Geology topics

Aaron Gerace

Publications and source records attributed to Aaron Gerace.

7 recordsLinked to original sources

USGS2026

Enhancements to the USGS Landsat Level 2 surface temperature and emissivity product for Collection 3 reprocessing

The Landsat program provides the longest continuous global record of thermal infrared observations of the Earth's surface, underpinning critical applications in climate monitoring, water resources, ecosystem dynamics, urban heat analysis, and natural hazard assessment. The release of a global inventory of Landsat Collection 2 Level 2 surface temperature products by the U.S. Geological Survey (USGS) marked a major milestone in operational provision of Landsat thermal infrared analysis-ready data. Ongoing validations and community uses of Collection 2 have identified opportunities to further improve accuracy, uncertainty characterization, and emissivity correction across diverse atmospheric and surface conditions. In preparation for the planned Landsat Collection 3 reprocessing of the Landsat data record in the late 2020s, the USGS is implementing a coordinated set of enhancements to the Level 2 surface temperature products. These include revised emissivity estimation that leverages external datasets, improved atmospheric characterization and uncertainty propagation, expanded dynamic range for high temperature targets, consideration of split window atmospheric correction algorithm for Landsat 8 and 9, and decoupling of thermal infrared processing from visible to shortwave infrared constraints to enable surface temperature retrievals under low or no solar illumination conditions. These changes are designed to improve product quality and consistency across the Landsat record. Beyond near-term performance gains, the Collection 3 design establishes a scalable processing architecture to accommodate the expanded spectral and radiometric measurement capabilities of the forthcoming Landsat 10 mission. By preserving continuity across the Landsat 4–9 record while enabling future algorithm evolution, Landsat Collection 3 will provide a foundation for long-term, multi-decadal Earth system thermal infrared observations.

Remote Sensing of Environment↗

USGS2025

Landsat surface product validation instrumentation: The BigMAC exercise

Users of Earth remotely sensed optical imagery are increasingly demanding a surface reflectance or surface temperature product instead of the top-of-atmosphere products that have been produced historically. Validating the accuracy of surface products remains a difficult task since it involves assessment across a range of atmospheric profiles, as well as many different land surface types. Thus, standard approaches from the satellite calibration community do not apply and new technologies need to be developed. The Big Multi-Agency Campaign (BigMAC) was developed to assess current technologies that might be used for validation of surface products derived from satellite imagery, with emphasis on Landsat. Conducted in August, 2021, in Brookings, SD, USA, a variety of measurement technologies were fielded and assessed for accuracy, precision, and deployability. Each technology exhibited its strengths and weaknesses. Handheld spectroradiometers are capable of surface reflectance measurements with accuracies in the 0.01 - 0.02 absolute reflectance units, but are expensive to deploy. Unmanned Aircraft System (UAS)-based radiometers have the potential of making measurements with similar accuracy, but are also difficult to deploy. Mirror-based empirical line methods showed improving accuracy potential, but deployment also remains an issue. However, there are inexpensive radiometers designed for long-term autonomous use that exhibited good accuracy and precision, as well as being easy to deploy. Thermal measurement technologies showed accuracy potential in the 1 - 2K range, and some easily deployable instruments are available. Results from BigMAC indicate there are technologies available today to begin making operational surface reflectance/temperature measurements and strong potential for improvements in the future.

Sensors↗

USGS2025

Toward a near-lossless image compression strategy for the NASA/USGS Landsat Next mission

As orbiting Earth imaging platforms carry more complex and capable instruments, efficient methods are needed to reduce the time and cost associated with storing and downlinking greater volumes of image data. The upcoming NASA/USGS Landsat Next mission, with an increase in spatial and spectral resolution over previous Landsat missions, is no exception. Landsat Next will produce nearly six times the amount of image data per day over either of the current Landsat 8 or Landsat 9 observatories. Near-lossless compression, where the image after compression is not identical to the original image, allows for the efficient storage and transmission of all image data while meeting the mission’s global coverage, temporal revisit frequency, and science measurement and performance requirements. Although the Landsat user community is understandably cautious about lossy compression, it is possible to constrain the maximum loss, or error, introduced during compression, ensuring that any added error remains within the intrinsic noise level of the instrument. The Consultative Committee for Space Data Systems image compression standard, CCSDS 123.0-B-2, was chosen for the Landsat Next mission because it is an internationally supported standard suited for integration with space hardware, and it allows control over the magnitude and distribution of compression error. Using several proxy datasets as a surrogate for Landsat Next image data, an investigation was performed to determine a preliminary set of parameter values that would keep the added compression error within acceptable limits. The results of these studies demonstrate that near-lossless image compression can be utilized by the Landsat Next instruments to store and downlink all science data without compromising image quality or mission requirements.

