A Simple and Robust Spectral Index for Identifying Lodged Maize Using Gaofen1 Satellite Data
文献类型：期刊论文
| 作者 | Chen, Yuanyuan1,2; Sun, Li1,2; Pei, Zhiyuan1,2; Sun, Juanying1,2; Li, He3; Jiao, Weijie1,2; You, Jiong1,2 |
| 刊名 | SENSORS
 |
| 出版日期 | 2022-02-01 |
| 卷号 | 22期号:3页码:14 |
| 关键词 | maize lodging spectral feature Gaofen1 index |
| DOI | 10.3390/s22030989 |
| 通讯作者 | Sun, Li(sunli@aape.org.cn) |
| 英文摘要 | Crop lodging is a major destructive factor for agricultural production. Developing a cost-efficient and accurate method to assess crop lodging is crucial for informing crop management decisions and reducing lodging losses. Satellite remote sensing can provide continuous data on a large scale; however, its utility in detecting lodging crops is limited due to the complexity of lodging events and the unavailability of high spatial and temporal resolution data. Gaofen1 satellite was launched in 2013. The short revisit cycle and wide orbit coverage of the Gaofen1 satellite make it suitable for lodging identification. However, few studies have explored lodging detection using Gaofen1 data, and the operational application of existing approaches over large spatial extents seems to be unrealistic. In this paper, we discuss the identification method of lodged maize and explore the potential of using Gaofen1 data. An analysis of the spectral features after maize lodging revealed that reflectance increased significantly in all bands, compared to non-lodged maize. A spectral sum index was proposed to distinguish lodged and non-lodged maize. Two study areas were considered: Zhaodong City in Heilongjiang Province and Ningjiang District in Jilin Province. The results of the identified lodged maize from the Gaofen1 data were validated based on three methods: first, ground sample points exhibited the overall accuracies of 92.86% and 88.24% for Zhaodong City and Ningjiang District, respectively; second, the cross-comparison differences of 1.01% for Zhaodong City and 1.13% for Ningjiang District were obtained, compared to the results acquired from the finer-resolution Planet data; and third, the identified results from Gaofen1 data and those from farmer survey questionnaires were found to be consistent. The validation results indicate that the proposed index is promising, and the Gaofen1 data have the potential for rapid lodging monitoring. |
| WOS关键词 | RADARSAT-2 ; YIELD ; WHEAT ; CLASSIFICATION ; REFLECTANCE |
| 资助项目 | National Key Research and Development Program of China[2016YFB0501505] ; Independent Research and Development Project of Academy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs[ZZYFXKFZ201904] ; National Natural Science Foundation of China[41801353] |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000759812100001 |
| 资助机构 | National Key Research and Development Program of China ; Independent Research and Development Project of Academy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/171366]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Sun, Li |
| 作者单位 | 1.Minist Agr & Rural Affairs, Big Data Dev Ctr, Beijing 100125, Peoples R China 2.Minist Agr & Rural Affairs, Acad Agr Planning & Engn, Beijing 100121, Peoples R China 3.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 | Chen, Yuanyuan,Sun, Li,Pei, Zhiyuan,et al. A Simple and Robust Spectral Index for Identifying Lodged Maize Using Gaofen1 Satellite Data[J]. SENSORS,2022,22(3):14. |
| APA | Chen, Yuanyuan.,Sun, Li.,Pei, Zhiyuan.,Sun, Juanying.,Li, He.,...&You, Jiong.(2022).A Simple and Robust Spectral Index for Identifying Lodged Maize Using Gaofen1 Satellite Data.SENSORS,22(3),14. |
| MLA | Chen, Yuanyuan,et al."A Simple and Robust Spectral Index for Identifying Lodged Maize Using Gaofen1 Satellite Data".SENSORS 22.3(2022):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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