Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
文献类型：期刊论文
| 作者 | Felegari, Shilan2; Sharifi, Alireza3; Moravej, Kamran2; Amin, Muhammad1; Golchin, Ahmad2; Muzirafuti, Anselme4; Tariq, Aqil5; Zhao, Na6 |
| 刊名 | APPLIED SCIENCES-BASEL
 |
| 出版日期 | 2021-11-01 |
| 卷号 | 11期号:21页码:14 |
| 关键词 | Sentinel 1 and 2 Copernicus Sentinels crop classification food security agricultural monitoring remote sensing data analysis SAR random forest |
| DOI | 10.3390/app112110104 |
| 通讯作者 | Zhao, Na(zhaon@lreis.ac.cn) |
| 英文摘要 | Crop identification is key to global food security. Due to the large scale of crop estimation, the science of remote sensing was able to do well in this field. The purpose of this study is to study the shortcomings and strengths of combined radar data and optical images to identify the type of crops in Tarom region (Iran). For this purpose, Sentinel 1 and Sentinel 2 images were used to create a map in the study area. The Sentinel 1 data came from Google Earth Engine's (GEE) Level-1 Ground Range Detected (GRD) Interferometric Wide Swath (IW) product. Sentinel 1 radar observations were projected onto a standard 10-m grid in GRD output. The Sen2Cor method was used to mask for clouds and cloud shadows, and the Sentinel 2 Level-1C data was sourced from the Copernicus Open Access Hub. To estimate the purpose of classification, stochastic forest classification method was used to predict classification accuracy. Using seven types of crops, the classification map of the 2020 growth season in Tarom was prepared using 10-day Sentinel 2 smooth mosaic NDVI and 12-day Sentinel 1 back mosaic. Kappa coefficient of 0.75 and a maximum accuracy of 85% were reported in this study. To achieve maximum classification accuracy, it is recommended to use a combination of radar and optical data, as this combination increases the chances of examining the details compared to the single-sensor classification method and achieves more reliable information. |
| WOS关键词 | FOREST ; AGRICULTURE ; MACHINE ; RADAR ; SAR |
| 资助项目 | National Natural Science Foundation of China[42071374] |
| WOS研究方向 | Chemistry ; Engineering ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000723188700001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/168024]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhao, Na |
| 作者单位 | 1.PMAS Arid Agr Univ Rawalpindi, Inst Geoinformat & Earth Observat, Rawalpindi 46300, Pakistan 2.Univ Zanjan, Dept Soil Sci, Fac Agr, Zanjan 4537138791, Iran 3.Shahid Rajaee Teacher Training Univ, Fac Civil Engn, Dept Surveying Engn, Tehran 1678815811, Iran 4.Univ Messina, Interreg Italia Malta Progetto Pocket Beach Manag, Via F Stagno dAlcontres 31, I-98166 Messina, Italy 5.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Felegari, Shilan,Sharifi, Alireza,Moravej, Kamran,et al. Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping[J]. APPLIED SCIENCES-BASEL,2021,11(21):14. |
| APA | Felegari, Shilan.,Sharifi, Alireza.,Moravej, Kamran.,Amin, Muhammad.,Golchin, Ahmad.,...&Zhao, Na.(2021).Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping.APPLIED SCIENCES-BASEL,11(21),14. |
| MLA | Felegari, Shilan,et al."Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping".APPLIED SCIENCES-BASEL 11.21(2021):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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