DOI: 10.5061/dryad.v41ns1s4z
title: Multitemporal multispectral imagery for rice yield and phenology prediction

description: Timeseries data captured by unoccupied aircraft systems (UASs) are
 increasingly used for agricultural applications requiring accurate
 prediction of plant phenotypes from remotely-sensed imagery. This
 benchmark dataset for rice supports the development of improved analytical
 approaches for phenotype prediction from multispectral timeseries of drone
 imagery. The dataset includes five experiments conducted at the
 USDA-ARS Dale Bumpers National Rice Research Center in Stuttgart, AR in
 2021 and 2022: two nitrogen rate studies, a private hybrid study, an
 inbred study, and a genetic diversity study. A randomized block design was
 established in both years, with 252 total plots in 2021 and 180 plots in
 2022. Plots were imaged at 12 timepoints throughout the season in both
 years. The dataset includes images for each plot as well as extracted
 features (49 features including vegetation indices, texture properties,
 and thermal features), and per-plot yield and phenology data.
description: A team of licensed and certified drone pilots collected UAS
 imagery during the 2021 and 2022 growing seasons. Multispectral and
 thermal imagery data were acquired using the DJI Matrice 210 V2 quadcopter
 (DJI, Shenzhen, Nanshan District, China) equipped with an Altum sensor
 (MicaSense, Seattle, Washington). The Altum sensor captured six bands
 across the electromagnetic spectrum, including blue (459-491 nm), green
 (547-573 nm), red (661-675 nm), red edge (711-723 nm), near-infrared
 (814-870 nm), and long-wave infrared (8,000-14,000 nm). Data were
 collected at approximately weekly intervals, weather permitting, within
 two hours of solar noon. Prior to each flight, images of a calibrated
 reflectance panel were collected using the Altum sensor and the
 downwelling light sensor (DLS) to generate radiometrically calibrated
 reflectance maps. Flights were conducted at a height of 120 m above ground
 level, providing a spatial resolution of 5 cm/px for the spectral bands
 and 81 cm/px for the thermal band (upsampled to 5 cm/px). In addition, the
 flight plans had a 75% front/side overlap to provide more consistent and
 smooth outputs. The Pix4Dmapper software (Pix4D SA, Switzerland) was used
 to perform radiometric calibrations and corrections, stitch raw imagery,
 and generate mosaic reflectance maps for each spectral band.
 <strong>Agronomic information:</strong>
 Genetic diversity study (2021): 
 Planted May 7, 2021 and emerged May 19, 2021.
 Fertilized June 14, 2021.
 Flooded June 15, 2021 and drained Sep. 8,
 2021. Harvested Sep. 15,
 2021. Hybrid/inbred study
 (2021) Planted May 7, 2021 and
 emerged May 19, 2021. Fertilized
 June 14, 2021. Flooded June 15,
 2021 and drained Sep. 1, 2021. 
 Harvested Sep. 13 
 Nitrogen rate study, early commercial varieties (2021)
 Planted May 7, 2021 and emerged May 20,
 2021. Fertilized June 14,
 2021. Flooded June 15, 2021 and
 drained Sep. 7, 2021. Nitrogen
 rate study, late commercial varieties (2021) 
 Planted May 7, 2021 and emerged May 20,
 2021. Fertilized June 14,
 2021. Flooded June 15, 2021 and
 drained Sep. 3, 2021. Harvested
 Sep. 10 (low N rate) or 9/13 (rest of N rate) 
 Genetic diversity study (2022): 
 Planted May 2, 2022 and emerged May 12,
 2022. Fertilized June 14,
 2022. Flooded June 14, 2022 and
 drained Sep. 8, 2022. Harvested
 Sep. 15, 2022. Hybrid/Inbred
 study (2022): Planted May 2, 2022
 and emerged May 13, 2022. 
 Fertilized June 14, 2022. 
 Flooded June 14, 2022 and drained Sep. 8, 2022.
 Harvested Sep. 14, 2022.
