Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States

doi:10.1016/j.rse.2009.08.011

Remote Sensing of Environment

Volume 114, Issue 1, 15 January 2010, Pages 35-49

https://doi.org/10.1016/j.rse.2009.08.011 Get rights and content

Abstract

Since January 2008, the U.S. Department of Interior / U.S. Geological Survey have been providing free terrain-corrected (Level 1T) Landsat Enhanced Thematic Mapper Plus (ETM+) data via the Internet, currently for acquisitions with less than 40% cloud cover. With this rich dataset, temporally composited, mosaics of the conterminous United States (CONUS) were generated on a monthly, seasonal, and annual basis using 6521 ETM+ acquisitions from December 2007 to November 2008. The composited mosaics are designed to provide consistent Landsat data that can be used to derive land cover and geo-physical and bio-physical products for detailed regional assessments of land-cover dynamics and to study Earth system functioning. The data layers in the composited mosaics are defined at 30 m and include top of atmosphere (TOA) reflectance, TOA brightness temperature, TOA normalized difference vegetation index (NDVI), the date each composited pixel was acquired on, per-band radiometric saturation status, cloud mask values, and the number of acquisitions considered in the compositing period. Reduced spatial resolution browse imagery, and top of atmosphere 30 m reflectance time series extracted from the monthly composites, capture the expected land surface phenological change, and illustrate the potential of the composited mosaic data for terrestrial monitoring at high spatial resolution. The composited mosaics are available in 501 tiles of 5000 × 5000 30 m pixels in the Albers equal area projection and are downloadable at http://landsat.usgs.gov/WELD.php. The research described in this paper demonstrates the potential of Landsat data processing to provide a consistent, long-term, large-area, data record.

Introduction

The Landsat satellite series, operated by the U.S. Department of Interior / U.S. Geological Survey (USGS) Landsat project, with satellite development and launches supported by the National Aeronautics and Space Administration (NASA), represents the longest temporal record of space-based land observations (Williams et al., 2006). Until recently the primary limitations to using Landsat data have been the cost and availability of data, which have precluded continental to global scale Landsat studies (Hansen et al., 2008). In January 2008, NASA and the USGS implemented a free Landsat Data Distribution Policy that provides Level 1 terrain corrected data for the entire U.S. Landsat archive, over 2.2 million globally distributed Landsat acquisitions, at no cost via the Internet. Landsat acquisitions with a cloud cover of less than or equal to 40% are processed and made freely available as they are acquired, and users may request any other scene in the U.S. Landsat archive to be processed and made available via the Internet at no cost. Free Landsat data will enable reconstruction of the history of the Earth's land surface back to 1972, with appropriate spatial resolution to enable chronicling of both anthropogenic and natural changes (Townshend & Justice, 1988), during a time when the human population has doubled and the impacts of climate change have become noticeable (Woodcock et al., 2008).

The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) is the most recent in a series of Landsat sensors that acquire high spatial resolution multi-spectral data over an approximately 183 km × 170 km extent, defined in a Worldwide Reference System of path (groundtrack parallel) and row (latitude parallel) coordinates, with a 16 day revisit capability (Williams et al., 2006). Every Landsat overpass of the conterminous United States (CONUS) is acquired by the U.S. Landsat Project, providing 22 or 23 acquisitions per year per path/row (Ju & Roy, 2008). The Landsat project does not acquire every acquisition globally due to ground system processing and archiving constraints (Arvidson et al., 2006). Cloud cover reduces the number of Landsat surface observations; for example, the annual mean Landsat ETM+ cloud cover for the CONUS and global scenes stored in the U.S. Landsat archive is about 40% and 35% respectively (Ju & Roy, 2008). In addition, in May 2003 the ETM+ scan line corrector (SLC) failed, reducing the usable data in each Landsat ETM+ SLC-off scene by 22% (Storey et al., 2005, Maxwell et al., 2007).

Arguably, the utility of Landsat data for long-term and/or large-area monitoring has not been fully assessed; to date, the majority of the data in the U.S. Landsat archive have not been used in applications science. A number of regional, continental and global Landsat data sets have been generated however. Regional mosaics of Landsat imagery are increasingly being developed to meet national monitoring and reporting needs across land-use and resource sectors, for example, in Canada (Wulder et al., 2002) and the Congo basin (Hansen et al., 2008). Large volume Landsat processing was developed by the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) that processed over 2100 Landsat Thematic Mapper and ETM+ acquisitions to provide wall-to-wall surface reflectance coverage for North America for the 1990s and 2000s (Masek et al., 2006). More recently, a Landsat mosaic of Antarctica was generated from nearly 1100 Landsat ETM+ austral summer acquisitions (Bindschadler et al., 2008). At global scale, the Global Land Survey decadal Landsat data set provides relatively cloud-free acquisitions selected for each path/row from the 1970s, 1990s and 2000s (Tucker et al., 2004). These data sets are composed of single date manually selected Landsat acquisitions. With the advent of free Landsat data it becomes feasible to apply temporal compositing approaches to multi-temporal Landsat acquisitions of the same path/row. Compositing procedures are applied independently on a per-pixel basis to gridded satellite time series data and provide a practical way to reduce cloud and aerosol contamination, fill missing values, and reduce the data volume of moderate resolution global near-daily coverage satellite data (Holben, 1986, Cihlar et al., 1994). Thus, instead of spatially mosaicing select relatively cloud-free Landsat acquisitions together (Zobrist et al., 1983), all the available multi-temporal acquisitions may be considered, and at each gridded pixel the acquisition that satisfies some compositing criteria selected. In this way, the Global Land Survey (GLS) 2005 Landsat ETM+ data set is generated by compositing up to three circa 2005 low cloud cover acquisitions per path/row (Gutman et al., 2008). Recently, Lindquist et al. (2008) examined the suitability of the GLS data sets compared to more data intensive Landsat compositing methods (Hansen et al., 2008) and showed that over the Congo Basin compositing an increasing number of acquisitions reduced the percentage of SLC-off gaps and pixels with a high likelihood of cloud, haze or shadow. Similar observations have been observed for compositing moderate and coarse spatial resolution satellite data (Holben, 1986, Cihlar et al., 1994, Roy, 2000).

