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

A Comparison of Fractional Vegetation Cover in Camarena, Spain from DESIS and EnMAP Observations

Fractional vegetation cover (FVC) is an important measure for the conservation, restoration and maintenance of biodiverse environments, giving the spatial patterns and distributions of photosynthetically active (PV) and non-photosynthetically active (NPV) vegetation as well as bare soil (BS) in a given region. Using hyperspectral remote sensing observations from DESIS and EnMAP (Environmental Mapping and Analysis Program), we derive FVC for Camarena, Spain, a semi-arid region southwest of Madrid and an important test site for the upcoming Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and compare the results from both sensors. DESIS and EnMAP are both hyperspectral remote sensing instruments with spatial resolutions of 30 m but they differ in other key aspects. DESIS has a spectral range of 400-1000 nm and a maximum spectral resolution of 2.55 nm whilst EnMAP has a range of 400-2500 nm and a resolution of 6.5-10 nm. The SWIR bands of EnMAP make it far more useful for the derivation of FVC than DESIS due to characteristic absorption features above 1500 nm which help to disentangle NPV and BS spectra. Nevertheless, abundances can still be derived from the FVC processing, accepting that the RMSEs are higher for the DESIS results (13% for PV, 18% for NPV, 9% for BS) than for the EnMAP results (12% for PV, 14% for NPV, 4% for BS). The FVC processing of the DESIS and EnMAP images consists of three steps. After some pre-processing (band removal and smoothing), pure spectra are retrieved from the image using the spatial-spectral endmember extraction method developed by Rogge. This method creates a global set of endmembers from the image after the masking of pixels which are not vegetation or soil. Secondly, the extracted endmembers are classified with a Logistic Regression (for DESIS) or a Random Forest (for EnMAP) classifier which were trained from a spectral library containing 631 samples. Three classes are used for the classification: PV, NPV amd BS. Unmixing is the final stage which uses a MESMA approach where each pixel is considered to be a linear combination of one PV spectrum, one NPV spectrum and one BS spectrum from the labelled endmember library. The class abundance are the weights found in the linear unmixing and an extra shade component is considered. In this work, we will present FVC maps derived from EnMAP and DESIS of Camarena which is a semi-arid region covering approximately 75 km2 in the Province of Toledo, Spain, where the land is mainly used for rainfed agriculture. It has an undulating topography with vegetation growing on sloping areas that were either not considered good enough for farming or later abandoned. Since June 2019, 60 cloud free images were acquired by DESIS over the region and EnMAP has so far acquired 8 cloud free images in this area since launch in April 2022. Several EnMAP images in July-August 2022 coincide closely with a DESIS observation which will enable quantifiable comparisons to be made between the two sensors and allow for an evaluation of the results considering the different wavelength ranges of each sensor

The DESIS Spaceborne Hyperspectral Instrument Calibration

Overview of the calibration methods used in the DESIS hyperspectral instrumen

Calibration of the DESIS Instrument

The DLR Earth Sensing Spectrometer (DESIS) on board the International Space Station (ISS) has been providing high quality hyperspectral data to the scientific community and commercial users since the start of operations in September 2018. Now 5~years in orbit, the DESIS instrument continues to operate correctly and provide hyperspectral data products for a wide variety of applications. This successful activity is supported by the calibration team that regularly analyzes the instrument data and provides calibration updates. DESIS calibration uses the on-board LED calibration unit to monitor the spectral response of the instrument. Over the years the spectral performance of DESIS shows little variation over time, what makes unnecessary spectral calibration updates in most occasions. However, there are as well variations in spectral calibration from one measurement to another. Part of this variability is related to temperature gradients inside the instrument that can be corrected during data processing. However, there are other variations that seem to have a random nature (RMS ~0.1 nm) and cannot be corrected. Radiometric calibration, on the other hand is based on vicarious calibration using RadCalNet sites as reference. We also use a harmonization of the radiometric response of all sensor elements, where small spectral adjustments can also be incorporated. This pixel harmonization is performed using different algorithms applied over a large selection of uniform scenes in several different steps. Comparison with RalCalNet reference data shows that the radiometric calibration of DESIS can change a 3.4% over one year above 500 nm. However, below 500 nm there is a larger variation that increases with decreasing wavelengths up to a maximum of 25% per year. This higher variation in the first 100 nm has been reducing over time, until July 2021, when seems to have stopped. Typical DESIS calibration updates are performed every 7 months, resulting in average variation of up to 2% above 500 nm. Finally, the geometric calibration of the sensor is performed by comparison of ground control points (GCPs) automatically extracted from the DESIS images and from reference images of higher geometric accuracy. When enough GCPs can be found on an image, DESIS images have a similar RMSE of ~21 m, which is similar in both North and East directions. RMSE when no GCPs can be obtained is typically 300 m in the across-track direction and 500 m in the along-track direction. In this contribution we present the concept used for the DESIS calibrations and the results obtained in these 5 years of operation. We offer here an overview of the uncertainties in the DESIS originating at the sensor calibration and how they vary with time