Estimating vegetation temperature from UAV multispectral imagery-based vegetation indices

Abstract

In agricultural research, the computation of temperature and water content indicators, such as the Crop Water Stress Index (CWSI), relies on expensive and specialized thermal imaging devices. To overcome this limitation, this study presents a novel and cost-effective methodology for precise temperature estimation. By using an affordable multispectral imaging system, the objective is to provide growers with low-cost Unmanned Aerial Systems (UAS) capable of estimating vegetation temperature without the need for thermal imagery. This investigation delves into the relationship between multispectral imagery-based vegetation indices and temperature derived from thermal imagery. After correcting and calibrating these data sources, an estimation model is established to compute vegetation temperature using only visible and near-infrared (NIR) radiation, effectively eliminating the need for thermal imagery. Among various vegetation indices tested, the Green Chlorophyll Index (GCI) demonstrates the highest correlation with ground truth temperature (R² = 0.71) in vegetation regions including a park and a cornfield. Consequently, GCI is used to compute the temperature estimate map and derive a CWSI estimate, which entirely foregoes thermal imagery. Rigorous quantitative comparisons are made between ground truth and estimated temperature to validate the accuracy of the results. Although the proposed approach is currently in the early stages, it appears promising as a practical tool for growers to assess water content features at low and high resolutions without compromising accuracy compared to the traditional thermal-based method. The open-source software developed for this research is available online as supplementary material, fostering transparency and repeatability.