Multitemporal Chlorophyll Mapping in Pome Fruit Orchards from Remotely Piloted Aircraft Systems

Yasmin Vanbrabant,Ben Somers,Wim Verjans,Stephanie Delalieux,Laurent Tits,Klaas Pauly

doi:10.3390/rs11121468

Abstract

Early and precise spatio-temporal monitoring of tree vitality is key for steering management decisions in pome fruit orchards. Spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while manned aircraft sensor-platform systems are very expensive. In order to address the shortcomings of these platforms, this study investigates the potential of Remotely Piloted Aircraft Systems (RPAS) to facilitate rapid, low cost, and flexible chlorophyll monitoring. Due to the complexity of orchard scenery a robust chlorophyll retrieval model on RPAS level has not yet been developed. In this study, specific focus therefore lies on evaluating the sensitivity of retrieval models to confounding factors. For this study, multispectral and hyperspectral imagery was collected over pome fruit orchards. Sensitivities of both univariate and multivariate retrieval models were demonstrated under different species, phenology, shade, and illumination scenes. Results illustrate that multivariate models have a significantly higher accuracy than univariate models as the former provide accuracies for the canopy chlorophyll content retrieval of R2 = 0.80 and Relative Root Mean Square Error (RRMSE) = 12% for the hyperspectral sensor. Random forest regression on multispectral imagery (R2 > 0.9 for May, June, July, and August, and R2 = 0.5 for October) and hyperspectral imagery (0.6 < R2 < 0.9) led to satisfactory high and consistent accuracies for all months.

Highlights

In pome fruit orchards, timely management decisions rely on the early and precise localization of sub-optimally performing trees
The main objective of this study is to develop a robust and reliable canopy chlorophyll content (CCC) retrieval model for pome fruit tree monitoring using Remotely Piloted Aircraft Systems (RPAS) platforms equipped with an optical sensor
Key in our study is to evaluate the robustness of CCC retrieval models of sensors with multispectral and hyperspectral resolution against: 1. shadow—we evaluate the CCC retrieval model shade sensitivity by comparing CCC retrieval models extracted from full and sunlit signals from both sensors; 2. species—we evaluate the leaf chlorophyll content (LCC) and CCC retrieval model sensitivity of apple and pear species and both species combined from multi- and hyperspectral sensor systems; 3. phenology—we evaluate the CCC retrieval model sensitivity to phenological stages by comparing the unitemporal with the multitemporal model performance; 4. illumination differences—we evaluate the CCC retrieval model sensitivity to illumination differences by comparing the performance of unitemporal and multitemporal models on image acquisition days with cloudy and clear skies

Summary

Introduction

Timely management decisions rely on the early and precise localization of sub-optimally performing trees. Optical contact sensors for in-field measurements of chlorophyll at leaf (e.g., SPAD; Minolta Camera Co., Japan) and canopy level (e.g., LiDAR-RGB systems) [5] provide a non-destructive alternative for chlorophyll content monitoring. These techniques remain labor-intensive, covering only a limited number of samples in space and time. The spectral resolution of most traditional satellite systems is currently too coarse compared to a suggested bandwidth of less than 10 nm for precision agriculture [10]. In short, traditional remote sensing platforms do not allow to provide timely information with sufficient spatial resolution which are demanded in precision horticulture

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