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Data collection strategies vary among different citizen science projects. This complicates the intercomparability of parameter values acquired in different studies (e.g., methodological and scale issues) and results in variable data quality. This creates problems regarding the merging of different data sets and hampers the reuse of data from different projects. Modular designed applications for mobile devices (Apps) represent a framework that helps to foster the standardisation of data collection methods. While they encourage the reuse of the software, they provide enough flexibility for an adjustment in accordance with the research question(s) of interest. The currently developed App FieldMApp offers such a framework running under Android and iOS. The related concept includes predefined frame functionalities, like settings for the user account and the user interface, and adaptable application-related functionalities. The latter comprise several modules that are categorized as sensor test, basic functionality, parameter collection and data quality collection modules. The interdependencies of these modules are documented in a wiki. This enables an individual and context-based selection of functionalities. The FieldMApp is based on open-source software libraries (Xamarin, Open Development Kit (ODK), SQLite, CoreCLR-NCalc, LusoV.YamarinUsbSerialForAndroid, Newtonsoft.Json, SharpZipLib) and will be published as open-source software. Hence, the existing catalogue of functionalities can be augmented in the future. The premise for such extensions is that modules are published together with smart, universally applicable data quality recording routines and a proper documentation in the wiki. In this contribution, we present the concept and the structure of the FieldMApp and some current fields of application that are related to the cultivation of arable land, soil mapping, forest monitoring, and Earth Observation. The extension of the functionality catalogue is exemplified by the newly implemented speech recognition module. A related quality recording routine will be introduced. With this contribution we would like to encourage citizens and scientists to elicit which requirements such an App should fulfil from their point of view.

The replenishment of aquifers depends mainly on precipitation rates, which is of vital 19 importance for determining water budgets in arid and semi-arid regions. El-Qaa Plain in Sinai 20 Peninsula is such a region which experiences a constant population growth. The local water budget 21 equilibrium is negatively affected by relatively frequent light rain events. This study compares the 22 performance of two sets of satellite-based data of precipitation and in situ rainfall measurements. The 23 dates selected refer to rainfall events between 2015 and 2018. For this purpose, 0.1° and 0.25° spatial 24 resolution TMPA (TRMM Multi-satellite Precipitation Analysis) and IMERG (Integrated Multi-25 satellitE Retrievals for GPM) data were retrieved and analyzed, employing appropriate statistical 26 metrics. The best-performing data set was determined as the data source capable to most accurately 27 bridge gaps in the limited rain gauge records, embracing both frequent light-intensity rain events 28 and rarer heavy-intensity events. With light-intensity events the corresponding satellite-based data 29 sets differ the least and correlate more, while the greatest differences and weakest correlations are 30 noted for the heavy-intensity events. The satellite-based records best match those of the rain gauges 31 during light-intensity events, when compared to the heaviest ones. IMERG data exhibit a superior 32 performance than TMPA, in all rainfall intensities.

The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m−2 and mean absolute error (MAE) of 0.51 m2m−2 . The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m−2 and MAE of 0.61 m2m−2 ) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m−2 and MAE of 0.30 m2m−2 ). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance. Fil: Hosseini, Mehdi. Carleton University. Department of Geography and Environmental Studies; Canadá. University of Maryland. Department of Geographical Sciences; Estados Unidos Fil: McNairn, Heather. Carleton University. Department of Geography and Environmental Studies; Canada. Agriculture and Agri-Food Canada. Science and Technology Branch; Canadá Fil: Mitchell, Scott. Carleton University. Department of Geography and Environmental Studies; Canadá. Fil: Dingle Robertson, Laura. University of Maryland. Department of Geographical Sciences; Estados Unidos Fil: Davidson, Andrew. Carleton University. Department of Geography and Environmental Studies; Canadá. University of Maryland. Department of Geographical Sciences; Estados Unidos Fil: Ahmadian, Nima. Julius-Maximilians-Universität; Alemania Fil: Bhattacharya, Avik. Indian Institute of Technology. Centre of Studies in Resources Engineering. Microwave Remote Sensing Lab; India Fil: Borg, Erik. German Aerospace Center. Department of National Ground Segment; Alemania Fil: Conrad, Christopher. University of Halle-Wittenberg. Institute of Geosciences and Geography; Alemania Fil: Dabrowska-Zielinska, Katarzyna. Institute of Geodesy and Cartography; Polonia Fil: De Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Gurdak, Radoslaw. Institute of Geodesy and Cartography; Polonia Fil: Kumar, Vineet. Indian Institute of Technology. Centre of Studies in Resources Engineering. Microwave Remote Sensing Lab; India. Delft University of Technology. Department of Water Management; Países Bajos Fil: Kussul, Nataliia. Space Research Institute of National Academy of Sciences of Ukraine and State Space Agency of Ukraine; Ucrania Fil: Mandal, Dipankar. Indian Institute of Technology. Centre of Studies in Resources Engineering. Microwave Remote Sensing Lab; India Fil: Rao, Y. S. Indian Institute of Technology. Centre of Studies in Resources Engineering. Microwave Remote Sensing Lab; India Fil: Saliendra, Nicanor. USDA-ARS Northern Great Plains Research Laboratory; Estados Unidos Fil: Shelestov, Andrii. Space Research Institute of National Academy of Sciences of Ukraine and State Space Agency of Ukraine; Ucrania Fil: Spengler, Daniel. Deutsches GeoForschungs Zentrum (GFZ). Division of Remote Sensing; Alemania Fil: Verón, Santiago Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentina Fil: Homayouni, Saeid. Institut National de la Recherche Scientifique (INRS). Center Eau Terre Environnement; Canadá Fil: Becker-Reshef, Inbal. University of Maryland, Department of Geographical Sciences; Estados Unidos

