<p>Crop mapping using Sentinel-1 and Sentinel-2 imagery and UAV-acquired ground truth data</p>

Robert Beach; Daniel Lapidus; Meghan Hegarty-Craver; Maggie O'Neil; James Rineer; Dorota Temple

doi:10.21955/gatesopenres.1116682.1

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Beach R, Lapidus D, Hegarty-Craver M et al. Crop mapping using Sentinel-1 and Sentinel-2 imagery and UAV-acquired ground truth data [version 1; not peer reviewed]. Gates Open Res 2020, 4:149 (poster) (https://doi.org/10.21955/gatesopenres.1116682.1)

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Crop mapping using Sentinel-1 and Sentinel-2 imagery and UAV-acquired ground truth data

Robert Beach¹, Daniel Lapidus, Meghan Hegarty-Craver, Maggie O'Neil, James Rineer, Dorota Temple

Published 26 Sep 2020 (https://doi.org/10.21955/gatesopenres.1116682.1)

Author Affiliations

¹ RTI International, USA

Copyright © 2020 Beach R et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 26 Sep 2020

Crop mapping using Sentinel-1 and Sentinel-2 imagery and UAV-acquired ground truth data
[version 1; not peer reviewed]

Robert Beach¹, Daniel Lapidus, Meghan Hegarty-Craver, Maggie O'Neil, James Rineer, Dorota Temple

Author Affiliations

¹ RTI International, USA

Abstract

Gates Foundation grant number

INV-GC2020

Competing Interests

No competing interests were disclosed

Keywords

Google Earth Engine, machine learning, random forest, satellite imagery, smallholder agriculture

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Crop mapping using Sentinel-1 and Sentinel-2 imagery and UAV-acquired ground truth data

Crop mapping using Sentinel-1 and Sentinel-2 imagery and UAV-acquired ground truth data
[version 1; not peer reviewed]

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