This project is a continuation of project F funded in the first phase of the DFG Research Group CAOS, where we evaluated the potential of different ground-based geophysical techniques for exploring hydrological systems regarding subsurface structures, characteristics, and processes. Building up on the results of this project, we now focus on further developing selected geophysical techniques (timelapse GPR imaging) for deepening our understanding of hydrological processes at the plot and hillslope scale. In addition, we propose to systematically evaluate modem remote sensing techniques because they cun-ently represent the only means to efficiently explore larger areas or entire catchments. Here, we focus on a combination of full-waveform laserscanning and hyperspectral imaging because they can provide detailed Information regarding geometrical and physical properties of earth's surface, respectively. To link remote sensing with point/plot/hillslope scale data as provided by geophysics and conventional hydrological field techniques, we believe that further methodological innovations are needed. For example, we plan to establish a unique field laboratory to better understand the responses of geophysical and remote sensing techniques to different natural and artificial hydrological events and to develop exploration strategies advancing the applicability of geophysics and remote sensing for hydrological applications at a variety of spatial scales.
Mit einem flugzeuggetragenen abbildenden Spektrometer wird das vom Boden reflektierte Sonnenspektrum vermessen und die registrierten spektralen Signaturen Landnutzungsklassen zugeordnet. Bei dieser Zuordnung werden auch neuronale Netze eingesetzt. Neben der Verarbeitung spektral aufgeloester Daten wird augenblicklich an der Einbindung von Texturinformationen (mit neuronalen Netzen) gearbeitet.
Airborne hyperspectral remote sensing surveys over various areas in Saxony.
This dataset is a global surface ocean compilation of high-performance liquid chromatography (HPLC) phytoplankton pigment concentrations and hyperspectral remote sensing reflectance (Rrs) data, with associated temperature and salinity measurements. The pigments measured include: total chlorophyll-a (Tchla), 19'-hexanoyloxyfucoxanthin (HexFuco), 19'-butanoyloxyfucoxanthin (ButFuco), alloxanthin (Allo), fucoxanthin (Fuco), peridinin (Perid), zeaxanthin (Zea), divinyl chlorophyll a (DVchla), monovinyl chlorophyll b (MVchlb), chlorophyll c1+c2 (Chlc12), chlorophyll c3 (Chlc3), neoxanthin (Neo), and violaxanthin (Viola). Rrs data are measured at 1 nm spectral resolution from 400-700 nm. The Rrs data from the ANT cruises were collected using a RAMSES hyperspectral radiometer, the Rrs data from the NAAMES, SABOR, Tara, RemSensPOC, BIOSOPE, and EXPORTS cruises were generated by a HyperPro (Satlantic, Inc.) hyperspectral radiometer. All samples presented in this dataset have previously been published and are publicly available, as referenced in the table: ANT: Bracher et al. (2015), https://doi.org/10.1594/PANGAEA.847820, NAAMES: Behrenfeld et al. (2014a), http://dx.doi.org/10.5067/SeaBASS/NAAMES/DATA001, Remote Sensing of POC: Cetinić (2013), http://dx.doi.org/10.5067/SeaBASS/REMSENSPOC/DATA001, SABOR: Behrenfeld et al. (2014b), http://dx.doi.org/10.5067/SeaBASS/SABOR/DATA001, Tara Oceans: Boss and Claustre (2009), http://dx.doi.org/10.5067/SeaBASS/TARA_OCEANS_EXPEDITION/DATA001, Tara Mediterranean: Boss and Claustre (2014), http://dx.doi.org/10.5067/SeaBASS/TARA_MEDITERRANEAN/DATA001, BIOSOPE: Claustre and Sciandra (2004), https://doi.org/10.17600/4010100 hosted at http://www.obs-vlfr.fr/proof/php/bio_open_access_data.php, EXPORTS: Behrenfeld et al. (2018), http://dx.doi.org/10.5067/SeaBASS/EXPORTS/DATA001. This compilation of these data is used in Kramer et al. (2021) to evaluate a model that reconstructs pigment concentrations from hyperspectral remote sensing reflectance.
