# What Does TERRA-REF’s High Resolution, Multi Sensor Plant Sensing Public
Domain Data Offer the Computer Vision Community?

David LeBauer
University of Arizona
<EMAIL_ADDRESS>Max Burnette
University of Illinois
<EMAIL_ADDRESS>
Noah Fahlgren
Donald Danforth Plant Science Center
<EMAIL_ADDRESS>Rob Kooper
University of Illinois
<EMAIL_ADDRESS>Kenton McHenry
University of Illinois
<EMAIL_ADDRESS>Abby Stylianou
St. Louis University
<EMAIL_ADDRESS>
###### Abstract

A core objective of the TERRA-REF project was to generate an open-access
reference dataset for the evaluation of sensing technologies to study plants
under field conditions. The TERRA-REF program deployed a suite of high-
resolution, cutting edge technology sensors on a gantry system with the aim of
scanning 1 hectare (104m) at around 1 mm2 spatial resolution multiple times
per week. The system contains co-located sensors including a stereo-pair RGB
camera, a thermal imager, a laser scanner to capture 3D structure, and two
hyperspectral cameras covering wavelengths of 300-2500nm. This sensor data is
provided alongside over sixty types of traditional plant phenotype
measurements that can be used to train new machine learning models. Associated
weather and environmental measurements, information about agronomic management
and experimental design, and the genomic sequences of hundreds of plant
varieties have been collected and are available alongside the sensor and plant
phenotype data.

Over the course of four years and ten growing seasons, the TERRA-REF system
generated over 1 PB of sensor data and almost 45 million files. The subset
that has been released to the public domain accounts for two seasons and about
half of the total data volume. This provides an unprecedented opportunity for
investigations far beyond the core biological scope of the project.

The focus of this paper is to provide the Computer Vision and Machine Learning
communities an overview of the available data and some potential applications
of this one of a kind data.

##  1 Introduction

In 2015, the Advanced Research Projects Agency for Energy (ARPA-E) funded the
TERRA-REF Phenotyping Platform (Figure 1). The scientific aim was to transform
plant breeding by providing a reference dataset generated by deploying a suite
of co-located high-resolution sensors under field conditions. The goal of
these sensors was to use proximate sensing from approximately 2m above the
plant canopy to quantify plant characteristics.

The study has evaluated diverse populations of sorghum, wheat, and lettuce
over the course of four years and ten cropping cycles. Future releases of
additional data will be informed by user interests.

Figure 1: TERRA-REF field scanner at the University of Arizona’s Maricopa
Agricultural Center.

The TERRA-REF reference dataset can be used to characterize phenotype-to-
genotype associations, on a genomic scale, that will enable knowledge-driven
breeding and the development of higher-yielding cultivars of sorghum and
wheat. The data is also being used to develop new algorithms for machine
learning, image analysis, genomics, and optical sensor engineering. Beyond
applications in plant breeding, the resulting dataset provides opportunities
for the study and integration of diverse remote sensing modalities.

###  1.1 Types of Data

The TERRA-REF field scanner platform utilizes a sensor suite of co-located
instruments (Figure 2 and Table 1). The TERRA-REF reference dataset includes
several data types (Figures 3 and 4, Table 2) including raw and processed
outputs from sensors, environmental sensor measurements, manually measured and
computationally derived phenotypes, and raw and processed genomics datasets
[16]. Extensive contextual measurements and metadata include sensor
information and extensive documentation for each of the sensors, the field
scanner, calibration targets, and the results of sensor validation tests [16].

In addition to raw sensor data, the first release of TERRA-REF data includes
derived sensor data products in enhanced formats including calibrated and
georeferenced images and point clouds (Table 2). Many of the data products are
provided in formats that follow Open Geospatial Consortium (OGC) standards and
work with GIS software.

Figure 2: TERRA-REF field scanner sensor suite.

###  1.2 Sensors

Sensors available on the TERRA-REF field scanner include snapshot and line-
scan imaging, multi-spectral radiometric, and environmental sensors. Table 1
and Figure 2) provide a high level overview of the sensors deployed on this
system. Full documentation and metadata for each sensor as well as the
configuration and geometry of the sensor box are provided as metadata
alongside the TERRA-REF data release.

