Aaron Gerace

Stray Light Artifacts in Imagery from the Landsat 8 Thermal Infrared Sensor

The Thermal Infrared Sensor (TIRS) has been collecting imagery of the Earth since its launch aboard Landsat 8 in early 2013. In many respects, TIRS has been exceeding its performance requirements on orbit, particularly in terms of noise and stability. However, several artifacts have been observed in the TIRS data which include banding and absolute calibration discrepancies that violate requirements in some scenes. Banding is undesired structure that appears within and between the focal plane array assemblies. In addition, in situ measurements have shown an error in the TIRS absolute radiometric calibration that appears to vary with season and location within the image. The source of these artifacts has been determined to be out-of-field radiance that scatters onto the detectors thereby adding a non-uniform signal across the field-of-view. The magnitude of this extra signal can be approximately 8% or higher (band 11) and is generally twice as large in band 11 as it is in band 10. A series of lunar scans were obtained to gather information on the source of this out-of-field radiance. Analyses of these scans have produced a preliminary map of stray light, or ghost, source locations in the TIRS out-of-field area. This dataset has been used to produce a synthetic TIRS scene that closely reproduces the banding effects seen in actual TIRS imagery. Now that the cause of the banding has been determined, a stray light optics model is in development that will pin-point the cause of the stray light source. Several methods are also being explored to correct for the banding and the absolute calibration error in TIRS imagery.

Increased potential to monitor water quality in the near-shore environment with Landsat’s next-generation satellite

Abstract The Operational Land Imager (OLI) is a new sensor developed by the joint USGS-NASA Landsat Data Continuity Mission that should become a valuable tool for studying inland and coastal waters. With upgrades to spectral coverage, 12-bit quantization, and increased signal-to-noise due to its new push-broom design, OLI exhibits the potential to become the first Landsat sensor with the radiometric resolution necessary for retrieval of the three primary constituents in Case 2 waters: chlorophyll, suspended materials, and colored-dissolved organic matter. Considering its traditional 30-m spatial resolution, this next-generation Landsat satellite will be especially useful for monitoring the near-shore environment. This work presents the relevant sensor parameters and results of experiments designed to determine if OLI will have the radiometric sensitivity necessary for water-based research. An OLI sensor model is developed, and its ability to retrieve water constituents from simulated data is compared with that of existing sensors. Results indicate that when atmospheric effects are properly accounted for, OLI introduces retrieval errors of less than 11% of the expected observable range for all three constituents. Furthermore, by spatially averaging a few OLI pixels, noise can be reduced to the Medium Resolution Imaging Spectrometer levels, making this next Landsat instrument an exciting option for monitoring inland and coastal waters.

The SHARE 2012 data campaign

A multi-modal (hyperspectral, multispectral, and LIDAR) imaging data collection campaign was conducted just south of Rochester New York in Avon, NY on September 20, 2012 by the Rochester Institute of Technology (RIT) in conjunction with SpecTIR, LLC, the Air Force Research Lab (AFRL), the Naval Research Lab (NRL), United Technologies Aerospace Systems (UTAS) and MITRE. The campaign was a follow on from the SpecTIR Hyperspectral Airborne Rochester Experiment (SHARE) from 2010. Data was collected in support of the eleven simultaneous experiments described here. The airborne imagery was collected over four different sites with hyperspectral, multispectral, and LIDAR sensors. The sites for data collection included Avon, NY, Conesus Lake, Hemlock Lake and forest, and a nearby quarry. Experiments included topics such as target unmixing, subpixel detection, material identification, impacts of illumination on materials, forest health, and in-water target detection. An extensive ground truthing effort was conducted in addition to collection of the airborne imagery. The ultimate goal of the data collection campaign is to provide the remote sensing community with a shareable resource to support future research. This paper details the experiments conducted and the data that was collected during this campaign.

Measuring Radiant Emissions from Entire Prescribed Fires with Ground, Airborne and Satellite Sensors RxCADRE 2012

Characterising radiation from wildland fires is an important focus of fire science because radiation relates directly to the combustion process and can be measured across a wide range of spatial extents and resolutions. As part of a more comprehensive set of measurements collected during the 2012 Prescribed Fire Combustion and Atmospheric Dynamics Research (RxCADRE) field campaign, we used ground, airborne and spaceborne sensors to measure fire radiative power (FRP) from whole fires, applying different methods to small (2 ha) and large (>100 ha) burn blocks. For small blocks (n = 6), FRP estimated from an obliquely oriented long-wave infrared (LWIR) camera mounted on a boom lift were compared with FRP derived from combined data from tower-mounted radiometers and remotely piloted aircraft systems (RPAS). For large burn blocks (n = 3), satellite FRP measurements from the Moderate-resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors were compared with near-coincident FRP measurements derived from a LWIR imaging system aboard a piloted aircraft. We describe measurements and consider their strengths and weaknesses. Until quantitative sensors exist for small RPAS, their use in fire research will remain limited. For oblique, airborne and satellite sensors, further FRP measurement development is needed along with greater replication of coincident measurements, which we show to be feasible.

