基于UBiaSTF时空融合模型的时序NDVI重建方法研究

Temporal NDVI Reconstruction Method Based on UBiaSTF Spatiotemporal Fusion Model

摘要:

高时空分辨率的NDVI数据在农业遥感应用中具有重要意义。时空融合（STF）模型可以作为提高NDVI数据时空分辨率的一种有效途径。提出了一种将UNet框架集成到BiaSTF中的STF模型UBiaSTF，并将其应用于内蒙古河套灌区解放闸灌域的Landsat 8和Sentinel-2与MODIS影像的时序NDVI融合中，并与ESTARFM和BiaSTF模型进行对比，分析其在遥感时序NDVI重建中的效果。结果表明，UBiaSTF模型在NDVI时间序列重建中表现优异，决定系数R2较其他模型显著提高，最高达到了0.930；同时UBiaSTF模型在长时间序列数据融合任务中的稳定性较强，能有效克服参考影像时相间隔改变对预测精度的影响；并且UBiaSTF模型在不同植被覆盖类别上的时间序列NDVI重建与实际变化最吻合，相较于ESTARFM和BiaSTF表现出更低的融合误差。该模型可作为植被覆盖区域时间序列NDVI重建的有效工具。

Abstract:

High spatial and temporal resolution NDVI data is of great significance in the application of agricultural remote sensing. Spatiotemporal fusion (STF) models can serve as an effective approach to enhance the spatiotemporal resolution of NDVI data. An STF model, UBiaSTF, was proposed which integrated the Unet framework into BiaSTF, and applied it to the spatiotemporal NDVI fusion of Landsat 8 and Sentinel-2 with MODIS imagery in the Jiefangzha Irrigation District. The model was compared with ESTARFM and BiaSTF models to analyze its effectiveness in the reconstruction of remote sensing time series NDVI. The results indicated that the UBiaSTF model performed excellently in the reconstruction of NDVI time series, with the coefficient of determination R2 significantly improved compared with that of other models, reaching a maximum of 0.930. Additionally, the UBiaSTF model demonstrated strong stability in long time series data fusion tasks, effectively overcoming the impact of reference image temporal interval changes on prediction accuracy. Furthermore, the UBiaSTF model showed the lowest fusion error in the reconstruction of time series NDVI across different vegetation coverage categories compared with ESTARFM and BiaSTF, closely matching the actual changes. This model can serve as an effective tool for the reconstruction of time series NDVI in areas with vegetation coverage.