An Inversion-Based Fusion Method for Inland Water Remote Monitoring
Although remote sensing technology has been widely used to monitor inland water bodies, the lack of suitable data with high spatial and spectral resolutions has seriously obstructed the practical development of inland water color remote sensing. An inversion-based fusion (IBF) algorithm is proposed to fuse water color and high-spatial resolution images. The algorithm was applied to two datasets: The Hyperion simulated dataset (dataset #1) and a pair of Environmental Satellite 1 (HJ1, launched by China in 2009) and medium resolution imaging spectrometer images (dataset #2). The fusions are quantitatively and qualitatively compared with three widely used algorithms. The results show that the IBF algorithm performs better using both evaluation indexes and visual comparisons. A discussion of the free parameter window size n and the contribution of the low-resolution image (LRI) to the spatial distribution (WLRI) shows that a largern will result in both greater model errors and better control of geometric errors, whereas WLRI helps stabilize the algorithm. Finally, chlorophyll-a concentration maps are developed from the fusions. The significant advantage of the IBF-derived chlorophyll-a concentration map indicates that the IBF algorithm has the potential to advance the monitoring of optical complex inland water.