Disruptive Trends of Earth Observation in Precision Farming

Dear Colleagues,

Recent technological advances and novel ideas has lead towards the development of new sensors (miniaturized hyperspectral, miniaturized lidar, dedicated online soil sensors), new satellite concepts (nano-satellite constellations, micro-satellite missions, on-board processing, educational satellites), new airborne platforms (low cost UAVs, Medium Altitude Long Endurance UAVs, High Altitude Pseudo Satellites), new digital image processing concepts (cloud computing, Software as a Service, Open Data Cube, real time processing, multiple data fusion, emerging algorithms), big data availability (PB of data from Copernicus, Google Earth Engine, Internet of Things, crowdsourcing 'farmer as a sensor') and new business models (vertical integration of Earth observation services, freely available high-resolution products).

In view of these developments, disruptive trends have emerged to provide valuable applications in site-specific management of agricultural resources for improved decision making and eventually optimizing agricultural production. These are essential steps towards sustainable application of precision agriculture and its promotion to new markets.

Capitalizing on the above disruptive trends, the aim of this special issue is to collect state-of-the-art research in the world of precision agriculture applications of Earth Observation. Submissions on the following topics are invited (but not limited to), as long as they present innovative methods and approaches, or novel applications of existing tools in precision agriculture applications:

• miniaturized hyperspectral, miniaturized lidar, dedicated online sensors,
• nano-satellite constellations, micro-satellite missions,
• low flight UAVs, Medium Altitude Long Endurance UAVs,
• sensors for proximal sensing at field level,
• GPS/GNSS,
• big data, IoT, crowdsourcing (farmer as a sensor),
• emerging algorithms, cloud computing, real time processing, on-board processing, multiple data fusion,
• spatio-temporal analysis of time series of agricultural parameters,
• analysis of hyperspectral data cubes,
• remote and proximal data assimilation in crop growth models,
• mapping and predicting crop yield and grassland biomass,
• mapping soil fertility parameters,
• monitoring soil moisture content,
• determining fertilization applications in the spatial domain,
• monitoring crop growth,
• monitoring water use and irrigation requirements at within-field scales,
• mapping and early detection of crop diseases, weed and insect infestations for site-specific management,
• crop damage assessment (frost, droughts, hail),
• site-specific applications and management of agricultural resources.

Dr. Thomas Alexandridis
Dr. Tomáš Řezník
Prof. Dr. Abdul M. Mouazen
Prof. Guo Huadong
Guest Editors

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