Time series normalized difference vegetation index (NDVI) is the primary data for agricultural remote sensing monitoring. Due to the tradeoff between a single sensor's spatial and temporal resolutions and the impacts of cloud coverage, the time series NDVI data cannot serve well for precision agriculture. In this study, a Gaussian kernel-based spatiotemporal fusion model (GKSFM) was developed to fuse high-resolution NDVI (Landsat) and low-resolution NDVI (MODIS) to produce a daily NDVI product at a 30-m spatial resolution. Considering that the NDVI curve of crop in each growing season can be characterized by Gaussian function, GKSFM used the Gaussian kernel to fit the nonlinear relationship between the high-resolution NDVI and the low-resolution NDVI, to obtain a more reasonable temporal increment. The experimental results show that GKSFM outperformed the comparative models in different proportions of cropland/noncropland and different crop phenology. In addition, GKSFM was also applied for crop mapping of Mishan County by fusing the NDVI images during the crop growing season. This study demonstrates that the accuracy of the proposed method can be improved in the midseason of crops.

中文翻译：

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基于高斯核的时空融合模型在农业遥感监测中的应用

时间序列归一化差异植被指数（NDVI）是农业遥感监测的主要数据。由于在单个传感器的空间和时间分辨率与云覆盖的影响之间进行权衡，因此时间序列NDVI数据不能很好地用于精确农业。在这项研究中，开发了一种基于高斯核的时空融合模型（GKSFM），以融合高分辨率NDVI（Landsat）和低分辨率NDVI（MODIS），以产生30 m空间分辨率的每日NDVI产品。考虑到每个生长季作物的NDVI曲线都可以用高斯函数表征，因此GKSFM使用高斯核拟合高分辨率NDVI和低分辨率NDVI之间的非线性关系，以获得更合理的时间增量。实验结果表明，在不同比例的耕地/非耕地和不同的作物物候方面，GKSFM优于比较模型。此外，GKSFM还通过在农作物生长季节融合NDVI图像，将其应用于密山县的农作物制图。这项研究表明，该方法的准确性可以在农作物的中期进行。