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Proceedings Paper

Crop classification using temporal stacks of multispectral satellite imagery
Author(s): Daniela I. Moody; Steven P. Brumby; Rick Chartrand; Ryan Keisler; Nathan Longbotham; Carly Mertes; Samuel W. Skillman; Michael S. Warren
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Paper Abstract

The increase in performance, availability, and coverage of multispectral satellite sensor constellations has led to a drastic increase in data volume and data rate. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. The data analysis capability, however, has lagged behind storage and compute developments, and has traditionally focused on individual scene processing. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and can scale with the high-rate and dimensionality of imagery being collected. We investigate and compare the performance of pixel-level crop identification using tree-based classifiers and its dependence on both temporal and spectral features. Classification performance is assessed using as ground-truth Cropland Data Layer (CDL) crop masks generated by the US Department of Agriculture (USDA). The CDL maps contain 30m spatial resolution, pixel-level labels for around 200 categories of land cover, but are however only available post-growing season. The analysis focuses on McCook county in South Dakota and shows crop classification using a temporal stack of Landsat 8 (L8) imagery over the growing season, from April through October. Specifically, we consider the temporal L8 stack depth, as well as different normalized band difference indices, and evaluate their contribution to crop identification. We also show an extension of our algorithm to map corn and soy crops in the state of Mato Grosso, Brazil.

Paper Details

Date Published: 5 May 2017
PDF: 12 pages
Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980G (5 May 2017); doi: 10.1117/12.2262804
Show Author Affiliations
Daniela I. Moody, Descartes Labs, Inc. (United States)
Steven P. Brumby, Descartes Labs, Inc. (United States)
Rick Chartrand, Descartes Labs, Inc. (United States)
Ryan Keisler, Descartes Labs, Inc. (United States)
Nathan Longbotham, Descartes Labs, Inc. (United States)
Carly Mertes, Descartes Labs, Inc. (United States)
Samuel W. Skillman, Descartes Labs, Inc. (United States)
Michael S. Warren, Descartes Labs, Inc. (United States)

Published in SPIE Proceedings Vol. 10198:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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