Synthetic Aperture Radar (SAR) and medium (MR) and very high (VHR) resolution optical satellite images are valuable sources of information for maritime situational awareness. This paper presents the Earth Observation Maritime Surveillance System (EO-MARISS), an integrated processing framework of remote sensing satellite data developed at DLR’s Maritime Safety and Security Lab Neustrelitz to support sea monitoring tasks. The system is currently developed for operational use at DLR’s Ground Station Neustrelitz to support service provider like AIRBUS or EUSI and processes image data from different satellite missions received directly at the ground station or provided by a network of ground stations and service providers.

Introduction: The destruction of habitats has not only reduced biological diversity but also affected essential ecosystem services of the Central European cultural landscape. Therefore, in the further development of the cultural landscape and in the management of natural resources, special importance must be attached to the habitat demands of species and the preservation of ecosystem services. The study of ecosystem services has extended its influence into spatial planning and landscape ecology, the integration of which can offer an opportunity to enhance the saliency, credibility, and legitimacy of landscape ecology in spatial planning issues.Objective: This paper proposes a methodology to detect red deer habitats for e.g. huntable game. The model is established on remote sensing based value-added information products, the derived landscape structure information and the use of spatially and temporally imprecise in-situ data (e.g. available hunting statistics). In order to realize this, four statistical model approaches were developed and their predictive performance assessed.Methods: Altogether, our results indicate that based on the data mentioned above, modeling of habitats is possible using a coherent statistical model approach. All four models showed an overall classification of > 60% and in the best case 71,4%. The models based on logistic regression using preference data derived from 5-year hunting statistics, which has been interpreted as habitat suitability. The landscape metrics (LSM) will be calculated on the basis of the Global Forest Change dataset (HANSEN et al. 2013b ). The interpolation of landcover data into landscape-level was made with the software FRAGSTAT and the moving window approach. Correlation analysis is used to identify relevant LSM serving as inputs; logistic regression was used to derive a final binary classifier for habitat suitability values. Three model variations with different sets of LSM are tested using the unstandardized regression coefficient. Results lead to an insight of the effect of each LSM but not on the strength of the effect. Furthermore, the predicted outcome is rather difficult to interpret as different units and scales for each LSM are used. Hence, we calculated the fourth model using the standardized regression coefficient. It harmonized the measurement units of the LSM and thus allowed a better comparison, interpretation, and evaluation.Conclusion: Our research reveals that applying a statistical model using coarse data is effective to identify potential red deer habitats in a significant qualitative manner. The presented approach can be analogously applied to other mammals if the relevant structural requirements and empirical habitat suitability data (e.g. home range, biotopes, and food resources) are known. The habitat preferences of red deer are best described by LSM concerning area-relation and wildlife-edge relations. Most important are edges between meadows, pastures or agricultural field and forest, as well as short paths between those elements for food resources. A large proportion of forest is important for species survival and positively influences the occurrence of red deer. Outcomes help to understand species –habitat relation and on which scale wildlife perceives the landscape. In addition, they support the practical habitat management and thus the overall species diversity.

