Time series image fusion: Application and improvement of STARFM for land cover map and production

Nowadays, several optical space-borne systems with high resolution, high temporal revisit frequency and constant viewing angles are preparing to be be launched: Venμs, Sentinel-2, etc. The usefulness of these data will be limited due to for instance cloud coverage over the scene. Image fusion techniques with other satellite products of even higher revisit frequency will dramatically promote the usefulness of the data. Therefore, our objective is to find the proper image fusion technique to adapt these new missions. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is one the techniques we implemented. During our research, this technique is modified to fit the parameters of our data, and the result shows an obvious improvement.

[1]  Olivier Hagolle,et al.  Low and high spatial resolution time series fusion for improved land cover map production , 2011, 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp).

[2]  Gérard Dedieu,et al.  A framework for the simulation of high temporal resolution image series , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[3]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[4]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[5]  F. Gao,et al.  Mapping Wildland Fire Scar Using Fused Landsat and MODIS Surface Reflectance , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.