The ARTMO toolbox for analyzing and processing of hyperspectral data
This tutorial will focus on the use of ARTMO’s (Automated Radiative Transfer Models Operator) radiative transfer models (RTMs), retrieval toolboxes and post-processing tools (https://artmotoolbox.com/) for the generation and interpretation of hyperspectral data. ARTMO brings together a diverse collection of leaf, canopy and atmosphere RTMs into a synchronised user-friendly GUI environment. Essential tools are provided to create all kinds of look-up tables (LUT). These LUTs can then subsequently be used for mapping applications from optical images. A LUT, or user-collected field data, can subsequently be inserted into three types of mapping toolboxes: (1) through parametric regression (e.g. vegetation indices), (2) nonparametric methods (e.g. machine learning methods), or (3) through LUT-based inversion strategies. In each of these toolboxes various optimization algorithms are provided so that the best-performing strategy can be applied for mapping applications. When coupled with an atmosphere RTM retrieval can take place directly from top-of-atmosphere radiance data.
Further, ARTMO’s RTM post-processing tools include: (1) global sensitivity analysis, (2) emulation, i.e. approximating RTMs through machine learning, and (3) synthetic scene generation.
The proposed tutorial will consist of a theoretical introduction and a practical session, where the following topics will be addressed:
- Basics of leaf, canopy and atmosphere RTMs: generation of RTM simulations
- Overview of retrieval methods: parametric, nonparametric, inversion and hybrid methods.
- Principles of emulation of hyperspectral data, and applications such as global sensitivity analysis and scene generation.
In the practical session we will learn to work with the ARTMO toolboxes. They provide practical solutions dealing with the abovementioned topics. Step-by-step tutorials, demonstration cases and demo data will be provided. No prior knowledge is needed.