Table 1: Summary of TERRA-REF sensor instruments.

Sensor Name | Model | Technical Specifications
---|---|---
Imaging Sensors |  |
Stereo RGB Camera | Allied Vision Prosilica GT3300C |
Laser Scanner | Custom Fraunhofer 3D | Spatial Resolution: 0.3 to 0.9 mm
Thermal Infrared | FLIR A615 | Thermal Sensitivity: $\leq$50mK @ 30 ∘C
PS II Camera | LemnaTec PS II Fluorescence Prototype | Illumination 635nm x 4000 $\mu$mol/m2/s, Camera 50 fps
Multi-spectral Radiometers |  |
Dedicated NDVI Multispectral Radiometer | Skye Instruments SKR 1860D/A | 650 nm, 800 nm $\pm$ 5 nm; 1 down, 1 up
Dedicated PRI Multispectral Radiometer | Skye Instruments SKR 1860ND/A | 531nm +/- 3nm; PRI = Photochemical Reflectance Index
Active Reflectance | Holland Scientific Crop Circle ACS-430 | 670 nm, 730 nm, 780 nm
Hyper-spectral Cameras |  |
VNIR Hyperspectral Imager | Headwall Inspector VNIR | 380-1000 nm @ 2/3 nm resolution
SWIR Hyperspectral Imager | Headwall Inspector SWIR | 900-2500 nm @ 12 nm resolution
Environmental Sensors |  |
Climate Sensors | Thies Clima 4.9200.00.000 |
VNIR Spectroradiometer | Ocean Optics STS-Vis | Range: 337-824 nm @ 1/2 nm
VNIR+SWIR Spectroradiometer | Spectral Evolution PSR+3500 | Range 800-2500nm @3-8 nm; Installed 2018
PAR Sensor | Quantum SQ–300 | Spectral Range 410 to 655 nm

Table 2: Summary of the sensor data products included in the first release of
TERRA-REF data.

Data Product | Sensor | Algorithm | File Format | Plot Clip | Full Field
---|---|---|---|---|---
Environment | Thies Clima | envlog2netcdf | netcdf | NA | NA
Thermal Image | FLIR | ir_geotiff | geotiff | + |
Point Cloud | Fraunhofer Laser 3D | laser3d_las | las | + |
Point Cloud | Fraunhofer Laser 3D | scanner3DTop | ply |  |
Images Time-Series | PSII Camera | ps2png | png |  |
Color Images | RGB Stereo | bin2tiff | geotiff | + | +
Plant Mask | RGB Stereo | rgb_mask | geotiff |  | x

Figure 3: Example data from the TERRA-REF gantry system. (top left) RGB data
(center top) RGB data with soil masked (top right) close up of RGB data.
3D-scanner data (center row, right to left) depth, reflectance, surface
normals, and point cloud data produced by the 3D scanner. (bottom row, right
to left) FLIR thermal image with transpiring leaves shown as cooler than soil;
Fv/Fm derived from active fluorescence PSII sensor providing a measure of
photosynthetic efficiency; the reflectance of light at 543nm wavelength
measured by the VNIR hyperspectral camera. Because these are two-dimensional
representations of three-dimensional systems, all scale bars are approximate.

##  2 Computer Vision and Machine Learning Problems

There are a variety of questions that the TERRA-REF dataset could be used to
answer that are of high importance to the agricultural and plant science
communities, while also posing extremely interesting and challenging computer
vision and machine learning problems. In this section, we consider example
research areas or topics within computer vision and discuss the relevant
agricultural and plant science questions those research communities could help
address using the TERRA-REF data.

#### Measurement, Prediction and Causal Inference.

The TERRA-REF sensor data can be used to drive development of vision-based
algorithms for fundamental problems in plant phenotyping, such as making
measurements of plant height, leaf length, flower counting, or estimating
environmental stress. Additional challenges include attempting to predict end
of season phenotypes, such as end of season yield, from early season sensor
data – an accurate predictor of end of season yield from early season visual
data, for example, could help growers and breeders invest resources only in
the most promising of candidate crops. There are additional opportunities to
investigate the causal relationship between genotypes or environmental
conditions and their expressed phenotypes, as the TERRA-REF dataset includes
both comprehensive genetic information, as well as high temporal resolution
environmental information. The TERRA-REF data contain over sixty hand
measurements that could be used to train models from one or more sensors. In
addition, there are opportunities to train models that predict plot-level
phenotypes measured by an expensive sensor with a less expensive sensor.
Further, many events including insect damage, heat stress, and plant lodging
(falling) could be labeled in new images.

