基于特征融合的精细归一化植被指数生成方法OA

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.