CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals

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Type
Article

Authors
Johansen, Kasper
Ziliani, Matteo G.
Houborg, Rasmus
Franz, Trenton E.
McCabe, Matthew

KAUST Department
Biological and Environmental Science and Engineering (BESE) Division
Earth System Observation and Modelling
Environmental Science and Engineering Program
Hydrology, Agriculture and Land Observation Group
Water Desalination and Reuse Research Center (WDRC)

Date
2022-03-28

Abstract
Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precision-level attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multi-satellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications.

Citation
Johansen, K., Ziliani, M. G., Houborg, R., Franz, T. E., & McCabe, M. F. (2022). CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-09376-6

Acknowledgements
Funding for these AmeriFlux core sites at the Eastern Nebraska Research and Extension Center was provided by the U.S. Department of Energy’s Office of Science. This research was a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture. The authors acknowledge the support of Dr Joseph Mascaro and the Planet Ambassadors program.
The work presented herein was funded by the King Abdullah University of Science and Technology. T.E.F. acknowledges the financial support of the USDA National Institute of Food and Agriculture, Hatch project #1009760, #1020768 and project #2019–67021-29312.

Publisher
Springer Science and Business Media LLC

DOI
10.1038/s41598-022-09376-6
10.21203/rs.3.rs-1134595/v1

PubMed ID
35347221

Additional Links
https://www.nature.com/articles/s41598-022-09376-6

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