A New Spatiotemporal Data Fusion Method to Reconstruct High-Quality Landsat Ndvi Time-Series Data

Landsat NDVI time-series data have great potential in global change research. However, Landsat NDVI time-series data are quite discontinuous due to frequent cloud contamination. Although spatiotemporal data fusion technology has been widely used to reconstruct Landsat NDVI time-series data, the existing spatiotemporal fusion methods usually ignore the effective use of partially cloud-contaminated images. In this study, we presented a new spatiotemporal fusion method, which employed the cloud-free pixels in the partially cloud-contaminated images to Correct the inconsistency between MODIS and Landsat data in Spatiotemporal DAta Fusion (called CSDAF). By comparing with recently developed advanced algorithm IFSDAF, we found that CSDAF performed better in terms of both visual inspections and quantitative evaluations. Besides, CSDAF is simple and its running time is about 1/4 of that of IFSDAF. Because of high accuracy and running efficiency, we expect that CSDAF has the potential to be widely used in forestry investigations, ecological evaluations and other relevant fields.