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The European Forest Fire Information System (EFFIS) provides timely and reliable information on wildland fires in Europe since 1998. EFFIS supports agencies in charge of the protection of forests within the European Union. The services comprise the daily analysis and publication of weather based fire danger information, as well as the timely delivery of active fire locations and recently occurred burnt areas. As part of Copernicus Emergency Management Service, EFFIS contributes directly to the disaster management in Europe and helps monitoring indicators of the Sustainable Development Goals. The Earth Observation Centre from the German Aerospace Centre (DLR) has been entrusted with modernizing the EFFIS service and with improving the near real time products delivery by shortening timeliness. The new main functionalities comprise analysis, derivation and delivery of burnt areas from Sentinel-3 and MODIS data. In this regard, a methodology has been developed, as well as implemented and tested, to efficiently derive reliable and accurate burnt area perimeters for different optical earth observation satellites. Another main functionality is the active fire and hotspot detection. This element has been updated with the newest available components which improve fire detection and reduces false alarms. The processing chain is fully automated and comprises the pre-processing of satellite data. In addition for burnt areas, it comprises histogram matching, mosaic generation and functions for the tracking growth over time. EFFIS new functionalities are integrated and validated in an operational environment. Results of these functionalities benefit the fire situation monitoring in Europe through faster, more reliable and accurate detections and bring considerable progress towards achieving the SDGs.

Das Weltraumwetter wird hauptsächlich durch die Sonne verursacht und wirkt über die Kette Magnetosphäre, Thermosphäre und Ionosphärein auf die Erdatmosphäre ein. Physik und Effekte des Weltraumwetters zeichnen sich durch hohe Dynamik aus. Von der systematischen Erforschung der komplexen Zusammenhänge des Weltraumwetters erwarten wir eine tieferes Verständnis der Beeinflussung unserer technologischen Umwelt und des menschlichen Lebens durch die Sonne und andere kosmische Quellen. Monitoring, Modellierung und Vorhersage des Weltraumwetters sind die Voraussetzungen zur Reduzierung potenzieller Gefährdungen der Funktionalität technischer Systeme oder des menschlichen Lebens.

Etablierung des Cal/Val-Standorts DEMMIN als internationales Testgebiet für fernerkundliche Methodenentwicklung

Measurements of environmental parameters for the validation of remote sensing data in DEMMIN are based on the measurements of stationary environmental monitoring stations. In addition to this network, these data are supplemented by in-situ campaigns to obtain plant and soil parameters for calibration and validation of the remote sensing data. The paper provides a status update for the German JECAM test site DEMMIN / TERENO-NO.

Innovative algorithms for Sentinel-1 (S1) satellite allow daily observations of meteo-marine parameters, tracking of storm propagation, study of local sea state variability and coastal processes. In order to investigate geophysical processes, the sea state and wind fields estimated simultaneously from S1 scenes acquired twice daily are combined with numerical forecast model results and in-situ measurements. The focus of the investigations are the evolution and propagation of storm peaks/centres, storm front movement, and arrival of swell. An example of efficient storm tracking in the Black Sea in April 2017 over three days is analysed. The HZG forecast spectral wave model running for the Black Sea reproduces the storm peak propagation near to the S1 observations. In detail, the storm peak observed by S1 is shifted ~80km towards the south in comparison to the model simulations. During this storm, the ship “Geroi Arsenala” of river-sea class licensed for inland waterways with access to the coastal seas was capsized about 40 kilometres to the south of the Kerch Strait in the open Black Sea, according to the associated press. The cargo ship was carrying grain from Russia to Turkey. Obviously, the course was taken too far from the coast to shorten the way across the sea with unexpected high sea state. Only one of the 12 people aboard was rescued. This is a tragic incident that proves the importance of Maritime Safety and Security. With the Sentinel satellites and the processing framework demonstrated in this work, we have appropriate tools to raise Maritime Situation Awareness (MSA) to unprecedented levels, which helps avoiding such accidents. The rapid development of satellite techniques, information extraction algorithms and ground infrastructures during the last years enabled a series of oceanographic applications with near-real-time (NRT) capabilities. Several minutes after acquisition, the produced data with geo-coded information on wind speed and wave height can be transferred to the weather services for validation of the forecasting models. The different kind of data like coastline, wave height, surface wind speed, ice coverage, oil spills etc. can be processed in parallel for the same image and combined with other information (e.g. model results, ship traffic) for supporting MSA. The algorithms currently developed for this purpose are integrated into a prototype processor for Sentinel-1 imagery. The DLR Ground Station Neustrelitz applies this prototype as part of a near real-time demonstrator MSA service. The presented scientific service involves daily provision of surface wind and sea state parameters estimated fully automatically from S1 Wide Swath Mode (IW) Synthetic Aperture Radar (SAR) images of North and Baltic Sea. S1 IW covers area-strips of thousand kilometres of earth and ocean surface with a resolution of ~10m by sequences of multiple individual IW images with an approximate size of 200km×250km. The data are free to use and provide unprecedented observation possibilities of ocean processes and natural phenomena worldwide at a high repetition rate. Due to the independence of sunlight and cloud coverage, SAR data are an indispensable source of 2D information of the ocean surface for open sea and for coastal applications.

