Spatio-temporal data fusion model is a feasible way to obtain high spatial resolution and high temporal resolution images in crop monitoring. As vegetation indices such as Normalized Difference Vegetation Index (NDVI) are generally used directly to monitor the vegetation growth, in this study, two recently proposed spatio-temporal data fusion methods (FSDAF and DPM-STVIFM) were evaluated for generating NDVI time series in cropland areas. It is found that both methods have limitations and the performances of the two methods vary with the dates of available fine-resolution images and the degree of land cover changes between the available fine-resolution images and the synthetic fine-resolution images.
['Chunhua Liao', 'Jinfei Wang']
Evaluation of spatio-temporal data fusion methods for generating NDVI time series in cropland areas