Sentinel-2 image based smallholder crops classification and accuracy assessment by UAV data
Author:
Affiliation:
1. College of Geography and Tourism, Xinjiang Normal University/Laboratory of Basin Information Integration and Ecological Security, Urumqi, China
2. Key Laboratory of Arid Lake Environment and Resources, Urumqi, China
Funder
the Xinjiang Uyghur Autonomous Region Science and Technology Innovation Base Construction Plan Project of China
the Natural Sciences Foundation of China
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/10106049.2024.2361733
Reference48 articles.
1. 15. Food and Agriculture Organization of the United Nations (FAO)
2. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis
3. Bizier V Rahija M. 2017. Twenty-fifth session. Food and Agriculture Organization of the United Nations. Entebbe Uganda. Retrieved from https://www.fao.org/3/bt858e/bt858e.pdf
4. A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach
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