Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China

被引:22
|
作者
Li, Changchun [1 ]
Chen, Weinan [1 ]
Wang, Yilin [1 ]
Wang, Yu [1 ]
Ma, Chunyan [1 ]
Li, Yacong [1 ]
Li, Jingbo [1 ,2 ]
Zhai, Weiguang [1 ]
机构
[1] Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454000, Henan, Peoples R China
[2] Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
基金
中国国家自然科学基金;
关键词
winter wheat; Sentinel; Google Earth Engine; image aggregation; integrated image; random forest; LANDSAT;
D O I
10.3390/rs14020284
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The timely and accurate acquisition of winter wheat acreage is crucial for food security. This study investigated the feasibility of extracting the spatial distribution map of winter wheat in Henan Province by using synthetic aperture radar (SAR, Sentinel-1A) and optical (Sentinel-2) images. Firstly, the SAR images were aggregated based on the growth period of winter wheat, and the optical images were aggregated based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS-NDVI) curve. Then, five spectral features, two polarization features, and four texture features were selected as feature variables. Finally, the Google Earth Engine (GEE) cloud platform was employed to extract winter wheat acreage through the random forest (RF) algorithm. The results show that: (1) aggregated images based on the growth period of winter wheat and sensor characteristics can improve the mapping accuracy and efficiency; (2) the extraction accuracy of using only SAR images was improved with the accumulation of growth period. The extraction accuracy of using the SAR images in the full growth period reached 80.1%; and (3) the identification effect of integrated images was relatively good, which makes up for the shortcomings of SAR and optical images and improves the extraction accuracy of winter wheat.
引用
收藏
页数:17
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