Related Identifier https://www.enmap.org/data_tools/flights/
Metadata Access http://doidb.wdc-terra.org/oaip/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:doidb.wdc-terra.org:6998
Provenance
Creator Milewski, Robert (GFZ German Research Centre for Geosciences, Potsdam, Germany); Chabrillat, Sabine (GFZ German Research Centre for Geosciences, Potsdam, Germany); Brell, Maximilian (GFZ German Research Centre for Geosciences, Potsdam, Germany); Behling, Robert (GFZ German Research Centre for Geosciences, Potsdam, Germany); Eichstaedt, Holger (Dimap HK Pty Limited, Freiberg, Germany)
Publisher GFZ Data Services
Contributor Milewski, Robert
Publication Year 2020
Funding Reference Bundesministerium für Wirtschaft und Energie; Bundesministerium für Bildung und Forschung
Rights CC BY 4.0
OpenAccess true
Contact Milewski, Robert (GFZ German Research Centre for Geosciences, Potsdam, Germany)
Representation
Resource Type Dataset
Discipline Geodesy, Geoinformatics and Remote Sensing
Spatial Coverage (19.335W, -23.734S, 19.428E, -23.639N)