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.
- Dataset conventions for raster datasets
- Altimetric datasets
- Atmospheric datasets
- Optical datasets
- Passive microwave datasets
- Radar datasets
- Vector datasets
Additionally, AVL provides access to other open data sets:
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