Space-borne Earth Observation (EO) data depicting vegetation covered land surfaces contain insufficient information for an unambiguous interpretation of the spectral signal in terms of variables that characterize the vegetation state (e.g., leaf area index, leaf chlorophyll content and proportion of senescent material). For the retrieval of vegetation properties from EO data, an optimal estimate of the state variables needs to be found. The uncertainty of such an estimate can be reduced by combining EO data and in situ data. Information provided by citizens represents a valuable and mostly inexpensive source for in situ data. Since the quality of such data can be diverse, the consideration of uncertainties is of great importance. In this contribution, we present a concept for the elicitation of local knowledge from citizens with respect to the application of management practices (e.g., sowing and harvesting date, irrigation) and the instantaneous state of crops. The concept includes the acquisition of in situ data as well as an uncertainty assessment (precision and/or accuracy). The latter involves a profiling in which inherent uncertainties are quantified for individual citizens. This concept was tested for agricultural fields of the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site in Northeast Germany. Within the frame of several field seminars, students were requested to assess management practices and the instantaneous state of crops. Furthermore, they assessed their own ability to create valid data. They filled in pseudonymized questionnaires from which we created corresponding datasets. At the same day, field data were collected with appropriate equipment and can be used as reference against which student estimates can be compared. The level of compliance between estimated and measured data was determined on an individual basis. The results of this analysis will be presented. Conclusions will be drawn regarding the ability of the students to evaluate their own skills. In addition, we will demonstrate an approach for a digital ascertainment of in situ data. In the future, this approach will be used to collect in situ data for the setup of refined prior information within the frame of the Earth Observation Land Data Assimilation System (EO-LDAS).

The focus of the study is analysing storm peak/center propagation, front movement and arrival of swell using newest remote sensing information, numerical models, in-situ measurements and their combination. For this purposes, a new empirical algorithm for sea state retrieval from satellite borne Sentinel-1 (S1) Synthetic Aperture Radar (SAR) imagery was developed. The algorithm is applied inside a new processor for meteo-marine parameter estimation for Near Real Time (NRT) applications. These NRT-applications include the investigation of geophysical processes using different satellite modes ranging from high resolution modes with small image coverage of ~20km in open ocean to low resolution modes with wide coverage of ~250km in shelf areas.The quick developments in satellite techniques, processors, algorithms and ground infrastructures provide new possibilities for series of oceanographic applications in the last years. These new techniques allow estimation of a wide range of oceanographic information including properties of surface waves and internal waves, surface wind speed, sub-meso scale fronts and eddies, ice coverage, oil spills, coastal bathymetry, currents and others. Generally, the new high resolution products from different models allow verification of meteo-marine parameters more accurately. Here, a cross validation with different sea state model results using WWIII (NOAA) and CMEMS (COPERNICUS), with in situ buoy measurements and with satellite estimated parameters allowed an significant improvement of the accuracy of the derived sea state and wind fields. The new empirical algorithm allows estimation of total integrated sea state parameters including significant wave height Hs, first moment wave period Tm1, second moment period Tm2, mean period Tm and also partial integrated parameters like swell and windsea wave heights and windsea period. The algorithm allows processing of different S1 Synthetic Aperture Radar (SAR) modes into sea state fields: S1 Wave Mode (WV) acquires multiple vignettes with an extent of ~20km×20km and each displaced by 100 km along satellite tracks in open ocean (global). About 60 tracks around the globe have been acquired per day. The relatively high spatial resolution of ~4 m allows estimating wave height with accuracy of ~35cm. This is comparable with the accuracy of satellite altimetry and a new achievement for SAR based techniques. S1 Interferometric Wide Swath Mode (IW) covers area-strips of thousand kilometres of earth and ocean surface in coastal areas with a resolution of ~30m by sequences of multiple images with an approximate size of 200km×250km. The accuracy of ~ 70cm (Hs) for this mode is not as so high as for S1-WV, because the short waves are not visible for S1-IW mode and imaged as noise. However, the accuracy is much higher than state-of-the-art methods for this mode. The algorithm has been integrated into a prototype processor for Sentinel-1 SAR 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 IW images of North and Baltic Sea in and around German territorial waters.

