Description: The EnPT Python package is an automated pre-processing pipeline for the new EnMAP hyperspectral satellite data. It provides free and open-source features to transform EnMAP Level-1B data to Level-2A. The package has been developed at the German Research Centre for Geosciences Potsdam (GFZ) as an alternative to the processing chain of the EnMAP Ground Segment.

Description: Geospatial co-registration is a mandatory prerequisite when dealing with remote sensing data. Inter- or intra-sensoral misregistration will negatively affect any subsequent image analysis, specifically when processing multi-sensoral or multi-temporal data. In recent decades, many algorithms have been developed to enable manual, semi- or fully automatic displacement correction. Especially in the context of big data processing and the development of automated processing chains that aim to be applicable to different remote sensing systems, there is a strong need for efficient, accurate and generally usable co-registration. Here, we present AROSICS (Automated and Robust Open-Source Image Co-Registration Software), a Python-based open-source software including an easy-to-use user interface for automatic detection and correction of sub-pixel misalignments between various remote sensing datasets. It is independent of spatial or spectral characteristics and robust against high degrees of cloud coverage and spectral and temporal land cover dynamics. The co-registration is based on phase correlation for sub-pixel shift estimation in the frequency domain utilizing the Fourier shift theorem in a moving-window manner. A dense grid of spatial shift vectors can be created and automatically filtered by combining various validation and quality estimation metrics. Additionally, the software supports the masking of, e.g., clouds and cloud shadows to exclude such areas from spatial shift detection. The software has been tested on more than 9000 satellite images acquired by different sensors. The results are evaluated exemplarily for two inter-sensoral and two intra-sensoral use cases and show registration results in the sub-pixel range with root mean square error fits around 0.3 pixels and better.

Description: EnMAP (Environmental Mapping and Analysis Program, www.enmap.org) is a German, Earth observing, imaging spectroscopy, spaceborne mission planned for launch in 2020. The data products will cover the spectral range from 420 nm to 2450 nm with a spectral sampling distance between 5 and 12 nm with an expected signal-to-noise-ratio of 400:1 in the visible near-infrared and 180:1 in the shortwave infrared parts of the electro-magnetic spectrum. The resulting images will cover an area of 30 km in the across- track direction with a ground sampling distance of 30 m. The across-track tilt-capability of 30° enables revisit times of less than four days. The resulting data products will be freely available to the scientific user community for measuring, deriving, and analyzing diagnostic parameters, which describe vital processes on the Earth's surface comprising agriculture, forestry, soil and geological environments, as well as coastal zones and inland waters. This work concentrates on the description of activities performed and facilities involved for the preparation of these products. It starts out by the description of the User Portals for observation requests and acquisition planning, touches the aspects of creating the time-lines, the commanding and controlling of the satellite, the downlink of the telemetry and payload data, the design of the processing chain and the archiving of data plus a set of activities flanking the above for the provision of high-quality data products.

Description: We describe EnMAP-like imaging spectroscopy data files to be used for mineral mapping with the EnMAPBOX software. It is simulated EnMAP satellite data, which is based on hyperspectral flight cam-paign data with the AVIRIS-NG and HyMap sensors. In preparation of the EnMAP satellite mission, an EnMAPBOX software package provides tools for visualization and scientific analysis of the data. Among many applications, the EnMAPBOX contains geological mapping tools (EnGeoMAP). Here we apply these tools to several representative test cases (Boesche, 2015; Boesche et al., 2016; Mielke et al., 2016). The test data comprise two study sites. The first scene covers the Mountain Pass open pit mine - a carbonatite deposit in California, USA. It contains calcitic rock units and rare earth element (REE) bearing minerals of the bastnaesite group, also called fluorocarbonates (Olson et al., 1954). The REE concentrations at mountain pass are 9.2% on average, among the highest in the world (Brüning and Böhmer, 2011). The high concentration and the open pit activities make Mountain Pass an ideal test site to investigate the rare earth element distribution in the surface layer. The airborne image data were collected in 2014 by Jet Propulsion Laboratory (JPL), USA, with the AVIRIS-NG sensor and form the basis for EnMAP simulations (Segl et al., 2012; Thompson et al., 2015). The second HyMap spectral image data covers part of the Miocene Cabo de Gata-Nίjar volcanic field, in southeast Spain. It comprises a subset of (Chabrillat et al., 2016) covering the Rodalquilar and Lomilla Calderas, which host the economically relevant gold-silver, lead-zinc-silver-gold and alunite deposits. It is a hydrothermal alteration complex, representing the silicic alteration, the advanced argillic alter-ation zone, which grades into the argillic and propylitic zone (Arribas et al., 1995, 1989). The image data are part of the Cabo de Gata-Nίjar HyMap imagery which was collected during the DLR HyEurope airborne campaign 2005 in the frame of the GFZ land degradation program (Chabrillat et al., 2016, 2005). We use these datasets to simulate EnMAP-like images for classification and mapping using spectro-scopic remote sensing techniques in the EnGeoMAP tools. The EnMAP end-to-end Simulation (EeteS) tool produced simulated EnMAP like data with a spatial sampling distance of 30 x 30 m and 242 spectral bands (Guanter et al., 2015; Segl et al., 2012).

