气候变化领域集成服务门户(CCPortal): An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China

CCPortal

DOI	10.3390/rs14051208
	An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China
	Hu, Yueran; Zeng, Hongwei; Tian, Fuyou; Zhang, Miao; Wu, Bingfang; Gilliams, Sven; Li, Sen; Li, Yuanchao; Lu, Yuming; Yang, Honghai
通讯作者	Zeng, HW (通讯作者)，Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China. ; Zeng, HW (通讯作者)，Univ Chinese Acad Sci, Beijing 100049, Peoples R China.
发表日期	2022
EISSN	2072-4292
卷号	14 期号:5
英文摘要	Crop type classification is critical for crop production estimation and optimal water allocation. Crop type data are challenging to generate if crop reference data are lacking, especially for target years with reference data missed in collection. Is it possible to transfer a trained crop type classification model to retrace the historical spatial distribution of crop types? Taking the Hetao Irrigation District (HID) in China as the study area, this study first designed a 10 m crop type classification framework based on the Google Earth Engine (GEE) for crop type mapping in the current season. Then, its interannual transferability to accurately retrace historical crop distributions was tested. The framework used Sentinel-1/2 data as the satellite data source, combined percentile, and monthly composite approaches to generate classification metrics and employed a random forest classifier with 300 trees for crop classification. Based on the proposed framework, this study first developed a 10 m crop type map of the HID for 2020 with an overall accuracy (OA) of 0.89 and then obtained a 10 m crop type map of the HID for 2019 with an OA of 0.92 by transferring the trained model for 2020 without crop reference samples. The results indicated that the designed framework could effectively identify HID crop types and have good transferability to obtain historical crop type data with acceptable accuracy. Our results found that SWIR1, Green, and Red Edge2 were the top three reflectance bands for crop classification. The land surface water index (LSWI), normalized difference water index (NDWI), and enhanced vegetation index (EVI) were the top three vegetation indices for crop classification. April to August was the most suitable time window for crop type classification in the HID. Sentinel-1 information played a positive role in the interannual transfer of the trained model, increasing the OA from 90.73% with Sentinel 2 alone to 91.58% with Sentinel-1 and Sentinel-2 together.
关键词	VIRTUAL WATER FLOWS GOOGLE EARTH ENGINE LAND-COVER NATIONAL-SCALE TIME-SERIES PADDY RICE SENTINEL-2 INDEX AGRICULTURE PERFORMANCE
英文关键词	crop type classification; random forest classifier; interannual transfer; GPS; video and GIS (GVG); Google Earth Engine; Hetao irrigation district
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000768506700001
来源期刊	REMOTE SENSING
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254911
作者单位	[Hu, Yueran; Zeng, Hongwei; Tian, Fuyou; Zhang, Miao; Wu, Bingfang; Li, Yuanchao; Lu, Yuming] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; [Hu, Yueran; Zeng, Hongwei; Wu, Bingfang; Li, Yuanchao; Lu, Yuming] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Gilliams, Sven] Vlaamse Instelling Technol Onderzoek VITO, Boeretang 200, B-2400 Mol, Belgium; [Li, Sen] Chinese Acad Sci, Northwest Inst EcoEnvironm & Resources, Key Lab Desert & Desertificat, Lanzhou 730000, Peoples R China; [Yang, Honghai] Big Data Ctr Geospatial & Nat Resources Qinghai P, Xining 810001, Peoples R China
推荐引用方式 GB/T 7714	Hu, Yueran,Zeng, Hongwei,Tian, Fuyou,et al. An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China[J]. 中国科学院西北生态环境资源研究院,2022,14(5).
APA	Hu, Yueran.,Zeng, Hongwei.,Tian, Fuyou.,Zhang, Miao.,Wu, Bingfang.,...&Yang, Honghai.(2022).An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China.REMOTE SENSING,14(5).
MLA	Hu, Yueran,et al."An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China".REMOTE SENSING 14.5(2022).