In this paper we describe the generation of Landsat ETM+ composited mosaics of the CONUS for December 2007 to November 2008. The composited mosaics are designed to provide consistent Landsat data that can be used to derive land cover and geo-physical and bio-physical products needed for detailed regional assessments of land-cover dynamics and to study Earth system functioning (Gutman et al., 2008, Wulder et al., 2008). The composited mosaics are generated on a monthly, seasonal, and annual basis to provide data that capture temporal surface variations. The compositing approach is designed to preferentially select valid land surface observations with minimal cloud, snow, and atmospheric contamination; consequently the composited mosaics are not appropriate for Landsat studies of cloud, snow or the atmosphere. The processing steps and data products are informed by our MODIS Land processing, quality assessment and validation experience (Justice et al., 2002). The processing approach is intentionally designed to facilitate automated processing with minimal human intervention, including the requirement for composited mosaics to be updated regardless of the chronological order of the Landsat acquisition and processing dates, and to provide processing in near-real time i.e., updating composited mosaics shortly after the Landsat data are acquired. Information on how to obtain the composited mosaic products and further research is described.

Section snippets

Input web-enabled level 1T Landsat ETM+ data

The Landsat data made freely available by the U.S. Landsat project are sometimes called “web-enabled” data as they are made available via the Internet. These data are nominally processed as Level 1 terrain-corrected (L1T) data. The L1T data are available in GeoTIFF format in the Universal Transverse Mercator (UTM) map projection with World Geodetic System 84 (WGS84) datum which is compatible with heritage GLS and Landsat MSS data sets (Tucker et al., 2004). The Level 1T processing includes

Top of atmosphere reflectance, brightness temperature, vegetation index, and band saturation computation

The spectral radiance sensed by each ETM+ detector is stored as an 8-bit digital number (Markham et al., 2006). The digital numbers should be converted to radiance (units: W m⁻² sr^− 1 μm^− 1), to minimize changes in the instrument radiometric calibration, and then converted to top of atmosphere reflectance to minimize remote sensing variations introduced by variations in the sun–earth distance, the solar geometry, and exoatmospheric solar irradiance arising from spectral band differences (Chander et

Cloud masking

It is well established that optically thick clouds preclude optical and thermal wavelength remote sensing of the land surface but that automated and reliable satellite data cloud detection is not trivial (Kaufman, 1987, Platnick et al., 2003). Recognizing that cloud detection errors, both of omission and commission, will always occur in large data sets, both the Landsat automatic cloud cover assessment (ACCA) algorithm and a classification tree based cloud detection approach were implemented.

Reprojection, resampling and tiling

The L1T data are defined in the UTM projection which is defined in zones, each 6° of longitude in width and centered over a meridian of longitude, with ten zones encompassing the CONUS (Snyder, 1993). Consequently, after each Landsat ETM+ L1T acquisition was processed (3 Top of atmosphere reflectance, brightness temperature, vegetation index, and band saturation computation, 4 Cloud masking), the 30 m TOA reflective bands, TOA NDVI, TOA brightness temperature bands, band saturation mask, and the

Compositing and composited mosaic format

Compositing was developed originally to reduce residual cloud and aerosol contamination in AVHRR time series to produce representative n-day data sets (Holben, 1986). Compositing procedures either select from colocated pixels in different orbits of geometrically registered data the pixel that best satisfies some compositing criteria, or combine the different pixel values together. Compositing criteria have included the maximum NDVI, maximum brightness temperature, maximum apparent surface

Browse generation

Browse images with reduced spatial resolution were generated from the composited mosaics to enable synoptic product evaluation with reduced data volume (Roy et al., 2002), and with the expectation that the browse imagery could be used for Internet data ordering. CONUS browse images were generated in the JPEG format with fixed contrast stretching and color look-up tables to enable consistent temporal comparison. The browse images were generated using the median pixel values falling in a given

Cloud

It is difficult to definitively validate cloud detection algorithms (Irish et al., 2006, Platnick et al., 2003). Consequently, the ACCA and Classification Tree cloud masks were compared statistically and also by visual inspection. The Classification Tree cloud mask was generated using unsaturated and saturated ETM+ L1T data; 12,979,302 unsaturated training pixels were used to generate a single tree defined by 1595 nodes that explained 98% of the tree variance, and 5,374,157 saturated training

Summary

The 2008 free Landsat Data Distribution Policy opens a new era for utilizing Landsat data; users are no longer restricted by data costs to selection of acquisitions with low cloud cover, rather, with the advent of free Landsat data it becomes feasible to analyze and process long-term and/or large-area Landsat data sets. For example, a total of 6521 Landsat acquisitions were used to generate the monthly, seasonal and annual composited mosaics of the CONUS described in this paper; this data

Acknowledgements

This Web-enabled Landsat Data (WELD) project is funded by NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, grant number NNX08AL93A. The LEDAPS team led by Dr. Masek is thanked for their feedback and provision of the ACCA cloud masking code. The U.S. Landsat project management and staff are thanked for provision of the Landsat ETM+ data.

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