Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data

By David P. Roy, J. Ju, Philip Lewis, Crystal Barker Schaaf, Feng Gao, Matthew C. Hansen, E. Lindquist. 2008

518 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/6d86cf960438ca47cd58d558e6f358019940a8f8

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Citations in the corpus

Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications (2017, background): “Roy et al. (2008) proposed a semi-physical fusion approach that uses MODIS Bidirectional Reﬂectance Distribution Function (BRDF)/Albedo land surface characterization products to predict Landsat images, and achieved good results.”
Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. (2016, background): “…introduced by factors including atmospheric and cloud contamination (Kaufman, 1987, Masek et al., 2006), variable sunsurface-sensor geometry (Roy et al., 2008, Nagol et al., 2015, Ju et al., 2010), sensor degradation and calibration changes (Markham & Helder, 2012), between sensor spectral…”
Recovering missing pixels for Landsat ETM + SLC-off imagery using multi-temporal regression analysis and a regularization method (2013, background): “Boloorani et al. (2008b) proposed to use the information observed by the Advanced Land Imager (ALI) on board the Earth Observing One (EO-1) satellite,while Roy et al. (2008) choseMODIS imagery as a source of complementary information.”
Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions (2018, methodology): “Weight function-based STARFM [8]; semi-physical fusion approach [19]; ESTARFM [11]; STAARCH [27]; SADFAT [21]; Topographically corrected downscaling [18]; STITFM [23]; STARFM-Sensor difference [54]; mESTARFM [45]; Bilateral Filter method [59]; ET fusion model [53]; STI-FM [64]; operational STARFM [3”
A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion (2015, methodology): “The MODIS MCD43A4 Nadir BRDF-Adjusted Reflectance product with a 463 m resolution was utilized, as previous studies have indicated that MODIS BRDF products provide more accurate data fusion results than daily MODIS surface reflectances (Roy et al., 2008; Walker et al., 2012).”
A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS (2009, methodology): “This can be a major concern, particularly in humid environments (Ranson et al., 2003; Roy et al., 2008; Ju & Roy, 2008), where the probability of acquiring cloud-free Landsat imagery for a given year (cloud cover b10”
Southern Africa Validation of the MODIS, L3JRC, and GlobCarbon Burned-Area Products (2009, unknown): “The Landsat L1G geolocation error is less than 250 m (1σ) and may be comparable to the MODIS error in areas of flat relief [32], [54].”
Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation (2016, methodology): “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.”
Examination of Sentinel-2A Multi-spectral Instrument (MSI) Reflectance Anisotropy and the Suitability of a General Method to Normalize MSI Reflectance to Nadir BRDF Adjusted Reflectance (2017, methodology): “…c-factor defined by the ratio of reflectances modeled using MODIS BRDF spectral model parameters for the observed and a fixed nadir view geometry (Roy et al., 2008, 2016a) as: NBARλ θν ¼ 0; θs ¼ kð Þ ¼ cλ ρλ θν ¼ θSentinelv ; θs ¼ θSentinels cλ ¼ ρ̂ MODIS λ θν ¼ 0; θs ¼ kð Þ ρ̂MODISλ θν ¼…”
Harmonic regression of Landsat time series for modeling attributes from national forest inventory data (2018, methodology): “…proposed to dealing with them, including compositing of several images (Roy et al., 2010), interpolation using SLC-on imagery (Maxwell et al., 2007; Chen et al., 2011), data fusion with MODIS imagery (Roy et al., 2008), and geostatistical methods (Zhang et al., 2007; Pringle et al., 2009).”
C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter (2011, methodology): “In this study, the Landsat and SPOT data were reflectance-normalized relative to Terra/Aqua MODIS Nadir-/BRDF-adjusted reflectance data from a corresponding acquisition date (Olthof et al. 2005; Reese et al. 2009; Roy et al. 2008).”
Generation of Spectral–Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications (2015, unknown): “[13] presented the spatial”
The Future of Earth Observation in Hydrology. (2017, unknown): “When considering the influence of cloud cover on the visible, shortwave infrared, and thermal infrared portions of the spectrum, data continuity and availability can be severely impeded (Roy et al., 2008).”
Missing Information Reconstruction of Remote Sensing Data: A Technical Review (2015, background): “[93] touched on this idea to reconstruct the missing information of remote sensing data, and their results showed certain advantages.”