---------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------- Important Information about the Test Data Products ---------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------- These test data are provided as sample EnMAP mission products. They are representative concerning the data formats of the user data products (L1B, L1C, L2A) [1] that will be available during EnMAP routine operations (>= Q4/2021). They are provided with the sole purpose to help the EnMAP user community to test the products data formats. These test data are based on simulated data [2][3][4] of the EnMAP science segment and the geometric simulator from DLR [5] (land pixels), and simulated data with MIP software [6][7] by EOMAP GmbH & Co.KG (water pixels). These data are then processed with the fully-automatic operational processors [8][9][10] of the EnMAP ground segment. These simulated data have also been used for the realization of the processors which is still ongoing. Namely, minor changes of the products data formats are possible. Known limitations of these simulated products include (and are not limited to): - Simulated land pixels in the EnMAP scene are based on Sentinel-2 images where artificial spectral mixtures were introduced at every EnMAP pixel location. All spectral and spatial information is not realistic and only artificially adapted to reality with a limited accuracy. - Water pixels have been separately simulated using the MIP processing system [6] and based on radiative transfer calculations in a coupled stratified atmosphere – water surface – water system using the Finite Elemente Model [7]. In this simulation, in a first step the environmental properties over water pixels are calculated from the underlying Sentinel-2 scene using the MIP processor. These properties include adjacency scattering, atmospheric aerosol concentrations and slopes, absorption and scattering properties in the water column for each pixel. In a second step, these environmental values are used to calculate the radiance at sensor altitude for each pixel in hyperspectral resolution, for the given date and sun-observer geometries. The base for this transformation are the hyperspectral specific optical properties as used in MIP and the FEM radiative transfer model [7] as controlled by MIP. - The merging of water and land simulations can result in unrealistic spectra in the water/land transition pixels - The selection and optimal display of the quicklook bands is not yet finalized. - Detector non-linearity is not simulated (and thus not corrected for in L1B). - Shutter thermal emission during dark phases is not simulated (and thus not corrected for in L1B). - Open points in simulation of orbit/attitude might cause inaccuracies in georeferencing. - Interior orientation is simulated, although it is given in the metadata, no implications on the actual geometric calibration can be derived, e.g. no keystone effect is simulated. - L2A Mixed land / water pixels currently can appear as black pixels (~0.0 signal) due to overcorrection during the water atmospheric correction process caused by a too high radiance signal in the IR range of input data. - L2A computes reflectance from all radiance values in L1C product. Typical uncertainties in the surface reflectance retrieval may cause large discrepancies for low values of radiance in a pixel/band. This is the case for bands transmitted inside atmospheric water absorption regions (e.g. 1320-1380 nm transmitted to use the cirrus information). Next version of the processors will interpolate the L2A reflectance values in that range, as agreed at the Instrument Working Group from 19.03.2020. - Thresholds for cloud/snow/shadow classification will be optimized on real data during commissioning. In the simulations current threshold may lead to a small number of pixels misclassified in very bright/dark areas - The files *pixelmask.vnir.tif & *pixelmask.swir.tif can cause ENVI to crash (GDAL works correctly) [1] EN-PCV-ICD-2009-2 EnMAP HSI Level 1 / Level 2 Product (1.5 excerpt and draft) [2] Segl, K.; Guanter, L.; Rogaß, C.; Küster, T.; Roessner, S.; Kaufmann, H.; Sang, B.; Mogulsky, V.; Hofer, S. (2012): EeteS - The EnMAP End-to-End Simulation Tool. - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 2, p. 522-530. http://doi.org/10.1109/JSTARS.2012.2188994 [3] Segl, K.; Guanter, L.; Kaufmann, H.; Schubert, J.; Kaiser, S.; Sang, B.; Hofer, S. (2010): Simulation of Spatial Sensor Characteristics in the Context of the EnMAP Hyperspectral Mission. - IEEE Transactions on Geoscience and Remote Sensing, 48, 7, p. 3046-3054. http://doi.org/10.1364/AO.51.000439 [4] Guanter, L.; Segl, K.; Kaufmann, H. (2009): Simulation of Optical Remote-Sensing Scenes With Application to the EnMAP Hyperspectral Mission. - IEEE Transactions on Geoscience and Remote Sensing, 47, 7, p. 2340-2351. http://doi.org/10.1364/OE.17.011594 [5] Schwind, P.; Müller, R.; Palubinskas, G.; Storch, T. (2012): An in-depth simulation of EnMAP acquisition geometry. ISPRS Journal of Photogrammetry and Remote Sensing, 70, p. 99-106. [6] Kiselev, V., Bulgarelli, B. and Heege, T., 2015. Sensor independent adjacency correction algorithm for coastal and inland water systems. Remote Sensing of Environment, 157: 85-95., ISSN 0034-4257. http://dx.doi.org/10.1016/j.rse.2014.07.025. [7] Kisselev, V.; Bulgarelli, B. (2004). Reflection of light from a rough water surface in numerical methods for solving the radiative transfer equation. Journal of Quantitative Spectroscopy and Radiative Transfer 85, 419-435. [8] Storch, T.; Honold, H-P.; und Guanter, L.; und Schwind, P.; und Mücke, M.; Segl, K.; Fischer, S.; (2018): The Imaging Spectroscopy Mission EnMAP - Its Status and Expected Products. In: 9th Workshop on Hyperspectral Image and Signal Processing (WHISPERS), Seiten 1-5. WHISPERS 2018, 23.-26. September 2018, Amsterdam, Netherlands. [9] Storch, T.; Heiden, U.; und Asamer, H.; und Dietrich, D.; und Fruth, T.; Schwind, P.; Ohndorf, A.; Palubinskas, G.; Habermeyer, M.; Fischer, S.; Chlebek, C. (2017): EnMAP - From Earth Observation Request, Planning, and Processing To Image Product Delivery. EARSeL SIG IS, 19 - 21 April 2017, Zürich, Switzerland. [10] Storch, T.; Bachmann, M.; Eberle, S; Habermeyer, M.; Makasy, C.; de Miguel, A.; Mühle, H.; Müller, R. (2013): EnMAP Ground Segment Design: An Overview and Its Hyperspectral Image Processing Chain. Earth Observation for Global Change, Earth Observation for Global Change, Lecture Notes in Geoinformation and Cartography, Springer, 49-62. ---------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------