摘要
Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years for agricultural research. High spatial and temporal resolution images obtained with UAVs are ideal for many applications in agriculture. The objective of this study was to evaluate the performance of vegetation indices (VIs) derived from UAV images for quantification of plant nitrogen (N) concentration of spring wheat, a major cereal crop worldwide. This study was conducted at three locations in Idaho, United States. A quadcopter UAV equipped with a red edge multispectral sensor was used to collect images during the 2016 growing season. Flight missions were successfully carried out at Feekes 5 and Feekes 10 growth stages of spring wheat. Plant samples were collected on the same days as UAV image data acquisition and were transferred to lab for N concentration analysis. Different VIs including Normalized Difference Vegetative Index (NDVI), Red Edge Normalized Difference Vegetation Index (NDVIred edge), Enhanced Vegetation Index 2 (EVI2), Red Edge Simple Ratio (SRred edge), Green Chlorophyll Index (CIgreen), Red Edge Chlorophyll Index (CIred edge), Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) and Red Edge Triangular Vegetation Index (core only) (RTVIcore) were calculated for each flight event. At Feekes 5 growth stage, red edge and green based VIs showed higher correlation with plant N concentration compare to the red based VIs. At Feekes 10 growth stage, all calculated VIs showed high correlation with plant N concentration. Empirical relationships between VIs and plant N concentration were cross validated using test data sets for each growth stage. At Feekes 5, the plant N concentration estimated based on NDVIred edge showed one to one correlation with measured N concentration. At Feekes 10, the estimated and measured N concentration were highly correlated for all empirical models, but the model based on CIgreen was the only model that had a one to one correlation between estimated and measured plant N concentration. The observed high correlations between VIs derived from UAV and the plant N concentration suggests the significance of VIs deriving from UAVs for within-season N concentration monitoring of agricultural crops such as spring wheat.
Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years for agricultural research. High spatial and temporal resolution images obtained with UAVs are ideal for many applications in agriculture. The objective of this study was to evaluate the performance of vegetation indices (VIs) derived from UAV images for quantification of plant nitrogen (N) concentration of spring wheat, a major cereal crop worldwide. This study was conducted at three locations in Idaho, United States. A quadcopter UAV equipped with a red edge multispectral sensor was used to collect images during the 2016 growing season. Flight missions were successfully carried out at Feekes 5 and Feekes 10 growth stages of spring wheat. Plant samples were collected on the same days as UAV image data acquisition and were transferred to lab for N concentration analysis. Different VIs including Normalized Difference Vegetative Index (NDVI), Red Edge Normalized Difference Vegetation Index (NDVIred edge), Enhanced Vegetation Index 2 (EVI2), Red Edge Simple Ratio (SRred edge), Green Chlorophyll Index (CIgreen), Red Edge Chlorophyll Index (CIred edge), Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) and Red Edge Triangular Vegetation Index (core only) (RTVIcore) were calculated for each flight event. At Feekes 5 growth stage, red edge and green based VIs showed higher correlation with plant N concentration compare to the red based VIs. At Feekes 10 growth stage, all calculated VIs showed high correlation with plant N concentration. Empirical relationships between VIs and plant N concentration were cross validated using test data sets for each growth stage. At Feekes 5, the plant N concentration estimated based on NDVIred edge showed one to one correlation with measured N concentration. At Feekes 10, the estimated and measured N concentration were highly correlated for all empirical models, but the model based on CIgreen was the only model that had a one to one correlation between estimated and measured plant N concentration. The observed high correlations between VIs derived from UAV and the plant N concentration suggests the significance of VIs deriving from UAVs for within-season N concentration monitoring of agricultural crops such as spring wheat.
