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

Remote Sensing Applications in Almond Orchards: A Comprehensive Systematic Review of Current Insights, Research Gaps, and Future Prospects

“…Another study was recorded in connection with the use of satellite data in combination with UAV data. Sandonís-Pozo et al [36] used satellite data to estimate geometric and structural parameters, bypassing the timeconsuming procedures associated with LiDAR or UAV photogrammetry. They estimated critical orchard parameters using LiDAR data.…”

Optimizing Wheat Yield Prediction Integrating Data from Sentinel-1 and Sentinel-2 with CatBoost Algorithm

“…This function allows to use all features in each split. After tests with different combinations of tree depth (4,5,6,7,8,10,45,50), the best result was obtained with 45 nodes.…”

“…The development of precision agriculture (PA) began in the late 1990s as a strategy for improving the sustainability of agricultural production through the consideration of temporal and spatial variability. The utilization of various sensors, including weather stations [9], multispectral cameras [10], electroconductivity meters [11], and LiDAR [12], is a common practice within the framework of PA. The implementation of PA allows input reduction while maintaining yield levels [13] through the targeted distribution of inputs according to specific crop requirements rather than a uniform application [14].…”

Research on Quality Detection of Jujube (Ziziphus jujuba Mill.) Fruit Based on UAV Multi-Spectrum

“…These data can be used to infer the fruit tree growth status and soil quality in orchards [8][9][10][11]. In recent years, this technology has gained attention from numerous scholars, who have conducted extensive research in areas such as processing [12,13], feature extraction [11,14], and recognition model construction [15][16][17]. Sun et al [18] employed UAV remote sensing technology to quantify the nitrogen nutrient content of leaves using multispectral images.…”