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Title:   Author  item
                                            FENG, MIN - University Of Marylanditem
                                            SEXTON, JOSEPH - University Of Marylanditem
                                            HUANG, CHENGQUAN - University Of Marylanditem
                                            MASEK, JEFFREY - National Aeronautics And Space Administration (NASA)item
                                            VERMOTE, ERIC - University Of Marylanditem
                                            Gao, Fengitem
                                            NARASIMHAN, RAGHURAM - University Of Marylanditem
                                            CHANNAN, SAURABH - University Of Marylanditem
                                            WOLFE, ROBERT - University Of Marylanditem
                                            TOWNSHEND, JOHN - University Of Maryland
  Submitted to: Remote Sensing of Environment
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 3/1/2013
                                    Publication  Date: 4/10/2013
                                    Citation: Feng, M., Sexton, J., Huang, C., Masek, J., Vermote, E., Gao, F.N., Narasimhan, R., Channan, S., Wolfe, R., Townshend, J. 2013. Global, long-term surface reflectance records from Landsat. Remote Sensing of Environment. 134:276-293.Interpretive Summary: Satellite images provide valuable information for monitoring land cover changes from local to global scales. However, changes detected from the raw satellite images may vary due to varying atmospheric conditions. Atmospheric correction procedure removes atmospheric effects from raw satellite observations and creates surface reflectance maps. Although surface reflectance (SR) provides the most accurate representation of Earth’s surface properties, there has never been a globally consistent SR dataset at the field scale (<1 hectare) or over the temporal extent (~40 years) of the Landsat mission. In this paper, the Landsat Global Land Survey (GLS) datasets for the 2000 and 2005 time frames were calibrated and atmospherically corrected using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). Good agreement between Landsat surface reflectances and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite measurements was obtained at global scale. Scientists, agricultural managers and agencies monitoring crop condition and yield at the field scale who need consistent, calibrated, multi-decadal Landsat images will benefit from this research.
                                Technical Abstract: 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 hectare) 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) 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. This paper presents a global assessment of this landmark dataset for ca. 2000 and 2005 against coincident Moderate Resolution Imaging Spectroradiometer (MODIS) daily SR and Normalized Bidirectional Distribution Function-Adjusted Reflectance (NBAR) measurements. Accuracy with respect to MODIS SR and NBAR data is very high, with overall discrepancies (Root-Mean-Squared Deviation (RMSD)) between 1.0 and 2.5 percent reflectance for Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and between 1.6 and 3.2 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. This new repository will provide consistent, calibrated, multi-decadal image data for robust land cover change detection and monitoring across the Earth sciences.


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