The utility of very-high resolution unmanned aerial vehicles (UAV) imagery in monitoring the spatial and temporal variations in leaf moisture content of smallholder maize farming systems.
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Date
2021
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Abstract
Maize moisture stress, resulting from rainfall variability, is a primary challenge in the
production of rain-fed maize farming, especially in water-scarce regions such as southern
Africa. Quantifying maize moisture variations throughout the growing season can support
agricultural decision-making and prompt the rapid and robust detection of smallholder maize
moisture stress. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral
sensors, provide spatially explicit near real-time information for determining maize moisture
content at farm scale. Therefore, this study evaluated the utility of UAV derived multispectral
imagery in estimating maize leaf moisture content indicators on smallholder farming systems
throughout the maize growing season. The first objective of the study was to conduct a
comparative analysis in order to evaluate the performance of five regression techniques
(support vector regression, random forest regression, decision trees regression, artificial neural
network regression and the partial least squares regression) in predicting maize water content
indicators (i.e. equivalent water thickness (EWT), fuel moisture content (FMC) and specific
leaf area (SLA)), and determine the most suitable indicator of smallholder maize water content
variability based on multispectral UAV data. The results illustrated that both NIR and red-edge
derived spectral variables were critical in characterising maize moisture indicators on
smallholder farms. Furthermore, the best models for estimating EWT, FMC and SLA were
derived from the random forest regression algorithm with a relative root mean square error
(rRMSE) of 3.13%, 1% and 3.48 %, respectively. Additionally, EWT and FMC yielded the
highest predictive performance of maize leaf moisture and demonstrated the best correlation
with remotely sensed data. The study’s second objective was to evaluate the utility of UAVderived
multispectral imagery in estimating the temporal variability of smallholder maize
moisture content across the maize growing season using the optimal maize moisture indicators.
The findings illustrated that the NIR and red-edge wavelengths were influential in
characterising maize moisture variability with the best models for estimating maize EWT and
FMC resulting in a rRMSE of 2.27 % and 1%, respectively. Furthermore, the early reproductive
stage was the most optimal for accurately estimating maize EWT and FMC using UAVproximal
remote sensing. The findings of this study demonstrate the prospects of UAV- derived
multispectral data for deriving insightful information on maize moisture availability and overall
health conditions. This study serves as fundamental step towards the creation of an early maize moisture stress detection and warning systems, and contributes towards climate change adaptation and resilience of smallholder maize farming.
Description
Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.