Today vessel detection from remote sensing images is increasingly becoming a crucial component in maritime surveillance applications. The increasing number of very high and medium resolution (VHR and MR) optical satellites shortens the revisit time as it was never before. This makes the technology especially attractive for a variety of maritime monitoring tasks. Nevertheless, it is quite a challenge to perform object detection on enormous large satellite images that cover several hundreds of square kilometers and derive results under near real time constraints. This thesis presents an end-to-end multiclass vessel detection method from optical satellite images. The proposed workflow covers the complete processing chain and involves rapid image enhancement techniques, the fusion with automatic identification system (AIS) data, and the detection algorithm based on convolutional neural networks (CNN). To train the CNNs, two versions of training datasets were generated. The VHR training dataset was produced from the set of WorldView-[1-3] and GeoEye-1 images and contains about 40 000 of uniquely annotated vessels divided into 14 different classes. The MR training dataset was generated from the set of Landsat-8 images and contains about 14 000 of uniquely annotated vessels of 7 different classes. The algorithms presented are implemented in the form of independent software processors and integrated in an automated processing chain as part of the Earth Observation Maritime Surveillance System (EO-MARISS). The solution developed from the methods presented has proven its usability within different projects and is used and further developed at the ground station of the German Aerospace Center (DLR) in Neustrelitz.

Imaging spectrometry provides densely sampled and finely structured spectral information for each image pixel over large areas, enabling the characterization of materials on the Earth's surface by measuring and analyzing quantitative parameters allowing the user to identify and characterize Earth surface materials such as minerals in rocks and soils, vegetation types and stress indicators, and water constituents. The recently launched DLR Earth Sensing Imaging Spectrometer (DESIS) installed on the International Space Station (ISS) closes the long-term gap of sparsely available spaceborne imaging spectrometry data and will be part of the upcoming fleet of such new instruments in orbit. DESIS measures in the spectral range from 400 and 1000 nm with a spectral sampling distance of 2.55 nm and a Full Width Half Maximum (FWHM) of about 3.5 nm. The various DESIS data products available for users are described with the focus on specific processing steps. A summary of the data quality results are given. The product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements.

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.

Space2030 is an agenda under the United Nations Committee on the Peaceful Uses of Outer Space, with the vision of using space as a driver for peace and the Sustainable Development Goals. The agenda aims to strengthen the contributions of space technologies by addressing global development challenges, building stronger partnerships, and bridging the space divide between developed and developing States. This paper is an extension of the Team Project "Space2030"; a major component of the 2019 Southern Hemisphere Space Studies Program, which was jointly coordinated by the International Space University and the University of South Australia, hosted in Adelaide, Australia. It identifies and recommends priority items to be included in the Space2030 Agenda from the perspective of the Economic South. These are States on or south of the Tropic of Cancer, which consists of developed and developing States, emerging space powers, and States of a growing influence on the world stage. Research and analysis of priority items fall under the established four pillars: Space Economy, Space Society, Space Accessibility, and Space Diplomacy; and a number of recommendations are presented for each pillar. These recommendations can serve as discussion points as the United Nations develop the structure for the implementation plan of the Space2030 Agenda.

DESIS (DLR Earth Sensing Imaging Spectrometer) is a space-based hyperspectral sensor currently installed and operated in the International Space Station (ISS). The instrument is the result of the collaboration between the German Aerospace Center (DLR) and Teledyne Brown Engineering (TBE). DLR has developed the instrument and the software for data processing, while TBE provides the Multi-User System for Earth Sensing (MUSES), where DESIS is installed, and the infrastructure for operation. After finishing the commissioning phase, DESIS data will be available to TBE commercial customers as well as DLR scientific users and collaborators. We present here an overview of the DESIS instrument and processing chain and the first results obtained during the commissioning phase of the instrument.

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.

