Article ID Journal Published Year Pages File Type
4459178 Remote Sensing of Environment 2013 18 Pages PDF
Abstract

•We atmospherically corrected the GLS Landsat dataset for ca. 2000 and 2005.•First global multi-temporal Landsat surface reflectance (SR) dataset.•Assessment of the Landsat SR against coincident MODIS measurements.•Agreement with respect to MODIS SR and NBAR data is very high.•The dataset is hosted on the GLCF web site for free download.

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 (~ 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 (ETM +) 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.

Related Topics
Physical Sciences and Engineering Earth and Planetary Sciences Computers in Earth Sciences
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