ALBERT

Description: Knowledge and technology transfer (hereafter referred to as KTT) between academia and society has long been recognized as a key driver of innovation and economic development. Knowledge transfer (KT) is defined by Bloedon and Stokes, (1994) “as the process by which knowledge concerning the making or doing of useful things contained within one organized setting is brought into use within another organizational context”. Similarly, technology transfer (TT) can be defined as the movement of a specific technology from one place to another. Technology transfer (TT) between academia and industry is an important source of innovation and economic development. Successful TT depends strongly on effective communication between academic and external partners. This is best facilitated by an academic intermediary who can provide meaningful interactions. An academic intermediary can ensure expertise and knowledge are communicated using a common language and that goals and expectations are clear between partners. In the field of remote sensing intermediaries play a particularly essential role. The rapid expansion of the remote sensing sector over the last decade, including sensor technologies and the volume of freely available data has created a bottleneck between available data and technology and ready to use products outside of academia. Regional and municipal government agencies, small-medium enterprises (SMEs), and Non-Governmental Organizations (NGOs) who would greatly benefit from remote sensing data often lack expertise and capacity to produce or access high quality products and technologies. Additionally, large private sector companies with expertise in internal Research and Development (R&D) departments who operate on profit-driven strategies may limit investment in new untested RS solutions because of limited market size or monetarization risks. Academic institutions are well positioned to act as intermediaries and address this bottleneck through the implementation of robust KTT strategies to help meet regional, national, and international demand for high quality RS data and technology. Several initiatives have been made by Space Agencies, academic institutions or private companies covering broad range of KTT work but upon closer examination. All these initiatives focus on profit-based operation predominantly by licensing developments and products or the creation of spin-offs. The following aspects are not considered: 1. Missing benefit for scientists. Most current KTT frameworks are viewed as mutually exclusive or as direct competition for resources and reputation with scientific work. This results in lost opportunities for further operational development of innovative ideas being developed in research settings. 2. Undervaluation of the social impact of KTT. KTT is often seen as a profit-driven initiative only. In our opinion More weight and value should be put on the social, political, and environmental impact of KTT activities to broaden the reach and participation. 3. Combining open science with commercial use. KTT can and should focus on the simultaneous development of open-source community and commercial versions with advanced functionalities to maintain the principles of open science which are central to current good scientific practice. 4. KTT should not only be viewed as an exit strategy for scientists to leave academia. KTT should be embedded in institutional frameworks to encourage and inspire scientific developments from scientists pursuing academic careers. To address this bottleneck and establish a long-term innovation platform and thematic TT infrastructure, FERN.Lab, Remote Sensing for Sustainable Use of Resources Helmholtz Innovation Lab was founded at the Geodesy Department of the German Centre for Geosciences (GFZ) in January 2020. FERN.Lab is funded by the Helmholtz Association, the largest scientific organization in Germany. The goal of FERN.Lab is to facilitate TT and deliver remote sensing products to commercial and non-commercial partners by acting as an expert intermediary platform. We will present two distinct approaches to improve this bottleneck from science market and society 1. The first is a “pull” approach to develop tailor-made technologies for and funded by external third parties. 2. The second is a “push” approach to promote existing departmental technologies with high market potential. The pull and push of technologies to external partners is accomplished by a combination of competencies and services. This includes business development, scientific development, software development, and public relations. All of them directly address institutional, financial and skills gap that can cause the TT process to fail. By implementing a robust TT framework for remote sensing products, the impact of research has the potential to be much broader and farther reaching. Additionally, these efforts can improve the acceptance of remote sensing outside of academia improving and modernizing methods used in diverse sectors which in turn can benefit not only individual partners but also politics, society, and the environment.

