The EnMAP L2A Water Processor: Operational Performance And Application Of EnMAP Dedicated Water Reflectance Products

Launched in April 2022, EnMAP is an optical (VNIR/SWIR) remote sensing mission with high spatial (30m GSD) and spectral (FWHM ~6-12nm) resolution [1]. As ans unique feature of the mission, the L2A processor of the EnMAP ground segment processing chain has been designed and developed to provide dedicated water reflectance products to the users. It is based on the output of the Modular Inversion and Processing System (MIP) developed by EOMAP GmbH [2]. EnMAP water reflectance can be provided in two different flavours: subsurface irradiance reflectance and normalised water leaving reflectance [3]. The contribution will show the performance of the EnMAP L2A water processor in terms of a quality assessment of the dedicated water products, evaluating the accuracy of the estimated reflectance based on in-situ measurements from several AERONET-OC stations under different water and atmospheric conditions. Furthermore, the two products will be highlighted in terms of their use and advantages, in particular for the (hyperspectral) water community, and a number of applications using the EnMAP hyperspectral water reflectance data will be presented

The EnMAP L2A Processor

Within the EnMAP Ground Segment Processor, the EnMAP L2A Processor performs the specific steps required for the atmospheric correction of orthorectified top-of-atmosphere (TOA) radiance data to produce bottom-of-atmosphere (BOA) land and water surface reflectance values. Depending on the user’s configuration, these values can be delivered in three different flavors: the land reflectance (remote sensing reflectance) product as provided by the L2A land processor based on the DLR internal atmospheric correction software PACO; water reflectance (normalized water-leaving reflectance, subsurface irradiance reflectance) products as provided by the L2A water processor based on the MIP atmospheric correction algorithm developed by EOMAP GmbH; a combined product that fuses the results of the two different processors. The presentation aims to provide users with detailed information on the EnMAP L2A user products from the developer’s perspective. Specifics of the L2A processor outputs will be addressed, clarifying different representations of land and water reflectance, provided ancillary data, and other characteristics of the spectra induced by processor design. A number of application examples will be shown for each of the L2A user products, highlighting the benefits and capabilities of each product and of EnMAP hyperspectral data in general

Specific data correction for EnMAP and DESIS

The processing chains of the upcoming hyperspectral missions DESIS (DLR Earth Sensing Imaging Spectrometer), and EnMAP (Environmental Mapping and Analysis Program) need to deal with several systematic errors. This work will present some of the existing problems and the selected correction and attenuation procedures. One common error is the smile effect, affecting push-broom hyperspectral sensors by shifting the central wavelength in the across-track direction. This spectral distortion is minimal in the center of the sensor, increasing towards the sensor edges. The smile effect is particularly noticeable on hyperspectral sensors as the band width is in the order of a few nanometers. EnMAP performs a column-wise smile-aware atmospheric correction, taking the shifted wavelengths into account and interpolating the BOA (Bottom Of Atmosphere) reflectances to the sensor's nominal wavelengths. On the other hand, DESIS addresses the smile correction interpolating TOA (Top Of Atmosphre) radiances. An even more common data error, not only to DESIS and EnMAP but to every type of imaging system, is the existence of abnormal or defective pixels. These abnormal pixels can be present due to sensor aging, errors during data acquisitions, saturation of the sensor, etc. EnMAP strategy for abnormal pixel is based on linear interpolations of the BOA reflectances in the spectral direction. In the case of DESIS, a hybrid interpolation method for abnormal pixels is used. The algorithm selects the optimum value between spectral and spatial cubic spline interpolations of the TOA radiances. The selection criterion is based on the spectral gradient difference between the interpolated pixels and spatial neighbors. Finally this work will present a DESIS specific effect which is introduced, during the data acquisition, by the imaging sensor’s shutter mode. The rolling shutter mode provides higher frame rate and better SNR (Signal to Noise Ratio) than global shutter. As a negative side effect, every individual band from any specific row in the along-track direction is acquired at slightly different time, and therefore, at slightly different position on the ground. To correct this effect, the speed of the ISS (International Space Station) and the band acquisition times are used in order to calculate the point to be interpolated via a cubic spline interpolation in the along-track direction

BOA Reflectance Based Dead and Defective Pixel Interpolation in the ENMAP Ground Segment Processing Chain

