Geology topics

Kelly Vanderwerff

Publications and source records attributed to Kelly Vanderwerff.

4 recordsLinked to original sources

USGS2015

Landsat-8 Operational Land Imager (OLI) radiometric performance on-orbit

Expectations of the Operational Land Imager (OLI) radiometric performance onboard Landsat-8 have been met or exceeded. The calibration activities that occurred prior to launch provided calibration parameters that enabled ground processing to produce imagery that met most requirements when data were transmitted to the ground. Since launch, calibration updates have improved the image quality even more, so that all requirements are met. These updates range from detector gain coefficients to reduce striping and banding to alignment parameters to improve the geometric accuracy. This paper concentrates on the on-orbit radiometric performance of the OLI, excepting the radiometric calibration performance. Topics discussed in this paper include: signal-to-noise ratios that are an order of magnitude higher than previous Landsat missions; radiometric uniformity that shows little residual banding and striping, and continues to improve; a dynamic range that limits saturation to extremely high radiance levels; extremely stable detectors; slight nonlinearity that is corrected in ground processing; detectors that are stable and 100% operable; and few image artifacts.

Remote Sensing↗

USGS2014

On-orbit performance of the Landsat 8 Operational Land Imager

The Landsat 8 satellite was launched on February 11, 2013, to systematically collect multispectral images for detection and quantitative analysis of changes on the Earth’s surface. The collected data are stored at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and continue the longest archive of medium resolution Earth images. There are two imaging instruments onboard the satellite: the Operational Land Imager (OLI) and the Thermal InfraRed Sensor (TIRS). This paper summarizes radiometric performance of the OLI including the bias stability, the system noise, saturation and other artifacts observed in its data during the first 1.5 years on orbit. Detector noise levels remain low and Signal-To-Noise Ratio high, largely exceeding the requirements. Impulse noise and saturation are present in imagery, but have negligible effect on Landsat 8 products. Oversaturation happens occasionally, but the affected detectors quickly restore their nominal responsivity. Overall, the OLI performs very well on orbit and provides high quality products to the user community. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Conference Paper↗

USGS2012

Dark and background response stability for the Landsat 8 Thermal Infrared Sensor

The Thermal Infrared Sensor (TIRS) is a pushbroom sensor that will be a part of the Landsat Data Continuity Mission (LDCM), which is a joint mission between NASA and the USGS. The TIRS instrument will continue to collect the thermal infrared data that are currently being collected by the Thematic Mapper and the Enhanced Thematic Mapper Plus on Landsats 5 and 7, respectively. One of the key requirements of the new sensor is that the dark and background response be stable to ensure proper data continuity from the legacy Landsat instruments. Pre launch testing of the instrument has recently been completed at the NASA Goddard Space Flight Center (GSFC), which included calibration collects that mimic those that will be performed on orbit. These collects include images of a cold plate meant to simulate the deep space calibration source as viewed by the instrument in flight. The data from these collects give insight into the stability of the instrument’s dark and background response, as well as factors that may cause these responses to vary. This paper quantifies the measured background and dark response of TIRS as well as its stability.

Conference Paper↗

USGS2011

Bias estimation for the Landsat 8 operational land imager

The Operational Land Imager (OLI) is a pushbroom sensor that will be a part of the Landsat Data Continuity Mission (LDCM). This instrument is the latest in the line of Landsat imagers, and will continue to expand the archive of calibrated earth imagery. An important step in producing a calibrated image from instrument data is accurately accounting for the bias of the imaging detectors. Bias variability is one factor that contributes to error in bias estimation for OLI. Typically, the bias is simply estimated by averaging dark data on a per-detector basis. However, data acquired during OLI pre-launch testing exhibited bias variation that correlated well with the variation in concurrently collected data from a special set of detectors on the focal plane. These detectors are sensitive to certain electronic effects but not directly to incoming electromagnetic radiation. A method of using data from these special detectors to estimate the bias of the imaging detectors was developed, but found not to be beneficial at typical radiance levels as the detectors respond slightly when the focal plane is illuminated. In addition to bias variability, a systematic bias error is introduced by the truncation performed by the spacecraft of the 14-bit instrument data to 12-bit integers. This systematic error can be estimated and removed on average, but the per pixel quantization error remains. This paper describes the variability of the bias, the effectiveness of a new approach to estimate and compensate for it, as well as the errors due to truncation and how they are reduced.

Conference Paper↗