Description: Information provided by satellite data is becoming increasingly important in the field of agriculture. Estimating biomass, nitrogen content or crop yield can improve farm management and optimize precision agriculture applications. A vast amount of data is made available both as map material and from space. However, it is up to the user to select the appropriate data for a particular problem. Without the appropriate knowledge, this may even entail an economic risk. This study therefore investigates the direct relationship between satellite data from six different optical sensors as well as different soil and relief parameters and yield data from cereal and canola recorded by the thresher in the field. A time series of 13 years is considered, with 947 yield data sets consisting of dense point data sets and 755 satellite images. To answer the question of how well the relationship between remote sensing data and yield is, the correlation coefficient r per field is calculated and interpreted in terms of crop type, phenology, and sensor characteristics. The correlation value r is particularly high when a field and its crop are spatially heterogeneous and when the correct phenological time of the crop is reached at the time of satellite imaging. Satellite images with higher resolution, such as RapidEye and Sentinel-2 performed better in comparison with lower resolution sensors of the Landsat series. The additional Red Edge spectral band also has advantage, especially for cereal yield estimation. The study concludes that there are high correlation values between yield data and satellite data, but several conditions must be met which are presented and discussed here.

Description: Several holistic approaches are based on the description of socio-ecological systems to address the sustainability challenge. Essential Variables (EVs) have the potential to support these approaches by describing the status of the Earth system through monitoring and modeling. The different classes of EVs can be organized along the environmental policy framework of Drivers, Pressures, States, Impacts and Responses. The EV concept represents an opportunity to strengthen monitoring systems by providing observations to seize the fundamental dimensions of the Earth system The Group on Earth Observation (GEO) is a partnership of 113 nations and 134 participating organizations in 2021 that are dedicated to making Earth Observation (EO) data available globally to inform about the state of the environment and enable data-driven decision processes. GEO is building the Global Earth Observation System of Systems, a set of coordinated and independent EO, information and processing systems that interoperate to provide access to EO for users in the public and private sectors. The progresses made in the development of various classes of EVs are described with their main policy targets, Internet links and key references The paper reviews the literature on EVs and describes the main contributions of the EU GEOEssential project to integrate EVs within the work plan of GEO in order to better address selected environmental policies and the SDGs. A new GEO-EVs community has been set to discuss about the current status of the EVs, exchange knowledge, experiences and assess the gaps to be solved in their communities of providers and users. A set of four traits characterizing an EV was put forward to describe the entire socio-ecological system of planet Earth: Essentiality, Evolvability, Unambiguity, and Feasibility. A workflow from the identification of EO data sources to the final visualization of SDG 15.3.1 indicators on land degradation is demonstrated, spanning through the use of different EVs, the definition of the knowledge base on this indicator, the implementation of the workflow in the VLab (a cloud-based processing infrastructure), the presentation of the outputs on a dedicated dashboard and the corresponding narrative through a story map. The concept of EV started in the climate sphere and spread to other domains of the earth system but less so in socio-economic activities. More work is therefore needed to converge on a common definition and criteria in order to complete the implementation of EVs in all GEO focus areas. EVs should screen the entire Earth's social-ecological system, providing a trusted and long-term foundation for interdisciplinary approaches such as ecological footprinting, planetary boundaries, disaster risk reduction, and nexus frameworks, as well as many other policy frameworks such as the SDGs

Description: The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters of agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines the potentials of entropy, anisotropy, and alpha angle derived from a dual-polarimetric decomposition of Sentinel-1 data to monitor crop development. The temporal profiles of these parameters are analysed for wheat and barley in the vegetation periods 2017 and 2018 for 13 fields in two test sites in Northeast Germany. The relation between polarimetric parameters and biophysical parameters observed in the field is investigated using linear and exponential regression models that are evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). The performance of single regression models is furthermore compared to those of multiple regression models, including backscatter coefficients in VV and VH polarisation as well as polarimetric decomposition parameters entropy and alpha. Characteristic temporal profiles of entropy, anisotropy, and alpha reflecting the main phenological changes in plants as well as the meteorological differences between the two years are observed for both crop types. The regression models perform best for data from the phenological growth stages tillering to booting. The highest R2 values of the single regression models are reached for the plant height of wheat related to entropy and anisotropy with R2 values of 0.64 and 0.61, respectively. The multiple regression models of VH, VV, entropy, and alpha outperform single regression models in most cases. R2 values of multiple regression models of plant height (0.76), wet biomass (0.7), dry biomass (0.7), and vegetation water content (0.69) improve those of single regression models slightly by up to 0.05. Additionally, the RMSE values of the multiple regression models are around 10% lower compared to those of single regression models. The results indicate the capability of dual-polarimetric decomposition parameters in serving as meaningful input parameters for multiple regression models to improve the prediction of biophysical parameters. Additionally, their temporal profiles indicate phenological development dependent on meteorological conditions. Knowledge about biophysical parameter development and phenology is important for farmers to monitor crop growth variability during the vegetation period to adapt and to optimize field management.

