ALBERT

Description: The paper presents the first retrievals of clean snow properties using spaceborne hyperspectral observations via the Environmental Mapping and Analysis Program (EnMAP). The location close to the Concordia station at the Dome C Plateau (Antarctica) was selected. At this location, the atmospheric effects (except molecular light scattering and absorption) are weak, and the simplified atmospheric correction scheme could be applied. The ice grain size, snow specific surface area, and snow spectral and broadband albedos were retrieved using single-view EnMAP measurements. In addition, we propose a technique to retrieve trace gas concentrations (e.g., water vapor and ozone) from EnMAP observations over the snow surfaces. A close correspondence of satellite and ground-measured parameters was found.

Description: The Environmental Mapping and Analysis Program (EnMAP) is a spaceborne German hyperspectral satellite mission that aims at monitoring and characterizing the Earth's environment on a global scale. The mission is now ready to start with the sensor being by end of 2021 in Flight Acceptance Review, ready to be shipped to the launch pad in early 2022. This paper presents first an update of the mission status with recent activities and developments from the space and the ground segment. Then, an update of selected highlights of the science segment activities at launch phase are presented including preparation and if possible early results for the validation of EnMAP products, updates on EnMAP science algorithms (EnMAP-Box) developed at GFZ, online education initiative (HYPERedu), and further mission support activities such as background mission.

Description: Multisensor data fusion has become a hot topic in the remote sensing research community. This is thanks to significant technological advances and the ability to extract information that would have been challenging with a single sensor. However, sensory enhancement requires advanced analysis that enables deep learning. A framework is designed to effectively fuse hyperspectral and lidar data for semantic segmentation in the urban environment. Our work proposes a method of reducing dimensions by exploring the most representative features from hyperspectral and lidar data and using them for supervised semantic segmentation. In addition, we chose to compare segmentation models based on 2D and 3D convolutional operations with two different model architectures, such as U-Net and ResU-Net. All algorithms have been tested with three loss functions: standard Categorical Cross-Entropy, Focal Loss and a combination of Focal Loss and Jaccard Distance—Focal–Jaccard Loss. Experimental results demonstrated that the 3D segmentation of U-Net and ResU-Net with Focal and Focal–Jaccard Loss functions had significantly improved performance compared to the standard Categorical Cross-Entropy models. The results show a high accuracy score and reflect reality by preserving the complex geometry of the objects.

Description: Technological innovations and advanced multidisciplinary research increase the demand for multisensor data fusion in Earth observations. Such fusion has great potential, especially in the remote sensing field. One sensor is often insufficient in analyzing urban environments to obtain comprehensive results. Inspired by the capabilities of hyperspectral and Light Detection and Ranging (LiDAR) data in multisensor data fusion at the feature level, we present a novel approach to the multitemporal analysis of urban land cover in a case study in Høvik, Norway. Our generic workflow is based on bitemporal datasets; however, it is designed to include datasets from other years. Our framework extracts representative endmembers in an unsupervised way, retrieves abundance maps fed into segmentation algorithms, and detects the main urban land cover classes by implementing 2D ResU-Net for segmentation without parameter regularizations and with effective optimization. Such segmentation optimization is based on updating initial features and providing them for a second iteration of segmentation. We compared segmentation optimization models with and without data augmentation, achieving up to 11% better accuracy after segmentation optimization. In addition, a stable spectral library is automatically generated for each land cover class, allowing local database extension. The main product of the multitemporal analysis is a map update, effectively detecting detailed changes in land cover classes.

Description: The Environmental Mapping and Analysis Program (EnMAP) is a spaceborne German hyperspectral satellite mission that aims at monitoring and characterizing the Earth’s environment on a global scale. EnMAP core themes are environmental changes, ecosystem responses to human activities, and management of natural resources. In 2021 major milestones were achieved in the sensor and satellite preparation which is by end-2021 in the final acceptance review and pre-launch phase, with a launch window opening April 2022 (Fischer et al., ESA LPS 2022). Accordingly, the mission science support shifted from science development to pre-launch and launch support. The EnMAP science preparation program has been run for more than a decade to support industrial and mission development, and scientific exploitation of the data by the user community. The program is led by the German Research Center for Geosciences (GFZ) Potsdam supported by several partners and is funded within the German Earth observation program by the DLR Space Agency with resources from the German Federal Ministry for Economic Affairs and Energy (BMWi). In 2020 a new 3+1-year project phase started during which specific activities are performed at the GFZ Potsdam together with the four project partners Humboldt-University (HU) Berlin, Alfred-Wegener Institute (AWI) Bremerhaven, Ludwig Maximilian University (LMU) Munich, and University Greifswald. These activities focus on the preparation for the scientific exploitation of the data by the user community as well as mission support during the commissioning phase and the start of the nominal phase, supported by the EnMAP Science Advisory Group. In this presentation, we aim at providing an update of the current science preparation activities performed at GFZ. This includes an update of the data product validation activities focusing on an independent validation of the EnMAP radiance and reflectance products. For smooth and efficient validation especially during the commissioning phase, a semi-automatic processing chain is being developed (EnVAL), which streamlines the validation sites and in-situ data management as well as the validation tasks and report generation. Also, an update on new resources in the online learning initiative HYPERedu will be presented. In particular, the first Massive Open Online Course (MOOC) on the basics of imaging spectroscopy titled ‘Beyond the Visible – Introduction to Hyperspectral Remote Sensing’ was successfully opened in November 2021. An update will be further provided on the status of algorithms included in the EnMAP-Box related to data pre-processing and derivation of geological and soil mapping. It includes the EnMAP processing tool (EnPT) that is developed as an alternative to the processing chain of the EnMAP ground segment and provides free and open-source features to process EnMAP Level-1B data to Level-2A bottom-of-atmosphere (BOA) reflectance, and the EnMAP geological Mapper (EnGeoMap) and Soil Mapper (EnSoMap) for users in bare Earth and Geosciences applications. Finally, a background mission plan is developed as mission internal to fully exploit the resources of the satellite in terms of functionalities and/or capacities when there are resources available after all user requests have been processed. It can be used to generate time series databases interesting for the user community and anticipate future user needs, or to prototype and validate new mission strategies, such as large mosaicking demonstrations and/or synergies with other hyperspectral missions.

