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

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

doi:10.1038/s41598-022-09376-6

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

Journal Article · Mon Mar 28 00:00:00 EDT 2022 · Scientific Reports

DOI:https://doi.org/10.1038/s41598-022-09376-6· OSTI ID:1982171

Johansen, Kasper ^[1]; Ziliani, Matteo G. ^[2]; Houborg, Rasmus ^[3]; Franz, Trenton E. ^[4]; McCabe, Matthew F. ^[1]

King Abdullah University of Science and Technology (KAUST), Thuwal (Saudi Arabia)
King Abdullah University of Science and Technology (KAUST), Thuwal (Saudi Arabia); Hydrosat (Luxembourg)
Planet Labs Inc., San Francisco, CA (United States)
Univ. of Nebraska, Lincoln, NE (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). AmeriFlux

Sponsoring Organization:: USDA; USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1982171

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 12; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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