A new empirical algorithm CWAVE_S1-IW for estimation of significant wave height Hs including swell and wind sea from C-band satellite-borne Synthetic Aperture Radar (SAR) data has been developed for Sentinel-1 (S1) Interferometric Wide Swath Mode (IW) imagery. The algorithm was implemented into the Sea Sate Processor (SSP) for fully automatic processing for near real time (NRT) services and allow the estimation of wave fields of thousands of kilometres in the flight direction and 250 km swath from S1 IW scenes consisting of a sequence of individual images. The priority of CWAVE_S1-IW development was an automatic, fast and robust raster processing independent from wave patterns, applicable even when only clutter is visible in the SAR images. The algorithm is based on the spectral analysis of subscenes in wavenumber space. The empirical function allows direct Hs estimation from image spectra without first converting them into wave spectra and uses integrated image spectra parameters as well as estimated local wind information. A texture analysis based on Grey Level Co-occurrence Matrices (GLCM) is also applied. In this way, also the parameters of short waves can be estimated, which are not visible in S1 IW images and are only represented by clutter. The algorithm was tuned worldwide using in-situ collocated measurements of 92 buoys with more than 2500 acquisitions. The validated SSP allows automatic processing of worldwide S1 IW images in VV or HH polarization, including Atlantic storms, cyclones and huge storms in the Gulf of Alaska with an root mean square error RMSE of 80 cm for Hs. For the closed seas like the North Sea, Baltic Seas and Black Sea the accuracy is higher with an RMSE = 55 cm. The algorithm is integrated into a demonstration service, used for further validation at the DLR ground station in Neustrelitz. The NRT processing has been tested by supporting a research ship cruise in the Antarctic Sea.

In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits.

Different metocean conditions have an impact on the detectability of ship signatures on Synthetic Aperture Radar (SAR) images. During the EMSec Project algorithms for retrieval of wind and sea state fields from TerraSAR-X data have been developed in conjunction with a near real-time-capable constant false alarm rate ship detection processor. This paper presents a new model connecting these three information extraction systems into a ship detectability model by setting the probability of detection in dependency to the four parameters: Wind speed, significant wave height, incidence angle and ship length. The model is based on a binary L2-regularized logistic regression classifier trained on a large dataset of X-band SAR ship samples, which are identified using Automatic Identification System messages co-located automatically in space and time and further checked manually to avoid possible mismatches. Results are compared to the state-of-the-art simulation-based ship detectability model available in literature. For the first time it has been possible to evaluate not only qualitatively but also quantitatively the effects of acquisition geometry and metocean conditions for the different image resolution classes obtainable with the high-flexible SAR sensor on-board the TerraSAR-X satellite.

The replenishment of aquifers depends mainly on precipitation rates, which is of vital importance for determining water budgets in arid and semi-arid regions. El-Qaa Plain in the Sinai Peninsula is a region that experiences constant population growth. This study compares the performance of two sets of satellite-based data of precipitation and in situ rainfall measurements. The dates selected refer to rainfall events between 2015 and 2018. For this purpose, 0.1° and 0.25° spatial resolution TMPA (Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis) and IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement) data were retrieved and analyzed, employing appropriate statistical metrics. The best-performing data set was determined as the data source capable to most accurately bridge gaps in the limited rain gauge records, embracing both frequent light-intensity rain events and more rare heavy-intensity events. With light-intensity events, the corresponding satellite-based data sets differ the least and correlate more, while the greatest differences and weakest correlations are noted for the heavy-intensity events. The satellite-based records best match those of the rain gauges during light-intensity events, when compared to the heaviest ones. IMERG data exhibit a superior performance than TMPA in all rainfall intensities.

This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-meter spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (>0.82), low RMSE (2) and significant p-value (2 (600 g/m2). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat).

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

From Synthetic Aperture Radar (SAR) imagery various types of maritime information are extractable, including surface winds, waves, oil slicks, waterline changes, changes of seabed morphology in shallow waters, wakes of ships, etc. The algorithms recently developed for this purpose are integrated into a prototype processor for Sentinel-1 (S-1) imagery. The DLR Ground Station Neustrelitz applies this prototype as part of a near real-time demonstrator service for support of Maritime Situation Awareness. The presented scientific service involves daily provision of surface wind and sea state parameters estimated fully automatic from S-1 Interferometric Wide Swath (IW) images for North and Baltic Sea.

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.

Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.

Synthetic Aperture Radar (SAR) satellites are able to observe small and large scale structures in sea ice - in any weather, through clouds and darkness. In order to assist ship navigation during polar campaigns, we acquired SAR images along the ship course and provided them to navigators on board in near real time, utilizing the operational data processing chain of DLR ground station Neustrelitz. These “exclusive” acquisitions already helped to optimize the routes. SAR data, however, contain more information that is not easily visible, e.g. information about the local sea ice drift. In this paper, we explore the capabilities of a new software processor that is intended to retrieve high resolution sea ice drift fields from pairs of colocated SAR images, combining TerraSAR-X and Radarsat-2 images. The processor is foreseen to be integrated into the operational data processing chain at DLR ground station network sites.

