Abstract
The free and open policy of Landsat data in 2008 completely changed the way that Landsat data was analyzed and used, particularly for applications such as time series analysis. Nine years later, the United States Geological Survey (USGS) released the first version of Landsat Analysis Ready Data (ARD) for the United States, which was another milestone in Landsat history. The Landsat time series is so convenient and easy to use and has triggered science that was not possible a few decades ago. In this Editorial, we review the current status of Landsat ARD, introduce scientific studies of Landsat ARD from this special issue, and discuss global Landsat ARD.
Highlights
The series of Landsats 1–8 has been the gold standard for satellite remote sensing, mainly because of its long history of relatively high radiometric, spatial, temporal, and spectral resolutions [1,2,3,4]
One of the major challenges of using the dense Landsat time series was the lack of standard and operational algorithms to create consistent cloud, cloud shadow, and snow free surface reflectance observations. This was no longer an issue when the United States Geological Survey (USGS) started to operationally produce Landsat surface reflectance with quality assessment (QA) bands, in which LEDAPS [6] and LaSRC [7] were used for atmospheric correction, and a C version of Fmask (CFmask) was used to create the QA bands [8,9,10]
The USGS made another step forward by providing Landsat Analysis Ready Data (ARD), which provides Landsat data for the conterminous United States (CONUS), Alaska, and Hawaii in formats that can be directly compared for change and time series analysis
Summary
The series of Landsats 1–8 has been the gold standard for satellite remote sensing, mainly because of its long history of relatively high radiometric, spatial, temporal, and spectral resolutions [1,2,3,4]. One of the major challenges of using the dense Landsat time series was the lack of standard and operational algorithms to create consistent cloud, cloud shadow, and snow free surface reflectance observations. This was no longer an issue when the United States Geological Survey (USGS) started to operationally produce Landsat surface reflectance with quality assessment (QA) bands, in which LEDAPS [6] and LaSRC [7] were used for atmospheric correction, and a C version of Fmask (CFmask) was used to create the QA bands [8,9,10]. For each Landsat ARD tile, there are five major layers including: (1) Top of atmosphere (TOA) reflectance; (2) TOA brightness temperature (BT); (3) surface reflectance (SR); (4) provisional surface temperature (PST); and (5) pixel QA band
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
