TY - GEN
T1 - Early-Season Industry-Wide Rice Maps Using Sentinel-2 Time Series
AU - Brinkhoff, James
PY - 2022/9/28
Y1 - 2022/9/28
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85140385266
U2 - 10.1109/IGARSS46834.2022.9883755
DO - 10.1109/IGARSS46834.2022.9883755
M3 - Conference contribution
SN - 9781665427920
SN - 9781665427913
SN - 9781665427937
T3 - IEEE International Geoscience and Remote Sensing Symposium proceedings
SP - 5854
EP - 5857
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers (IEEE)
CY - Piscataway, United States of America
T2 - IGARSS 2022: 2022 IEEE International Geoscience and Remote Sensing Symposium
Y2 - 17 July 2022 through 22 July 2022
ER -