In the context of the project real-time services for maritime security (Echtzeitdienste fur die Maritime Sicherheit—security), an experimental research platform for validation of maritime products derived from remote sensing data, was developed. This article describes the work carried out to derive ship-, wind-, and wave detection products out of Sentinel-1 remote-sensing data by DLR’s Maritime Safety and Security Lab in Neustrelitz, part of the German Remote Data Center DFD. The activity aims to the fulfilment of project requirements, primarily to support the need for near real-time performance up to 15 min, as those in maritime situational awareness. The development and implementation cover the task of level 0 processing, based on DLR’s front end processor, the implementation of the framework for real-time processing up to level 2 (value adding), as well as the development of a hardware-independent virtual-processing platform.

The satellite of the TerraSAR-X mission, called TSX, launched on June 15th 2007 and its identically constructed twin satellite TDX, which is required by the mission TanDEM-X, launched on 21st of June 2010. Together they supply high-quality radar data in order to serve two mission goals: Scientific observation of the Earth and provision of remote sensing data for the commercial market (TerraSAR-X mission) and the generation of a global digital elevation model (DEM) of the Earth’s surface (TanDEM-X mission). On the occasion of the 10th anniversary of the mission, the focus will be on the development of the TerraSAR-X system during this period, including the extension of the ground segment, the evolution of the product portfolio, dedicated mission campaigns, radar experiments, refinement of the satellite operations and orbit control and results of the performance monitoring. Despite numerous interventions in the overall system, mainly driven by science and commercial demands in the past ten years we managed to incorporate new scientific and commercial requirements and to improve and enhance the overall system in order to fulfill the increasing demand for earth observation data without noticeable interruptions to ongoing operations.

Space-borne Synthetic Aperture Radar (SAR) images have proven to be very useful tool in challenging Polar environment - due to its active radar antenna which can acquire valuable data in all weathers, though clouds and darkness. In several polar expeditions, German Aerospace Center (DLR) acquired high-resolution SAR images from the satellite mission TerraSAR-X/TanDEM-X over the planned course, and provided them to navigators on board the ship in near real-time. It has been shown that these targeted acquisitions helped the onboard navigators to identify cost effective routes. During the course of MOSAiC campaign German Aerospace Center (DLR) plans to provide SAR derived sea ice related products such as sea ice type, sea ice dirft information within Near Real Time through AWI’s MapViewer. The sea ice type product is based on dual polarized SAR acquisitions and capable to discriminate between open water and different types of sea ice (e.g. First Year Ice, Multi Year Ice). The sea ice drift algorithm is based on two consecutive SAR acquisition can capable to provide high resolution drift information within Near Real Time. During the Potsdam workshop we will show examples of above mentioned products which is not only helpful for campaign planning but also might provide useful information to scientists across different scientific domains.

Gas flaring is a disposal process widely used in the oil extraction and processing industry. It consists in the burning of unwanted gas at the tip of a stack and due to its thermal characteristic and the thermal emission it is possible to observe and to quantify it from space. Spaceborne observations allows us to collect information across regions and hence to provide a base for estimation of emissions on global scale. We have successfully adapted the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire algorithm for the detection and characterisation of persistent hot spots, including gas flares, to the Sea and Land Surface Temperature Radiometer (SLSTR) observations on-board the Sentinel-3 satellites. A hot event at temperatures typical of a gas flare will produce a local maximum in the night-time readings of the shortwave and mid-infrared (SWIR and MIR) channels of SLSTR. The SWIR band centered at 1.61 micrometer is closest to the expected spectral radiance maximum and serves as the primary detection band. The hot source is characterised in terms of temperature and area by fitting the sum of two Planck curves, one for the hot source and another for the background, to the radiances from all the available SWIR, MIR and thermal infra-red channels of SLSTR. The flaring radiative power is calculated from the gas flare temperature and area. Our algorithm differs from the original VIIRS Nightfire algorithm in three key aspects: (1) It uses a granule-based contextual thresholding to detect hot pixels, being independent of the number of hot sources present and their intensity. (2) It analyses entire clusters of hot source detections instead of individual pixels. This is arguably a more comprehensive use of the available information. (3) The co-registration errors between hot source clusters in the different spectral bands are calculated and corrected. This also contributes to the SLSTR instrument validation. Cross-comparisons of the new gas flare characterisation with temporally close observations by the higher resolution German FireBIRD TET-1 small satellite and with the Nightfire product based on VIIRS on-board the Suomi-NPP satellite show general agreement for an individual flaring site in Siberia and for several flaring regions around the world. Small systematic differences to VIIRS Nightfire are nevertheless apparent. Based on the hot spot characterisation, gas flares can be identified and flared gas volumes and pollutant emissions can be calculated with previously published methods.

