Journal ArticleDOI
A Landsat surface reflectance dataset for North America, 1990-2000
Jeffrey G. Masek,Eric Vermote,Nazmi Saleous,Robert E. Wolfe,Forrest G. Hall,Karl F. Huemmrich,Feng Gao,J. Kutler,Teng-Kui Lim +8 more
TLDR
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.Abstract:
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.read more
Citations
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Google Earth Engine: Planetary-scale geospatial analysis for everyone
TL;DR: Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection.
Journal ArticleDOI
Landsat-8: Science and Product Vision for Terrestrial Global Change Research
David P. Roy,Michael A. Wulder,Thomas R. Loveland,Curtis E. Woodcock,Richard G. Allen,Martha C. Anderson,Dennis L. Helder,James R. Irons,Daniel M. Johnson,Robert E. Kennedy,Ted Scambos,Crystal B. Schaaf,John R. Schott,Yongwei Sheng,Eric Vermote,Alan Belward,Robert Bindschadler,Warren B. Cohen,Feng Gao,James D. Hipple,Patrick Hostert,Justin L. Huntington,Christopher O. Justice,Ayse Kilic,V. Kovalskyy,Zhongping Lee,Leo Lymburner,Jeffrey G. Masek,Joel McCorkel,Yanmin Shuai,Ricardo Trezza,James E. Vogelmann,Randolph H. Wynne,Zhe Zhu +33 more
TL;DR: Landsat 8, a NASA and USGS collaboration, acquires global moderate-resolution measurements of the Earth's terrestrial and polar regions in the visible, near-infrared, short wave, and thermal infrared as mentioned in this paper.
Journal ArticleDOI
Object-based cloud and cloud shadow detection in Landsat imagery
Zhe Zhu,Curtis E. Woodcock +1 more
TL;DR: The goal is development of a cloud and cloud shadow detection algorithm suitable for routine usage with Landsat images and as high as 96.4%.
Journal ArticleDOI
On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance
TL;DR: A new spatial and temporal adaptive reflectance fusion model (STARFM) algorithm is presented to blend Landsat and MODIS surface reflectance so that high-frequency temporal information from MODIS and high-resolution spatial information from Landsat can be blended for applications that require high resolution in both time and space.
Journal ArticleDOI
Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images
TL;DR: In this article, a new cirrus band was introduced for detecting clouds, especially for thin cirrus clouds, and a new version of the Fmask algorithm was developed for use with Landsat 8 images.
References
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AERONET-a federated instrument network and data archive for aerosol Characterization
Brent N. Holben,Thomas F. Eck,Ilya Slutsker,Didier Tanré,J. P. Buis,Alberto Setzer,Eric Vermote,John A. Reagan,Yoram J. Kaufman,Teruyuki Nakajima,François Lavenu,I. Jankowiak,Alexander Smirnov +12 more
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TL;DR: In this article, the authors provide a summary of the MODIS instrument performance and status, the data production system, the products, their status and availability for land studies, and a partnership between Science Team members and MODIS Science Data Support Team is producing data sets of unprecedented volume and number for the land research and applications.
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The MODIS 2.1-/spl mu/m channel-correlation with visible reflectance for use in remote sensing of aerosol
TL;DR: A new technique for remote sensing of aerosol over the land and for atmospheric correction of Earth imagery is developed, based on detection of dark surface targets in the blue and red channels, but uses the 2.1-/spl mu/m channel, instead of the 3.75-/Spl mu/M channel, for their detection.
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The annual net flux of carbon to the atmosphere from changes in land use 1850–1990*
TL;DR: In this article, rates of land use change, including clearing for agriculture and harvest of wood, were reconstructed from statistical and historic documents for 9 world regions and used, along with the per ha changes in vegetation and soil that result from land management, to calculate the annual flux of carbon between land and atmosphere.
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Atmospheric correction of MODIS data in the visible to middle infrared: first results
TL;DR: In this paper, the first evaluation of the MODIS surface reflectance product accuracy, in comparison with other data products and in the context of MODIS instrument performance since launch, is presented.