Early-Season Industry-Wide Rice Maps Using Sentinel-2 Time Series

Title
Early-Season Industry-Wide Rice Maps Using Sentinel-2 Time Series
Publication Date
2022-09-28
Author(s)
Brinkhoff, James
( author )
OrcID: https://orcid.org/0000-0002-0721-2458
Email: jbrinkho@une.edu.au
UNE Id une-id:jbrinkho
Type of document
Conference Publication
Language
en
Entity Type
Publication
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Place of publication
Piscataway, United States of America
Series
IEEE International Geoscience and Remote Sensing Symposium proceedings
DOI
10.1109/IGARSS46834.2022.9883755
UNE publication id
une:1959.11/54683
Abstract

Regional maps of rice fields provided early in each growing season facilitate production estimates, planning around harvest logistics, marketing and targeted agronomic recommendations. This work develops maps of all irrigated rice fields in New South Wales, Australia. Classification models were trained on reference maps from the 2019 and 2020 harvest seasons. Model predictions were tested against a reference rice map from the 2021 harvest season, covering 60,000 km 2 . The random forest algorithm was used, with features from aggregated time-series of Sentinel-2 imagery. A sequence of maps were generated at intervals of 15 days, from early to late in the growing season, with accuracy assessed at each time. The maps achieved 95% overall accuracy against point samples at 16 January 2021 ( ≈80 days after sowing). Pixel-based F1-scores against the reference map were above 80% for the 1, 16 and 31 January classified maps.

Link
Citation
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, p. 5854-5857
ISBN
9781665427920
9781665427913
9781665427937
Start page
5854
End page
5857

Files:

NameSizeformatDescriptionLink