DOI: 10.5061/dryad.905qftttm
title: Crop performance, aerial, and satellite data from multistate maize yield trials

description: Accurate genotype-specific early yield estimates at fields and plots offer
 potential benefits to farmers in optimizing their agronomic practices,
 breeders in screening hundreds and thousands of varieties, and
 policymakers in decisions contributing to the overall improvement of
 agriculture and food production systems. Effective, generalizable
 approaches to track plant growth and predict yield at the individual plot
 level require large matched datasets of remote sensing and ground truth
 data collected across multiple environments. Low-altitude drone flights
 are increasingly being used to collect data from field evaluations of new
 crop varieties, while satellite imagery is being explored to track yield
 and management practices at the regional and field scales. Despite their
 lower spatial resolution, satellite platforms exhibit multiple logistical
 and technical advantages in scalability and accessibility, and could
 facilitate plot-level predictions, especially with steadily improving
 spatial resolution. However, genotype-specific, plot-level,
 high-resolution satellite images from multiple environments integrated
 with the ground truth measurements are not yet publicly available. Here we
 generated, described, and evaluated a set of more than 20,000 plot-level
 images of over 80 hybrid maize (Zea mays) varieties grown in six locations
 across the US corn belt under various management practices collected from
 (near simultaneous) satellite and drone flights integrated with ground
 truth measurements of crop yield. Of the six baseline models examined,
 models employing data collected from satellite images often matched or
 exceeded the performance of models employing data collected from drones
 for both within-environment and cross-environment yield prediction. Large,
 multimodal, multi-environment, genetically diverse training datasets such
 as those generated in this study, along with more complex models could
 help unlock the power of satellite imagery as an important new addition to
 the tool of farmers, plant geneticists, crop breeders, and policymakers.
description: UAV Image Acquisition and Processing UAV
 visible spectral (RGB) imagery was collected at three time points per
 location. The goal was to acquire images of maize during the vegetative,
 reproductive, and post-flowering growth stages from the fields at each
 location, capturing images at three different time points (Supplemental
 Data Set S1). In Scottsbluff, NE, images were acquired with DJI Matrice
 600 Pro with DJI Zenmuse X3 and a 12 Mega Pixel (MP) RGB (red, green,
 blue) camera as an image acquisition sensor. Images were acquired at an
 altitude of 100 ft (30.48 m) with a front overlap of 90% and a side
 overlap of 65%. In North Platte, images were acquired using DJI Inspire 2
 with a Sentra Double 4K AG+ RGB camera as an image sensor at an altitude
 of 50 ft (15.24 m) with front and side overlap of 70%. In Lincoln, images
 were acquired with a DJI Phantom 4 RTK with a DJI Zenmuse P1 camera, with
 a 45 MP RGB camera as an image acquisition sensor. Images were acquired at
 an altitude of 115 ft (35 m) with front and side overlap of 80%. In
 Missouri Valley, Ames, and Crawfordsville, IA, DJI Phantom 4 Pro V2.0 with
 DJI 20 MP RGB cameras was used as an image acquisition sensor, and images
 were acquired at an altitude of 100 ft (30.48 m) with front and side
 overlap of 80%. The UAV images were processed and stitched using Pix4D
 Mapper 4.8.4 (Pix4D 2024) and AgiSoft Metashape 1.8.4 (Agisoft Metashape
 2024), photogrammetric software to create RGB orthomosaic images using
 default parameters during image processing.  Satellite
 Image Acquisition Pléiades Neo was used to capture
 images at all locations at six different time points (approximately two
 weeks apart), with the first three time points close to the dates of the
 three UAV image acquisitions at each location. Table 1 shows the
 specifications of this satellite constellation. The average widths of the
 six bands in satellite multispectral images are as follows: Red (620 – 690
 nm), Green (530 – 590 nm), Blue (450 – 520 nm), Near-infrared (NIR, 770 –
 880 nm), Red Edge (700 – 750 nm), and Deep Blue (400 – 450 nm). Along with
 multispectral images, a single-band panchromatic raster file with a wide
 width band of approximately 450-800 nm was generated. Each image captured
 a total area of 100 km × 100 km per location, covering the entire
 experimental field at each location simultaneously. Final 16-bit GeoTIFF
 satellite images with 30-cm resolution were generated and provided to us
 after panchromatic sharpening or pan-sharpening using panchromatic band
 image files, manual ortho-rectification, and atmospheric correction by
 Pleiades Neo.  Assigning Plot-level Labels to
 Images For precise segmentation of small plots from
 satellite images, a single UAV image captured at a single time point at
 each location was used as a reference for plot segmentation due to the low
 resolution of the satellite image.  With available UAV images, before the
 plot-level segmentation, satellite images were registered to the UAV image
 captured at the nearest date to the date of satellite image capture using
 ArcGIS Pro 10.8.2 through georeferencing. Registration was performed using
 the ground control points installed at either side of each field when
 available. able field features in satellite and UAV images were used for
 accurate registration as much as possible. After registration, the
 first-time point registered UAV and satellite image pair were used for
 plot segmentation for each location. All computation was performed using
 the ArcPy Jupyter Notebook environment implemented in ArcGIS Pro V3.2.0.
 Plot grids generated as described above were used to crop field images
 into plots. For each plot grid, a minimum bounding rectangle with four
 corners of rectangles surrounding/enclosing the plot grids was generated.