Joint Experiment for Crop Assessment and Monitoring (JECAM) - Test Site DEMMIN 2018 Project Overview: Test site and project team

The Earth Observation Center (EOC) is a cluster institute of the German Aerospace Center (DLR). The EOC comprises the German Remote Sensing Data Center (DFD) and the Remote Sensing Technology Institute (IMF), at work in Oberpfaffenhofen, Neustrelitz, Berlin-Adlershof and Bremen. With its national and international receiving stations, DFD offers direct access to data from earth observation missions, derives information products from the raw data, disseminates these products to users, and safeguards all data in the National Remote Sensing Data Library for long-term use. The EOC operates the National Ground Station Neustrelitz (NSG), which is used as main ground station for the payload data reception of high rate data stream (X-Band, Ka-Band). In the beginning of the 90th, after finishing the INTERKOSMOS program, the station development has started into a new era. First projects have been OKEAN, the solar mission CORONAS-I/F, and the joint project MOMS/PRIRODA. Nowadays, the ground station is involved operationally for remote sensing missions like TerraSAR-X, TanDEM-X, Landsat-8, Landsat-7, KOMPSAT, Sentinel-1A/B, OCEANSAT-2, AQUA, TERRA, and for several small explorer missions. A number of Indian and international projects have been successfully supported. On behalf of ESA, the EOC, especially NGS with specific front end processing systems, has developed and operates the Sentinel-5P Ground System inclusive the operations of ground station elements at Svalbard and Inuvik. The paper describes the evolution process over two decades with respect to the station site and infrastructure, and the main hardware and software design. It focuses on the automation system of the station, which is either today station specific. From the beginning, the development goal was full integration in a highly automated multi-mission system. Relevant working areas are reception planning, reception automation, and quality control. Starting from long-term experience in data reception a software system supporting operations planner and operators has been developed. This system realizes a fast and reliable adopting of changing operation scenarios, a stable station monitoring and control and an objective collection of reception quality data. Furthermore, the paper gives a detailed view on the main integration and production component: the Front-End Processing system (FEP). In this role, the FEP is capable to be easily integrated in local and external facilities and provides a rich bundle of functions beside an excellent expandability and maintainability having limited resources.

The TERENO network: 1. Regional different effects of global climate change on terrestrial systems, 2. Global change affects all terrestrial compartments (water, soil, vegetation, atmosphere), 3. Most existing observation networks focus on specific compartments and/or scientific questions TERENO: 1. Long-term observations (> 15 years) of hydrological and ecological parameters on different scales, 2. Investigation of interaction between the different compartments, 3. Bridging the gap between measurement, modelling and management, 4. Currently 4 observatories, each operated by one individual Helmholtz Center, 5. Project duration: 2008 until >2023

Inferring conditions about the earth’s surface using remotely sensed electro-optical measurements almost always requires the use of ground truth data. Due to the heterogeneity and diversity of the land cover, as well as the distinctions in spectral and geometric resolution of various remote sensing applications an adaptive ground-based reference system is required for an adequate calibration and Validation of the data. Wireless sensor networks are a promising application for a sufficient solution of ground truthing multispectral remotely sensed data. Due to the quick installation and their self-organising behaviour iterative optimal sampling strategies can be performed straightforward. Especially the improvement of atmospheric corrections as well as resampling algorithms of single multispectral channels or derived vegetation indices are great potentials for the data quality management of remote sensing products.

