Abstract
Abstract. Canopy height (CH) and leaf area index (LAI) provide key information about crop growth and productivity. A rapid and accurate retrieval of CH and LAI is critical for a variety of agricultural applications. LiDAR and RGB photogrammetry have been increasingly used in plant phenotyping in recent years thanks to the developments in Unmanned Aerial Vehicle (UAV) and sensor technology. The goal of this study is to investigate the potential of UAV LiDAR and RGB photogrammetry in estimating crop CH and LAI. To this end, a high resolution 32 channel LiDAR and RGB cameras mounted on DJI Matrice 600 Pro UAV were employed to collect data at sorghum fields near Maricopa, Arizona, USA. A series of canopy structure metrics were extracted using LiDAR and RGB photogrammetry-based point clouds. Random Forest Regression (RFR) models were established based on the UAV-LiDAR and photogrammetry-derived metrics and field-measured LAI. The results show that both UAV-LiDAR and RGB photogrammetry demonstrated promising accuracies in CH extraction and LAI estimation. Overall, UAV-LiDAR yielded superior performance than RGB photogrammetry in both low and high canopy density sorghum fields. In addition, Pearson’s correlation coefficient, as well as RFR-based variable importance analysis demonstrated that height-based metrics from both LiDAR and photogrammetric point clouds were more useful than density-based metrics in LAI estimation. This study proved that UAV-based LiDAR and photogrammetry are important tool in sustainable field management and high-throughput phenotyping, but LiDAR is more accurate than RGB photogrammetry due to its greater canopy penetration capability.
Highlights
Monitoring crop growth and development is of great significance for agricultural studies (Weiss et al, 2020)
With high spatial resolution, which fulfils the requirements for fine-scale applications, as well as high flexibility and controllability in data collection compared to satellite remote sensing, recently emerged Unmanned Aerial Vehicle (UAV) has advanced the applications of remote sensing technologies, especially in precision agriculture and high-throughput field phenotyping (Maimaitijiang et al, 2020b; ten Harkel et al, 2020)
Canopy structure matrices extracted from LiDAR or RGB photogrammetry point clouds were used as input variables for Random Forest Regression (RFR), to estimate sorghum leaf area index (LAI)
Summary
Monitoring crop growth and development is of great significance for agricultural studies (Weiss et al, 2020). An accurate retrieval of crop growth parameters such as CH and LAI with high efficiency and low cost is critical, in precision agriculture and high-throughput field phenotyping. Field-based direct measurement of CH and LAI is accurate but often labour-intensive, time-consuming and destructive, while remote sensing techniques provide rapid and non-destructive measurements at larger spatial and higher temporal scales, and have been known as alternative approaches (Tao et al, 2020). With high spatial resolution, which fulfils the requirements for fine-scale applications (i.e., agricultural field or plot scale), as well as high flexibility and controllability in data collection compared to satellite remote sensing, recently emerged Unmanned Aerial Vehicle (UAV) has advanced the applications of remote sensing technologies, especially in precision agriculture and high-throughput field phenotyping (Maimaitijiang et al, 2020b; ten Harkel et al, 2020).
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