#### Fine Grained Visual Categorization.

The TERRA-REF data is a rich source of visual sensor data collected from crop
species that are visually similar. Differentiating between data with low
inter-class variance is an interesting categorization challenge, requiring
visual models that learn the fine-grained differences between varieties of the
same crop.

#### Transfer Learning.

There are a variety of interesting transfer learning challenges of utmost
importance to the agricultural and plant science communities, including
discovering approaches that generalize across sensors, across crops, or across
environmental conditions. The TERRA-REF data additionally presents an
opportunity to help solve the greenhouse-to-field gap, where models that
perform well in greenhouse conditions tend to not generalize to field
conditions; because the TERRA-REF data includes both greenhouse and field data
for the exact same varieties, researchers in transfer learning could help
build models that bridge this gap.

#### Multi-sensor Integration.

The TERRA-REF data includes data captured from a variety of visual sensors
(described in Section 1.2). These sensors have similar, but not identical,
viewpoints from within the gantry box, may not have captured data at the exact
same time, and may have captured different perspectives of the same part of
the field on different days. This presents interesting challenges in terms of
how to incorporate information across the various sensors, and how to work
with time-series data that is not necessarily well-aligned or continuously
captured.

#### Explainable Models.

All too often in machine learning research, datasets and models are built
solely to drive the development of machine learning algorithms. When building
models to answer questions like “should I cut this plant down because it won’t
produce sufficient yield?” or “is this plant under environmental stress?,” it
is important not just to have maximally accurate models but to also understand
why the models make the determinations that they make. This makes the TERRA-
REF data, and the biologically relevant questions it supports, an excellent
opportunity to drive development of new approaches for explainable machine
learning, conveying the decisions made by algorithms to non-machine learning
experts.

#### Information Content.

The TERRA-REF field scanner and sensors represent a substantial investment,
and it is still not clear which sensors, sensor configurations, and spatial
and temporal resolutions are useful to answer a particular question.
Presently, much less expensive sensors and sensing platforms are available
[11, 1]. What do we gain from the 1mm spatial resolution on this platform
relative to unoccupied aerial systems (UAS) that are quickly approaching 1cm
spatial resolution? Or, which subset of hyperspectral wavelengths provide the
most useful information? Can we predict the useful parts of a hyperspectral
image from RGB images? Or get most of the information from a multispectral
camera with a half-dozen bands? At the outset, the team recognized that this
configuration would be oversampling the plant subjects, but it wasn’t clear
what the appropriate resolutions or most useful sensors would be.

#### Overall Challenges.

Within all of these topic areas in computer vision and machine learning, the
challenges that must be addressed require addressing interesting questions
such as determining the most appropriate sensors and data processing choices
for specific questions, addressing difficult domain transfer issues,
considering how to integrate noisy side channels of information, such as
genetic information that may conflict or conflate with each other, or dealing
with nuisance parameters like environmental or weather variations that
simultaneously influence plant subjects and the sensor data content.

##  3 Algorithm Development.

The process of converting raw sensor outputs into usable data products
required geometric, radiometric, and geospatial calibration. In this regard,
each sensor presented its own challenges. Combining these steps into an
automated computing pipeline also represented a substantial effort that is
described by Burnette _et al_. [3].

Radiometric calibration was particularly challenging, owing that many images
contain both sunlit and shaded areas. In the case of hyperspectral images, the
white sensor box and scans spread out over multiple days confounded an already
challenging problem. Radiometric calibration of images taken by the two
hyperspectral cameras exemplifies these challenges, and a robust solution is
described by Sagan _et al_. [21] and implemented in [19]. Even processing
images from an RGB camera was challenging due to fixed settings resulting in
high variability in quality and exposure, requiring the novel approach
described by Li _et al_. [18]. Herritt _et al_. [14, 13] demonstrate and
provide software used in analysis of a sequence of images that capture plant
fluorescence response to a pulse of light.

