On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans Geosci Remote Sens

Earth Resources Technology Inc, Jessup, MD
IEEE Transactions on Geoscience and Remote Sensing (Impact Factor: 3.51). 08/2006; 44(8):2207-2218. DOI: 10.1109/TGRS.2006.872081
Source: DBLP

ABSTRACT The 16-day revisit cycle of Landsat has long limited its use for studying global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors' ability to quantify biophysical processes in heterogeneous landscapes. In this paper, the authors present a new spatial and temporal adaptive reflectance fusion model (STARFM) algorithm to blend Landsat and MODIS surface reflectance. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from Landsat can be blended for applications that require high resolution in both time and space. The MODIS daily 500-m surface reflectance and the 16-day repeat cycle Landsat Enhanced Thematic Mapper Plus (ETM+) 30-m surface reflectance are used to produce a synthetic "daily" surface reflectance product at ETM+ spatial resolution. The authors present results both with simulated (model) data and actual Landsat/MODIS acquisitions. In general, the STARFM accurately predicts surface reflectance at an effective resolution close to that of the ETM+. However, the performance depends on the characteristic patch size of the landscape and degrades somewhat when used on extremely heterogeneous fine-grained landscapes

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Available from: Forrest G. Hall, Jan 31, 2015
    • "STARFM relies on the assumption that land cover does not change between the estimation-and reference-time periods. A series of weights (spatial, temporal, and distance weights) were introduced to increase the ability of the fusion model to detect land cover change (Gao et al., 2006). STARFM has been proven useful to detect gradual changes but was shown to be less effective in detecting abrupt changes often caused by disturbances (Hilker et al., 2009). "
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    • "When considering international satellite missions such as Sentinel, CBERS-2, and IRS, the rich source of medium-resolution remotely sensed data suggests that we may now move urban mapping from the local and regional, to the global scale. Despite the great potential for the combined use of existing and future medium-resolution imagery, many issues deserve to be studied further, including cross-sensor comparison and normalization (Schroeder et al. 2006; Wulder et al. 2008), multisensor fusion (Gao et al. 2006; Weng et al. 2014), and utilization of full suite of Landsat-like data for any location and date (Powell et al. 2007; Gao et al. 2012). Significant challenges remain for mapping urbanization over large areas, in terms of validation and systematically processing data from multiple times, various sources/instruments , and different seasons (Gao et al. 2012). "
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    • "These approaches are especially suited when the input images exhibit significantly different spatial resolutions or temporal revisit times [50]. This assumption was used by the spatial and temporal adaptive reflectance fusion model (STARFM) [46], [51] for combining information from Landsat (30 m resolution) and MODIS (250 m to 1 km resolution, more frequent overpass), and by a full family of methods based on [52] for increasing the spatial resolution of MERIS (300 m) by mapping fractional abundances from Landsat through classification [53]–[55]. These methods are particularly interesting examples of fusion as multiple (spatial, spectral, temporal) information modes are jointly considered. "
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