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Hyperspectral time series datasets of maize during the grain filling period
BMC Research Notes volume 15, Article number: 152 (2022)
Remotely sensed hyperspectral data are increasingly being used to assess crop development and growth throughout the growing season. Large datasets capturing key growth stages can be useful to researchers studying many physiological plant responses. A time series analysis of hyperspectral reflectance measurements taken during the grain filling period and published within a publicly accessible database are described herein. These datasets document the spectral reflectance pattern of the canopy within the visible and near-infrared portion of the electromagnetic spectrum during the late stages of the grain filling period as plants approach and reach physiological maturity.
Included within the data repository are canopy-level hyperspectral datasets collected in 2017 and 2018. Data is included in its raw form, as well as with several manipulations to smooth and standardize the raw data. Data are released as comma separated value spreadsheets as well as Microsoft Excel open XLSX spreadsheets. These are accompanied by README text files which further describe the data and supplemental files that record hybrids used and plant phenology for each year of data collection.
Characterization of plant development and growth throughout the plant’s lifecycle using hyperspectral remote sensing technologies is an attractive alternative to many traditional phenotyping approaches . Time series analyses of crop growth is particularly useful for researchers to analyze the spectral changes occurring through different growth stages or critical phases of development . Maize (Zea mays L.) has several key growth stages: vegetative growth, flowering, the grain-filling period, and physiological maturity (a.k.a., black layer) . Many spectral reflectance-based phenotyping approaches are being examined for their utility in maize [3, 4]. In this paper we describe a set of spectral reflectance data collected from 16 single-cross maize hybrids during the later stages of growth for 2 years (2017 & 2018). This dataset specifically describes canopy reflectance in 3 nm increments from the visible through the near infrared portion of the electromagnetic spectrum starting at 3-weeks post silking and continuing until physiological maturity.
Included within the data repository are several hyperspectral reflectance datasets and their manipulations. Files follow a naming structure of YEAR_DATA TYPE_MANIPULATION (Data Files 1–20, Table 1), where YEAR can be either 2017 or 2018, DATA TYPE is either the full dataset or the data averaged for every genotype on each sampling date across replications, and MANIPULATION is raw data, data that has been standardized by dividing reflectance at every wavelength by reflectance at 849.5 nm, data smoothed by calculating a rolling average every 9.9 nm, or data which has been standardized and smoothed. Missing values from the hyperspectral sensor and skipped scans are populated with a decimal and treated as missing data for all manipulations. In addition to these spectral reflectance data files, supplementary files are also present in the repository. Data collection methods and manipulations are described in the Data File 21 README file, and variable names and descriptions are included in the Data File 22 CODEBOOK file (Table 1). Information about the single-cross hybrids used in the study and their inbred parents are found in the Data File 23 GENETIC_MATERIALS datafile (Table 1). Date of flowering (i.e., appearance of the silks) and of physiological maturity (i.e., appearance of black layer) are found in Data File 24 and 25 PHENOLOGY datafiles for 2017 and 2018 (Table 1). A SUPPLEMENTARY_IMAGES file is also included (Data File 26), which contains images of the experimental setup, examples of black layer, and an example graph of spectral reflectance (Table 1). All files contained within the data repository and their file type can be found in Table 1.
The experiment was set up as a randomized complete block design with four replications, planted on May 12, 2017 and May 10, 2018 at the Elora Research Station (Elora, Ontario; 43° 38 ′ 27.0456" N, 80° 24′ 18.6948" W). Genotypes consisted of four extremely short-season hybrids, referred to as the Set 1 hybrids, and 12 short-season hybrids referred to as the Set 2 hybrids. The inbred line parents for two of the hybrids in Set 1 and for all Set 2 hybrids are publicly available and have been genotyped . Set 1 hybrids were planted in a late planted experiment (May 29, 2017 and May 21, 2018) to sample spectral reflectance differences within a growing season. Environmental conditions such as air temperature, rainfall, wind speed and direction, and solar radiation were recorded at the Elora Weather Station, located on the Elora Research station. This information is publicly available for both 2017  and 2018 .
