Abstract

The availability of concurrently high spatiotemporal resolution remote sensing data is highly desirable as they represent a key element for effective monitoring in various environmental applications. However, due to the tradeoff between the spatial resolution and acquisition frequency of current satellites, such data are still lacking. Many studies have been undertaken trying to overcome these problems; however, a couple of long-standing limitations remain, including accommodating abrupt temporal changes, dealing with complex and heterogeneous landscapes, and integrating other satellite datasets as well. Accordingly, this paper proposes a deep learning spatiotemporal data fusion approach based on Very Deep Super-Resolution (VDSR) to fuse the NDVI retrievals from Sentinel-2 and Landsat 8 images. The performances of VDSR are analyzed in comparison with those of two other classical methods, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF) method. The results obtained indicate that VDSR outperforms other data fusion algorithms as it generated the least blurred images and the most accurate predictions of synthetic NDVI values, particularly in areas with heterogeneous landscapes and abrupt land-cover changes. The proposed algorithm has broad prospects to improve near-real-time agricultural monitoring purposes and derivation of crop status conditions in the field-scale.

摘要

迫切需要同时提供高时空分辨率遥感数据，因为它们代表了在各种环境应用中进行有效监控的关键要素。但是，由于在空间分辨率和当前卫星的获取频率之间进行权衡，因此仍缺乏此类数据。为了克服这些问题，进行了许多研究。然而，仍然存在一些长期的局限性，包括适应突然的时间变化，处理复杂多样的景观以及整合其他卫星数据集。因此，本文提出了一种基于超深度超分辨率（VDSR）的深度学习时空数据融合方法，以融合Sentinel-2和Landsat 8图像的NDVI检索。与其他两种经典方法（增强的时空自适应反射融合模型（ESTARFM）和灵活的时空数据融合（FSDAF）方法）相比，分析了VDSR的性能。获得的结果表明，VDSR在生成最少的模糊图像和最准确的合成NDVI值预测方面，胜过其他数据融合算法，尤其是在地形异质和土地覆盖突然变化的地区。所提出的算法在改善近实时农业监测目的和在田间尺度上获得作物状况条件方面具有广阔的前景。获得的结果表明，VDSR在生成最少的模糊图像和最准确的合成NDVI值预测方面，胜过其他数据融合算法，尤其是在地形异质和土地覆盖突然变化的地区。所提出的算法在改善近实时农业监测目的和在田间尺度上获得作物状况条件方面具有广阔的前景。获得的结果表明，VDSR在生成最少的模糊图像和最准确的合成NDVI值预测方面，胜过其他数据融合算法，尤其是在地形异质和土地覆盖突然变化的地区。所提出的算法在改善近实时农业监测目的和在田间尺度上获得作物状况条件方面具有广阔的前景。