Most of the algorithms used to generate data products have not been published
as papers but are made available on GitHub (https://github.com/terraref); code
used to release the data publication in 2020 is available on Zenodo [25, 15,
10, 6, 4, 19, 8, 7, 5, 9, 17].

Pipeline development continues to support ongoing use of the field scanner as
well as more general applications in plant sensing pipelines. Recent advances
have improved pipeline scalability and modularity by adopting workflow tools
and making use of heterogeneous computing environments. The TERRA-REF
computing pipeline has been adapted and extended for continuing use with the
Field Scanner with the new name ”PhytoOracle” and is available at
https://github.com/LyonsLab/PhytoOracle. Related work generalizing the
pipeline for other phenomics applications has been released under the name
”AgPipeline” https://github.com/agpipeline with applications to aerial imaging
described by Schnaufer _et al_. [22]. All of these software are made available
with permissive open source licenses on GitHub to enable access and community
development.

Figure 4: Summary of public sensor datasets from Seasons 4 and 6. Each dot
represents the dates for which a particular data product is available, and the
size of the dot indicates the number of files available.

##  4 Uses to date.

TERRA-REF is being used in a variety of ways. For example, hyperspectral
images have been used to measure soil moisture [2], but the potential to
predict leaf chemical composition and biophysical traits related to
photosynthesis and water use are particularly promising based on prior work
[23, 24].

#### Plant Science and Computer Vision Research.

A few projects are developing curated datasets for specific machine learning
challenges related to classification, object recognition, and prediction.

We currently know of at least three datasets curated for CVPPA 2021. The
Sorghum-100 dataset was created to support development of algorithms that can
classify sorghum varieties from RGB images from Ren _et al_. [20]. Another set
of RGB images curated for the Sorghum Biomass Prediction Challenge on Kaggle
was developed with the goal of developing methods to predict end of season
biomass from images taken of different sorghum genotypes over the course of
the growing season. Finally, RGB images from the TERRA-REF field scanner in
Maricopa accounted for 250 of the 6000 1024x1024 pixel images in the Global
Wheat Head Dataset 2021 [12]. The goal of the Global Wheat Challenge 2021 on
AIcrowd is to develop an algorithm that can identify wheat heads from a
collection of images from around the world that represent diverse fields
conditions, sensors, settings, varieties, and growth stages.

Most of the research applications to date have focused on analysis of plot-
level phenotypes and genomic data rather than the full resolution sensor data.

##  5 Data Access

#### Public Domain Data.

A curated subset of the TERRA-REF data was released to the public domain in
2020 (Figure 4) [16]. These data are intended to be re-used and are accessible
as a combination of files and databases linked by spatial, temporal, and
genomic information. In addition to providing open access data, the entire
computational pipeline is open source, and we can assist academic users with
access to high-performance computing environments.

The total size of raw (Level 0) data generated by these sensors is 60 TB.
Combined, the Level 1 and Level 2 sensor data products are 490 TB. This size
could be substantially reduced through compression and removal of duplicate
data. For example, the same images at the same resolution appear in the
georeferenced Level 1 files, the full field mosaics, and the plot-level clip.

#### Other Data Available.

The complete TERRA-REF dataset is not publicly available because of the effort
and cost of processing, reviewing, curating, describing, and hosting the data.
Instead, we focused on an initial public release and plan to make new datasets
available based on need. Access to unpublished data can be requested from the
authors, and as data are curated they will be added to subsequent versions of
the public domain release (https://terraref.org/data/access-data).

In addition to hosting an archival copy of data on Dryad [16], the
documentation includes instructions for browsing and accessing these data
through a variety of online portals. These portals provide access to web user
interfaces as well as databases, APIs, and R and Python clients. In some cases
it will be easier to access data through these portals using web interfaces
and software libraries.

The public domain data is archived on Dryad, with the exception of the large
sensor data files. The Dryad archive provides a catalog of these files that
can be accessed via Globus or directly on the host computer at the National
Center for Supercomputing Applications.

##  6 Acknowledgements

The work presented herein was funded in part by the Advanced Research Projects
Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Numbers DE-
AR0000598 and DE-AR0001101, and the National Science Foundation, under Award
Numbers 1835834 and 1835543.

