Abstract
In recent decades, remote sensing has increasingly been used to estimate the spatio-temporal evolution of crop biophysical parameters such as the above-ground biomass (AGB). On a local scale, the advent of unmanned aerial vehicles (UAVs) seems to be a promising trade-off between satellite/airborne and terrestrial remote sensing. This study aims to evaluate the potential of a low-cost UAV RGB solution to predict the final AGB of Zea mays. Besides evaluating the interest of 3D data and multitemporality, our study aims to answer operational questions such as when one should plan a combination of two UAV flights for AGB modeling. In this case, study, final AGB prediction model performance reached 0.55 (R-square) using only UAV information and 0.8 (R-square) when combining UAV information from a single flight with a single-field AGB measurement. The adding of UAV height information to the model improves the quality of the AGB prediction. Performing two flights provides almost systematically an improvement in AGB prediction ability in comparison to most single flights. Our study provides clear insight about how we can counter the low spectral resolution of consumer-grade RGB cameras using height information and multitemporality. Our results highlight the importance of the height information which can be derived from UAV data on one hand, and on the other hand, the lower relative importance of RGB spectral information.
Highlights
Maize, rice, and wheat provide 30% of the food calories to more than 4.5 billion people in almost 100 developing countries [1]
Adding height information to the model improves the quality of the above-ground biomass (AGB) prediction, especially after 100 days after sowing (DAS)
unmanned aerial vehicles (UAVs) data acquired more than 80 DAS does not contribute notably to the quality of the final AGB prediction (Figure 3c)
Summary
Rice, and wheat provide 30% of the food calories to more than 4.5 billion people in almost 100 developing countries [1]. At the local and farm scale, the estimation of crop biomass production is of great importance and remains one of the basic indicators to assess the performance of agricultural practices (e.g., crop response to tillage or residue management [4]), to study environmental processes in the agro-ecosystem (e.g., estimation of carbon stocks or light use efficiency [5]), to analyze plant health status (e.g., estimation of crop losses due to disease severity), to predict and plan logistical aspects (e.g., estimating feed production available on the farm, or planning grain delivery and stock in grain depots) or for the purpose of precision agriculture (e.g., site-specific N management [6]) In such a global and local context, techniques allowing for a rapid, economical and quantitative estimation of crop biomass and yield production are of great importance for accessibility risk management, global markets, policy-making, and decision-making from farm over regional to even global scale [7]. Satisfactory relationships have been proposed in the literature between remotely sensed spectral variables, usually combined in and expressed through vegetation indices (VI), and crop biophysical parameters such as phenology [9], leaf area index (LAI [10]), and above-ground biomass (AGB [11])
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