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: 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: Surface soil moisture information is needed for monitoring and modeling surface processes at various spatial scales. While many reflectance based soil moisture quantification models have been developed and validated in laboratories, only few were applied from remote sensing platforms and thoroughly validated in the field. This paper addresses the issues of a) quantifying surface soil moisture with very high resolution spectral measurements from remote sensors in a landscape with sandy substrates and low vegetation cover as well as b) comprehensively validating these results in the field. For this purpose, the recently developed Normalized Soil Moisture Index (NSMI) has been analyzed for its applicability to airborne hyperspectral remote sensing data. Three HyMap scenes from 2004 and 2005 were collected from a lignite mining area in southern Brandenburg, Germany. An NSMI model was calibrated (R2=0.92) and surface soil moisture maps were calculated based on this model. An in-situ surface soil moisture map based on a combination of Frequency Domain Reflectometry (FDR) and gravimetric data allowed for validating each image pixel (R2=0.82). In addition, a qualitative multitemporal comparison between two consecutive NSMI datasets from 2004 was performed and validated, showing an increase in estimated surface soil moisture corresponding with field measurements and precipitation data. The study shows that the NSMI is appropriate for modeling surface soil moisture from high spectral-resolution remote sensing data. The index leads to valid estimations of soil moisture values below field capacity in an area with sandy substrates and low vegetation cover (NDVI 〈 0.3). Further studies will analyze the validity of the NSMI for surface soil moisture estimation from spaceborne hyperspectral sensors like the Environmental Mapping and Analysis Program (EnMap) in different landscapes.

Description: Berlin-Urban-Gradient is a ready-to-use imaging spectrometry dataset for multi-scale unmixing and hard classification analyses in urban environments. The dataset comprises two airborne HyMap scenes at 3.6 and 9 m resolution, a simulated spaceborne EnMAP scene at 30 m resolution, an im-age endmember spectral library and detailed land cover reference information. All images are pro-vided as geocoded reflectance products and cover the same subset along Berlin’s urban-rural gra-dient. The variety of land cover and land use patterns captured make the dataset an ideal play-ground for testing the transfer of methods and research approaches at multiple spatial scales.

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: The influence of plant and canopy architecture on canopy bidirectional reflectance and the bidirectional reflectance distribution function (BRDF) is the subject of this paper. To understand BRDF-influenced reflectance signals, this influence must be identified and quantified, which requires detailed knowledge concerning the structure and BRDF of the observed canopies. In situ BRDF measurements of canopies are time consuming and depend on the availability of a field goniometer. In contrast to field measurements, computer-based simulations of the canopy BRDF offer an alternative approach that considers parameter-driven setups of virtual canopies under constant illumination conditions. This paper presents the hyperspectral simulation of canopy reflectance (HySimCaR) system, which has been developed in the context of the EnMAP mission. This spectral, spatial, and temporal simulation system consists of detailed virtual 3-D cereal canopies of different phenological stages, whose geometries are linked to the corresponding spectral information. The system enables the simulation of realistic bidirectional reflectance spectra on the basis of virtual 3-D scenarios by incorporating any possible viewing position with ray- tracing techniques. The parameterization of a number of canopy structure parameters, such as phe nological stage, row distance, and row orientation, enables the modeling of the bidirectional reflectance and, based on them, the approximation of the BRDF for many structurally different cereal canopies. HySimCaR has been validated with respect to structural and spectral accuracy using three cereal types, namely, wheat, rye, and barley, at 13 different phenological stages. The results show that the virtual cereal canopies are re-created in a realistic way, and it is possible to model their detailed canopy bidirectional reflectance and their BRDF using HySimCaR.