This dataset accompanying the MOOC on soil applications contains an airborne hyperspectral HySpex image over the study site Demmin in Northern Germany which was recorded in October 2015. The surrounding area of Demmin is characterized by its glacial past and is largely used for agriculture. Here you can find relics of the ice age such as kettle holes - small, completely closed hollow shapes whose formation is attributed to the burial and subsequent thawing of an ice lens. Mostly overgrown nowadays by vegetation, SOC accumulates in these areas and higher contents are measured. The image dataset is fully pre-processed – all non-soil pixels are masked, the spectra were smoothed using a Savitzky-Golay Filter and transformed to first derivatives – and provided in BSQ format. In addition to the HySpex image, this dataset contains a point data shapefile with 27 sampling locations, as well as information on the soil organic carbon (SOC) contents [g/kg]. The dataset is made publicly available as part of the Massive Open Online Course (MOOC) "Beyond the Visible - Imaging Spectroscopy for Soil Applications ", available from Spring 2023. Guidance on how to derive quantitative soil maps (SOC content) using the EnMAP-Box (QGIS plugin) are provided as videos at the HYPERedu YouTube channel, the soil MOOC course pages and the regression workflow documentation.
The effective translation of Earth observation (EO) measurements into actionable information for agriculture and land monitoring is critical to support policy implementation on climate, environment, and sustainable development. However, this translation remains challenging, as EO evolves from an awareness-raising instrument into an operational tool for evidence-based policymaking. To address this gap, we systematically link, for the first time, European Union (EU) land-related agricultural and environmental policies to EO-derived variables that can be generated from enhanced optical satellites expected in the next decade. We present a comprehensive framework for assessing the technology readiness levels (TRLs) of EO variables used to map, monitor, and manage crop, forest, soil, mineral, and water resources, thereby facilitating policy implementation and compliance. Upcoming Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and the Sentinel-2 Next Generation (S2NG) missions, both developed by the European Space Agency (ESA), will deliver substantial technological advancements for high-level EO-based products, enabling applications such as plant nitrogen and soil organic carbon content (SOC) estimation, species identification, and water quality characterization. Realizing the full potential of CHIME and S2NG for agricultural and environmental policy implementation will require advancing current products from prototype stages (TRL 4–6) to full operational readiness (TRL 9) through robust science-policy interfaces. Within such interfaces, we recommend exploiting existing (hyperspectral) EO data and time series, strengthening in-situ observations for robust model development and validation, and testing synergies between systems. Co-design of tailored products with policymakers is then essential to refine algorithms and align EO outputs with regulatory needs and scales. Upcoming spaceborne imaging spectroscopy and enhanced multispectral data streams thus have the potential to become game-changers and indispensable tools for EU policy implementation, providing greater traceability of key environmental and agricultural processes. © The Author(s) 2026
Hyperspectral image (HSI) scanning of the composite record from Holzmaar (HZM19) was measured using a Specim PFD-CL-65-V10 E line scan camera (University of Bern, Switzerland). Data were processed using the ENVI software following the workflow of Butz et al. (2015, doi10.1117/1.JRS.9.096031): data were white-corrected, masked for cracks in the sediment surface and Relative Absorption Band Depths (RABDs) were computed for 2mm wide subsets. RABD671 (band depths from 640 to 702 nm) for Total Chloropigments-a (TChl-a), RABD845 (790 - 900 nm) for Bacteriopheopigments-a (Bphe-a), and RABD620 (600 - 640 nm) for Phycocyanin (PhyCy). To translate HSI indices into absolute concentrations, a pigment extraction was performed at the University of Bern using 23 samples covering the full range of RABD671 and RABD845 index values. Ca 1 g of wet sediment was treated with 100 % acetone following the method of Lami et al. (1994, doi:10.1007/BF00684032) and extractions were measured using a Shimadzu UV-1800 spectrophotometer to obtain bulk concentrations of TChl-a and Bphe-a in µg/g dry sediment using a molar extinction coefficient for TChl-a and Bphe-a. A proxy-proxy calibration was carried out using an ordinary least square regression. After all, only 1.42 % and 0.77 % of datapoints are outside of the calibration ranges for Chl-a (calibration range: 12.75 – 1202.68 µg/g, intercept = -4799.52, slope= 4756,45, r² = 0.8, p-val = 0.00, RMSEP 10-fold = 169.03, RMSEP % = 14.05) and Bphe-a (calibration range 0.38 – 345.12 µg/g, intercept = -1295,8, slope= 1319,7, r² = 0.94, p-val = 0.00, RMSEP 10-fold = 25.26, RMSEP % = 7.32). Ages refer to Birlo et al. (2023) and the related dataset is Model D available via doi:10.1594/PANGAEA.949292.
