The German Research Center for Geosciences also known as GFZ-Potsdam has a long history in the definition and development of new spaceborne sensors such as for gravity and optical Earth Observation missions with GRACE, GRACE-FO, MOMS, and very recently with the launch of EnMAP on 1st April 2022. The Environmental Mapping and Analysis Program (EnMAP) is the first German spaceborne hyperspectral satellite mission. EnMAP aims at monitoring and characterizing the Earth’s environment on a global scale. Core science objectives are toward studying environmental changes, ecosystem responses to human activities, and management of natural resources. The EnMAP mission consortium is composed of the DLR Space Administration in Bonn that is responsible for the overall project management, OHB is responsible of the space segment, DLR Earth Observation Center is responsible of the ground segment, and GFZ Potsdam is responsible for the science related activities and science mission support.
In particular, EnMAP is accompanied by an extensive scientific exploitation preparation program that has been run for more than a decade to support industrial and mission development, and scientific exploitation of the data by the user community. In the current EnMAP phase, this program includes mission support during the current commissioning phase and the start of the nominal phase planned toward end of October, supported by the EnMAP Science Advisory Group. In that frame, large activities in the GFZ remote sensing group are dedicated to a) hyperspectral sensor simulation, data quality and validation of EnMAP data products, b) development of methods and open softwares toolboxes such as in the QGIS EnMAP-Box for the pre-processing of radiance to reflectance, and for the retrieval of geo- and bio-physical parameters, c) user community training and workshops, development of new educational resources such as in the EnMAP online learning initiative HYPERedu, opening of a Massive Open Online Course (MOOC) on the basics of imaging spectroscopy, d) mission support and development of validation and background mission plan, and EnMAP announcement of opportunities.
KEYWORDS: Soil science, Reflectivity, Vegetation, Data modeling, Calibration, Remote sensing, Sensors, Global system for mobile communications, Spectral resolution, Statistical modeling
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.
The goal of this study is to develop remote sensing desertification indicators for drylands, in particular using the capabilities of imaging spectroscopy (hyperspectral imagery) to derive soil and vegetation specific properties linked to land degradation status. The Cabo de Gata-Nijar Natural Park in SE Spain presents a still-preserved semiarid Mediterranean ecosystem that has undergone several changes in landscape patterns and vegetation cover due to human activity. Previous studies have revealed that traditional land uses, particularly grazing, favoured in the Park the transition from tall arid brush to tall grass steppe. In the past ~40 years, tall grass steppes and arid garrigues increased while crop field decreased, and tall arid brushes decreased but then recovered after the area was declared a Natural Park in 1987. Presently, major risk is observed from a potential effect of exponential tourism and agricultural growth. A monitoring program has been recently established in the Park. Several land degradation parcels presenting variable levels of soil development and biological activity were defined in summer 2003 in agricultural lands, calcareous and volcanic areas, covering the park spatial dynamics. Intensive field spectral campaigns took place in Summer 2003 and May 2004 to monitor inter-annual changes, and assess the landscape spectral variability in spatial and temporal dimension, from the dry to the green season. Up to total 1200 field spectra were acquired over ~120 targets each year in the land degradation parcels. The targets were chosen to encompass the whole range of rocks, soils, lichens, and vegetation that can be observed in the park. Simultaneously, acquisition of hyperspectral images was performed with the HyMap sensor. This paper presents preliminary results from mainly the field spectral campaigns. Identifying sources of variability in the spectra, in relation with the ecosystem dynamics, will allow the definition of spectral indicators of change that can be used directly to derive the desertification status of a land.
Desertification is a land degradation problem of major importance in the arid regions of the world. Deterioration in soil and plant cover have adversely affected nearly 70 percent of the drylands as mainly the result of human mismanagement of cultivated and range lands. Overgrazing, woodcutting, cultivation practices inducing accelerated water and wind erosion, improper water management leading to salinisation, are all causes of land degradation. In addition to vegetation deterioration, erosion, and salinisation, desertification effects can be seen in loss of soil fertility, soil compaction, and soil crusting. Combating desertification involves having an accurate knowledge on a current land degradation status and the magnitude of the potential hazard. We present here a new project that aims at deriving a global simplified Land Degradation Index (LDI) from hyperspectral remote sensing data. Indeed, specific soil properties directly linked to soil degradation status, such as chemical properties, organic matter content, mineralogical content, soil crusting, and runoff, as well as vegetation content and degradation status, could be derived from high-spectral resolution imagery. Then, global maps assessing drylands desertification status could be routinely developed. This paper, after a brief review of land degradation processes and assessment, discusses the capabilities of hyperspectral imagery for land degradation assessment.
KEYWORDS: Minerals, Associative arrays, Vegetation, Reflectivity, Calcite, Data acquisition, Spectral models, Mica, Signal to noise ratio, Short wave infrared radiation
The ESF-LSF 1997 flight campaign conducted by the German Aerospace Center (DLR) recorded several transects across the island of Naxos using the airborne hyperspectral scanner DAIS. The geological targets cover all major litho-tectonic units of a metamorphic dome with the transition of metamorphic zonations from the outer meta-sedimentary greenschist envelope to the gneissic amphibolite facies and migmatitic core. Mineral identification of alternating marble-dolomite sequences and interlayered schists bearing muscovite and biotite has been accomplished using the airborne hyperspectral DAIS 7915 sensor. Data have been noise filtered based on maximum noise fraction (MNF) and fast Fourier transform (FFT) and converted from radiance to reflectance. For mineral identification, constrained linear spectral unmixing and spectral angle mapper (SAM) algorithms were tested. Due to their unsatisfying results a new approach was developed which consists of a linear mixture modeling and spectral feature fitting. This approach provides more detailed and accurate information. Results are discussed in comparison with detailed geological mapping and additional information. Calcites are clearly separated from dolomites as well as the mica-schist sequences by a good resolution of the mineral muscovite. Thereon an outstanding result represents the very good resolution of the chlorite/mica (muscovite, biotite)-transition defining a metamorphic isograde.
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