Paper
30 August 2023 Research on the extraction of winter wheat planting area in Henan Province based on time-series EVI
Siyi Li, Guowang Jin, Zhengfang Lou
Author Affiliations +
Proceedings Volume 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023); 1279710 (2023) https://doi.org/10.1117/12.3007540
Event: 2nd International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 2023, Qingdao, China
Abstract
Winter wheat is one of the major food crops in China, and timely and accurate access to winter wheat planting distribution information is important for the growth detection and yield estimation of winter wheat. In this paper, Henan Province was selected as the study area, and the MODIS time-series EVI data were smoothed and filtered by S-G filtering method, while a decision tree extraction model under different municipalities was constructed by combining winter wheat phenology calendar to extract the winter wheat planting area and spatial distribution in Henan Province in 2018. The experimental results show that: the wheat planting area in the eastern part of Henan Province is more than the wheat planting area in the western part; compared with the statistical data, the extraction accuracy of winter wheat remote sensing in each city ranges from 81% to 96%, and the extraction accuracy of the province reaches 93.94%; the overall average accuracy of MODIS extraction results is 78.43% compared with the extraction results of Sentinel-2. The experiment proved the effectiveness of the method in this paper, which can provide reference and reference for the formulation of grain planting plan and related planting policies in Henan Province.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Siyi Li, Guowang Jin, and Zhengfang Lou "Research on the extraction of winter wheat planting area in Henan Province based on time-series EVI", Proc. SPIE 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 1279710 (30 August 2023); https://doi.org/10.1117/12.3007540
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KEYWORDS
Data modeling

Tunable filters

Contour extraction

Phenology

Remote sensing

Image classification

Data conversion

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