GEE与多源遥感在冬小麦自动样本生成与分类中的应用——以邯郸市为例

doi:10.14088/j.cnki.issn0439-8114.2025.09.034

湖北农业科学 ›› 2025, Vol. 64 ›› Issue (9): 220-228.doi: 10.14088/j.cnki.issn0439-8114.2025.09.034

GEE与多源遥感在冬小麦自动样本生成与分类中的应用——以邯郸市为例

李亚强^1,2, 曹俊涛^1,2, 常宇飞^1,2, 孟成真^1,2, 张珺³, 刀剑⁴, 赵春雷^1,2, 权畅^1,2

1.中国气象局雄安大气边界层重点开放实验室,河北雄安 071800;
2.河北省气象局河北省气象科学研究所/河北省气象与生态环境重点实验室,石家庄 050000;
3.邯郸市气象局,河北邯郸 056002;
4.云南农业大学植物保护学院,昆明 650108

收稿日期:2025-07-17 出版日期:2025-09-25 发布日期:2025-10-28
通讯作者: 权畅（1987-）,男,河北石家庄人,高级工程师,主要从事生态遥感、作物分类研究,（电话）15633855594（电子信箱）quanchang1987@163.com。
作者简介:李亚强（1995-）,男,山西运城人,助理工程师,主要从事生态遥感、作物分类研究,（电话）18703448048（电子信箱）1978278407@qq.com。
基金资助:
中国气象局青年创新团队“高标准农田智慧气象保障技术”项目; 风云卫星应用先行计划（2023）项目（FY-APP-ZX-2023.01）; 河北省气象局青年基金项目（2025ky18; 2023ky05）; 海河流域气象科技创新项目（HHXM202507）

Application of GEE and multi-source remote sensing in automated sample generation and classification of winter wheat：Taking Handan City as an example

LI Ya-qiang^1,2, CAO Jun-tao^1,2, CHANG Yu-fei^1,2, MENG Cheng-zhen^1,2, ZHANG Jun³, DAO Jian⁴, ZHAO Chun-lei^1,2, QUAN Chang^1,2

1. China Meteorological Administration Xiong’an Atmospheric Boundary Layer Key Laboratory, Xiong’an 071800, Hebei, China;
2. Hebei Institute of Meteorological Sciences/Key Laboratory of Meteorology and Ecological Environment of Hebei, Hebei Meteorological Bureau, Shijiazhuang 050000, China;
3. Handan Meteorological Bureau, Handan 056002, Hebei, China;
4. College of Plant Protection, Yunnan Agricultural University, Kunming 650108, China

Received:2025-07-17 Published:2025-09-25 Online:2025-10-28

摘要/Abstract

摘要： 以邯郸市为研究对象,基于Google Earth Engine（GEE）平台与多源遥感数据,构建一种冬小麦训练样本自动生成与分类的方法。通过融合SNIC分割、物候特征筛选与随机森林算法,建立融合光谱、植被指数及纹理的多特征组合方案。结果显示,特征组合③（光谱+植被指数+纹理）的提取效果最优,其相对误差连续3年均为最低（2023年0.21%、2024年1.33%、2025年0.44%）,总体精度和Kappa系数逐年提升。基于该方案生成了2023—2025年邯郸市冬小麦种植空间分布图,邯郸市冬小麦种植空间分布呈东部平原集中、西部山区较少的分异特征。长势监测显示,2025年邯郸市冬小麦在整个生育期内光照、温度、降水量、湿度条件匹配良好,NDVI增量以偏好为主,整体长势优于2023年和2024年。自动化样本生成方法在大范围作物分类中具备良好的适用性与稳定性。

关键词: Google Earth Engine（GEE）, 多源遥感, 冬小麦, 样本生成, 作物分类, 邯郸市

Abstract: Taking Handan City as the study area, a method for automatic generation of training samples and classification of winter wheat was constructed based on the Google Earth Engine (GEE) platform and multi-source remote sensing data.By integrating SNIC segmentation, phenological feature screening, and the random forest algorithm, a multi-feature combination scheme incorporating spectra, vegetation indices, and texture was established. The results showed that feature combination ③ (spectra + vegetation indices + texture) achieved the best extraction performance, with the lowest relative error for three consecutive years (0.21% in 2023, 1.33% in 2024, and 0.44% in 2025), and the overall accuracy and Kappa coefficient improved annually.Based on this scheme, spatial distribution maps of winter wheat planting in Handan City from 2023 to 2025 were generated, revealing a distribution pattern concentrated in the eastern plains and sparse in the western mountainous areas. Growth monitoring indicated that the light, temperature, precipitation, and humidity conditions during the entire growth period of winter wheat in Handan City in 2025 were well-matched, the NDVI increment was predominantly favorable, and the overall growth status was better than that in 2023 and 2024. The automatic generation sample method demonstrated good applicability and stability in large-scale crop classification.

Key words: Google Earth Engine (GEE), multi-source remote sensing, winter wheat, sample generation, crop classification, Handan City

中图分类号:

P237

李亚强, 曹俊涛, 常宇飞, 孟成真, 张珺, 刀剑, 赵春雷, 权畅. GEE与多源遥感在冬小麦自动样本生成与分类中的应用——以邯郸市为例[J]. 湖北农业科学, 2025, 64(9): 220-228.

LI Ya-qiang, CAO Jun-tao, CHANG Yu-fei, MENG Cheng-zhen, ZHANG Jun, DAO Jian, ZHAO Chun-lei, QUAN Chang. Application of GEE and multi-source remote sensing in automated sample generation and classification of winter wheat：Taking Handan City as an example[J]. HUBEI AGRICULTURAL SCIENCES, 2025, 64(9): 220-228.

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GEE与多源遥感在冬小麦自动样本生成与分类中的应用——以邯郸市为例

Application of GEE and multi-source remote sensing in automated sample generation and classification of winter wheat：Taking Handan City as an example

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