基于深度学习的干旱绿洲地区Sentinel-2时间序列物候提取与作物分类

王春莉; 池佳男; 张晓; 张楠楠

doi:10.1631/jzus.B2500403

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Research Article | Updated：2026-05-15

基于深度学习的干旱绿洲地区Sentinel-2时间序列物候提取与作物分类
Deep learning-based phenology extraction and crop classification in arid oasis using Sentinel-2 time series
浙江大学学报（英文版）（B辑：生物医学和生物技术） 2026年27卷第5期页码：537-560
Affiliations：

1.College of Information Engineering, Tarim University, Alar 843300, China
2.Key Laboratory of Oasis Agriculture in Tarim, Tarim University, Alar 843300, China
Funds：

the National Natural Science Foundation of China(32101621;62061041);the Bingtuan Science and Technology Program(2022CB001-05);the Graduate Scientific Research Innovation Project of Tarim University(TDGRI2024092)
DOI：10.1631/jzus.B2500403
中图分类号：
CSTR：
收稿：2025-07-11，

修回：2025-12-03，

网络首发：2026-04-15，

纸质出版：2026-05-15
Accepted：

王春莉,池佳男,张晓等.基于深度学习的干旱绿洲地区Sentinel-2时间序列物候提取与作物分类[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2026,27(05):537-560.

Chunli WANG, Jianan CHI, Xiao ZHANG, et al. Deep learning-based phenology extraction and crop classification in arid oasis using Sentinel-2 time series[J]. Journal of Zhejiang University-SCIENCE B, 2026, 27(5): 537-560.
王春莉,池佳男,张晓等.基于深度学习的干旱绿洲地区Sentinel-2时间序列物候提取与作物分类[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2026,27(05):537-560. DOI： 10.1631/jzus.B2500403.

Chunli WANG, Jianan CHI, Xiao ZHANG, et al. Deep learning-based phenology extraction and crop classification in arid oasis using Sentinel-2 time series[J]. Journal of Zhejiang University-SCIENCE B, 2026, 27(5): 537-560. DOI： 10.1631/jzus.B2500403.

摘要

多时相遥感数据在大规模作物物候识别与分类中应用日益广泛，尤其适用于种植结构复杂的干旱绿洲农业区域的精细化管理。本研究构建了一个融合Sentinel-2多时相影像与归一化植被指数（NDVI）时间序列的深度学习框架，用于对中国新疆图木舒克市的棉花、冬枣和油莎豆进行分类制图。采用最小冗余最大相关（mRMR）算法进行光谱与植被指数特征选择，结合Savitzky-Golay（S-G）滤波与双逻辑函数拟合，自动提取关键物候参数（生长季开始（SOS）、生长季峰值（POS）和生长季结束（EOS）），显著提升了物候特征提取的精度。通过融合多时相Sentinel-2数据与多尺度特征融合策略，系统比较了五种分类模型（MLP、ResNet-18、ConvLSTM、Transformer和RFC），结果表明高分辨率空间细节能显著增强复杂环境下作物边界划定与分类一致性。通过多尺度窗口分析进一步优化Transformer的空间表征能力，发现1×1+3×3+5×5卷积窗口在精度与计算效率间达到最佳平衡。在未参与训练区域的独立验证表明模型具有良好的泛化能力，三种作物（冬枣、棉花和油莎豆）的F1分数分别达到94.37%、87.75%和86.35%。综上所述，本研究验证了Sentinel-2时间序列数据与深度神经网络在多作物环境中具有高精度识别潜力，且能够实现作物分布的精确空间制图，为干旱绿洲地区的智慧农业决策提供方法支持。

Abstract

Multi-temporal remote sensing data in large-scale crop phenology identification and classification have become increasingly utilized

particularly for precision management in arid oasis agricultural regions with complex cropping systems. In this study

we developed a deep learning framework integrating Sentinel-2 multi-temporal imagery and normalized difference vegetation index (NDVI) time series for mapping cotton

winter jujube

and tiger nut crops in Tumushuke City

Xinjiang Uygur Autonomous Region

China. We employed the minimum redundancy maximum relevance (mRMR) algorithm for spectral and vegetation index feature selection

followed by Savitzky-Golay (S-G) filtering and double logistic function fitting

to automatically extract the key phenological parameters (start of season (SOS)

peak of season (POS)

and end of season (EOS))

significantly improving phenological feature extraction accuracy. By incorporating multi-temporal Sentinel-2 data and a multi-scale feature fusion approach

we could systematically compare five classification models (multi-layer perceptron (MLP)

residual network-18 (ResNet-18)

convolutional long short-term memory (ConvLSTM)

Transformer

and random forest classifier (RFC))

demonstrating that high-resolution spatial details substantially enhance crop boundary delineation and classification consistency in complex environments. Further optimization of Transformer’s spatial representation through multi-scale window analysis revealed that the use of 1×1+3×3+5×5 convolutional windows achieves an optimal balance between accuracy and computational efficiency. Independent validation on unseen areas confirmed robust model transferability

with F1 scores of 94.37%

87.75%

and 86.35% for the three crops (winter jujube

cotton

and tiger nut)

respectively. This study validates the high-precision identification potential of Sentinel-2 temporal data and deep neural networks for multi-crop environments

enabling the precise spatial mapping of crop distributions and providing methodological support for smart agricultural decision-making in arid oasis regions.

关键词

Keywords

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