Description: Heat waves have tremendous ecological and socioeconomic consequences for many countries and initiate complex event chains that reach from the land surface to the upper atmosphere. Although it is well known that global change affects the Earth and environment on many different time and length scales, currently, only very limited knowledge is available on the importance of such distinct dynamic events for the long-term development of the Earth system. To investigate the impact of extended heat periods and droughts on our terrestrial ecosystems and natural resources, the Helmholtz MOSES project implements a modular infrastructure that is designed to capture such highly dynamic events in event-driven campaigns. As part of this infrastructure initiative a new hyperspectral thermal instrument, the Telops Hyper-Cam LW, was recently acquired at the Potsdam German Research Centre for Geosciences (GFZ) and capabilities for airborne surveys, laboratory and field deployment, as well as data processing in the context of heat wave impacts are currently developed. The Telops Hyper-Cam LW is a Fourier-transform imaging spectrometer (~8–12 μm) with adjustable spectral resolution from 0.25 to 150 cm−1 that can be operated at various scales from ground and airborne platforms. The hyperspectral longwave infrared shows great potential for the characterization of soil and vegetation properties and their variability related to heat wave impacts. However, this spectral imagery can only be used to fullest advantage when the signal is corrected, e.g. path radiance of the atmosphere, as well as the downwelling radiance component have been removed from the measured signal and temperature is separated from emissivity. In this context, this contribution describes the recent developments at GFZ toward (i) The development of suitable field sampling strategy & protocols related to the acquisition of field thermal hyperspectral data including calibration and validation measurements, (ii) Establishment of preliminary protocols for field data processing to temperature and emissivity, (iii) Test and mounting of the Hyper-cam on the Cessna-T207A airborne platform from the Free University Berlin (FUB) and (iv) Flight testing and calibration, and establishment of preliminary protocols and strategies for the development of a processing chain from raw data to temperature and emissivity imagery and extraction of relevant thematic parameters. In particular, first results will be shown based on the MOSES/ScaleX-2019 campaign where field Hyper-Cam measurements were acquired in different configurations at the Fendt grassland test site located in the German Pre-Alpine foreland. Different approaches for temperature emissivity separation are tested and compared, e.g. normalization emissivity method and spectral smoothness based emissivity separation. Furthermore, calibration and validation activities are presented in the frame of several airborne surveys over different targets to correct and validate the thermal signal. Preliminary airborne results will be shown over different locations in Germany and Greece that indicate good geometric and radiometric data accuracy, as well as high potential for the differentiation of surface materials from the spectral emissivity and surface temperature.

Description: The dataset is composed of Neo HySpex (VNIR/SWIR) hyperspectral imagery acquired during airplane overflights on June 6th, 2015 covering the Omongwa Pan located in the South-West Kalahari, Namibia. The dataset includes three cloud-free flight lines with 408 spectral bands ranging from VNIR to SWIR wavelength regions (0.4-2.5 µm). The dataset also includes Level 2A EnMAP-like imagery simulated using the end-to-end Simulation tool (EeteS). The overall goal of the campaign was to acquire imagery over the Omongwa Pan and use the spectral reflectance for the analyses of surface sediments, specifically the mineralogical composition of exposed surface evaporites / salts on the airborne and spaceborne scale. The data are highly novel and can be used to test estimation of surface sediment properties in a highly saline and dynamic environment.

Description: The dataset is composed of Neo HySpex (VNIR/SWIR) hyperspectral imagery acquired during the GFZ/DIMAP Geoarchive airborne campaign on June 6th, 2015 covering the Omongwa salt pan located in the South-West Kalahari, Namibia. The dataset includes 9 merged cloud-free flight lines with 408 spectral bands ranging from VNIR to SWIR wavelength regions (0.4-2.5 μm). The dataset also includes Level 2A EnMAP-like imagery simulated using the end-to-end Simulation tool (EeteS). The overall goal of the campaign was to test the potential of advanced optical hyperspectral remote sensing, or imaging spectroscopy, for the analysis of surface processes in the Omongwa salt pan and for the quantification of surface sediments. Specifically, the mineralogical composition of exposed evaporites such as halite, gypsum and calcite were investigated at the airborne and spaceborne scale, associated with comprehensive field campaigns, ich which spectral reflectance and ground-truth chemical data of field samples have been collected. The data are highly novel and can be used as testbeds for the development and validation of retrieval algorithms based on air- and space-borne hyperspectral imagery for estimation of surface sediment properties in a highly saline and dynamic environment.

Description: The scope of the Science Plan is to describe the scientific background, applications, and activities of the Environmental Mapping and Analysis Program (EnMAP) imaging spectroscopy mission. Primarily, this document addresses scientists and funding institutions, but it may also be of interest to environmental stakeholders and governmental agencies. It is designed to be a living document that will be updated throughout the entire mission lifetime. 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 impact on international programs as well as major environmental and societal challenges. Chapter 2 describes the EnMAP system together with data products and access, calibration/validation, and synergies with other missions. Chapter 3 gives an overview of the major fields of application such as vegetation and forests, geology and soils, coastal and inland waters, cryosphere, urban areas, atmosphere and hazards. 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 and an extended glossary of terms and abbreviations is available on the EnMAP website.

Description: Soils are an essential factor contributing to the agricultural production of rainfed crops such as barley and triticale cereals. Changing environmental conditions and inadequate land management are endangering soil quality and productivity and, in turn, crop quality and productivity are affected. Advances in hyperspectral remote sensing are of great use for the spatial characterization and monitoring of the soil degradation status, as well as its impact on crop growth and agricultural productivity. In this study, hyperspectral airborne data covering the visible, near-infrared, short-wave infrared, and thermal infrared (VNIR–SWIR–TIR, 0.4–12 µm) were acquired in a Mediterranean agricultural area of central Spain and used to analyze the spatial differences in vegetation vitality and grain yield in relation to the soil degradation status. Specifically, leaf area index (LAI), crop water stress index (CWSI), and the biomass of the crop yield are derived from the remote sensing data and discussed regarding their spatial differences and relationship to a classification of erosion and accumulation stages (SEAS) based on previous remote sensing analyses during bare soil conditions. LAI and harvested crop biomass yield could be well estimated by PLS regression based on the hyperspectral and in situ reference data (R2 of 0.83, r of 0.91, and an RMSE of 0.2 m2 m−2 for LAI and an R2 of 0.85, r of 0.92, and an RMSE of 0.48 t ha−1 for grain yield). In addition, the soil erosion and accumulation stages (SEAS) were successfully predicted based on the canopy spectral signal of vegetated crop fields using a random forest machine learning approach. Overall accuracy was achieved above 71% by combining the VNIR–SWIR–TIR canopy reflectance and emissivity of the growing season with topographic information after reducing the redundancy in the spectral dataset. The results show that the estimated crop traits are spatially related to the soil’s degradation status, with shallow and highly eroded soils, as well as sandy accumulation zones being associated with areas of low LAI, crop yield, and high crop water stress. Overall, the results of this study illustrate the enormous potential of imaging spectroscopy for a combined analysis of the plant-soil system in the frame of land and soil degradation monitoring.