## 1 Introduction
Whilst these technologies have been found to be highly effective for measuring yield in row crops [16; 17; 18; 19; 20], generally attributed to harvest index (HI) (i.e., fraction of biomass allocated to yield components divided by the total above ground biomass) [21; 22; 23], similar studies in perennial fruit tree crops, such as citrus [24; 25], apple [7; 26], pear [27], peach [28], olives [28], mango [29], and grapevines [30; 31] have produced varying levels of success. For avocado, there has only been limited remote sensing research investigating fruit size and yield mapping as well as tree number auditing [4]. It is hypothesised that remote sensing may provide an accurate measure of avocado yield via the relationship between tree canopy size/health measured after final fruit set. The recent advent of very high resolution (VHR) satellites, such as WorldView-2 (WV2) (1.85 m multispectral resolution), WorldView-3 (WV3) (1.2 m multispectral resolution) and GeoEYE-1 (1.84 m resolution), allows the spectral reflectance characteristics of individual tree canopies to be measured. Additionally, the increased spectral resolution of these sensors i.e., up to 16 spectral wavebands for WV3, and the high temporal resolution support an increased capacity to measure variations in tree crop health. Vegetation indices (VIs) derived from multispectral imagery have been used effectively to predict yield in many cropping systems. These VIs are dimensionless, radiometric measures usually formed from combinations of two wavebands in visible and NIR spectrum that function as indicators of relative abundance and activity of green vegetation [32]. Two of the earliest and most commonly used VIs are the normalized difference vegetation index (NDVI) [33] and simple ratio (SR = NIR/Red) [34]. However, with the advancement of multispectral and hyperspectral imagery, there is greater capacity to calculate a large number of structural and pigment based VIs that better correlate to crop attributes and yield parameters [35]. If a large number of potential predictors are available for model development, variable selection and dimension reduction is an important first step. Principal component analysis (PCA) and Partial least squares (PLS) are two popular methodologies when some of the predictive variables might be correlated [36], such as the VIs used in this study. When the key area of application is to reduce the number of variables while maximizing the separation of classes, such as in a yield forecasting scenario, PLS is a better approach [37] as it accounts for both the explanatory and predicted variable as part of the process. In contrast, the use of PCA combined with biplots allows selection of a subset of those VIs with the strongest correlation with yield and least correlation with the other VIs. Once identified, regression models can be fit to mathematically relate the selected VIs to yield. This feature selection approach facilities greater understanding of VI importance whilst also reducing the data sampling requirements of the model. This paper investigates the accuracies of the high spatial resolution WV imagery for measuring the spatial and temporal variability of avocado yield and fruit size on the individual tree and orchard level. More specifically, the objectives are (i) to determine if the yield parameters of avocado (average fruit size (g) per tree and total fruit weight (kg\(\cdot\)tree\({}^{-1}\))) are correlated to the canopy reflectance characteristics extracted from high resolution satellite imagery, (ii) to identify optimal VIs and specific algorithms that strongly correlate to measured yield parameters, and (iii) to extrapolate those algorithms to estimate total yield as a validation of the model and to derive yield maps at the orchard block level.