## References

  * [1] Jonathan A Atkinson, Robert J Jackson, Alison R Bentley, Eric Ober, and Darren M Wells.  Field Phenotyping for the Future, pages 1–18.  John Wiley & Sons, Ltd, Chichester, UK, Nov. 2018.
  * [2] Ebrahim Babaeian, Paheding Sidike, Maria S Newcomb, Maitiniyazi Maimaitijiang, Scott A White, Jeffrey Demieville, Richard W Ward, Morteza Sadeghi, David S LeBauer, Scott B Jones, et al.  A new optical remote sensing technique for high-resolution mapping of soil moisture.  Frontiers in Big Data, 2:37, 2019.
  * [3] Maxwell Burnette, Rob Kooper, J D Maloney, Gareth S Rohde, Jeffrey A Terstriep, Craig Willis, Noah Fahlgren, Todd Mockler, Maria Newcomb, Vasit Sagan, Pedro Andrade-Sanchez, Nadia Shakoor, Paheding Sidike, Rick Ward, and David LeBauer.  TERRA-REF data processing infrastructure.  In Proceedings of the Practice and Experience on Advanced Research Computing, page 27. ACM, July 2018.
  * [4] Max Burnette, David LeBauer, Solmaz Hajmohammadi, ZongyangLi, Craig Willis, Wei Qin, Sidke Paheding, and JD Maloney.  terraref/extractors-multispectral: Season 6 Data Publication (2019), Sept. 2019.
  * [5] Max Burnette, David LeBauer, Wei Qin, and Yan Liu.  terraref/extractors-metadata: Season 6 Data Publication (2019), Sept. 2019.
  * [6] Max Burnette, David LeBauer, ZongyangLi, Wei Qin, Solmaz Hajmohammadi, Craig Willis, Sidke Paheding, and Nick Heyek.  terraref/extractors-stereo-rgb: Season 6 Data Publication (2019), Sept. 2019.
  * [7] Max Burnette, Zongyang Li, Solmaz Hajmohammadi, David LeBauer, Nick Heyek, and Craig Willis.  terraref/extractors-3dscanner: Season 6 Data Publication (2019), Sept. 2019.
  * [8] Max Burnette, Jerome Mao, David LeBauer, Charlie Zender, and Harsh Agrawal.  terraref/extractors-environmental: Season 6 Data Publication (2019), Sept. 2019.
  * [9] Max Burnette, Craig Willis, Chris Schnaufer, David LeBauer, Nick Heyek, Wei Qin, Solmaz Hajmohammadi, and Kristina Riemer.  terraref/terrautils: Season 6 Data Publication (2019), Sept. 2019.
  * [10] Max Burnette, Charlie Zender, JeromeMao, David LeBauer, Rachel Shekar, Noah Fahlgren, Craig Willis, Henry Butowsky, Xingchen Hong, ZongyangLi, Fengling Wang, TinoDornbusch, JD Maloney, Wei Qin, Stuart Marshall, Abby Stylianou, and Ting Li.  terraref/computing-pipeline: Season 4 & 6 Data Publication (2019), Feb. 2020.
  * [11] Anna L Casto, Haley Schuhl, Jose C Tovar, Qi Wang, Rebecca S Bart, Noah Fahlgren, and Malia A Gehan.  Picturing the future of food.  Plant phenome j., 4(1), Jan. 2021.
  * [12] Etienne David, Mario Serouart, Daniel Smith, Simon Madec, Kaaviya Velumani, Shouyang Liu, Xu Wang, Francisco Pinto Espinosa, Shahameh Shafiee, Izzat S. A. Tahir, Hisashi Tsujimoto, Shuhei Nasuda, Bangyou Zheng, Norbert Kichgessner, Helge Aasen, Andreas Hund, Pouria Sadhegi-Tehran, Koichi Nagasawa, Goro Ishikawa, Sébastien Dandrifosse, Alexis Carlier, Benoit Mercatoris, Ken Kuroki, Haozhou Wang, Masanori Ishii, Minhajul A. Badhon, Curtis Pozniak, David Shaner LeBauer, Morten Lilimo, Jesse Poland, Scott Chapman, Benoit de Solan, Frédéric Baret, Ian Stavness, and Wei Guo.  Global wheat head dataset 2021: more diversity to improve the benchmarking of wheat head localization methods, 2021.
