Instructor: Jochem Verrelst (University of Valencia, Spain) & Jorge Vicent (Magellium, France)
Tutorial description: This tutorial will focus on the use of ARTMO’s (Automated Radiative Transfer Models Operator) and ALG’s (Atmospheric Look-up table Generator) radiative transfer models (RTMs), retrieval toolboxes and post-processing tools (https://artmotoolbox.com/) for the generation and interpretation of hyperspectral data. ARTMO and ALG bring together a diverse collection of leaf, canopy and atmosphere RTMs into a synchronized 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.
The proposed tutorial will consist of a brief theoretical session 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. Coupling of top-of-canopy simulations with simulations from atmospheric RTMs for the generation of top-of-atmosphere radiance data.
- Practical exercise for the retrieval of vegetation biophysical properties from bottom-of-atmosphere and top-of-atmosphere Sentinel-2 data. 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.
Prerequisites: ARTMO Matlab in Windows and MySQL is required. In case Matlab is not available, students will be asked to team up in small groups. More information about ARTMO and ALG can be found here: https://artmotoolbox.com. Students are also recommended to install and compile the 6SV (http://6s.ltdri.org) or libRadtran (http://www.libradtran.org/doku.php) atmospheric models.
Number of participants: max. 13-24
Date: 21st June 2022, 15:30-18:00
Location: Seminar room (2.06, 1st upper floor) of building C4 (google maps) at Albert Einstein Science Park, 14473 Potsdam