Description: In times of rising world population, increasing use of agricultural products as energy sources, and climate change, the area-wide monitoring of agricultural land is of considerable economic, ecological, and political significance. Crop type information is a crucial requirement for yield forecasts, agricultural water balance models, remote sensing based derivation of biophysical parameters, and precision farming. To allow for long enough forecast intervals that are meaningful for agricultural management purposes, knowledge about types of crops is needed as early as possible, i.e. several months before harvest. Thus, such early-season crop-type information is relevant for a variety of user groups such as public institutions (subsidy control, statistics) or private actors (farmers, agropharma companies, dependent industries). The identification of crop types has been a long research topic in remote sensing, starting from mono-temporal Landsat scenes in the 1980s to multi-sensor satellite time series data nowadays. However, most often crop types are identified in a late cultivation phase or retrospectively after harvest. Existing products are mainly static and not available in a timely manner and therefore cannot be included in the decision-making and control processes of the users during the cultivation phase. We currently develop a web-based service for dynamic intra-season crop type classification using multi-sensor satellite time series data and machine learning. We make use of the dense time series the Copernicus Sentinel satellite fleet offers and combine optical (Sentinel-2), and SAR (Sentinel-1) data providing detailed information about the temporal development of the phenological state of the crop growing phases. This synergetic use of optic and radar sensors allows a multi-modal characterization of crops over time using passive optical reflectance spectra and SAR-based derivatives (i.e. backscatter intensities and structural parameters derived by polarimetric decompositions). The automatic data processing pipeline of data retrieval, data pre-processing, and data preparation as a prerequisite for applying machine learning algorithms is based on open-source tools using SNAP and python libraries as main functionalities. The developed AI-based model uses the multi-modal remote sensing time series data stream to predict crop types early in their growing season. This model is based on previous work by Garnot et al. 2020, who leverage the Attention mechanism originally introduced in the famous Transformer architecture in order to better exploit the information about crop type included in the change of appearance in satellite images over time. The original model focuses on prediction of crop type based on Sentinel-2 acquisitions from within a single Sentinel-2 tile, which leads to very similar acquisition time points for all parcels in the dataset. This is not the case when applying the model to larger regions or including other data sources, such as Sentinel-1 (polarimetric and backscatter), which have vastly different acquisition time points. By implementing a modified form of positional encoding, we are able to train and predict on regions and data sources with differing acquisition time points as we provide implicit information about the acquisition time point directly to the model. This means we don’t need any temporal data preprocessing (e. g. weekly/monthly averages) and allows us to seamlessly fuse data from different sources (Sentinel-1 and Sentinel-2), leading to good prediction performance also in periods where there is no Sentinel-2 data available due to cloud occlusion. In order to improve the generalisation abilities of our model across regions and different years, we also study the effect of fusing satellite data with geolocalised temperature and precipitation measurements to account for the dependence of growth periods on these two parameters. We will present insights on the developed dynamic crop type classification service based on the use case for the federal states Mecklenburg-Vorpommern and Brandenburg in Germany. For both states, official reference information from the municipalities about the cultivation information for approx. 200,000 fields are used for training and testing the algorithm. Predicted crop types are winter wheat, winter barley, winter rye, maize, potatoes, rapeseed, and sugar beet. We will show model performances in different cultivation stages (from early season to late season) and with different remote sensing data streams by using Sentinel-1 or Sentinel-2 data separately or in conjunction. Moreover, the transferability of the approach will be evaluated by applying a trained model of one year to other years not included in the training phase.

