Contribution to a conference proceedings

FZJ-2022-03881

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png LAI and Leaf Chlorophyll Content Retrieval Under Changing Spatial Scale Using a UAV-Mounted Multispectral Camera

Chakhvashvili, E. (Corresponding author)FZJ* ; Bendig, J.FZJ* ; Siegmann, B.FZJ* ; Muller, O.FZJ* ; Verrelst, J. ; Rascher, U.FZJ*

2022
IEEE

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Please use a persistent id in citations: doi:10.1109/IGARSS46834.2022.9883446

Abstract: Recent advancements in unmanned aerial vehicle (UAV) technologies made it possible to monitor agricultural fields at higher spatial and temporal resolution than commonly possible by aerial and satellite surveys. Mapping crop variables such as leaf area index (LAI) and leaf chlorophyll content (LCC) from low-cost UAV-based multispectral cameras can deliver vital information about crop status to farmers and plant breeders. Retrieval of these variables using radiative transfer models (RTMs) has been widely studied in the satellite remote sensing community but is still not well explored in the UAV remote sensing community. This study aims to investigate the advantages of high spatial resolution UAV image data for retrieving LAI and LCC using RTM inversion. A breeding experiment consisting of soybean plots has shown that high-resolution imagery (0.015m) delivers better retrieval accuracy compared to coarser resampled image data. Particularly, biochemical parameters, such as LCC, benefit from high spatial resolution.

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Contributing Institute(s):

1. Pflanzenwissenschaften (IBG-2)

Research Program(s):

1. 2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217) (POF4-217)
2. EXC 2070:  PhenoRob - Robotics and Phenotyping for Sustainable Crop Production (390732324) (390732324)

Appears in the scientific report 2022

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