Feng, M; Sexton, JO; Huang, CQ; Masek, JG; Vermote, EF; Gao, F; Narasimhan, R; Channan, S; Wolfe, RE; Townshend, JR (2013). Global surface reflectance products from Landsat: Assessment using coincident MODIS observations. REMOTE SENSING OF ENVIRONMENT, 134, 276-293.
Global, long-term monitoring of changes in Earth's land surface requires quantitative comparisons of satellite images acquired under widely varying atmospheric conditions. Although physically based estimates of surface reflectance (SR) ultimately provide the most accurate representation of Earth's surface properties, there has never been a globally consistent SR dataset at the spatial resolution (<1 ha) or temporal extent (similar to 40 years) of the Landsat mission. To increase the consistency and robustness of Landsat-based land cover monitoring, we atmospherically corrected the Global Land Survey (GLS) Landsat dataset using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) implementation of the Second Simulation of the Satellite Signal in the Solar Spectrum ( 6S) radiative transfer model. The GLS provides synoptic, orthorectified, cloud-free Landsat coverage of Earth's land area in four nominal epochs (1975, 1990, 2000, and 2005). This paper presents the resulting GLS surface reflectance dataset and a global assessment of the 2000- and 2005-epoch data against coincident Moderate Resolution Imaging Spectroradiometer (MODIS) daily SR and Normalized Bidirectional Distribution Function-Adjusted Reflectance (NBAR) measurements. Agreement with respect to MODIS SR and NBAR data is very high, with overall discrepancies (Root-Mean-Squared Deviation (RMSD)) between 1.3 and 2.8 percent reflectance for Landsat-7 Enhanced Thematic Mapper Plus (ELM +) and between 2.2 and 3.5 percent reflectance for Landsat-5 Thematic Mapper (TM). The resulting Landsat surface reflectance dataset and the associated quality metrics for each image are hosted on the Global Land Cover Facility web site for free download (http://www.landcover.org/data/gls_SR). This new repository will provide consistent, calibrated, multi-decadal image data for robust land cover change detection and monitoring across the Earth sciences. (C) 2013 Elsevier Inc. All rights reserved.