Identifying and mapping open-boll cotton fields in the southeastern United States with time series PhenoCam, Sentinel-2 and Sentinel-1 images
Cotton is a leading cash crop and ranks first in value-added crops in the United States of America. Timely and accurate information on the spatial distribution of cotton fields is vital for cotton management and production prediction. In this study, we combined hourly PhenoCam pictures and time seri...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S156984322500370X |
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| Summary: | Cotton is a leading cash crop and ranks first in value-added crops in the United States of America. Timely and accurate information on the spatial distribution of cotton fields is vital for cotton management and production prediction. In this study, we combined hourly PhenoCam pictures and time series Sentinel-2 images and identified the unique white feature in the cotton open-boll period from spectroscopy analysis. We then developed a color-based algorithm to automatically identify and map open-boll cotton fields at the 10-m spatial resolution in the southeastern United States using time series Sentinel-2 and Sentinel-1 images in 2019. We also generated the starting date map of the open-boll period. The area of the open-boll cotton fields was 17.90 (± 1.08) × 103 km2, with the largest area in Georgia of 4.05 × 103 km2, about 23 % of the total open-boll cotton fields. Based on the stratified random sampling ground references, the open-boll cotton field map had a high overall accuracy of 95 % (± 1 %). The starting dates of the open-boll period varied across the southeastern United States, mainly ranging from the day of the year (DOY) 244 to 304 (September and October). The color-based algorithm performed well in multiple years and different regions, demonstrating the algorithm’s robustness and potential for regional application. This study could provide data and methodological support for the management of cotton fields and cotton supply chain management to achieve the No Poverty and No Hunger Sustainable Development Goals of the United Nations. |
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| ISSN: | 1569-8432 |