Zahlreiche Studien befassen sich mit der Ableitung von Pflanzenparametern anhand von spektralen Vegetationsindizes und deren Anfälligkeit gegenüber wesentlichen Störfaktoren, wie zum Beispiel dem Einfluss des Bodenhintergrunds. Die Korrektur der Vegetationsindizes beziehungsweise des Vegetationssignals hinsichtlich des Bodeneinflusses stellt eine in der Fernerkundung seit langer Zeit bekannte Herausforderung dar und ist insbesondere im Bereich der Landwirtschaft für eine zuverlässige Abschätzung bedeutender Pflanzenparameter von großem Interesse. In Anbetracht dessen beschäftigt sich die vorliegende Arbeit mit einer Methode zur direkten Ableitung des Bodensignals aus luftgestützten Fernerkundungsdaten landwirtschaftlich genutzter und vegetationsbedeckter Flächen, mit der die Korrektur des Vegetationssignals von Pflanzenbeständen in einer übergeordneten Zielstellung ermöglicht werden kann. Die Extraktion des Bodensignals erfolgt durch einen wissensbasierten Modellansatz zur linearen spektralen Entmischung der Fahrgassen unter Zuhilfenahme eines Fahrspurmodells. Eine exemplarische Umsetzung dieser Methodik umfasst unter anderem eine Fahrgassenselektion sowie einen geometrischen Ansatz zur Modellierung der Fahrspuren auf Subpixel-Niveau. Es wird ein nicht-lineares alternierendes Optimierungsverfahren zur Entmischung der Fahrgassenpixel vorgestellt, mit dem wesentliche Modellunsicherheiten reduziert werden können und die Bestimmung von Referenzspektren entfällt. Eine Bewertung der Entmischungsergebnisse und eine Einschätzung der resultierenden Methode sind aufgrund fehlender Validierungsdaten nicht möglich und erfordern weiterführende Untersuchungen. Die Übertragbarkeit der Methode unterliegt einigen Einschränkungen, bedingt durch die geometrische und spektrale Auflösung der Daten sowie das Untersuchungsgebiet selbst. Unter geeigneten Bedingungen verspricht dieser Ansatz nichtsdestotrotz, eine Möglichkeit zur Abschätzung der Bodenreflexion und somit eine verbesserte Ableitung des Vegetationssignals gewährleisten zu können.

The Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) is a calibration and Validation test site for remote sensing Technologies and derived Information products. The test site is located within the TERENO German Northeastern Lowlands Observatory. DEMMIN Objectives are: i) combination of in-situ data and remote sensing data analysis for: Crop parameter estimation (crop type, crop status, crop pattern) Soil parameter retrieval (soil moisture, organic matter) Evapotranspiration modelling, ii) high resolution data analysis (automatic data processing and analysis of multi sensor data (e.g. TSX, Sentinel-1 & 2, Landsat-8 + in-situ + modelling), and iii) Cal/Val site for new sensors and missions.

Presentation of DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) including the scientific campaigns Techs4Times and Didl, and comparison of first results.

Since 2007, the TerraSAR-X mission provides high-resolution SAR data with high quality and an unprecedented geometric accuracy to both commercial and science users. The first satellite TSX was supplemented by a second, mostly identical, satellite TDX in 2010 for the generation of a global Digital Elevation Model (DEM) in the frame of the TanDEM-X (TerraSAR-X Add-On For Digital Elevation Measurements) mission. Since then the two missions share a joint space segment consisting of both TSX and TDX and a common ground segment which was first developed for TerraSAR-X and had been later extended for TanDEM-X. While TanDEM-X uses both satellites (nominally one in receive-only mode) for an acquisition, TerraSAR-X data are acquired by either one of the two satellites. It is this sharing of one ground segment which necessitated on-going updates of the TerraSAR-X ground segment in accordance with the TanDEM-X mission constraints. Furthermore, both satellites show a good health and resources, specifically the TSX which has considerably exceeded its nominal life time of 5.5 years by now, and thus justify on-going improvements to even better support user needs. This presentation summarizes the current SAR imaging modes and product portfolio and describes the recent ground segment system updates. Among these are the following: After having completed the global DEM data acquisition, the TanDEM-X mission currently focuses on the provision of radar data products for a number of new science technology related applications based not only on a bistatic, but also on a pursuit mono-static flight configuration. This pursuit mono-static flight configuration with its time lag of about 10 seconds between the two satellites allowed the processing of TanDEM-X acquisitions into TerraSAR-X product pairs which are offered to both the TerraSAR-X and the TanDEM-X science community. This includes fully polarimetric and along-track interferometry data acquired in the dual-receive antenna configuration. Receiving stations may be combined in a so-called TerraSAR-X ground station pool. Data takes ordered for such a station pool (instead of a single receiving station) are then planned for downlink to one or even several stations (in case of a partial downlink) by the mission planning leading to a much better exploitation of the limited downlink resources. Originally, the TerraSAR-X mission was not designed for near-real time (NRT) applications. Thus, the NRT functionality had to be included “as best as possible” within given constraints. Due to a growing demand for TerraSAR-X NRT products, the NRT capability was extended considerably over the last years. The TerraSAR-X product portfolio now supports maritime applications like ship and oil detection, wind and wave products are currently under integration. An improved mission planning and the downlink station pool concept leads to reduced product latencies.

