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Current Version

STARFM, Version 1.2.2


Landsat 30m resolution observations provide sufficient spatial details for monitoring land surface and changes. However, the 16-day revisit cycle and cloud contamination have limited its use for studying global biophysical processes, which evolve rapidly during the growing season. Meanwhile, MODIS sensors aboard the NASA EOS Terra and Aqua satellites provide daily global observations valuable for capturing rapid surface changes. However, the spatial resolution of 250m to 1000m may not good enough for heterogeneous areas. To better utilize Landsat and MODIS data, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was developed (Gao et al., 2006). The STARFM algorithm uses spatial information from fine-resolution Landsat imagery and temporal information from coarse-resolution MODIS imagery to produce estimates of surface reflectance that are high resolution in both space and time. In essence, the collection of daily MODIS imagery and seasonal Landsat imagery allows the generation of synthetic •daily• Landsat-like views of the Earth•s surface.

The STARFM algorithm uses comparisons of one or more pairs of observed Landsat/MODIS maps, collected on the same day, to predict maps at Landsat-scale on other MODIS observation dates. STARFM was initially developed at the NASA Goddard Space Flight Center. This version (v1.2) has been greatly improved in computing efficiency (e.g. one run for multiple dates and parallel computing) for large-area processing (Gao et al., 2015). Additional improvements (e.g. Landsat and MODIS images co-registration, daily MODIS nadir BRDF-adjusted reflectance) in the operational data fusion system (Wang et al., 2014) are beyond the STARFM program and are not included in this package. Improvement and continuous maintenance are being undertaken in the USDA-ARS Hydrology and Remote Sensing Laboratory (HRSL), Beltsville, MD by Dr. Feng Gao.

Gao, F., Masek, J., Schwaller M. and Hall, F. On the blending of the Landsat and MODIS surface reflectance: predict daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing. 44 (8): 2207-2218. 2006.

Wang, P., Gao, F. and Masek, J. Operational data fusion framework for building frequent Landsat-like images in cloudy regions. IEEE Transactions on Geoscience and Remote Sensing. 52(11):7353-7365. doi: 10.1109/TGRS.2014.2311445. 2014.

Gao, F., Hilker, T., Zhu, X., Anderson, M. A., Masek, J., Wang, P. and Yang, Y. Fusing Landsat and MODIS data for vegetation monitoring, IEEE Geoscience and Remote Sensing Magazine. 3(3): 47-60. doi: 10.1109/MGRS.2015.2434351. 2015.

Unconditional Release

The package is now being released at no cost to the public for further development. ARS is releasing the STARFM so that interested parties can use the software tool for their own needs and purposes. ARS does not foresee providing monetary or technical support to refine, adapt, or use this software tool, and provides no warranty for its use for any purpose. ARS does not reserve any rights or interests in the work that may be performed by others to refine or adapt it. ARS does reserve the right to continue its own refinement of the current version of the software package at a later date, should program needs require it.



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