An effective corn and soybean mapping model integrating phenological and biophysical information based on seasonal median composite satellite imagery
文献类型：期刊论文
| 作者 | Chen, Hui1,4; Chao, Aosheng1,4; Dong, Jinwei2,3; Li, Zhichao2,3; Yang, Peng1,4; Sun, Jing1,4; Wu, Wenbin1,4 |
| 刊名 | GISCIENCE & REMOTE SENSING
 |
| 出版日期 | 2026-12-31 |
| 卷号 | 63期号:1页码:2609467 |
| 关键词 | Corn and soybean mapping crop index-based classification method Sentinel-2 image pigment index |
| ISSN号 | 1548-1603 |
| DOI | 10.1080/15481603.2025.2609467 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Accurate and timely monitoring of corn and soybean distribution is crucial for ensuring food security, nutritional diets, and environmental conservation. While crop-specific classifiers have garnered considerable attention, distinguishing between corn and soybean remains challenging due to their similar phenological trajectories, spectral characteristics, and the lack of effective indicative features. Efforts to improve their separability typically rely on integrating multi-source data, which incur substantial temporal and economic costs, and often falls short of meeting the demands of large-scale, time-sensitive applications. To address these limitations, we present a highly-effective model for mapping corn and soybean that leverages two robust time-series-independent crop indices, derived from a seasonal median composite Sentinel-2 imagery, that integrate phenological and biophysical characteristics. We first develop an integrative index to identify corn by capturing carotenoid accumulation patterns across key phenological stages. Subsequently, we construct an unsupervised classifier that synergistically integrates the discriminative features of the proposed corn index and a soybean-specific mapping index during their peak growth season. We evaluate the model across five environmentally diverse study sites (A-E) spanning three major agricultural countries. Applying a grid search method in site A, we identify two optimal thresholds, which are then applied to all study sites without site-specific calibration. The model achieves an average overall accuracy (OA) of 88.90% and F1 score of 0.88., outperforming a random forest classifier with an average OA of 82.77% and F1 score of 0.80. Threshold sensitivity analysis indicates the optimal thresholds for each study site yields an average OA gain of 0.49% compared with the applied thresholds, demonstrating the model's robustness to threshold variations. Our results highlight the potential of efficient, crop-specific indices to enable low data-dependent classification frameworks for large-scale, time-sensitive crop mapping. |
| URL标识 | 查看原文 |
| WOS关键词 | TIME-SERIES ; WATER-CONTENT ; INDEX ; SENTINEL-1 ; EXTENT ; MAIZE |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001651743600001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219691]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 |
| 通讯作者 | Li, Zhichao; Sun, Jing; Wu, Wenbin |
| 作者单位 | 1.Chinese Acad Agr Sci, Key Lab Agr Remote Sensing, Minist Agr & Rural Affairs, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Resource Use & Environm Remediat, Beijing 100101, Peoples R China; 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arable Land China, Beijing, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Chen, Hui,Chao, Aosheng,Dong, Jinwei,et al. An effective corn and soybean mapping model integrating phenological and biophysical information based on seasonal median composite satellite imagery[J]. GISCIENCE & REMOTE SENSING,2026,63(1):2609467. |
| APA | Chen, Hui.,Chao, Aosheng.,Dong, Jinwei.,Li, Zhichao.,Yang, Peng.,...&Wu, Wenbin.(2026).An effective corn and soybean mapping model integrating phenological and biophysical information based on seasonal median composite satellite imagery.GISCIENCE & REMOTE SENSING,63(1),2609467. |
| MLA | Chen, Hui,et al."An effective corn and soybean mapping model integrating phenological and biophysical information based on seasonal median composite satellite imagery".GISCIENCE & REMOTE SENSING 63.1(2026):2609467. |
入库方式： OAI收割
来源：地理科学与资源研究所
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