Description: Peatlands are areas with naturally accumulated thick layers of dead organic materials. While peatlands cover about 3% of the world’s land area, their carbon storage is estimated equivalent to ~30% of all soil carbon, ~75% of all atmospheric carbon, and as much carbon as all terrestrial biomass. Drained peatlands due to past human uses can emit carbon and be a key source of greenhouse gases while rewetted peatlands usually have significantly reduced CO2 emissions and can even become a carbon sink. However, quantifying the potential and limitations of reducing emissions by peatland rewetting is challenging. Carbon fluxes on peatlands are both spatially complex and temporally dynamic owing to their microtopography, changing water levels and associated vegetation status. Here we demonstrated the generation of temporally consistent ~biweekly 5-m images over 8 years (2013-2020) at visible and near infrared bands (VNIR) to track the temporal trajectories of vegetation and surface water and estimate cover-specific carbon fluxes at a rewetted peatland site in northeastern Germany (Figure 1). To ensure temporally-consistent multispectral images for the subsequent analyses of vegetation/water covers, we set up a two-stage normalization procedure that normalized the images from RapidEye (SmallSats) and PlanetScope (CubeSats) to rigorously calibrated multispectral sensors onboard large satellites (Landsat-7/8 and Sentinel-2). The two-stage normalization procedure produced two levels of image normalization that allows for downstream applications to balance between the quality and the quantity of available normalized CubeSat images in a time series. A quantitative evaluation approach using daily MODIS images as bridging benchmark data revealed that the temporal consistency in CubeSat images was comparable to that in Landsat and Sentinel-2 images, which confirmed the efficacy of the normalization procedure. The temporal information in the stack of normalized 5-m images helped us estimate the vegetation types and the changes in vegetation/surface water covers throughout the 8 years. The within-year time series of CubeSat images at the three visible and one near-infrared bands showed discernible differences among vegetation types at this peatland sites, which promises systemic mapping of vegetation compositions in peatlands using very-high-resolution CubeSat imagery time series over heterogeneous peatlands. We aggregated vegetation and surface water covers within each year into three condition categories at the peatland site, always emergent vegetation, always surface water, and alternating between vegetation and water. The estimated areas of the three condition categories closely covary with the measured water table depths at the site (Figure 2). The substantial areal expansion of always emergent vegetation at the site, that are captured by the CubeSat imagery time series, aligns well with the timing of three drought events (2016, 2018 and 2019) in this region. These surface covers and conditions at both high temporal and spatial resolutions from CubeSat images allow us to disaggregate ecosystem-scale measurements of CO2 and CH4 fluxes by the eddy covariance (EC) tower at the site into cover-specific fluxes. We attribute CO¬2 and CH4 fluxes measured by EC over 8 years to the three surface condition categories through a nonparametric approach to flux decomposition using annual maps of surface condition categories and half-hourly EC-measurement footprints. The disaggregated carbon fluxes improve our upscaled estimates of carbon emission/sequestration over rewetted peatland sites. Such spatial-temporally-resolved carbon fluxes in dynamic and heterogeneous peatlands will contribute to better informed restoration and protection of peatlands.