Remote Sensing of Environment↗

USGS2023

The 50-year Landsat collection 2 archive

The Landsat global consolidated data archive now exceeds 50 years. In recognition of the need for consistently processed data across the Landsat satellite series, the U.S. Geological Survey (USGS) initiated collection-based processing of the entire archive that was processed as Collection 1 in 2016. In preparation for the data from the now successfully launched Landsat 9, the USGS reprocessed the Landsat archive as Collection 2 in 2020. This paper describes the rationale for, and the contents and advancements provided by Collection 2, and highlights the differences between the Collection 1 and Collection 2 products. Notably, the Collection 2 products have improved geolocation and, for the first time, the USGS provides a global inventory of Level 2 surface reflectance and surface temperature products. Also for the first time, the USGS used a commercial cloud computing architecture to efficiently process the archive and enable direct cloud access of the Landsat products. The paper concludes with discussion of likely improvements expected in Collection 3 in preparation for the Landsat Next mission that is planned for launch in the early 2030s.

Science of Remote Sensing↗

USGS2022

Landsat 9 cross calibration under-fly of Landsat 8: Planning, and execution

During the early post-launch phase of the Landsat 9 mission, the Landsat 8 and 9 mission teams conducted a successful under-fly of Landsat 8 by Landsat 9, allowing for the near-simultaneous data collection of common Earth targets by the on-board sensors for cross-calibration. This effort, coordinated by the Landsat Calibration and Validation team, required contributions from various entities across National Aeronautics and Space Administration and U.S. Geological Survey such as Flight Dynamics, Systems, Mission Planning, and Flight Operations teams, beginning about 18 months prior to launch. Plans existed to allow this under-fly for any possible launch date of Landsat 9. This included 16 ascent plans and 16 data acquisition plans, one for every day of the Landsat orbital repeat period, with a minimum of 5 days of useful coverage overlap between the sensors. After the Landsat 9 launch, the plan executed, and led to the acquisition of over 2000 partial to full overlapping scene pairs. Although containing less than the expected number of scenes, this dataset was larger than previous Landsat mission under-fly efforts and more than sufficient for performing cross-calibration of the Landsat 8 and Landsat 9 sensors. The details of the planning process and execution of this under-fly are presented.

Remote Sensing↗

USGS2018

Landsat-8 on-orbit and Landsat-9 pre-launch sensor radiometric characterization

Landsat-8 has been operating on-orbit for 5+ years. Its two sensors, the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), are continuing to produce high quality data. The OLI has been radiometrically stable at the better than 0.3% level on a band average basis for all but the shortest wavelength (443 nm) band, which has degraded about 1.3% since launch. All on-board calibration devices continue to perform well and consistently. No gaps in across track coverage exist as 100% operability of the detectors is maintained. The variability over time of detector responsivity within a band relative to the average is better than 0.05% (1 sigma), though there are occasional detectors that jump up to 1.5% in response in the Short-Wave InfraRed (SWIR) bands. Signal-to-Noise performance continues at 2-3x better than requirements, with a small degradation in the 443 nm band commensurate with the loss in sensitivity. Pre-launch error analysis, combined with the stability of the OLI indicates that the absolute reflectance calibration uncertainty is better than 3%; comparisons to ground measurements and comparisons to other sensors are consistent with this. The Landsat-8 TIRS is similarly radiometrically stable, showing changes of at most 0.3% over the mission. The uncertainty in the absolute calibration as well as the detector to detector variability are largely driven by the stray light response of TIRS. The current processing corrects most of the stray light effects, resulting in absolute uncertainties of ~1% and reduced striping. Efforts continue to further reduce the striping. Noise equivalent delta temperature is about 50 mK at typical temperatures and 100% detector operability is maintained. Landsat-9 is currently under development with a launch no earlier than December 2020. The nearly identical OLI-2 and upgraded TIRS-2 sensors have completed integration and are in the process of instrument level performance characterization including spectral, spatial, radiometric and geometric testing. Component and assembly level measurements of the OLI-2, which include spectral response, radiometric response and stray light indicate comparable performance to OLI. The first functional tests occurred in July 2018 and spatial performance testing in vacuum is scheduled for August 2018. Similarly, for TIRS-2, partially integrated instrument level testing indicated spectral and spatial responses comparable to TIRS, with stray light reduced by approximately an order of magnitude from TIRS.

Conference Paper↗

USGS2017

Landsat-8 TIRS thermal radiometric calibration status

The Thermal Infrared Sensor (TIRS) instrument is the thermal-band imager on the Landsat-8 platform. The initial onorbit calibration estimates of the two TIRS spectral bands indicated large average radiometric calibration errors, -0.29 and -0.51 W/m 2 sr μm or -2.1K and -4.4K at 300K in Bands 10 and 11, respectively, as well as high variability in the errors, 0.87K and 1.67K (1-σ), respectively. The average error was corrected in operational processing in January 2014, though, this adjustment did not improve the variability. The source of the variability was determined to be stray light from far outside the field of view of the telescope. An algorithm for modeling the stray light effect was developed and implemented in the Landsat-8 processing system in February 2017. The new process has improved the overall calibration of the two TIRS bands, reducing the residual variability in the calibration from 0.87K to 0.51K at 300K for Band 10 and from 1.67K to 0.84K at 300K for Band 11. There are residual average lifetime bias errors in each band: 0.04 W/m 2 sr μm (0.30K) and -0.04 W/m 2 sr μm (-0.29K), for Bands 10 and 11, respectively.

Conference Paper↗