 Nitrogen rate study, early commercial
 varieties (2022) Planted May 2,
 2022 and emerged May 13, 2022. 
 Fertilized June 14, 2022. 
 Flooded June 14, 2022 and drained Sep. 6, 2022.
 Harvested Sep. 14, 2022.
 Nitrogen rate study, late commercial
 varieties (2022) Planted May 2,
 2022 and emerged May 12, 2022. 
 Fertilized June 14, 2022. 
 Flooded June 14, 2022 and drained Sep. 8, 2022.
description: # Multitemporal multispectral imagery for rice yield and phenology
 prediction
 [https://doi.org/10.5061/dryad.v41ns1s4z](https://doi.org/10.5061/dryad.v41ns1s4z) The dataset includes five experiments conducted at the USDA-ARS Dale Bumpers National Rice Research Center in Stuttgart, AR in 2021 and 2022: two nitrogen rate studies, a private hybrid study, an inbred study, and a genetic diversity study. A randomized block design was established in both years, with 252 total plots in 2021 and 180 plots in 2022. Plots were imaged at 12 timepoints throughout the season in both years. ## Description of the data and file structure The dataset includes images for each plot as well as extracted features (49 features including vegetation indices, texture properties, and thermal features), and per-plot yield and phenology data. Images are named by plot within separate directories for each flyover date. Test and train sets are based on a 'leave-one-replicate-out' approach, where one replicate plot for each experimental treatment was randomly assigned to the test set. Additionally, the dataset includes weather data at the site for 2021 and 2022 (`RREC_DAILY_2021.xlsx` and `RREC_DAILY_2022.xlsx`). ### File structure ``` Agronomic |- 2021/ | |- constant_agronomic_traits_2021.csv |- 2022/ | |- constant_agronomic_traits_2022.csv Images |- Train/ | |- Flyover-date/ | | |- Plot-number/ | | | |- blue [.tfw, .tiff, .tiff.ovr, .tiff.aux.xml, .tiff.xml] | | | |- green [.tfw, .tiff, .tiff.ovr, .tiff.aux.xml, .tiff.xml] | | | |- red [.tfw, .tiff, .tiff.ovr, .tiff.aux.xml, .tiff.xml] | | | |- red_edge [.tfw, .tiff, .tiff.ovr, .tiff.aux.xml, .tiff.xml] | | | |- nir [.tfw, .tiff, .tiff.ovr, .tiff.aux.xml, .tiff.xml] |- Test/ | |- Flyover-date/ | | |- Plot-number/ | | | |- blue [.tfw, .tiff, .tiff.ovr, .tiff.aux.xml, .tiff.xml] | | | |- green [.tfw, .tiff, .tiff.ovr, .tiff.aux.xml, .tiff.xml] | | | |- red [.tfw, .tiff, .tiff.ovr, .tiff.aux.xml, .tiff.xml] | | | |- red_edge [.tfw, .tiff, .tiff.ovr, .tiff.aux.xml, .tiff.xml] | | | |- nir [.tfw, .tiff, .tiff.ovr, .tiff.aux.xml, .tiff.xml] ``` ### Agronomic and trait data #### Column headers: Seeding_Rate: seeding rate in kilograms/hectare Nitrogen_Rate: fertilizer rate in lbs/acre Yield: yield in metric tons/hectare Emergence_Date_DOY: emergence date, Julian day Heading_25: days from emergence to 25% heading Heading_50: days from emergence to 50% heading Heading_100: days from emergence to 100% heading Final_Lodge: % lodging final ### Weather data #### Tabs: `UAV growing season` is a subset of daily precipitation and temperature values from `RREC DAILY 2021` or `RREC DAILY 2022` during the growing season. #### Abbreviations: PAR: photosynthetically active radiation Deg: degree RH: relative humidity ## Code/Software Code to reproduce the analyses is available at [https://github.com/FareedFarag/TPPJ-Modeling-Code](https://github.com/FareedFarag/TPPJ-Modeling-Code). A Geographic Information System (GIS) such as [QGIS](https://www.qgis.org) is required to open the image files.