Imaging spectroscopy has been a recognized and established remote sensing technology since the 1980s, mainly using airborne and field-based platforms to identify and quantify key bio- and geo-chemical surface and atmospheric compounds, based on characteristic spectral reflectance features in the visible-near infrared (VNIR) and short-wave infrared (SWIR). Spaceborne missions, a leap in technology, were sparse, starting with the CHRIS/PROBA and EO1/Hyperion missions in the early 2000s, and providing spectroscopy data with limited spectral coverage and/or low data quality in the SWIR. Since 2019, several countries and agencies have successfully launched a number of spaceborne imaging spectroscopy systems into orbit or deployed them on the International Space Station (ISS) such as DESIS, PRISMA, HISUI, GF-5, EnMAP and EMIT. Among these recent missions, the German Environmental Mapping and Analysis Program (EnMAP) stands for its long-term development, sophisticated design with on-board calibration, high data quality requirements, and extensive accompanying science program. EnMAP was launched in April 2022 and, following a successful commissioning phase, started its operational activities in November 2022. The EnMAP mission encompasses global coverage from 80 degrees N to 80 degrees S through on-demand data acquisitions. Data are free and open access with 30 m spatial resolution, a high spectral resolution with a spectral sampling distance of 6.5 nm and 10 nm in the VNIR and SWIR regions respectively, and a high signal-to-noise ratio. In this paper, we aim to present the mission's current status, coverage, science capabilities and performance two years after launch. We show the potential of EnMAP for space-based imaging spectroscopy to operate in various environments, including high and low light levels, dense forests, Antarctic glaciers, and arid agricultural areas. EnMAP enables various applications in fields such as agriculture and forestry, soil compositional, raw materials, and methane mapping, as well as water quality assessment, and snow and ice properties. The results show that EnMAP's performance exceeds the mission requirements, and highlights the significant potential for contribution to scientific exploitation in various geo- and biochemical sciences. EnMAP is also expected to serve as a key tool for the development and testing of data processing algorithms for upcoming global operational missions.

Satellite missions like EnMAP and PRISMA generate raster images that describe the Earth's environment with hyperspectral resolution. Such imaging spectroscopy data is of high value for applications in, e.g., geological, vegetation, or hydrological research. Due to its high dimensionality, analyzing imaging spectroscopy data is still challenging and often requires the use of proprietary software. The motivation for the EnMAP-Box is to close this gap and to foster the use of imaging spectroscopy data with state-of-the art remote sensing methods. Developed as Python plugin for the QGIS geoinformation system, the EnMAP-Box integrates into a well-established, platform-independent, and free-and-open-source software ecosystem to analyze geospatial data. The EnMAP-Box offers advanced functionalities to visualize and process hyper- and multispectral, and multi-temporal remote sensing data, it implements novel spectral libraries concept, and provides easy access to published algorithms from different fields of environmental research. Already been widely used in the past, the EnMAP-Box can now unfold its full potential as first operational EnMAP data became available in 2022. (c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

This dataset contains extended material for the paper: Frantz, D., Rufin, P., Janz, A., Ernst, S., Pflugmacher, D., Schug, F., Hostert, P.: Understanding the robustness of spectral-temporal metrics across the global Landsat archive from 1984-2019 – a quantitative evaluation. In revision.

