Calibration of forage maize plant cover using UAVs and satellite imagery in Google Earth Engine

Abstract

Objective: Estimate vegetation cover (VC) using Unmanned Aerial Vehicle (UAV) and Sentinel-2 satellite images for monitoring agricultural areas.

Design/methodology/approach: Sixteen vegetation indices (VI) were evaluated to automatically estimate VC. Observed VC was obtained from high-resolution georeferenced orthomosaics generated by a multispectral camera mounted on a UAV. These observed VC values were correlated with vegetation indices (VI) calculated from Sentinel-2 satellite imagery.

Results: The VIs with the best statistical performance in estimating VC were identified. It was found that the ARVI vegetation index can be used to accurately monitor VC for forage maize in Cuauhtémoc, Chihuahua.

Limitations/implications: The results obtained are representative of the specific conditions of the study area; however, their application in other regions requires prior calibration.

Findings/conclusions: Based on the coefficients obtained in this study, vegetation cover (VC) can be automatically monitored using the VICAL tool. This feature enables the monitoring of VC status across different plots and time periods during the agricultural cycle, facilitating spatial and temporal analysis of crop development.