An improved approach to estimating crop lodging percentage with Sentinel-2 imagery using machine learning
文献类型：期刊论文
| 作者 | Guan, Haixiang2; Huang, Jianxi2,3,5; Li, Xuecao2,3; Zeng, Yelu2,3; Su, Wei2,3; Ma, Yuyang1; Dong, Jinwei4; Niu, Quandi2; Wang, Wei2 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2022-09-01 |
| 卷号 | 113页码:17 |
| 关键词 | Crop lodging Spatial aggregation Lodging percentage map Spectral bands Vegetation indexes Region scale |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2022.102992 |
| 通讯作者 | Huang, Jianxi(jxhuang@cau.edu.cn) |
| 英文摘要 | It is imperative to rapidly and precisely acquire crop lodging area and severity for disaster prevention and yield prediction. However, estimation of crop lodging area at a large scale remains challenging due to the relatively low sensitivity of remote sensing signal to the lodging variation, limited availability of remote sensing images, and lodging statistical data. This study proposes a new method for lodging area estimation based on the optimal grid cell of Sentinel-2 and crop lodging percentage, overcoming the limitation of traditional pixel-based mapping approaches that fail to obtain quantitative lodging information. Basing the spatial aggregation method, we analyzed the optimal grid size of Sentinel-2 data for lodging percentage estimation. Then we investigated the spectral response for different lodging percentage levels and analyzed the potential of lodging percentage esti-mation for Sentinel-2 metrics (including selected spectral bands and their derived vegetation indexes (VIs)). A quantitative model was established between the training set and the Sentinel-2 metrics using the random forest (RF) algorithm. Finally, around 1462.62 ha fields from six counties or districts in Heilongjiang province in China were estimated for lodging percentage. Results indicate that the proposed method can estimate the crop lodging percentage on the testing set with an R2 and RMSE of 0.64 and 25.24, respectively, which can explain around 95 % spatial variation of lodging crop. Moreover, the overall magnitude of reflectance increased with the increase in lodging percentage. Among all Sentinel-2 optimal metrics, the Green, SWIR1, and Red edge 1 bands are the most crucial indicators for lodging percentage estimation. Our results on lodging percentage estimation in the study area indicate that there is more lodging maize in the Meilisidawoerzu district than in other areas. Although typhoons passed over Fuyu and Lindian counties, the lodging percentage in these areas is relatively low. The lodging percentage map has great value in agriculture management and insurance claim. |
| WOS关键词 | ANCHORAGE STRENGTH ; OPTIMAL SCALE ; WINTER-WHEAT ; MAIZE ; RADARSAT-2 ; VEGETATION ; INDEXES ; WIND ; AGGREGATION ; RESISTANCE |
| 资助项目 | National Natural Science Founda-tion of China[41971383] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000849822800001 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Founda-tion of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/182111]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Huang, Jianxi |
| 作者单位 | 1.China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China 2.China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China 3.Minist Agr & Rural Affairs, Key Lab Remote Sensing Agrihazards, Beijing 100083, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 5.China Agr Univ, Coll Land Sci & Technol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guan, Haixiang,Huang, Jianxi,Li, Xuecao,et al. An improved approach to estimating crop lodging percentage with Sentinel-2 imagery using machine learning[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2022,113:17. |
| APA | Guan, Haixiang.,Huang, Jianxi.,Li, Xuecao.,Zeng, Yelu.,Su, Wei.,...&Wang, Wei.(2022).An improved approach to estimating crop lodging percentage with Sentinel-2 imagery using machine learning.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,113,17. |
| MLA | Guan, Haixiang,et al."An improved approach to estimating crop lodging percentage with Sentinel-2 imagery using machine learning".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 113(2022):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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