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Proceedings Paper

The use of Sentinel-1 and -2 data for monitoring maize production in Rwanda
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Paper Abstract

Although Rwanda has accomplished significant improvements in food production in recent years, one fifth of its population remains food insecure. Agricultural information is currently collected through seasonal agricultural surveys, but more frequent and timely data collection is needed to adequately inform public and private decision-makers about the status of crops during the growing season. Sentinel-1 and -2 data are freely available with new images provided every 4-5 days. While analysis of these multispectral images has been used for agricultural applications, there are few applications to smallholder agriculture. Major challenges for satellite image analysis in the context of Rwanda include heavily clouded scenes and small plot sizes that are often intercropped. Sentinel-2 scenes corresponding to mid-season were analyzed, and spectral signatures of maize could be distinguished from those of other crops. Seasonal mean filtering was applied to Sentinel-1 scenes, and there was significant overlap in the spectral signatures across different types of vegetation. Random Forest models for classification of Sentinel scenes were developed using a training dataset that was constructed from high-resolution multispectral images acquired by unmanned aerial vehicles (UAVs) in several different locations in Rwanda and labeled as to the crop type by trained observers. The models were applied to satellite images of the whole country of Rwanda and validated using a test dataset from the UAV images. The Sentinel-2 model had the user’s accuracy for maize classification of 75%, while the Sentinel-1 model overestimated the maize area resulting in a user’s accuracy of <50%.

Paper Details

Date Published: 21 October 2019
PDF: 6 pages
Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 111491Y (21 October 2019); doi: 10.1117/12.2533221
Show Author Affiliations
Jason Polly, RTI International (United States)
Meghan Hegarty-Craver, RTI International (United States)
James Rineer, RTI International (United States)
Margaret O'Neil, RTI International (United States)
Daniel Lapidus, RTI International (United States)
Robert Beach, RTI International (United States)
Dorota S. Temple, RTI International (United States)

Published in SPIE Proceedings Vol. 11149:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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