Description: The Minimal Sampling Classifier – MiSa.C - is an innovative classification webservice for remote sensing image raster data that allows a classification of complex surfaces over space and time by considering only a minimal amount of training samples as reference input data. Based on only one reference sample per class, the tool incorporates images statistics and machine learning to automatically create an enlarged set of unbiased and comprehensive training data, which is used as foundation for the creation of a large number of models representing the classification results. Users with expert knowledge for at least a small, representative region of the entire classification area can then evaluate the model outputs in a step-wise approach, that provides the flexibility to fine-tune each classification decision by thresholding the number of models used as basis for the final classification. A big surplus of MiSa.C is the ability to process multiple source imagery together that are provided in one 4D data-cube (areal, spectral, time domain), e.g. a combination of optical imagery, radar imagery and a digital elevation model or else. Originating from the GFZ Potsdam German Research Centre for Geosciences in-house software development called “Habitat Sampler”, MiSa.C is an example for successful technology transfer from an innovative open source code to a user-friendly Software-as-a-Service (SaaS) webservice. The development was undertaken by the Helmholtz Innovation Lab FERN.Lab, which is part of the research centre. The lab planned and implemented the webservice in close cooperation with the lead scientist and developed an intuitive graphical user interface suitable for all kind of users coming from both the public and the private sector. MiSa.C is especially designed for close monitoring of locally delimited regions that are dominated by mixed-pixel areas with typical spectral patterns and phenological changes over the time such as habitats and other ecosystem areas. We demonstrate applications of the Non-Governmental Organisation Heinz-Sielmann-Stiftung where MiSa.C is used to classify different habitat types in former military areas, which are not allowed to access nowadays due to ammunition load. Results of the change of habitat types over the time allow caretakers and decision makes to fulfil their report duties and to carefully plan their future restoration measurements. Further examples show forest applications, such as tree species detection and classifications of crops within the agriculture domain. In an outlook, the authors also encourage users to use MiSa.C as an automatic training data generator that provides the necessary source data for other supervised, AI-based classifiers.

Description: The increasing demand for food due to the rising population and the simultaneous shortage of agricultural land challenges agriculture in particular in the light of ongoing global change, such as climate change, threats to water quantity and quality, soil degradation, environmental pollution, destruction of ecosystems, and biodiversity loss. Efficient and sustainable solutions for adapting to the effects of global change are therefore of central importance. The digitization of agriculture offers the opportunity to optimize and automate processes, but also poses challenges for farmers. International activities such as GEOGLAM defined essential agricultural variables and core information products from remote sensing. However, major shortcomings are the lack of workflows for bringing scientifically based knowledge and methods into practice as well as insufficiently specified interfaces between data sources, farmer and machine. Methodological challenges are, above all, the robustness of the procedures, the handling of multi-sensor data, standards regarding data quality and the trustworthiness of scientific models as well as their temporal and spatial transferability. Open and solution-oriented communication with farmers regarding potentials, accuracies and limitations of remote sensing products is also strongly deficient. Technical questions about data management, user-friendliness, data integrity and data protection as well as high investment costs are further barriers. Farmers often lack awareness of the added value of the data, while scientists often fail to provide easily understandable data interpretations. However, it is also evident that practicing farmers are open to new technical solutions. The project "AgriSens - DEMMIN 4.0" (project duration 02/2020-01/2023), funded by the German Ministry for Nutrition and Agriculture (BMEL), has the goal to identify practical applications based on remote sensing data originated from satellites, aircrafts, and UAV-supported systems, for crop production. It further aims at developing new methods and making this knowledge easily available to the farmer and the public. Therefore, four use casas are implemented targeting at crop growth monitoring and yield prediction, sustainable use of low yield zones, irrigation monitoring, and detection of glacial stones at fields. The presentation will show and explain the first results of these use cases: The first use case focuses on a more resource-efficient management in winter wheat by the integration of re-mote sensing-based information on current crop status, crop development and potential crop yield. For this purpose, key