Paddy rice is an important agricultural product, and accurate mapping of paddy rice fields is essential for enhancing food security, promoting sustainable agriculture, increasing crop yields, and facilitating technological advancements.
A research group led by Prof. Sun Xiaobing from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences developed a method for accurately mapping paddy rice cultivation in Anhui, a province in eastern China. The work is published in the journal Agriculture.
Researchers combined annual phenological features with Sentinel-1/2 imagery, leveraging satellite remote sensing and machine learning to enhance agricultural monitoring.
They derived annual phenological variations from verified ground truth data and assigned several vegetation indices to different phenological phases.
This helps them get pixel-level rice planting distribution maps through machine learning.
The research team used an automatic sample expansion technique to increase the sample size and stratified different grids within the study area.
Researchers validated the results of this method with a confusion matrix, the Anhui Statistical Yearbook, and other rice mapping algorithms of similar resolutions. The method demonstrated high accuracy in primary grain-producing areas of Anhui with less than 10% of error and showed practical value in agriculture.
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