Estimating Leaf Area Index of Arid Land Cotton Crops with Unmanned Aerial System (UAS) Multispectral Data

Abstract

The use of unmanned aerial systems (UASs) can aid in assessing plant health via leaf area index (LAI) and percent canopy cover more efficiently than traditional methods and without the destruction of plant matter from taking ground-based plant measurements. Equipping a UAS with a five band multispectral camera will expand on prior research at a finer scale than previously possible with satellite imaging or other remote sensing techniques. In this study, a comparative analysis of traditional ground-based plant measurement methods to emerging UAS and multispectral technology was used to determine the advantages of UASs for crop monitoring. Ground truth data included canopy cover and plant height ground-based plant measurements, LI COR 2200C Plant Canopy Analyzer LAI data, and LI-COR Li-3100 Area Meter LAI data. Results indicate plant height may be assessed using UAS multispectral data up to mid-season growth before boll development, which may be attributed to canopy closure. This study suggests that LAI and percent canopy cover can be predicted through multispectral image analysis, which implies a strong role of UASs for crop monitoring in the future.