variables such as above-ground biomass and leaf area index are derived from Copernicus Sentinel images. They are coupled with a crop growth model to provide spatial explicit, daily information on the status of crops as new base layer for the application of plant protection products, fertilizers or growth regulators. On-farm experiments planned for 2022 shall provide insights on the potential economic and ecological benefits of this new source of information. Additionally, the potential of airborne images is evaluated The second use case is dedicated to sustainable management of agricultural land. Considering intra-field heterogeneity (e.g., through precision farming) can increase or stabilise yields while reducing the use of operating resources. Furthermore, this can contribute to the reduction of ammonia and nitrogen oxide concentrations in the soil and thus to the improvement of water quality. The aim of this use case is to provide an information layer that allows the identification, location, and typification of low-yield areas to support their optimised management. For this purpose, local knowledge about these areas is repeatedly digitally captured by farmers during field work using the "FieldMApp" application on mobile devices. The captured FieldMApp data are fused and afterwards blended with satellite data. The functionality and design of the "FieldMApp" are defined in a cooperative collaboration between farmers and scientists (citizen science approach) in order to create a solution that meets the requirements of both. The third use case deals with the detection of stones on agricultural land, which can cause major damage to agricultural machinery and have so far been removed manually by driving off the entire field. The aim is to develop a marketable workflow for the drone-based detection of stones in order to reduce personnel and machine costs. In this way, only selected areas on the fields need to be targeted and operating resources are saved and area compaction is reduced. Stones are detected at different field trials in 2021 using different camera techniques, e.g. optical, thermal, Lidar, in combination with object-based image analysis algorithms. The topic of the fourth use case is irrigation technology. In times of increasing extreme weather events, which became particularly visible in 2018 and 2019 due to a pronounced drought throughout Germany, many farmers are focusing on the expansion or re-installation of irrigation infrastructures. In order to apply the resource water in a demand-oriented and cost-conscious manner. The use case presents field trials in 2021 with different irrigation strategies and the corresponding detection of growth and quality parameters for potato. This information is combined with soil water modelling and remote sensing data analysis for supporting future site-specific irrigation strategy. The use cases are supporting with its results for greater acceptance and wider use of these valuable data sources for operational processes in crop production. This presentation spotlights on the experimental field DEMMIN, the project AgriSens - DEMMIN 4.0 and first results from it. AgriSens DEMMIN 4.0 located at the only German test site in the Joint Experiment of Crop Assessment and Monitoring (JECAM) an initiative for product development and validation in GEOGLAM. Thanks to its many years of research activities and its national and international networking, DEMMIN is ideally suited as a test site for the AgriSens - DEMMIN 4.0 project. DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) experimental field is located about 180 km north of Berlin in the federal state of Mecklenburg-Western Pomerania in the north-eastern German lowlands. The young moraine landscape with its numerous lakes and bogs is characterised by typical periglacial landscape elements such as extensive, flat sandy areas, hills and depressions. DEMMIN has been operated as large facility for calibration and validation of remote sensing data by the German Aerospace Centre for since 2000. Among others, it is equipped with 43 environmental measuring stations, 63 soil moisture stations, a lysimeter hexagon, an eddy covariance measuring station and a research crane. Since 2011, it has also been part of the TERENO Observatory Northeast of the German Research Centre for Geosciences Potsdam. The combination of the infrastructure facility and close cooperation between farmers and researchers at the site DEMMIN enables a large potential to combine high quality method development with quality assessment based on in-situ data and farmers information. Furthermore, the research can consider directly the needs of farmers related to remote sensing based information products. This exchange is a further key element of the AgriSens – DEMMIN 4.0. Regular local workshops, like AgriSens DEMMIN field day 2021 with 125 participants are supporting this exchange, but also includes now aspects like the knowledge and role of agricultural advice services. The oral talk of AgriSens DEMMIN 4.0 will present the described project and especially focus on the first results of the use cases and field trials achieved in 2021. Furthermore, other aspects, like the information of the status of use of remote sensing-based products and data handling options within the project, including access to farmers for the developed services will presented.