  * [13] Matthew T Herritt, Jacob C Long, Mike D Roybal, David C Moller Jr, Todd C Mockler, Duke Pauli, and Alison L Thompson.  Flip: Fluorescence imaging pipeline for field-based chlorophyll fluorescence images.  SoftwareX, 14:100685, 2021.
  * [14] Matthew T Herritt, Duke Pauli, Todd C Mockler, and Alison L Thompson.  Chlorophyll fluorescence imaging captures photochemical efficiency of grain sorghum (sorghum bicolor) in a field setting.  Plant methods, 16(1):1–13, 2020.
  * [15] David LeBauer, Nick Heyek, Rachel Shekar, Katrin Leinweber, JD Maloney, and Tino Dornbusch.  terraref/reference-data: Season 4 & 6 Data Publication (2019), Feb. 2020.
  * [16] David LeBauer, Burnette Maxwell, Jeffrey Demieville, Noah Fahlgren, Andrew French, Roman Garnett, Zhenbin Hu, Kimberly Huynh, Rob Kooper, Zongyang Li, Maitiniyazi Maimaitijiang, Jerome Mao, Todd Mockler, Geoffrey Morris, Maria Newcomb, Michael Ottman, Philip Ozersky, Sidike Paheding, Duke Pauli, Robert Pless, Wei Qin, Kristina Riemer, Gareth Rohde, William Rooney, Vasit Sagan, Nadia Shakoor, Abby Stylianou, Kelly Thorp, Richard Ward, Jeffrey White, Craig Willis, and Charles Zender.  Data from: TERRA-REF, an open reference data set from high resolution genomics, phenomics, and imaging sensors, Aug. 2020.
  * [17] David LeBauer, Craig Willis, Rachel Shekar, Max Burnette, Ting Li, Scott Rohde, Yan Liu, JD Maloney, Noah Fahlgren, Charlie Zender, Rob Kooper, Jerome Mao, Harsh Agrawal, Xingchen Hong, Shannon Bradley, Samy Pessé, Katrin Leinweber, Justin Manzo, Jeff Terstriep, and Abby Stylianou.  terraref/documentation: Season 6 Data Publication (2019), Feb. 2020\.
  * [18] Zongyang Li, Abby Stylianou, and Robert Pless.  Learning to correct for bad camera settings in large scale plant monitoring.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019.
  * [19] Jerome Mao, Max Burnette, Henry Butowsky, Charlie Zender, David LeBauer, and Sidke Paheding.  terraref/extractors-hyperspectral: Season 6 Data Publication (2019), Sept. 2019.
  * [20] Chao Ren, Justin Dulay, Gregory Rolwes, Duke Pauli, Nadia Shakoor, and Abby Stylianou.  Multi-resolution outlier pooling for sorghum classification.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages 2931–2939, June 2021.
  * [21] Vasit Sagan, Maitiniyazi Maimaitijiang, Sidke Paheding, Sourav Bhadra, Nichole Gosselin, Max Burnette, Jeffrey Demieville, Sean Hartling, David LeBauer, Maria Newcomb, Duke Pauli, Kyle T. Peterson, Nadia Shakoor, Abby Stylianous, Charlie Zender, and Todd C. Mockler.  Data-driven artificial intelligence for calibration of hyperspectral big data.  Transactions on Geoscience and Remote Sensing, 2021.
  * [22] Christophe Schnaufer, Julian L Pistorius, and David S LeBauer.  An open, scalable, and flexible framework for automated aerial measurement of field experiments.  In Proceedings of SPIE, 2020.
  * [23] Shawn P Serbin, Dylan N Dillaway, Eric L Kruger, and Philip A Townsend.  Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature.  J. Exp. Bot., 63(1):489–502, Jan. 2012.
  * [24] Shawn P Serbin, Aditya Singh, Ankur R Desai, Sean G Dubois, Andrew D Jablonski, Clayton C Kingdon, Eric L Kruger, and Philip A Townsend.  Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy.  Remote Sens. Environ., 167:78–87, Sept. 2015.
  * [25] Craig Willis, David LeBauer, Max Burnette, and Rachel Shekar.  terraref/sensor-metadata: Season 4 & 6 Data Publication (2019), Feb. 2020.