An implementation for the classification of remote sensing images with support vector machines (SVM) is introduced. This tool, called imageSVM, allows a user-friendly work, especially with large, highly-resolved data sets in the ENVI/IDL environment. imageSVM uses LIBSVM for the training of the SVM in combination with a user-defined grid search. Parameter settings can be set flexibly during the entire workflow and a time-efficient processing becomes possible. First tests underline the high-accuracy of SVM classification using heterogeneous hyperspectral data and the good performance of SVM in the context of multi-sensoral studies. INTRODUCTION Over the past two decades, a variety of non-parametric classifiers has been introduced into remote sensing image analysis, e.g. artificial neural networks (i), decision tree classifiers (ii), and support vector machines (iii,iv). SVM do not assume specific class distributions and are well suited for complex hyperspectral data sets (v). They are one of the more recent developments in the field of machine learning and outperformed other approaches under varying conditions in the very most cases or performed at least equally well (vi,vii), especially when small numbers of training samples were used. However, the processing of image data with latest machine learning developments for the SVM classification is often complicated due to missing user interfaces and inappropriate data formats like ASCII. In addition, the processing of large, highly resolved data sets often leads to long processing times. Furthermore, the classification of remote sensing images usually includes more than two classes and strategies for the originally binary SVM have to be applied to solve multi-class problems (iv,viii). First implementations of SVM in commercial image processing software packages are rather limited in performance, especially in the context of searching ideal SVM parameters or optimized training and classification procedures. This work aimed at developing a flexible SVM implementation for remote sensing image analysis called imageSVM. Objectives for the design were (1) offering the full flexibility of state-of-the-art algorithms for the training of the SVM, (2) the possibility to use standard remote sensing formats, (3) a semi-automized and time-saving search for optimal training parameters, (4) the possibility for a quick validation of the classification accuracy for methodological studies, and (5) SVM models and the intermediate results of the classification have to be flexibly accessible, e.g. for data fusion or classifier ensemble approaches. METHODS AND IMPLEMENTATION SVM delineate two classes by fitting an optimal separating hyperplane to the training data in the multi-dimensional feature space (ix). For linearly not separable classes, the input data are mapped into a higher dimensional space by a kernel function, wherein the new data distribution enables the fitting of a linear hyperplane. A detailed description on the concept of SVM is given in (x), comprehensive introductions in a remote sensing context in (iii,iv). Two parameters need to be set for the training of SVM with Gaussian kernels: the parameter γ that controls the width of the kernel and a Proceedings 5 EARSeL Workshop on Imaging Spectroscopy. Bruges, Belgium, April 23-25 2007 2 regularization parameter, either C or ν, depending on the preferred SVM formulation. The regularization parameter C controls the trade-off between maximizing the margin and the penalization of training errors (more precisely margin errors). ν is an upper bound on the fraction of training errors and a lower bound of the fraction of support vectors (xi). Besides the Gaussian kernel, any kernel function that meets the Mercer condition (ix) can be used with SVM, e.g. polynomials or sigmoidal functions. Since Gaussian kernels are most frequently used, only these were implemented in imageSVM. The widely accepted LIBSVM approach by (xii) was integrated in the ENVI/IDL Environment for the training of the SVM. This way the training and classification can be performed on common image file formats. Samples for training and validation are taken from ROIs. Both SVM-types, Cor ν-SVM, and the two common multi-class strategies one-against-all and one-against-one can be selected (Fig. 1, left). Figure 1: dialogue for SVM formulation and multi-class strategy (left) and for grid search and cross validation settings (right). To determine an optimal set of the parameters γ and C or ν a grid search with internal cross validation is performed. Besides the ranges and step-size of the parameter search the user can define the number of folds for the cross validation (CV) and the tolerance for the termination criterion of the quadratic optimization during the cross-validation trainings (Fig. 1, right). All parameters have great influence on the time needed for the calculation of the grid search and thus the determination of appropriate parameters is very important. The CV results for all parameter combinations during the grid search are saved as images. These so-called CV images can be used to assess the quality of the grid search and to identify the ideal parameters for the training of the final SVM (Fig. 2). The user can choose, whether the best parameters shall be selected individually for each binary SVM or if one best overall parameter set from a mean CV error image shall be used for the final training of all binary classifiers. This final training is independent from the previous grid search and the number of features, samples or the termination criterion can be changed compared to the grid search. This way, a time saving calculation during grid search is performed while equal final accuracy is achieved. The results from the final training with the best parameter settings are saved as so-called SVM models. These can be viewed in the imageSVM model viewer, which shows the training parameters and gives information on the data used during the training (Fig. 3). From the model viewer the user can choose between the classification of entire images or a quick validation, where only reference pixels from an ROI are classified and used for an accuracy assessment. Proceedings 5 EARSeL Workshop on Imaging Spectroscopy. Bruges, Belgium, April 23-25 2007 3

Classifying remotely sensed images from urban environments is challenging. Urban land cover classes are spectrally heterogeneous and materials from different classes have similar spectral properties. Image segmentation has become a common preprocessing step that helped to overcome such problems. However, little attention has been paid to impacts of segmentation on the data's spectral information content. Here, urban hyperspectral data is spectrally classified using support vector machines (SVM). By training a SVM on pixel information and applying it to the image before segmentation and after segmentation at different levels, the classification framework is maintained and the influence of the spectral generalization during image segmentation hence directly investigated. In addition, a straightforward multi-level approach was performed, which combines information from different levels into one final map. A stratified accuracy assessment by urban structure types is applied. The classification of the unsegmented data achieves an overall accuracy of 88.7%. Accuracy of the segment-based classification is lower and decreases with increasing segment size. Highest accuracies for the different urban structure types are achieved at varying segmentation levels. The accuracy of the multi-level approach is similar to that of unsegmented data but comprises the positive effects of more homogeneous segment-based classifications at different levels in one map.