---------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------- 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], that have been processed with the fully-automatic operational processors [6][7][8] 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): - Test Products were updated on 18.05.2020 to correct problems opening quicklooks and L1B products with QGIS. - Simulated EnMAP scenes 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. - 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 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. - The focus of the simulations is on land surface and therefore, the snow simulations are less accurate which results in inaccuracies in the resulting snow mask of the products. - The focus of the simulations is on land surface and therefore, the water simulations are less accurate which results in inaccuracies in the resulting water spectra of the products. - The focus of the simulations is on land surface and therefore, the cloud simulations are less accurate which results in inaccuracies in the resulting cloud mask of the products. - EnMAP L2A land processor delivers two types of shadows (cloud shadows and topographic) shadows. Due to a software bug, topographic shadows are also included in the EnMAP cloud shadows mask. This will be fixed for the next EnMAP L2A land processor software release. - 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] 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. [7] 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. [8] 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. ---------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------