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.

The automatic daily processing of coastlines from Sentinel-1 satellite data for updating the bathymetry to support modelling as part of a Near Real Time chain is being developed. In shallow waters, the physical processes caused by the interaction between waves, currents and the sea bottom become important and are crucial for modelling, requiring current and accurate bathymetry data. The spatial properties of sea state and currents change strongly in coastal areas and especially in the Wadden Sea at the Danish, German and Dutch North Sea coast with large areas of intertidal flats. The morphodynamics of seabed structures are significant in such littoral zones; the soft seabed can change within days during severe storms. Data from past measurement campaigns, which are very expensive using ship soundings and airborne LIDAR scanning, deprecates quickly as the positions of islands, sandbanks or tidal inlets change. Acquisitions from the Sentinel-1 (S1) Synthetic Aperture Radar (SAR) satellites cover most coasts worldwide. With two satellites currently in orbit, the acquisition interval is at most 6 days at the equator and almost daily in our latitudes. The Interferometric Wide Swath (IW) mode covers lands and coastal waters with a swath width of 250km and an image resolution of 10m. In contrast to optical satellites, SAR acquisitions are independent of illumination and cloud cover; hence, they deliver new images very reliably. The DLR satellite ground station in Neustrelitz receives these data and processes and delivers them in Near Real Time (NRT), usually within 20 minutes after acquisition. We have developed automated algorithms to retrieve the waterline from SAR images. In the tidal flat areas of the Wadden Sea, analyzing time series of acquisitions at similar tidal states allows estimating rates of change and sediment transport; a combination of several images acquired within a short time frame at different tidal states allows estimating the topography. Our algorithms for Sentinel-1 SAR images offer a quick and cheap way to spot ongoing processes, verify modelling results and identify regions of major change for future measurement campaigns.

The interpretation of optical Earth observation data (remote sensing data from satellites) requires knowledge of the exact geographic position of each pixel as well as the exact local acquisition time. But these parameters are not available in each case. If a satellite has a sun-synchronous orbit, equator crossing time (ECT) can be used to determine the local crossing time (LCT) and its corresponding solar zenith distance. Relation between local equator crossing time (LECT) and LCT is given by orbit geometry. The calculation is based on ECT of satellite. The method of actual ECT determination for different satellites on basis of the two-line-elements (TLE), available for their full lifetime period and with help of orbit prediction package is well known. For land surface temperature (LST) studies mean solar conditions are commonly used in the relation between ECT given in Coordinated Universal Time (UTC) and LECT given in hours, thus neglecting the difference between mean and real Sun time (MST, RST). Its difference is described by the equation of time (ET). Of particular importance is the variation of LECT during the year within about ±15 minutes. This is in each case the variation of LECT of a satellite, including satellites with stable orbit as LANDSAT (L8 around 10:05 a.m.) or ENVISAT (around 10:00 a.m.). In case of NOAA satellites the variation of LECT is overlaid by a long-term orbital drift. Ignatov et al. (2004) developed a method to describe the drift-based variation of LECT that can be viewed as a formal mathematical approximation of a periodic function with one or two Fourier terms. But, nevertheless, ET is not included in actual studies of LST. Our paper aims to demonstrate the possible influence of equation of time on simple examples of data interpretation, e.g. NDVI.

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.

Maritime Situational Awareness is of global importance and satellite remote sensing technologies are well suited to derive maritime value added products. Processing systems to derive information about oil spill, ship or iceberg detection from spaceborne Synthetic Aperture Radar (SAR) image data are being developed and integrated at the Ground Station Neustrelitz, part of the German Remote Sensing Data Center (DFD). Shortly, products of meteo-marine parameters like wind and wave will complement the product portfolio. Research and development aim at the implementation of highly automated services for operational use. At this time SAR images are widely used because of the possibility to provide maritime products with high spatial resolution over wide swaths and under all weather conditions. In combination with other information sources like Automatic Identification System (AIS) data fusion products are available to support the Maritime Situational Awareness.

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.

