Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping

Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at 5 time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into 2 groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.

Methods
The UAV system contains a DJI S900 UAV and an NIR converted multispectral Canon Rebel SL1 DSLR camera with an intervalometer and GPS. We conducted 5 UAV overflights across the growing season in 2017. Overflights were scheduled around 5 growth stages (V4, V8, V12, VT, and R5). The following image processing steps were applied to obtain high quality data from the raw UAV images: image pre-processing, orthomosaic generation, VIs calculation, and plot-level data extraction. The plot-level NDVI mean was calculated from the reflectance measurements in the red and NIR portion of the spectrum from the transect area of each plot. NDVI values generated on the -1 to 1 scale were rescaled by adding 1 and then multiplying by 128 to convert them into the [0 – 255] range. Time series NDVI values were obtained from five overflights for 1,752 diverse maize accessions. GWAS was conducted for the static NDVI values and the curve parameters derived across stages.