Description: The scope of the Science Plan is to describe the scientific background, applications, and activities related to the Environmental Mapping and Analysis Program (EnMAP) mission. Primarily, the document addresses scientists and funding institutions, but it may also be of interest for environmental stakeholders and governmental bodies. It is conceived to be a living document that will be updated throughout the entire mission. Chapter 1 provides a brief overview of the principles and current state of imaging spectroscopy. This is followed by an introduction to the EnMAP mission, including its objectives and potential impact on international programs as well as major environmental and societal challenges to their understanding and management EnMAP can contribute. Chapter 2 describes the EnMAP system together with data products and access, calibration/validation issues, and synergies with other missions. Chapter 3 gives an overview of the relevance, current lines of research, and potential contributions of EnMAP for major fields of application, such as vegetation, geology and soils, coastal and inland waters, cryosphere, urban areas, atmosphere and hazards to address the environmental and societal challenges presented in Chapter 1. Finally, Chapter 4 outlines the scientific exploitation strategy, which includes the strategy for community building and training, preparatory flight campaigns and software developments. A list of abbreviations is provided in the annex to this document, while an extended glossary of terms and abbreviations is available at the EnMAP website.

Description: The scope of this Science Plan is to describe the scientific background, applications, and activities related to the EnMAP mission. Primarily, the Science Plan addresses scientists and funding institutions, but it may also be of interest for environmental stakeholders and governmental bodies. It is conceived to be a living document that will be updated throughout the whole mission. Current global challenges call for interdisciplinary approaches. Hence, the science plan is not structured in the traditional disciplinary way. Instead, it builds on overarching research themes to which EnMAP can contribute. This Science Plan comprises the following five chapters presenting the significance, background, framework, applications, and strategy of the EnMAP mission: Chapter 2 highlights the need for EnMAP data with respect to major environmental issues and various stakeholders. This chapter states the mission’s main objectives and provides a list of research themes addressing global challenges to whose understanding and management EnMAP can contribute. Chapter 3 presents an overview of the EnMAP mission from a scientific point of view including a brief description of the mission parameters, data products and access, and calibration/validation issues. Chapter 4 provides an overview of hyperspectral remote sensing regarding its principles, development, and current state and synergies to other satellite missions. Chapter 5 describes current lines of research and EnMAP applications to address the research themes presented in Chapter 2. Finally, Chapter 6 outlines the scientific exploitation strategy, which includes the strategy for community building, dissemination of knowledge and increasing public awareness.

Description: Remote sensing data analysis retrieves spatial-temporal information about the Earth‘s surface from remotely sensed optical and radar images. For this purpose accurate and efficient classification or parameter quantification techniques must be used. Consequently, there exists a long tradition in remote sensing to employ methods and techniques from the field of machine learning. They can be regarded as „universal function approximators“ that are able to link any data in order to derive connections, conclusions and predictions efficiently using different learning strategies. In the following, current research topics of the Remote Sensing section of the GFZ are presented, in which different forms of machine learning are used.

Description: Against the background of EnMAP preparation analyses have been carried out on the status of research in various areas of hyperspectral remote sensing for the use of existing algorithms in the EnMAP application box. The aim was to compose the status of research in Germany and internationally. Therefore, in various fields of expertise applied and accordingly available algorithms and products based on hyperspectral data has been evaluated and documented. The intention has been to demonstrate and to evaluate the added value of hyperspectral remote sensing to multispectral methods for each product. For this purpose analyses have been carried out by different research groups which were based on the specialty and the experience of each group. Hereby a summary was created of each major application perspective and the relevant remote sensing derived variables and significant processing algorithms (state-of-the-art) belonging to this context. Based on this, the research delivers, as a result, a recommendation which of the algorithms should be implemented into the Applikationsbox of EnMAP. Tests of the algorithms or their implementation were not part of the analyses. However, notes have been given on what algorithms should be tested in the context of a detailed preparation phase. An assessment to the further R & D requirements in the development of algorithms has been made on this basis.

Description: Satellite remote sensing offers the possibility to continuously, globally collect information about Earth’s surface properties, and thus possesses an enormous scientific and economic potential. The spatio-temporal recording of information opens up a wide range of possibilities for large-scale monitoring of changes and influencing factors, e.g. in atmospheric research, agriculture and forestry, geology, urban areas and the analysis of natural hazards. The European Union‘s Copernicus programme marks the beginning of a new era in satellite-based Earth observation by designing and launching the Sentinel series, an operational fleet of high-continuity, multi-combinable observation systems. This enables completely new objects of investigation, but also requires a new level of automated data processing in order to make the huge amounts of data available for analysis. This article provides an insight into current remote sensing research at the GFZ. First, it presents the activities related to the creation of preprocessing standards and interfaces for data evaluation, geo-data management and visualization. In the following, examples for multi-sensor analysis of land use potentials and natural hazards are shown, in order to finally discuss the role of the GFZ in the development of hyperspectral satellite missions and related data analysis.