A Landsat surface reflectance dataset for North America, 1990-2000

By J. Masek, Eric F. Vermote, N. Saleous, R. Wolfe, F. Hall, K. Huemmrich, Feng Gao, J. Kutler, T. Lim. IEEE Geoscience and Remote Sensing Letters, 2006

1,570 citations recorded by the source corpus.

The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center has processed and released 2100 Landsat Thematic Mapper and Enhanced Thematic Mapper Plus surface reflectance scenes, providing 30-m resolution wall-to-wall reflectance coverage for North America for epochs centered on 1990 and 2000. This dataset can support decadal assessments of environmental and land-cover change, production of reflectance-based biophysical products, and applications that merge reflectance data from multiple sensors [e.g., the Advanced Spaceborne Thermal Emission and Reflection Radiometer, Multiangle Imaging Spectroradiometer, Moderate Resolution Imaging Spectroradiometer (MODIS)]. The raw imagery was obtained from the orthorectified Landsat GeoCover dataset, purchased by NASA from the Earth Satellite Corporation. Through the LEDAPS project, these data were calibrated, converted to top-of-atmosphere reflectance, and then atmospherically corrected using the MODIS/6S methodology. Initial comparisons with ground-based optical thickness measurements and simultaneously acquired MODIS imagery indicate comparable uncertainty in Landsat surface reflectance compared to the standard MODIS reflectance product (the greater of 0.5% absolute reflectance or 5% of the recorded reflectance value). The rapid automated nature of the processing stream also paves the way for routine high-level products from future Landsat sensors.

Subfields: Aerospace Engineering, Ecology, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/4c4bbf8b4620e947e6135964397200fa80f3f450

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Citations in the corpus

A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping (2013, methodology): “Furthermore, the atmospheric correction algorithm applied to MODIS data is similar to the correction method applied to the Landsat data in this study [36].”
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, neutral): “The 6S and MODTRAN models are two commonly employed methods (Adler-Golden et al., 1999; Masek et al., 2006).”
Assessing Land Degradation and Desertification Using Vegetation Index Data: Current Frameworks and Future Directions (2014, neutral): “Recent developments in the provision of both free and pre-processed Landsat data [141,142] may lead to an increased focus on higher-resolution sensor data, therefore the impact of varying pixel size on trend estimation should be a research priority prior to the undertaking analysis.”
Continuous change detection and classification of land cover using all available Landsat data (2014, methodology): “Atmospheric correction is performed using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) atmospheric correction tool (Masek et al., 2006; Vermote et al., 1997), in which the raw DN values are converted into Surface Re ﬂ ectance (SR) and Bright-ness Temperature (BT).”
A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm (2016, methodology): “The GLS Landsat images have been atmospherically corrected using the Landsat Ecosystem Disturbance Adaptive Processing System implementation of the 6S radiative transfer algorithm (Masek et al. 2006) (http://www. landcover.org/data/gls_SR).”
Canadian boreal forest greening and browning trends: an analysis of biogeographic patterns and the relative roles of disturbance versus climate drivers (2017, methodology): “The sites were numbered in alphabetical order and their names are listed below the map. by the USGS following standard best practices (Masek et al 2006, Loveland and Dwyer 2012, Markham and Helder 2012).”
Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data (2008, methodology): “The MODIS BRDF model, Ross-Thick/Li-Sparse-Reciprocal, is a weighted sum of an isotropic parameter and two functions (or kernels) of viewing and illumination geometry and has been shown to be well suited to describing the surface reflectance anisotropy of global land cover types ( Lucht et al., 2000”
Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error (2013, methodology): “Atmospheric inputs and parameterization of LEDAPS are described by Feng et al. (in press).”
Current status of Landsat program, science, and applications (2019, methodology): “, 2016) and Landsat-4, -5, and -7 (Landsat Ecosystem Disturbance Adaptive Processing System, LEDAPS) (Masek et al., 2006) data.”
Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change (2014, methodology): “Recently, many new automated algorithms have been developed based on a single Landsat image (Huang, Goward, et al., 2010; Huang, Thomas, et al., 2010; Irish et al., 2006; Masek et al., 2006; Oreopoulos, Wilson, & Várnai, 2011; Roy et al., 2010; Scaramuzza, Bouchard, & Dwyer, 2012; Zhu & Woodcock, 20”
Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects (2009, background): “2003; Schowengerdt, 1997) Increasingly, pre-processing steps are becoming automated and resulting in free datasets of relatively high quality (Fraser et al., 2009-this issue; Masek et al., 2006).”
A review of large area monitoring of land cover change using Landsat data (2012, methodology): “The LEDAPS North American forest disturbance map employs a full surface correction application based on the MODIS 6S radiative transfer approach (Masek et al., 2006; Vermote et al., 1997).”
Analysis Ready Data: Enabling Analysis of the Landsat Archive (2018, methodology): “The Landsat 4 and 5 TM and Landsat 7 ETM+ ARD reflectance bands are atmospherically corrected using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) Surface Reflectance Algorithm [34].”