Abstract
Crop above-ground biomass (AGB) is a key parameter used for monitoring crop growth and predicting yield in precision agriculture. Estimating the crop AGB at a field scale through the use of unmanned aerial vehicles (UAVs) is promising for agronomic application, but the robustness of the methods used for estimation needs to be balanced with practical application. In this study, three UAV remote sensing flight missions (using a multiSPEC-4C multispectral camera, a Micasense RedEdge-M multispectral camera, and an Alpha Series AL3-32 Light Detection and Ranging (LiDAR) sensor onboard three different UAV platforms) were conducted above three long-term experimental plots with different tillage treatments in 2018. We investigated the performances of the multi-source UAV-based 3D point clouds at multi-spatial scales using the traditional multi-variable linear regression model (OLS), random forest (RF), backpropagation neural network (BP), and support vector machine (SVM) methods for accurate AGB estimation. Results showed that crop height (CH) was a robust proxy for AGB estimation, and that high spatial resolution in CH datasets helps to improve maize AGB estimation. Furthermore, the OLS, RF, BP, and SVM methods all maintained an acceptable accuracy for AGB estimation; however, the SVM and RF methods performed slightly more robustly. This study is expected to optimize UAV systems and algorithms for specific agronomic applications.
Highlights
Maize, as one of the most dominant global food crops, together with wheat, rice, soybeans, barley, and sorghum, accounts for over 40% of global cropland, 55% of non-meat calories, and over 70% of animal feed [1,2]
The results show that crop height (CH) correlated significantly with both the fresh and dry Above-ground biomass (AGB), with r values of 0.92 and 0.90 (p < 0.01, n = 89), respectively, indicating that using CH as an important 3D parameter of canopy structure was feasible for AGB estimation
The results of this study demonstrated that crop height was an essential parameter for the accurate estimation of AGB, and that the high spatial resolution of CH datasets is a key factor for precise maize AGB estimation
Summary
As one of the most dominant global food crops, together with wheat, rice, soybeans, barley, and sorghum, accounts for over 40% of global cropland, 55% of non-meat calories, and over 70% of animal feed [1,2]. Destructive sampling is the most precise method for measuring vegetation AGB at a field-scale, but it is direct and reliable, it is labor-intensive and time consuming for use in large study areas. Remote sensing systems (i.e., satellite-, airborne-, ground-based platforms and sensors) have been already applied as non-destructive methods to monitor the growth status of vegetation [8,9,10]. Ground-based spectrometers can fulfill the practical requirements of high spatial and spectral resolutions, but are limited with regard to use in large study areas and the dependence on soil conditions [15]. Unmanned aerial vehicles (UAVs), characterized by low cost and easy use, can capture information concerning crop growth at a fine spatial resolution (centimeter-level), and have become an emerging tool applied in agronomic study at the field-scale over recent years [16]
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