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


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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    • "These methods rely on models with various degrees of complexity that are based on the empirical relationships between simultaneous images with different resolutions. Gao et al. (2006) developed an empirical fusion technique that blended 500-m MODIS and Landsat TM surface reflectance values using an initial Landsat image and a pair of MODIS images. Roy et al. (2008) developed a semi-empirical fusion approach using MODIS/Bidirectional Reflectance Distribution Function (BRDF) albedo data and Landsat TM surface reflectance data to generate synthetic Landsat images that accounted for the directional dependence of surface reflectance. "

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    • "However, they face challenges in heterogeneous regions with abrupt land cover type changes. Most weighted function based methods assume no land cover type changes between input and prediction date (Fu et al., 2013; Gao et al., 2006; Weng, Fu, & Gao, 2014; Zhu et al., 2010). As a result, they can successfully predict pixels with changes in attributes like vegetation phenology or soil moisture, because these changes are strongly related to the changes in similar pixels selected from the input imagery. "

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