Das Deutsches Fernerkundungsdatenzentrum mit seiner Empfangsstation in Neustrelitz ist seit 2009 Teil des Real Time Solar Wind (RTSW) Netzwerks zum Empfang des Satellite Advanced Composition Explorer (ACE). Die empfangenen Daten werden in Echtzeit an das Space Weather Prediction Center (SWPC) in Boulder weitergeleitet, stehen aber beispielsweise auch zeitnah den Wissenschaftlern zur Ableitung von Warnungen im Zusammenhang von Sonnenstürmen zur Verfügung. Die erfolgreiche Zusammenarbeit mit der National Oceanic and Atmospheric Administration (NOAA) wird mit der Erweiterung der Empfangskapazitäten auf den Anfang des Jahres gestarteten Nachfolge-Satelliten Deep Space Climate Observatory (DSCOVR) fortgesetzt. Der Vortrag stellt die genutzten technischen Systeme und ihre Einbindung in internationale Kooperationen vor.

Presentation of DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) including environmental measurement network, operative processing chain for in-situ-data, in-situ-data browse products and validation of corresponding products. Objectives for placing a L-band radiometer will be discussed.

Aktueller Stand und Ausblick auf die Langzeitarchivierung von Satellitendaten zur Erdbeobachtung und In-situ Daten im NRSDA (National Remote Sensing Data Archiv)

Presentation of DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) including environmental measurement network, operative processing chain for in-situ-data, in-situ-data browse products and validation of corresponding products.

The calibration and validation test site DEMMIN is infrastructured for hydrological applications based on remote sensing. Operational and automated measurement systems delivers parameters as air temperature and moisture, wind speed and direction, rain, leaf wetness, barometric preasure, incident and reflected solar radiation and incident and emitted thermal radiation, soil temperature and moisture in different depths.

The presentation exemplify the challenge of complex environmental processors and the motivation for operational remote sensing test sites. Remote Sensing includes diverse e.g. platforms, sensors, methods for interpretation. The result is an urgent need for cal / val data from a number of environmental parameters. For the development of value-added information products based on remote sensing cal / dal data from a variety of environmental parameters are urgently needed. The test site DEMMIN has an operational automated measurement infrastructure that can provide the relevant in-situ-data for the calibration and validation of remote sesning.

The goal of the TERENO initiative is the long-term monitoring and documentation of proposed climate change, changes in land use, social-economic developments and anthropogenic influences on terrestrial ecosystems. Therefore long-term observations of multitemporal environmental parameters at different scale are the basis for gathering information on changes of the available water resources, material flows, or further ecological changes. The Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN®) located in the North-east of Germany was installed by German Aerospace Center in 1999 for Calibration and Validation of Remote Sensing data at agricultural areas. Due to its physiographic composition and typical agricultural management the area is a perfect test site for monitoring of relevant environmental parameters. Since 2009 the DEMMIN test field of 50 x 50 km is part of the Northeastern German Lowland Observatory (TERENONE), which is coordinated by Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences. Because of the remote sensing focus of the DEMMIN test sites it is a perfect area to evaluate the potential of linking ground based in-situ data with remote sensing data analysis with view to the COPERNICUS initiative of the European Union (EU) and the European Space Agency (ESA).

