Rapid early-season maize mapping without crop labels
文献类型：期刊论文
| 作者 | You, Nanshan4,5; Dong, Jinwei; Li, Jing3; Huang, Jianxi2; Jin, Zhenong4 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2023-05-15 |
| 卷号 | 290 |
| ISSN号 | 1879-0704 |
| 关键词 | Early-season maize mapping Red-edge position Gaussian mixture model Corn belts Google Earth Engine |
| DOI | 10.1016/j.rse.2023.113496 |
| 文献子类 | Article |
| 英文摘要 | Maize (Zea mays), the second most-produced crop worldwide, serves as the cornerstone for global food security and human livelihood. Early-season maize mapping benefits maize production forecasting and other pre-harvest decision-making applications. However, most existing early-season mapping efforts rely heavily on either the current-year or historical crop labels to train classifiers, limiting the potential applications to new regions lacking crop labels. To explore the possibility of maize mapping only using satellite data in the early season, we proposed a Multi-temporal Gaussian Mixture Model (MGMM) to map maize planting areas without any crop labels. A chlorophyll content relevant proxy, named the Red-edge position (REP), was selected as model input, based on the truth that summer maize tends to show a higher chlorophyll content than other summer crops (e.g., soybean, cotton, peanut, sunflowers, etc.) in the peak season. The novel early-season mapping framework using the REP-based MGMM (MGMM-REP) was applied in four diverse areas (Iowa and Georgia in the US, Heilongjiang province (HLJ) in China, and Grand-Est in France). The MGMM-REP could generate maize maps more than two months before harvest with reasonable accuracy (F1 >= 77%) using all the available Sentinel-2 (S2) images and the Google Earth Engine platform (GEE). Our early-season maps agreed well with the existing crop maps and official statistics. The correlation coefficient (R) of the maize acreage between our early-season maps and sta-tistics was higher than 0.94. The high inter-class difference of REP between maize and other summer crops could increase the F1 score by 2-47% compared to the other commonly used Vegetation indices (VIs). Since MGMM-REP does not rely on crop labels, it had the potential to be transferred to label-scarce maize-cropped regions and contribute to the international commodity trade and food security forecast. |
| WOS关键词 | LAND-COVER ; RANDOM FOREST ; QUANTITATIVE ESTIMATION ; FEATURE-SELECTION ; NATIONAL-SCALE ; AREA ; RED ; SENTINEL-2 ; INDEX ; SYSTEM |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 出版者 | ELSEVIER SCIENCE INC |
| WOS记录号 | WOS:000951996700001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/190237]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 作者单位 | 1.China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China 2.Chinese Acad Agr Sci, Minist Agr & Rural Affairs, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing, Beijing 100081, Peoples R China 3.Univ Minnesota Twin Cities, Dept Bioprod & Biosyst Engn, St Paul, MN 55108 USA 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | You, Nanshan,Dong, Jinwei,Li, Jing,et al. Rapid early-season maize mapping without crop labels[J]. REMOTE SENSING OF ENVIRONMENT,2023,290. |
| APA | You, Nanshan,Dong, Jinwei,Li, Jing,Huang, Jianxi,&Jin, Zhenong.(2023).Rapid early-season maize mapping without crop labels.REMOTE SENSING OF ENVIRONMENT,290. |
| MLA | You, Nanshan,et al."Rapid early-season maize mapping without crop labels".REMOTE SENSING OF ENVIRONMENT 290(2023). |
入库方式： OAI收割
来源：地理科学与资源研究所
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