Abstract
Above-ground biomass (AGB) is an important indicator for effectively assessing crop growth and yield and, in addition, is an important ecological indicator for assessing the efficiency with which crops use light and store carbon in ecosystems. However, most existing methods using optical remote sensing to estimate AGB cannot observe structures below the maize canopy, which may lead to poor estimation accuracy. This paper proposes to use the stem-leaf separation strategy integrated with unmanned aerial vehicle LiDAR and multispectral image data to estimate the AGB in maize. First, the correlation matrix was used to screen optimal the LiDAR structural parameters (LSPs) and the spectral vegetation indices (SVIs). According to the screened indicators, the SVIs and the LSPs were subjected to multivariable linear regression (MLR) with the above-ground leaf biomass (AGLB) and above-ground stem biomass (AGSB), respectively. At the same time, all SVIs derived from multispectral data and all LSPs derived from LiDAR data were subjected to partial least squares regression (PLSR) with the AGLB and AGSB, respectively. Finally, the AGB was computed by adding the AGLB and the AGSB, and each was estimated by using the MLR and the PLSR methods, respectively. The results indicate a strong correlation between the estimated and field-observed AGB using the MLR method (R2 = 0.82, RMSE = 79.80 g/m2, NRMSE = 11.12%) and the PLSR method (R2 = 0.86, RMSE = 72.28 g/m2, NRMSE = 10.07%). The results indicate that PLSR more accurately estimates AGB than MLR, with R2 increasing by 0.04, root mean square error (RMSE) decreasing by 7.52 g/m2, and normalized root mean square error (NRMSE) decreasing by 1.05%. In addition, the AGB is more accurately estimated by combining LiDAR with multispectral data than LiDAR and multispectral data alone, with R2 increasing by 0.13 and 0.30, respectively, RMSE decreasing by 22.89 and 54.92 g/m2, respectively, and NRMSE decreasing by 4.46% and 7.65%, respectively. This study improves the prediction accuracy of AGB and provides a new guideline for monitoring based on the fusion of multispectral and LiDAR data.
Highlights
Maize is one of the main food crops today and is planted on a large scale worldwide
This paper proposes a method to estimate the above-ground biomass (AGB) of maize based on estimates of above-ground leaf biomass (AGLB) and above-ground stem biomass (AGSB) from multispectral data and LiDAR point cloud data, respectively, with both acquired from a unmanned aerial vehicle (UAV) platform
According to the screened indicators, the spectral vegetation indices (SVIs) derived from multispectral data and the LiDAR structural parameters (LSPs) derived from LiDAR data were subjected to multivariable linear regression (MLR) with the AGLB and AGSB, respectively
Summary
Maize is one of the main food crops today and is planted on a large scale worldwide. The timely and effective access to high-resolution spatial crop-development information provides important guidance for precision agricultural management, which allows the implementation of effective fertilization programs (Cilia et al, 2014; Gracia-Romero et al, 2017; Samborski, Tremblay & Fallon, 2009), irrigation measures (Barker et al, 2018; Ma et al, 2018; Maresma, Lloveras & Martinez-Casasnovas, 2018), and early production forecasts (Elazab et al, 2016; Kitchen et al, 2003; Vergara-Diaz et al, 2016). Unmanned aerial vehicles have been key to solve different problems in agriculture, which require high-precision crop data, such as crop pest detection (Albetis et al, 2017), crop yield estimation (Zhou et al, 2017), and crop variable measurement (Bendig et al, 2015). This approach allows high-efficiency and dynamic remote sensing monitoring of large areas in a convenient and nondestructive manner and with high throughput. Remote sensing from UAVs has become an important part of precision agriculture (Liebisch et al, 2015; Marshall & Thenkabail, 2015; Yang et al, 2017)
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