Description: Remote sensing and soil spectroscopy applications are valuable techniques for soil property estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil quality, and although organic matter is well studied, calcium carbonates require more investigation. In this study, we validated the performance of laboratory soil spectroscopy for estimating the aforementioned properties with referenced in situ data. We also examined the performance of imaging spectroscopy sensors, such as the airborne HySpex and the spaceborne PRISMA. For this purpose, we applied four commonly used machine learning algorithms and six preprocessing methods for the evaluation of the best fitting algorithm.. The study took place over crop areas of Amyntaio in Northern Greece, where extensive soil sampling was conducted. This is an area with a very variable mineralogical environment (from lignite mine to mountainous area). The SOM results were very good at the laboratory scale and for both remote sensing sensors with R2 = 0.79 for HySpex and R2 = 0.76 for PRISMA. Regarding the calcium carbonate estimations, the remote sensing accuracy was R2 = 0.82 for HySpex and R2 = 0.36 for PRISMA. PRISMA was still in the commissioning phase at the time of the study, and therefore, the acquired image did not cover the whole study area. Accuracies for calcium carbonates may be lower due to the smaller sample size used for the modeling procedure. The results show the potential for using quantitative predictions of SOM and the carbonate content based on soil and imaging spectroscopy at the air and spaceborne scales and for future applications using larger datasets.

Description: In support of the Environmental Mapping & Analysis Program (EnMAP) mission [1], the acquisition of accurate and comparable spectroradiometric in-situ measurements is crucial for vicarious validation of the official EnMAP data products [2]. This document provides a guide on properly conducting spectroradiometric field measurements within the scope of EnMAP. It is a summary, of the detailed technical handbook developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) [3], the approach established by the Remote Sensing Laboratories (RSL, University of Zurich) [4], on the bases of „Progress in field spectroscopy“ [5], “Field and airborne spectroscopy cross validation - Some considerations” [6] and the experience gained throughout numerous validation efforts for air- and spaceborne sensors by the Remote Sensing and Geoinformatics section at the GFZ Potsdam that have been specially adapted for EnMAP purposes. The following procedure should be used when conducting in-situ measurements of terrestrial surfaces to obtain consistent measurements by applying a repeatable approach throughout the validation phase of the EnMAP mission.

Description: The efficiency of spectral-based assessments of soil attributes using soil spectral libraries (SSLs) covering the visible–near-infrared–shortwave-infrared (VNIR–SWIR: 400–2500 nm) region has been proven in many studies. Nevertheless, as traditional SSLs are commonly developed under laboratory conditions, their application is limited for the assessment of soil surface-dependent properties such as water-infiltration rate (WIR) into the soil profile due to the sampling procedure. Currently, few studies are based on field SSLs for the prediction of physical soil properties. This study used a field-based protocol to measure soil reflectance data and WIR simultaneously in the field, and generate spectral-based decision tree models to predict WIR solely from field spectral measurements using the SoilPRO® assembly. The obtained models were applied to both airborne hyperspectral (HySpex) and satellite multispectral (Sentinel 2) data on a pixel-by-pixel basis to generate raster maps of WIR. The study areas were located in Macedonia (Greece), and were optimal for mapping WIR because the soil crust was well developed, and sites were characterized by bare soils (no vegetation coverage) with a sandy structure. Whereas the WIR map generated with the satellite data was poor due to the low spatial and spectral resolution of Sentinel 2 (20 m, 9 bands), the results obtained with the airborne hyperspectral HySpex sensor (5 m, 408 bands) were satisfactorily validated in the ground-truth stage with good prediction accuracy due to high spatial and spectral resolution. Validation accuracy of the HySpex observations using all field samples gave R2 = 0.68, whereas the predictions of the ground-truth samples that were not part of the calibration stage (field validation group) of the model gave R2 = 0.59. We concluded that these results are favourable for rapid estimation of soil surface conditions and pave the way for a wider spatial view from orbital hyperspectral remote-sensing sensors.

Description: EnMAP (Environmental Mapping and Analysis Program) is a high-resolution imaging spectroscopy remote sensing mission that was successfully launched on April 1st, 2022. Equipped with a prism-based dual-spectrometer, EnMAP performs observations in the spectral range between 418.2 nm and 2445.5 nm with 224 bands and a high radiometric and spectral accuracy and stability. EnMAP products, with a ground instantaneous field-of-view of 30 m x 30 m at a swath width of 30 km, allow for the qualitative and quantitative analysis of surface variables from frequently and consistently acquired observations on a global scale. This article presents the EnMAP mission and details the activities and results of the Launch and Early Orbit and Commissioning Phases until November 1st, 2022. The mission capabilities and expected performances for the operational Routine Phase are provided for existing and future EnMAP users.