Leaf area index (LAI) is a plant development indicator that as an input parameter strongly influences several relevant hydrological processes represented in Soil–Vegetation–Atmosphere-Transfer (SVAT) models. Generally, temporal measurement or monitoring of LAI is challenging or even impossible in remote areas. High-temporal resolution remote sensing imaging can be used to estimate LAI from vegetation indices calculated from band ratios. This paper shows the sensitivity of LAI estimation from satellite imaging to atmospheric correction (with ATCOR) and evaluates the effects of LAI uncertainty on water balance modelling. LAI as a SVAT model input parameter was estimated based on the empirical relationship between field measurements, and the vegetation indices NDVI (Normalized-Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and SARVI (Soil–Atmosphere Resistant Vegetation Index) for six RapidEye images obtained between 2011 and 2012. In summary, we found that the ATCOR parameter ‘visibility’ has the strongest influence on LAI estimation. Likewise, atmospherically corrected successive images gathered from around the same time period had low LAI differences (mean absolute difference of 0.09 ± 0.08) on overlapping image areas. This uncertainty is negligible in SVAT modelling in most cases, thereby allowing mosaicked successive atmospherically corrected images to be used. We showed that LAI uncertainties arising from atmospheric correction (ATCOR 3) can translate into small (LAI ± 0.1 ≈ evapotranspiration ± 0.9%, interception ± 2.5%, evaporation ± 3.3%, transpiration ± 0.7%) to moderate (LAI ± 0.3 ≈ evapotranspiration ± 4.1%, interception ± 7.5%, evaporation ± 9.9%, transpiration ± 2.4%) SVAT model uncertainty.

The near real time framework development, as well as the service chain implementation is done by the Maritime Security Lab at the Ground Station in Neustrelitz, part of the German Remote Sensing Data Center (DFD). The algorithms for SAR image analysis are being developed by the Maritime Security Lab in Bremen, part of the Remote Sensing Technology Institute (IMF). IMF and DFD are institutes of the Earth Observation Center (EOC), which is part of the German Aerospace Center (DLR).

The Sea Sate Processor (SSP) was developed for fully automatic processing of high-resolution Synthetic Aperture Radar (SAR) data from TerraSAR-X (TS-X) satellites and implemented into the processing chain for Near Real Time (NRT) services in the DLR Ground Station "Neustrelitz". The NRT chain was organised and tested to provide the processed data to the German Weather Service (DWD) in order to validate the new coastal forecast model CWAM (Coastal WAve Model) in the German Bight of the North Sea with 900 m horizontal resolution. The NRT test-runs, wherein the processed TS-X data were transferred to DWD and then incorporated into forecast products reach the best performance about 10 min for delivery of processed TS-X data to DWD server after scene acquisition. To do this, a new empirical algorithm XWAVE_C (C = coastal) for estimation of significant wave height from X-band satellite-borne SAR data has been designed for coastal applications. The algorithm is based on the spectral analysis of subscenes and the empirical model function yields an estimation of integrated sea state parameters directly from SAR image spectra without transformation into wave spectra. To provide the raster coverage analysis, the SSP intends three steps of recognising and removing the influence of non-sea-state-produced signals in the Wadden Sea areas such as ships, buoys, dry sandbars as well as nonlinear SAR image distortions produced by e.g. short and breaking waves. For the validation, more than 150 TS-X StripMap scene sequences with a coverage of ~30 km × 300 km across the German Bight since 2013 were analysed and compared with in situ Buoy measurements from 6 different locations. On this basis, the SSP autonomous processing of TS-X Stripmap images has been confirmed to have a high accuracy with an error RMSE = 25 cm for the total significant wave height.

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.

Overview about framework and product development as examples for near real time applications for the maritime domain. Applications to derive maritime value added products are presently developed and integrated at the ground station Neustrelitz, part of the German Remote Sensing Data Center.

The presentation focus on the framework and product development as an example for near real time applications for the maritime domain. Applications to derive maritime value added products like ship detection based on remote sensing SAR image data from TerraSAR-X are presently developed and integrated at the ground station Neustrelitz, part of the German Remote Sensing Data Center.

High resolution remote sensing Synthetic Aperture Radar (SAR) data from TerraSAR-X/Tandem-X Sentinel and RADARSAT 2 satellites are used to determine and monitor the sea surface in near real time and all weather and illumination conditions. The radar backscatter of the sea surface is determined by the sea surface roughness caused by the wind field and the sea state. An Automatic Wind Detection Processing System Workflow as implemented in DLR-Neustrelitz (DFD-NBS) is described.