Pathways to using remote sensing in crop cultivation and management - the German digital test field AgriSens DEMMIN 4.0
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.
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