Satellite Remote Sensing, GIS, and ICT for Precision Farming

Syllabus

GIS data and knowledge selection (1 hour lecture and 1 hour exercise)

• Take advantage of the 3D+time GIS datasets and products such as Cadastre data and base maps, Elevation datasets, Soil datasets, Meteo datasets, and Land use/cover maps.

EO data selection (and downloading) (1 hour lecture and 1 hour exercise and creating a customised project)

• Take advantage of the rich source of a wide variety of Multi-temporal, Multi-spectral, and Multi-resolution satellite imagery and tools for user-defined RoI/Date (space, time).

Analytics and Tools (algorithms and services)

1. Teaching and providing students with digital tools (selection of algorithm, parametres, and visualisation) for pista monitoring and assessment, including uncertainties analysis (4 hours lecture and 4 hours exercises)
2. The training will cover practical exercises on using cloud-based services/tools for crop monitoring (2 hours lectures and 2 hours exercises)
3. Computer-assisted information extraction from remotely sensed data for decision support, including case studies based on the needs of the Under Sun. (Case studies to identify water deficiency or surplus, weed infestations, and plant populations) (4 hours lectures and 4 hours exercises)
4. Computing Management Zones: Methods for computing management zones and practical exercises in computing management zones using FieldCalc/QGIS (2 hours lectures and 4 hours exercises)

Integration of Management Zones (MZ) with the climate and IoT sensor networks such as soil moisture and improving the MZ (6 hours lecture and 4 hours exercises)

The homework will include: 20 hours literature analysis, PF terminology, and individual study.

Objectives and Competences

The main objectives are:

• Build capacities to use integrated remote sensing and GIS data, and interpret them for Pista orchards/trees monitoring.
• RS data analysis and modelling for Precision Farming.
• Enriching EO data through IoT sensor networks, focusing on pista crop conditions monitoring (tree height, canopy vol, weed, etc.).
• Monitoring pista orchards (soil, trees) and processes (user/location/time-specific).
• Selection of algorithms, parametres, and visualisation of crop monitoring and assessment, including uncertainties analysis. With the correctly validated algorithms and parametres, agricultural inputs such as fertilisers and water can be optimised spatially using homogenous production zones (called Management Zones).
• The course will cover practical exercises using cloud-based services/tools for crop monitoring, removing the complexity of processing large volumes of satellite data.

Intended Learning Outcomes

Students who successfully complete the course will be able to:

• Identify basic concepts and methods applied in remote sensing and GIS data integration.
• Understand a user-defined location (RoI) and pista health issues, adjacent objects and agricultural landscapes, and then provide a selection of images for RoI for further image analysis (user-defined period).
• Understand the fundamentals of using near real-time EO data, including key enabling technologies like Geo-ICT services pulling IoT sensor data and climate data/products.
• Understand the role of Remote Sensing Image Analysis in a GIS environment for Precision Farming (PF) on a cost-effective basis.
• Understanding available Prior Knowledge and Sampling Desing and the need for additional (extra) information.