HyperBlend : Simulating Spectral Reflectance and Transmittance of Leaf Tissue with Blender
Abstract
Remotely sensing vegetation condition and health hazards requires modeling the connection of plants’ biophysical and biochemical parameters to their spectral response. Even though many models exist already, the field suffers from lack of access to program code. In this study, we will assess the feasibility of open-source 3D-modeling and rendering software Blender in simulating hyperspectral reflectance and transmittance of leaf tissue to serve as a base for a more advanced large-scale simulator. This is the first phase of a larger HyperBlend project, which will provide a fully open-source, canopy scale leaf optical properties model for simulating remotely sensed hyperspectral images. Test results of the current HyperBlend model show good agreement with real-world measurements with root mean squared error around 1‰. The program code is available at https://github.com/silmae/ hyperblend.
Main Authors
Format
Conferences
Conference paper
Published
2022
Series
Subjects
Publication in research information system
Publisher
Copernicus Publications
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202208194267Use this for linking
Review status
Peer reviewed
ISSN
2194-9042
DOI
https://doi.org/10.5194/isprs-annals-V-3-2022-471-2022
Conference
International Society for Photogrammetry and Remote Sensing Congress
Language
English
Published in
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Is part of publication
XXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, Commission III
Citation
- Riihiaho, K. A., Rossi, T., & Pölönen, I. (2022). HyperBlend : Simulating Spectral Reflectance and Transmittance of Leaf Tissue with Blender. In J. Jiang, A. Shaker, & H. Zhang (Eds.), XXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, Commission III (V-3-2022, pp. 471-476). Copernicus Publications. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. https://doi.org/10.5194/isprs-annals-V-3-2022-471-2022
Funder(s)
Research Council of Finland
Funding program(s)
Academy Project, AoF
Akatemiahanke, SA

Additional information about funding
This study was funded by Academy of Finland (327862).
Copyright© Author(s) 2022