On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance

By Feng Gao, J. Masek, M. Schwaller, F. Hall. IEEE Transactions on Geoscience and Remote Sensing, 2006

1,809 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Oceanography

View Paper: https://www.semanticscholar.org/paper/a5626016960d7f8ad1225084f98900e887b907cb

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Citations in the corpus

Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: A 26-year case study of the city of Wuhan in China (2016, positive): “where A is a scale factor, which is equal to 10,000 (Gao et al., 2006), and”
Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error (2013, background): “The mathematical properties of continuous data have been known for some time (Stevens 1946), and recent studies have demonstrated consistent scaling behavior of remotely sensed continuous fields (Gao et al. 2006, 2010; Hilker et al. 2009; Feng et al. in press).”
A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS (2009, methodology): “Previous research has shown this general approach to be successful for monitoring seasonal patterns in vegetation cover (Gao et al. 2006; Hilker et al. 2008) and larger”
Spatiotemporal image-fusion model for enhancing the temporal resolution of Landsat-8 surface reflectance images using MODIS images (2015, methodology): “2 13 – 18 In addition, some researchers have used other data, such as: (i) Landsat images and medium resolution imaging spectrometer (MERIS) having spatial resolution in the range 300 to 1200 m with 3 days temporal resolution; 19,20 and (ii) HJ-1 CCD satellite images having 30-m spatial resolution…”
Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach (2012, methodology): “In addition, multiresolution methods may also be appropriate, such as new work to synthesize Landsat and MODIS observations (Gao et al., 2006, 2010).”
A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm (2016, background): “Surface reflectance provides a more precise basis for discriminating various cover types than raw or scaled radiance values and enables data fusion between measurements from Landsat and other sensors (Gao et al. 2006; Feng et al. 2012).”
Opening the archive: How free data has enabled the science and monitoring promise of Landsat (2012, methodology): “Examples include the image compositing approach used in the WELD system (Roy et al., 2010), andmethods that blend information fromMODIS and Landsat either thematically (Hansen et al., 2008) or directly (Gao et al., 2006), with these approaches and options summarized in Wulder et al. (2011).”
A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion (2015, methodology): “…andL (xω/2, yω/2, t0) and M (xω/2, yω/2, t0) are the values of the Landsat and MODIS pixels of the base pair images: L xω 2 ; yω 2 ; tk ¼ Xω i¼1 Xω j¼1 Xn k¼1 Wijk M xi; yj; tk þ L xi; yj; t0 −M xi; yj; t0 ð1Þ For amore detailed description of the STARFM algorithm,we refer to Gao et al. (2006).”
Plant phenology and global climate change: Current progresses and challenges (2019, background): “Among these models, the spatial and temporal adaptive reflectance fusion models (STARFM, Gao et al. (2006)) and the enhanced STARFM (ESTARFM, Zhu et al. (2010)) are the most popular ones for plant phenology monitoring because of their simplicity and robustness.”
Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, methodology): “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 r”
Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions (2018, methodology): “The pixels of these objects are named as similar pixels in the STARFM method and other STARFM-like methods (Figure 5a) [8,11,26].”
Current status of Landsat program, science, and applications (2019, methodology): “Using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) (Gao et al., 2006), Gao et al. (2017) generated daily surface reflectance and vegetation index at Landsat spatial resolution for central Iowa from 2001 to 2015.”
Land cover 2.0 (2018, methodology): “Data fusion approaches, such as those based upon blending of MODIS and Landsat data (Gao et al. 2006; Hilker et al. 2009) may be implemented to produce synthetic gap-free composites for classi ﬁ cation purposes (Senf et al. 2015).”