Agriculture Virtual Laboratory Documentation#
The Agriculture Virtual Laboratory (AVL) is an integrated, user-friendly online environment that helps scientists to discover, explore, analyse, and visualize a wide variety of agricultural earth observation data.
The AVL integrates a data access layer, a thematic processing subsystem (TAO), a Python scientific stack including the xcube suite for data cube handling, a web-based interactive lab notebook (JupyterLab), and an online geodata viewer.
This section provides a guide for scientific users of the Agriculture Virtual Laboratory, including both the thematic processing and exploitation subsystems, and descriptions of the AVL-specific command-line and Python interfaces.
- Exploitation subsystem: JupyterLab
- Exploitation subsystem: xcube viewer
- Thematic processing subsystem
- AVL Python API
- AVL command-line tools
AVL provides a variety of EO data products from multiple sources (or collections). They follow a well-defined dataset convention and are grouped according to sensor type. Most of these datasets are provided via the thematic processing system but many are also available in the exploitation system via various xcube data stores.
- Dataset conventions for raster datasets
- Altimetric datasets (processing system)
- Atmospheric datasets (processing system)
- Optical datasets (processing system)
- Passive microwave datasets (processing system)
- Radar datasets (processing system)
- Open datasets (processing system)
- Vector datasets (exploitation system)
- xcube data store datasets (exploitation system)
This section documents the system design, development resources, test procedures, and test results.
- Design overview
- Architecture and common components
- Processing system design
- Exploitation system design
- Development infrastructure
- Software reuse file
- Processing system test procedures and results
- Exploitation system test procedures and results