Integrating remote sensing and climate data for olive grove classification and yield estimation

Keywords: Irrigated agriculture, NDVI, Sentinel-2, Dynamic Time Warping, Machine learning

The agricultural sector confronts escalating challenges amid uncertainties associated with water resources, underscoring the imperative for innovative solutions. Hence, a profound comprehension of the production dynamics of forthcoming productions becomes paramount for effective water management and the optimization of irrigation strategies, leveraging algorithms such as Dynamic Time Warping (DTW).

This study delves into forward-thinking methodologies encompassing delineation in both rainfed and irrigated olive groves, furnishing a comprehensive panorama of the cultivation landscape. Utilizing information derived from satellite images, particularly the Normalised Difference Vegetation Index (NDVI), enables the comparison between olive groves dedicated to either irrigated or rainfed production. This comparison helps quantify and comprehend the impact of irrigation on olive groves, correlating it with climatic factors such as rainfall and temperature. Essentially, it could aid in identifying optimal conditions for irrigation and when it may not be necessary.

Simultaneously, it facilitates accurate estimation of olive yields based on the prevailing water conditions. Harnessing vegetation indices such as NDVI from remote sensing allows us to forecast how diverse olive groves react to varying climatic conditions. This monitoring facilitates proactive irrigation to avert water stress affecting production levels deeply.

Moreover, this comparison, anchored in NDVI, lays the groundwork for subsequent analyses incorporating soil and other climate data. Therefore, it enhances the precision of irrigation decisions, contributing to preparedness for droughts and formulating well-informed policies.

In conclusion, this study pushes the boundaries of intelligent irrigation management in olive cultivation, fostering sustainability, cost-effective technology, and optimal resource utilization. The technical insights presented herein constitute a comprehensive resource for any stakeholder seeking solutions in agriculture.