"AgriSens DEMMIN 4.0 - remote sensing technologies for digitization in crop production" identifies concrete applications for remote sensing data use via the regional experimental field in Mecklenburg-Western Pomerania in order to answer practical questions of crop production with digital methods.
The main objective of the project is the development of an end-to-end satellite simulator for ESA, which is able to simulate realistically and very accurately the whole chain starting from data recording, sensor calibration and data pre-processing to sensor products up to final surface parameter maps.
Funded by: ESA
Funding period: 01.09.2018 - 31.12.2020
Significant advances in Earth system understanding will only be achieved through better integration of data and knowledge from the different Earth science disciplines and earth compartments.Improvement in this field strongly depends on our capabilities of dealing with fast growing multi-parameter data and on our effort employing Data Science methods, adapting new algorithms and developing digital workflows tailored to specific scientific needs.With Digital Earth we will address these challenges within and between the Helmholtz partners.
Funded by: Initiative and Networking Fund by Helmholtz Association
Funding period: 01.06.2018 - 31.05.2021
The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that aims at monitoring and characterizing the Earth’s environment on a global scale. EnMAP will provide accurate and diagnostic surface parameters for terrestrial and aquatic ecosystems to be used in a range of application fields. It will allow to quantify and model crucial ecosystem processes, to study the diverse effects of human interventions and to support the management of natural resources.
Remote sensing for a sustainable use of resources - FERN.Lab is a Helmholtz Innovation Lab, which actively promotes the transfer activities of the Department of Geodesy. The technology platform supports the operationalisation of application-oriented, transdisciplinary method developments for the analysis of remote sensing data.
MOSES (Modular Observation Solutions for Earth Systems) has been developed as a project and earth observation system within the “Earth and Environment” research field of the Helmholtz Association to decipher the interactions of short-term events and long-term trends in Earth and environmental systems.
Nachtlicht-BüHNE addresses Citizen Science in the context of the topic areas Astronomy, Space Research, Sustainability Science and Light Pollution. The general aim is the development of a co-design framework for App-based Citizen Science projects that brings together citizens and Helmholtz scientists for research purposes. The results of the project will be provided within a web platform, along with tools for supporting collaborative work of citizens and Helmholtz scientists.
The project NaTec – KRH aims to develop remote sensing based methods for the monitoring of species and habitats in nature conservation areas. In different ecological scales (from satellite sensors over drones to field spectroscopy) it will be examined how habitat management impacts spatial patterns of biodiversity and supports ecosystem resilience.
Ocean Scan - Marine litter database from earth and space is a platform that brings together in-situ observations of marine plastic litter and archived remote sensing images into one searchable database ensuring a consistent data format and schema to fit the requirements of remote sensing users and machine learning algorithms.
Using daily high-resolution optical, SAR, and hyperspectral satellite data, this project aims to obtain precise and reliable data on large pieces of floating litter, regarding their quantity, trajectories and accumulation zones, material properties, floating depth, and sources. This information may serve as a basis for the recovery of floating litter, the elimination of its sources, and to prevent its dispersing.
WORLDSOILS aims to develop a Soil Monitoring System to provide yearly estimations of Soil Organic Carbon (SOC) at global scale, exploiting space-based Earth observation data leveraging large soil data archives and modelling techniques to improve the spatial resolution and accuracy of SOC maps.