Evaluating the Effect of Environmental Factors on Quality Characteristics of Lavandula angustifolia Essential Oil Using Satellite Images and Remote Sensing

Abstract

Medicinal and aromatic plants have been used for centuries due to their beneficial properties. One such plant is lavender (Lavandula angustifolia), widely known for its essential oil. The composition and quality of lavender essential oil are influenced by various environmental factors. The present study aimed to investigate the impact of climate on the quality characteristics of L. angustifolia essential oil, using satellite images and remote sensing. The research was carried out on a 0.5 ha commercial lavender field at Kozani, Greece. Qualitative analysis of the essential oil was performed in a chemical laboratory from 2019 to 2023, with substance identification carried out using gas chromatography. Climate data, including temperature, relative humidity, precipitation, wind, and solar radiation, were obtained from NASA Power for five years (2019 – 2023). Additionally, NDVI (Normalised Difference Vegetation Index) and NDWI (Normalised Difference Water Index) indices were extracted from Sentinel 2 satellite imagery. The values were retrieved from climateengine.org. It is a freely available remote sensing application powered by Google Earth Engine. This platform allows users to download spatial and temporal data from several satellites, including Landsat and Sentinel. The collected data, as well as quality parameters of essential oil, were statistically analysed using IBM SPSS Statistics to identify correlations and specific trends. The results showed that lavender NDVI is significantly positively correlated with relative humidity (0.74, p<0.01). On the other hand, NDWI is significantly positively correlated with precipitation (0.71, p<0.05) and relative humidity (0.86, p<0.01). NDWI was significantly positively correlated only with cis-beta-OCIMENE (0.712, p<0.05) and significantly negatively correlated with 1,8-CINEOLE (-0.707, p<0.05), TERPINENE-4-OL (-0.818, p<0.05), Lavandulyl Acetate (-0.709, p<0.05), Lavandulol (-0.680, p<0.05) and alpha-TERPINEOL (-0.650, p<0.05). From all the above, it is clear that remotely sensed indices such as NDVI and NDWI could be utilised to help farmers and crop consultants predict the variability of essential oil quality within a field.