| Author(s) |
Brinkhoff, James
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| Publication Date |
2022-09-28
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| Abstract |
<p>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 <sup>2</sup> . 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.</p>
|
| Citation |
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, p. 5854-5857
|
| ISBN |
9781665427920
9781665427913
9781665427937
|
| Link | |
| Language |
en
|
| Publisher |
Institute of Electrical and Electronics Engineers (IEEE)
|
| Series |
IEEE International Geoscience and Remote Sensing Symposium proceedings
|
| Title |
Early-Season Industry-Wide Rice Maps Using Sentinel-2 Time Series
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| Type of document |
Conference Publication
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| Entity Type |
Publication
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