To calibrate the hyperspectral imaging (HSI) index values from the sediments of Holzmaar (HZM19) to concentration, a spectrophotometrically measured pigment analysis (Butz et al., 2015; doi:10.1117/1.JRS.9.096031) was performed for 23 samples. These samples were selected to cover a wide range of pigment concentrations as documented by HSI scanning. Approximately 1 g of wet sediment was treated with 100 % acetone according to the method of Lami et al. (1994; doi:10.1007/BF00684032), and the extracts were measured with a Shimadzu UV-1800 spectrophotometer to obtain the mass concentration of Chl-a and Bphe-a in µg/g dry sediment using a mass extinction coefficient for Chl-a (Fiedor et al, 2002; doi:10.1562/0031-8655(2002)0760145POTBCS2.0.CO2) and for Bphe-a (Jeffrey and Humphrey, 1975; doi:10.1016/S0015-3796(17)30778-3).
This dataset accompanying the MOOC on forest applications contains an airborne hyperspectral HyMap image over the study site north of Karlsruhe in Southwest Germany which was recorded in August 2010. The surrounding area of Karlsruhe is characterized by its relatively warm climate due to the influence of the Upper-Rhine and its climate can be considered more continental than typical German conditions. Additionally it is characterized by its flat terrain. Here you can find a diversity of tree species growing in the mixed forests. These include coniferous trees such as Scots Pine, Douglas Fir, Norway Spruce, Silver Fir and Larch as well as deciduous tree species like European Beech, Oak and Red Oak. The image dataset is fully pre-processed –it was atmospherically and topographically corrected by the DLR using ATCOR4 and ORTH software – and provided in TIF format. In addition to the HyMap image, this dataset contains a point data shapefile with 250 sampling locations, which represents 5 tree species with 50 reference positions each. These reference positions were collected using visual interpretation of high-resolution images in combination with reference tree species maps provided by the local forest administration. These reference tree species maps are also provided as tif-files. The dataset is made publicly available as part of the Massive Open Online Course (MOOC) "Beyond the Visible - Imaging Spectroscopy for Forest Applications ", available from Summer 2025. Guidance on how to derive tree species classification maps using the EnMAP-Box (QGIS plugin) are provided as videos at the HYPERedu YouTube channel, the forest MOOC course pages and the regression workflow documentation. HYPERedu is an education initiative within the Environmental Mapping and Analysis Program (EnMAP), a German hyperspectral satellite mission that aims at monitoring and characterizing the Earth’s environment on a global scale. EnMAP serves to measure and model key dynamic processes of the Earth’s ecosystems by extracting geochemical, biochemical and biophysical variables, which provide information on the status and evolution of various terrestrial and aquatic ecosystems.
The dataset contains a spaceborne hyperspectral image acquired by EnMAP over Berlin, Germany, and surrounding areas on July 24th, 2022. The data was preprocessed to Level 1B format (systematically and radiometrically corrected) and is provided in separate BSQ files for the VNIR and SWIR sensor of the instrument, respectively. The Level 1B product is accompanied by a history file (xml), a metadata file (xml), six quality masks (cirrus, classes, cloud, cloud shadow, haze and snow) as well as quality test flags and pixel masks for the VNIR and SWIR files separately (all TIF format). In addition, this dataset comes with a digital elevation model, COP-DEM-GLO-30-R (ESA, Copernicus) and a Sentinel-2 scene (ESA, Copernicus) as references for geometric and atmospheric correction with the EnMAP processing tool (EnPT). Please note that the two datasets described above are NOT part of the same license as the EnMAP data. The dataset is made publicly available as part of the Massive Open Online Course (MOOC) "Beyond the Visible - EnMAP data access and image preprocessing techniques", available from July 2023. Guidance on preprocessing hyperspectral imagery in general, access to EnMAP data and a hands-on tutorial on preprocessing of EnMAP data with EnPT in the EnMAP-Box (QGIS plugin) are provided as videos at the HYPERedu YouTube channel, the MOOC course page and the EnPT documentation. More information about the EnMAP mission can be found on the mission website and in Guanter et al. (2016) and Storch et al. (2023).
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