Satellite multispectral indices to estimate canopy parameters and within-field management zones in super-intensive almond orchards

Abstract Continuous canopy status monitoring is an essential factor to support and precisely apply orchard management actions such as pruning, pesticide foliar treatment applications, or fertirrigation, among others. For that, this work proposes the use of multispectral vegetation indices estimate geometric structural parameters from remote sensing images (high temporal spatial resolution) alternative more time-consuming processing techniques, LiDAR surveys UAV photogrammetry. A super-intensive almond ( Prunus dulcis ) was scanned using a mobile terrestrial laser (LiDAR) in two different vegetative stages (after spring pruning before harvesting). From point cloud, parameters, including maximum height width, cross-sectional area porosity, were summarized every 0.5 m along rows interpolated block kriging pixel centroids PlanetScope (3 × 3 m) Sentinel-2 (10 10 image grids. To study association between LiDAR-derived 4 indices. canonical correlation analysis carried out, showing normalized difference index (NDVI) green (GNDVI) have best correlations. cluster also performed. Results can be considered optimistic both for delimit within-field zones, being supported by significant differences parameters.