 When pixels inside the rectangle lacked values due to the geographical
 orientation of the fields, those regions were masked with zero pixel
 values for further data acquisition. Plot grids generated from the images
 at the first time point were used to segment the corresponding satellite
 images within the same location from all other time points.
description: # Crop performance, aerial, and satellite data from multistate maize yield
 trials
 [https://doi.org/10.5061/dryad.905qftttm](https://doi.org/10.5061/dryad.905qftttm) Maize (*Zea mays*) field experiments were conducted at five locations in 2022: Scottsbluff, NE, Lincoln, NE, Missouri Valley, IA, and Ames, IA with two-thirds of the experiment in one field and one-third of the experiment in another field and Crawfordsville, IA. This file describes the organization and format of the satellite, UAV, and ground truth data published in Shrestha et al. In addition, we provide an example code for searching and retrieving images from individual hybrid corn research plots as well as calculating the wavelength intensity values and indices employed in our study. ## Directory Layout: * **Satellite** * Five directories correspond to five of the six locations from which satellite imagery was collected (North Platte, NE was excluded because of issues with plot segmentation). * Six directories numbered sequentially "TP1", "TP2", "TP3", "TP4", "TP5", and "TP6" corresponding to the order of satellite images collected at each location. Note that the timing of time points with the same number are not identical across locations, and are not guaranteed to be similar. The time of acquisition for each time point at each location is provided in the file **DateofCollection.xlsx** in the folder **"GroundTruth"**. * .TIF formatted images segmented for each plot at a given location at a given time point. Rows often were at orientations other than 0 or 90 degrees in satellite images. Segmented images consist of the minimum bounding box encompassing the plot of interest, with real pixel data for all plot pixels and zero values entered for all pixels within the minimum bounding box but not part of the plot of interest. * Plot image names follow the format location-time-experiment_range_row.tif (Lincoln-TP1-hybirds_2_2.TIF). An example implementation of how to map between image names, plot IDs, and ground truth information is provided later in this document. The necessary information to map between ground truth, plot ID, and images is provided in the file **HYBRID_HIPS_V3.5_ALLPLOTS.csv** located in the **"GroundTruth"** folder. * Each image contains data on six bands per pixel: near-infrared, red edge, red, green, blue, and deep blue. * **FieldLevelImages** contains field level images before cropping/segmenting to produce plot level images with copyright attribution **© Airbus DS (2022)**. .PNG formatted images names follow location-time-experiment.PNG (Lincoln-TP1-hybrids.PNG). * **UAV** * Five directories corresponding to five of the six locations from which UAV imagery was collected (as with the satellite images, North Platte, NE UAV images were excluded because of issues with plot segmentation). * Three directories numbered sequentially "TP1", "TP2", and "TP3" corresponding to the order of UAV flights conducted at each location. Note that the timing of time points with the same number is not identical, and is not guaranteed to be similar across locations. The time of acquisition for each time point at each location is provided in the file **DateofCollection.xlsx** in folder **"GroundTruth"**. * .PNG formatted images segmented for each plot at a given location at a given time point. Rows often were at orientations other than 0 or 90 degrees in UAV image mosaics. Segmented images consist of the minimum bounding box encompassing the plot of interest, with real pixel data for all plot pixels and zero values entered for all pixels within the minimum bounding box but not part of the plot of interest. * Plot image names follow the format location-time-experiment_range_row.png (Crawfordsville-TP1-4351_3_15.PNG). An example implementation of how to map between image names, plot IDs, and ground truth information is provided later in this document. The necessary information to map between ground truth, plot ID, and images is provided in the file **HYBRID_HIPS_V3.5_ALLPLOTS.csv** located in the **"GroundTruth"** folder. * Each image contains data on three bands per pixel: red, green, and blue. * **GroundTruth** * **HYBRID_HIPS_V3.5_ALLPLOTS.csv** contains one record per hybrid maize plot grown at each of the six locations in this study. Each record includes information on the field, location within the field (row and column), the hybrid genotype planted in that plot, and a set of ground truth data collected from that plot. The individual ground truth measurements are defined below * plantingDate: the date the plot was planted. * totalStandCount: The number of living plants observed in the middle two rows of the four-row plot. Note that plots are variable length between locations, so this needs to be corrected for plot size to calculate the density of plants per unit area. * daysToAnthesis: The difference between the planting date and the first date where at least 50% of living plants in the plot had visible anthers present on their tassels (syn. "male flowering"). Not present for all locations. * GDDToAnthesis: This is a method for quantifying flowering time that corrects for the fact plants grow faster on warm days and slower in the cold. GDD stands for growing degree days. The number of growing degree days per day was calculated using temperatures in Fahrenheit with a crop base temperature of 50 degrees and a crop maximum temperature of 86 degrees. * yieldPerAcre: estimated grain yield per plot. This measurement starts with the direct measurement of grain pass per plot and grain moisture percentage and then corrects for variation in moisture content and differences in plot size across locations to calculate bushels per acre yield at a standardized 15.5% moisture content. * **DateofCollection.xlsx** translates location + TP1/2/3/etc into the specific date of image collection for both UAV and Satellite images collected at all locations included in this study. * **Documentation** * Documentation.ipynb: example code for extracting images, calculating different indices, and linking images/indices to the ground truth data. * Readme.md: explaination of data types and layout.