ABSTRACT:Applications to derive maritime value added products like oil spill and ship detection based on remote sensing SAR image data are being developed and integrated at the Ground Station Neustrelitz, part of the German Remote Sensing Data Center. 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. SAR images are being 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 like Automatic Identification System (AIS) data fusion products are available to support the Maritime Situational Awareness.

Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.

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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.

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