Monitoring above ground biomass production contributes important indicators for crop growth and is thus relevant for numerous agricultural applications such as biomass and yield estimation and farming practices (fertilizing, irrigation). The study targets the quantification of total growth stress impacts on wheat and maize fields in order to estimate actual biomass production. Two biomass modeling approaches are compared. On one hand the Leaf Area Index (LAI), is modelled using the mechanistic crop growth model CropSyst (LAImod), on the other hand the LAI will be derived from optical remote sensing data (LAIRE) in form of RapidEye images provided by the RapidEye Science Archive Project (RESA – 50 EE 07010) funded by the BlackBridge AG. Eventually, two estimates for the LAI will be available LAImod and LAIRE. The advantage of remotely sensed data is that the actual situation of the vegetation is represented, already accounting for a mixture of stress factors. Subsequently the two LAIs will be separately used as input to the CropSyst model in order to estimate biomass production. We state the hypothesis that the biomass calculations of the two model runs differ, whereas the results of the model driven by LAIRE are to be inferior to the model driven by the LAImod, which is estimated under optimal conditions and accordingly represents the potential biomass. This relation between the two calculations can be used as a first approximation of the actual biomass, without the exact quantification of each possible stress factor. The study area DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) is an agricultural test site hosted by the German Aerospace Center (DLR), which is explicitly used for the calibration and validation of remote sensing applications in the context of ongoing and upcoming research projects (TERENO, CEOS, QA4EO, SMOS). This test site offers a dense environmental monitoring network and excellent coverage of remote sensing data. In order to estimate potential biomass of the targeted fields the CropSyst model is purely driven by daily meteorological data from the DEMMIN test site. Two model runs are applied, one using mean temperature development and another based on the actual situation 2014. Within this modelling approach the LAImod is calculated through the accumulation of thermal time (Growing Degree Days), which describes the transition of the plants phenological stages. This approach works well under optimal circumstances but stress factors such as water scarcity, high temperatures, variations of nitrogen and others modify the plants growth and are difficult to account for. Therefore, they are initially neglected in this mechanistic approach. Fortnightly RapidEye data of 2014 is used to estimate the LAIRE by a regression analysis between field measurements and suitable spectral indices (SAVI, NDVI, EVI). Since the beginning of 2014 a extensive field campaign is being conducted on multiple wheat and maize plots, where an established sampling scheme with each three repetitions is applied. On these plots weekly measurements were and are recorded using the LAI2000 device, so that a time series will be available for the complete growth cycle of 2014. Additionally plant nitrogen content and plant height are measured weekly. In June 2014 the LAI was scanned manually on six wheat and three maize plots to calibrate the handheld sensor. The presented research is subject to an ongoing master thesis in the framework of a new Sentinel mission preparatory project. At present the climate data based model runs are established and the field data is processed for the derivation of the LAI. Further research has to be dedicated to identify the explicit stress factors, which the LAI accounts for. For the quantification of water, heat and nitrogen stress this can be accomplished by running multiple complete CropSyst models, including the estimation of the above mentioned stress factors resulting in the actual biomass. Other stress factors will be difficult to account for without changing the model to a more physical based approach. The DEMMIN test site offers optimal conditions to supply the adequate environmental information leading the way to achieve these objectives.

The main goal of TERENO was to create observation platforms on the basis of an interdisciplinary and long‐term aimed research program. By the aid of SoilCan‐lysimeters, an ideal network was built between the four different TERENO‐observatories with their highly instrumented test sites. Within the four TERENO‐observatories at 13 different experimental sites, in total 126 lysimeters were filled monolithically and the measurements were started in October 2010. The lysimeters have been cultivated as grassland or arable land with a standardized crop rotation: winter wheat – winter barley – winter rye – oat.

General conditions of remote sensing: In the next few years, there will be a lot of advanced remote sensing systems availablefor Earth observation. The data collected with help of these systems will be used in the context of GMES. But the analysis of the situation of remote sensing shows that different problems for the data provider and the data users are relevant. The papaer dicusses the problems and possibilities for overcomming some of the identified problems. For this background, the development of DEMMIN as a test site for calibrating and validating remote sensing missions, sensors, data, and value added products (information products) is shown.