The high-resolution imaging spectroscopy remote sensing mission "Environmental Mapping and Analysis Program" (EnMAP) [1] was successfully launched on April 1st, 2022 and entered operational phase on November 2nd, 2022. The data acquired by remote sensing platforms might be affected by different types of pixel defects, due to aging, degradation of electronic components, mechanical vibrations and data transmission failures [2]. These can produce from missing to low quality data (i.e. low- and high-gain linearity effects, non-uniformity effects (photo-response non-uniformity (PRNU), dark-signal non-uniformity (DSNU)) as well as low and high radiance values outside of the allowed dynamic range). In addition, sensor noise can reduce the quality of the data in some pixels in strong atmospheric absorption spectral regions. In particular in strong atmospheric absorption regions the narrow spectral bands may suffer from low signal to noise values. This paper gives a description of the dead and defective pixel correction algorithm as implemented in the EnMAP L1B processor. Results are evaluated intrinsically by generating artificial dead-pixel maps, masking healthy nominal pixels of an acquired EnMAP datatake in order to be able to compare the interpolated results with valid reference values. Further interpolation results are extrinsically and quantitatively compared to the dead-pixel interpolated processor output of the DESIS hyperspectral sensor, for which dead-pixel correction is conducted by common means of interpolating in spectral dimension on top-of-atmosphere (TOA) radiances and only on hardware-based defects (in contrary to the EnMAP dead and defective pixel masks which includes quality flagging). Additionally, the dataset is artificially damaged to simulate partial loss of the radiance data to present the overall performance of the dead-pixel correction reconstruction capabilities within the frame of the file and data deletion conditions

DESIS and Copernicus Sentinel-2 Surface Reflectance, AOT and WV Products compared to measurements on ground

PACO atmospheric correction software is implemented in the DESIS L2A processor providing Bottom-Of-Atmosphere (BOA) ground reflectance spectral image cube together with aerosol optical thickness (AOT) and integrated water vapour (WV) maps. PACO can also be applied to Sentinel-2 data providing equivalent outputs. This presentation will rely on reference measurements of SR, AOT and WV which had been performed on ground in parallel to DESIS acquisitions in August 2019 and 2020. Microtops photometers are used for measurements of the atmospheric parameters and SR measurements on ground used a hyperspectral SVC spectroradiometer covering the spectral range from 380 nm to 2.5 µm. There are Sentinel-2 overpasses over the same area at the same day. Both DESIS and Sentinel-2 data were be processed with PACO and then compared to the available reference measurements

Validation of DESIS Surface Reflectance Product with Measurements on Ground

The hyperspectral instrument "DLR Earth Sensing Imaging Spectrometer" (DESIS) was developed within a collaboration between the US company Teledyne Brown Engineering (TBE) and the German Aerospace Center (DLR). DESIS is operating onboard of the International Space Station (ISS) since August 2018 and is in operational phase since October 2019. The validation of the L2A products in remote sensing is performed by different approaches. One of them is the comparison of the ground surface reflectance as delivered by the L2A-remote sensing products with in-situ measurements. Those in-situ measurements are performed with spectroradiometers at the same wavelength of the remote sensor and at the time of a satellite overpass. The presentation provides recent validation results for the DESIS BOA reflectance product on basis of ground-based reference measurements. Accuracy represents the mean difference of BOA-reflectance retrieval to a reference value and uncertainty gives the rms around the reference. Accuracy and uncertainty of surface reflectance retrieval from DESIS data is benchmarked by comparison with surface reflectance retrieval from Sentinel-2 data

Validation of a new atmospheric correction Software using AERONET reference data

Atmospheric correction of satellite images based on radiative transfer calculations is a prerequisite for many applications. The program ATCOR, developed at the German Aerospace Center (DLR), is a rather versatile atmospheric correction software, capable of processing data acquired by different optical satellite sensors. A Python-based version of this code is currently being developed to process L2A products of Sentinel-2, Landsat-8 and of new space-based hyper-spectral sensors such as DESIS and EnMAP. In this contribution we will present the first validation results of this software, comparing L2A products generated from Sentinel-2 L1C images with in-situ (AERONET) data

The DESIS L2A processor and validation of L2A products using AERONET and RadCalNet data

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 400 to 1000 nm. In this contribution 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