Canopy-level hyperspectral measurements were taken using a ground based dual-channel reflectance spectrometer (Unispec-DC; PP Systems). This is a 243-channel sensor with a spectral range of 300.4–1101.8 nm, and a spectral resolution of 3.3 nm. Scans were calibrated using a spectralon tile (spectralon 12 × 12 inch calibrated white; ASD Inc) with 99.5% reflectance across the visual and near infrared spectrum. Sampling was done within 3 h of solar noon, typically after dew was gone from plants and on days without rain. Scans were taken starting 3 weeks post silking and continued every 2 to 4 days until physiological maturity, weather permitting.
Errors and missing data were dealt with in a defined manner. For the hyperspectral data collection, when machine errors occur, they are automatically given the value 9999 within the dataset, which were then replaced with a decimal and treated as missing data. Scans that were completely missed were populated with decimals and likewise treated as missing data. Zeros in the dataset were treated as true zeros, although it is possible that some of these are machine errors or the machine rounding down extremely small values rather than true zeros.
Although both years of data were planted at the Elora Research Station, due to crop rotation practices, the field which the trial was planted in changed year-to-year, and thus different soil environments may have been present. Weather played a large role on when sampling could occur, as conditions had to be dry as to not damage the machine, leading to different lengths of time between sampling dates.
Li L, Zhang Q, Huang D. A review of imaging techniques for plant phenotyping. Sensors. 2014;14(11):20078–111. https://doi.org/10.3390/s141120078.
Zarco-Tejada PJ, Ustin SL, Whiting ML. Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agron J. 2005;97(3):641–53. https://doi.org/10.2134/agronj2003.0257.
Vina A, Gitelson AA, Rundquist DC, Keydan G, Leavitt B, Schepers J. Monitoring maize (Zea mays L.) phenology with remote sensing. Remote Sens. 2004;96:1139–47.
Sibley AM, Grassini P, Thomas NE, Cassman KG, Lobell DB. Testing remote sensing approaches for assessing yield variability among maize fields. Agron J. 2014;106(1):24–32. https://doi.org/10.2134/agronj2013.0314.
Craig V, Earl H, Sulik J, Lee EA. Hyperspectral time series datasets of maize during the grain filling period. Scholars Portal Dataverse. 2021. https://doi.org/10.5683/SP2/1ZVWFV.
Lawrence-Dill, C. Genomes to fields 2014 v.3. 3. CyVerse Data Commons. 2017. https://doi.org/10.7946/P2V888.
Agricultural and Forest Meteorology Group, Elora Research Station/Guelph Turfgrass Institute. Weather records for the Elora research station, Elora, Ontario [Canada]: Meteorological data 2017 v.5. Scholars Portal Dataverse. 2017. https://doi.org/10.5683/SP/KYKL9M.
Agricultural and Forest Meteorology Group, Elora Research Station/Guelph Turfgrass Institute. Weather records for the Elora research station, Elora, Ontario [Canada]: Meteorological data 2018 v.3. Scholars Portal Dataverse. 2017. https://doi.org/10.5683/SP/RQRDSH.
We gratefully acknowledge the contributions of Carrie Breton in data management and organization within the Scholars Portal Dataverse Repository.
This research is graciously funded by a Natural Sciences and Engineering Research Council (NSERC) Discovery Grant, and by an NSERC Collaborative Research and Development (CRD) grant with the support of Maizex Seeds Inc. The funders had no role in the design and conduct of the study, data collection, and writing of the manuscript.
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Craig, V., Earl, H., Sulik, J. et al. Hyperspectral time series datasets of maize during the grain filling period. BMC Res Notes 15, 152 (2022). https://doi.org/10.1186/s13104-022-06029-9
- Zea mays L
- Remote sensing
- Grain filling period
- Physiological maturity
- Dual-channel unispec