山东农业大学学报（自然科学版）2026，Vol.57Issue(4)：675-689,15.DOI:10.3969/j.issn.1000-2324.2026.04.009
基于特征融合的精细归一化植被指数生成方法
A Spatiotemporal Feature Fusion-Based Super-Resolution Method for Normalized Difference Vegetation Index
摘要
Abstract
High-resolution large-scale Normalized Difference Vegetation Index(NDVI)is an important parameter for assessing crop growth,and obtaining high-quality NDVI data is of great significance for agricultural remote sensing.To address the need for generating high spatiotemporal resolution NDVI data using time-series Landsat 8 and Sentinel-2 data,this study constructs a temporal super-resolution model for NDVI data generation(Temporal Super-Resolution Network,TSRNet).Based on an encoder-fusion-decoder architecture,the model employs a temporal attention mechanism to calculate the feature contribution of each time step,capturing dependencies across multiple temporal phases.Additionally,a spatial enhancement module integrating channel attention,spatial attention,and global attention is designed to achieve multi-level,multi-scale feature enhancement.By balancing the contribution of temporal features and multidimensional enhancement of spatial features,TSRNet significantly improves the accuracy of spatiotemporal feature extraction.It can generate NDVI data with quality consistent with that extracted directly from high-resolution images,better preserving the variation trend of the original NDVI data and effectively retaining crop phenology information.Three models—HighResNet,UTAE,and TR-MISR—are selected for comparison.Experimental results show that TSRNet outperforms the compared models in metrics such as R²,PSNR,SSIM,indicating its significant advantages in spatiotemporal feature extraction and image detail reconstruction.关键词
深度学习/农作物/归一化植被指数/超分辨率/物候信息Key words
Deep Learning/crops/Normalized Difference Vegetation Index(NDVI)/Super-Resolution/temporal information分类
信息技术与安全科学引用本文复制引用
王奕涵,李峰,侯学会,李兆龙,林鹏瑜,刘晨阳,常万炬,杨晓霞,张承明..基于特征融合的精细归一化植被指数生成方法[J].山东农业大学学报（自然科学版）,2026,57(4):675-689,15.基金项目
风云卫星应用先行计划(FY-APP-ZX-2023.01) （FY-APP-ZX-2023.01）
山东省气象局创新团队专项(2024sdcxtd04) （2024sdcxtd04）
长岛国家气候观象台开放基金项目(2024cdkfz04) （2024cdkfz04）
山东省自然科学基金(ZR2021MD097) （ZR2021MD097）
