The FieldMApp - a flexible tool for the standardised acquisition of in-situ data

Kurzfassung

Earth observation data acquired by the Copernicus satellites can be deployed for the frequent large-scale monitoring of vegetation parameters on agricultural fields. From the perspective of agriculture counsellors and farmers, an added value arises in particular, if those data are used to derive products, which address parameters that are relevant to management decisions. Such products should come along with a sound uncertainty assessment and management recommendations. For this, in-situ data, knowledge of processes, and local knowledge are required. In-situ data are mainly deployed to calibrate, update and validate satellite-aided retrieval models. Knowledge of processes and local knowledge provide a basis to develop management recommendations. In-situ data and local knowledge are collected in different projects from a wide variety of people, including scientists as well as co-researchers (e.g., citizens). Thus, the measurement and sampling design vary for measured and observed parameters, which causes variable data quality. This makes it difficult to merge data of the same kind from different projects and therefore hampers the reuse and automatic analysis of data. Modular designed and customizable applications for mobile devices (Apps) represent a framework that can help to foster the standardization of data sampling methods and strategies. At the same time, they provide enough flexibility to be adjusted for the use in various scenarios. The FieldMApp represents such a modular designed application. Its open structure permits the reuse of existing software components, customizable adjustments, and the addition of new modules that are necessary to fulfil specific requirements of a research project at hand. The FieldMApp was built exclusively on open-source libraries. It can be compiled to run on an Android or iOS operating system, allows for the integration of internal and external sensors, and is designed to work either in an online or offline mode. Acquired data are stored in a machine-readable format. The FieldMApp concept includes tools that may support data validation and uncertainty assessments. The overall uncertainty of acquired data is estimated by considering sources of systematic and random errors, which depend on the modular set-up. Accordingly, the FieldMApp provides mutually compatible data sets, thus increasing their reuse potential. In this contribution, the concept and structure of the FieldMApp will be presented. The application of the FieldMApp within the frame of a use case of the project Agrisens DEMMIN 4.0 will be demonstrated. In this use case, low yield areas on acreages were identified and characterised by farmers during on-site agricultural operations. The relevance of such data for agricultural management and the common agriculture policy will be outlined. Examples for other fields of application within the agricultural sector will be highlighted.