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Crop Leaf Area Estimation Using Hemispherical Photos and Hyperspectral Images

Authors: Ali Al Wafi*, University of Toronto - Mississauga, Yuhong He, University of Toronto - Mississauga
Topics: Physical Geography, Remote Sensing, Agricultural Geography
Keywords: Leaf Area Index, Hyperspectral, Agriculture
Session Type: Paper
Presentation File: No File Uploaded

The incorporation of remote sensing data into the field of agriculture gave birth to the promising new area of precision agriculture. Many studies have used multispectral remote sensing to map crop leaf area that receive different nutrient applications. The development of affordable hyperspectral remote sensing instruments quickly expanded the number of promising hyperspectral applications in vegetation monitoring, this is due to its capability in capturing continuous spectral characteristics of ground features with greater detail when compared to multispectral sensors. However, the use of hyperspectral remote sensing in crops is still in its infancy and more hyperspectral indices need to be explored for their ability to estimate crop leaf area index. Our research objective is to map leaf area index using in-situ and airborne hyperspectral data and to investigate differences in crop properties relative to different nutrient treatments. An airborne hyperspectral image, 105 leaf samples for chlorophyll measurements and 200 hemispherical photo sets for leaf area measurements were collected from an experimental field located in Chatham-Kent, Ontario on August 9, 2019. The hyperspectral image is pre-processed by performing georeferencing, mosaicking, and atmospheric correction. Remote sensing spectral indices are calculated from the hyperspectral image spectra and related to field data to establish regression models. The spectral index with the highest estimation accuracy will then be used to map leaf area index. Finally, the spatial differences in the leaf area map will be explored to investigate how crops responded to different treatments.

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