融合自动样本生成与可解释学习的风云三号时序NDVI冬小麦识别&#x0002A;

王军强; 孙震辉; 孟庆岩; 张琳琳; 孙云晓

doi:10.7523/j.ucas.2026.010

中国科学院大学学报 >

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DOI: https://doi.org/10.7523/j.ucas.2026.010

融合自动样本生成与可解释学习的风云三号时序NDVI冬小麦识别^*

王军强 ,
孙震辉 ,
孟庆岩 ,
张琳琳 ,
孙云晓

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1.生态环境部土壤与农业农村生态环境监管技术中心, 北京 100012;
2.天津城建大学地质与测绘学院, 天津 300384;
3.中国科学院空天信息创新研究院, 北京 100101;
4.中国科学院大学, 北京 100049;
5.海南空天信息研究院海南省地球观测重点实验室, 海南三亚 572029

收稿日期: 2025-11-21

修回日期: 2026-03-23

网络出版日期: 2026-04-21

基金资助

^*生态环境部土壤与农业农村生态环境监管技术中心双碳课题(2024-11),风云三号03批气象卫星工程地面应用系统生态监测评估应用项目（第一期）(ZQC-R22227)和天津市科学技术普及项目（24KPHDRC00240）资助

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Winter wheat identification from FengYun-3 NDVI time series with automatic sample generation and interpretable learning

WANG Junqiang ,
SUN Zhenhui ,
MENG Qingyan ,
ZHANG Linlin ,
SUN Yunxiao

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1 Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100012, China;
2 School of Geology and Geomatics, Tianjin Chengjian University, Tianjin 300384, China;
3 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China;
4 University of Chinese Academy of Sciences, Beijing 100049, China;
5 Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, Hainan, China

Received date: 2025-11-21

Revised date: 2026-03-23

Online published: 2026-04-21

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摘要

及时、准确获取冬小麦信息至关重要,高质量样本的自动生成和识别模型的可解释性依然是制约当前作物提取和制图的重要问题。基于风云三号D星NDVI时序产品,本文提出了一种自动样本生成与可解释CatBoost模型的冬小麦识别方法,高质量分割一切模型(HQ-SAM)用于自动生成高质量样本,Shapley加性解释模型（SHAP）对CatBoost进行可视化解释。结果表明：（1）HQ-SAM可以生成高质量训练样本,降低样本制作成本;（2）鲸鱼优化算法优化后的CatBoost准确度和F1值分别为96.43%和96.36%;（3）SHAP模型分析发现时相的重要性排序及正负贡献方向与冬小麦生长规律一致;（4）基于SHAP模型时相重要性排序结果,前17个时相形成的组合取得了最高精度。本研究为风云气象卫星在农作物提取的应用中提供了技术支撑。

关键词： 风云三号; 样本生成; 模型可解释; 冬小麦; 作物制图

本文引用格式

王军强 , 孙震辉 , 孟庆岩 , 张琳琳 , 孙云晓 . 融合自动样本生成与可解释学习的风云三号时序NDVI冬小麦识别^*[J]. 中国科学院大学学报, 0 : 70 . DOI: 10.7523/j.ucas.2026.010

Abstract

Winter wheat is one of the most important staple crops in China and worldwide, and the timely and accurate acquisition of winter wheat information is crucial for agricultural monitoring, yield assessment, and food security. However, the automatic generation of high-quality training samples and the interpretability of classification models remain key challenges limiting the reliability and applicability of current crop identification and mapping approaches. Based on the NDVI time-series product derived from the FengYun-3D meteorological satellite, this study proposes a winter wheat identification method integrating automatic sample generation with an interpretable CatBoost model. A High-Quality Segment Anything Model (HQ-SAM) is introduced to automatically generate high-quality training samples, and effectively reduce labor cost and uncertainty associated with manual sample collection. To further enhance classification performance, the Whale Optimization Algorithm (WOA) is employed to optimize the CatBoost classifier. In addition, the Shapley Additive Explanations (SHAP) method is applied to provide quantitative and visual interpretations of model predictions, enabling analysis of temporal feature contributions. Experimental results demonstrate that: (1) HQ-SAM can reliably generate high-quality training samples for winter wheat identification; (2) the WOA-optimized CatBoost model achieves an overall accuracy of 96.43% and an F1 value of 96.36%; (3) SHAP-based analysis reveals that the importance ranking and contribution directions of temporal phases are consistent with winter wheat phenology; and (4) the combination of the top 17 most important temporal phases yields the highest classification accuracy.

Key words： FengYun-3D; sample generation; model interpretability; winter wheat; crop mapping

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