Toward an operational stray light correction for the Landsat 8 Thermal Infrared Sensor

The Thermal Infrared Sensor (TIRS) onboard Landsat 8 was tasked with continuing thermal band measurements of Earth as part of the Landsat program. From first light in early 2013, there were obvious indications, such as nonuniform banding and varying absolute calibration errors, that stray light was contaminating the thermal image data collected from the instrument. Stray light in this case refers to unwanted radiance from outside the field-of-view entering the optical system and being recorded by the focal plane. Standard calibration techniques used to flat-field and radiometrically correct the data were not sufficient to adjust the image products to within the accuracy that the Landsat community has come to expect. The development of an operational technique to remove the effects of the stray light in the TIRS data has become a high priority. A methodology is presented that makes use of a stray light optical model developed for the instrument along with knowledge of the out-of-field area surrounding the TIRS earth scene. Two versions of the algorithm are proposed in which one method utilizes near-coincident image data from an external sensor while another novel method is proposed that makes use of TIRS image data itself without the need for external data. Preliminary results of the algorithm indicate that banding artifacts due to stray light are significantly reduced when the methods are applied. Additionally, initial absolute calibration error estimates of over 9K are reduced to within 2K when applying the correction methods. Although both variations of the proposed algorithm have significantly reduced the stray light effects, the fact that the latter method utilizing TIRS image data itself does not rely on any external data is a significant advantage toward the development of an operational stray light correction solution. Ongoing work is focused on operationalizing the algorithm and identifying and quantifying potential sources of error when applying the method.

INTEGRATING LANDSAT-7 IMAGERY WITH PHYSICS-BASED MODELS FOR QUANTITATIVE MAPPING OF COASTAL WATERS NEAR RIVER DISCHARGES

Remote sensing has traditionally been used to retrieve water constituents by establishing a relationship between in- situ measured quantities and image-derived products. Motivated by the dramatically improved potential of the Landsat Data Continuity Mission (LDCM), this paper describes a different approach for water constituent retrieval where both thermal and visible spectral bands of the Enhanced Thematic Mapper Plus (ETM+) instrument on board Landsat-7 are utilized. In this effort, Landsat data is integrated with a 3D hydrodynamic model to obtain profiles of particles and dissolved matter in the near shore zone in the vicinity of two river discharges. The procedure is based upon performing many hydrodynamic simulations by adjusting input environmental/physical variables and generating Look-Up-Tables (LUTs). This is conducted in two phases, namely the model calibration and the constituent retrieval. In the calibration phase, the best model output is determined by searching the LUT for the optimal surface temperature map compared to the Landsat-derived surface temperature map. The profiles of particles and dissolved matter are retrieved in the second step by comparing several modeled surface reflectance maps with atmospherically compensated Landsat-7 imagery. Various case scenarios of simulated water constituent profiles drive an in-water radiative transfer code, i.e. Hydrolight, which simulates water-leaving reflectance ( d r ). The best match, obtained via optimization, demonstrated an average root-mean-squared-error (RMSE) of 0.68%, i.e., 0.0068 reflectance units, calculated over the two river plumes. It is concluded that calibrating a physics-based model using the Landsat-7 imagery can provide a more lucid insight into the dynamics of spatially non-uniform waters. Ongoing efforts show that, due to its enhanced radiometric fidelity, the LDCM should significantly improve our proposed approach for the retrieval of water constituents.

Over-water atmospheric correction for Landsat's new OLI sensor

The Operational Land Imager (OLI) is a new sensor being developed by the joint USGS-NASA Landsat Data Continuity Mission that exhibits an exciting potential to be used for monitoring Case 2 waters. With upgrades such as a Coastal Aerosol band, 12 bit quantization, and improved signal-to-noise, preliminary studies indicate that OLI should be radiometrically superior to its predecessors. Considering that OLI will have the traditional 30m resolution of other Landsat instruments, and that its data is free to the community, this sensor should be an invaluable tool for long-term monitoring of water quality in Case 2 waters, especially in the nearshore environment. Through the use of simulated data, previous research indicates that OLI can retrieve the levels of three main water quality indicators (chlorophyll, suspended materials, and colored-dissolved organic matter (CDOM)) to within 7% of their expected range when atmospheric effects are ignored. Since the atmosphere typically represents a major source of error when quantifying water constituents from space, significant efforts have been made to develop techniques that will accurately remove atmospheric effects from OLI data. As OLI is an instrument designed for land-based studies, it will not be equipped with the appropriate bands required by traditional water-based atmospheric correction algorithms. This work presents a new atmospheric correction technique that was developed specifically for the OLI instrument. Preliminary results indicate that when atmospheric effects are included, OLI can retrieve the levels of the three water parameters to within 15% of their expected range, which is within the desired error range for this type of research.

The increased potential for the Landsat Data Continuity Mission to contribute to case 2 water quality studies

The ability to achieve continuous monitoring of the global water supply from satellite imagery is an ongoing effort in the remote sensing community. Historically, sensors such as SeaWiFs and MODIS have been used over the open ocean and along coastal regions to determine the constituents in the water body. Due to their poor spatial resolution, these satellites are ineffective in monitoring many inland and near shore, case 2 waters whose constituents can have large spatial variability. Alternatively, current Landsat instruments have adequate spatial resolution but lack the radiometric fidelity necessary to perform constituent retrieval. In this paper, a new sensor being developed by NASA is introduced that is potentially both spectrally and spatially sufficient for the monitoring of case 2 waters. This study presents the relevant sensor design parameters and initial results of an experiment to determine what impact the improved features of the Landsat Data Continuity Missions (LDCM) Operational Land Imager (OLI) will have on water resource assessment. Specifically, we investigate how the addition of a deep blue band, 12-bit quantization, and improved signal-to-noise ratios affect our ability to retrieve water constituents. Preliminary results of a simulated case study indicate that the LDCM instrument introduces retrieval errors of less than 6% for three constituents while its predecessor, the Enhanced Thematic Mapper Plus (ETM+), introduces errors of over 20%. This suggests that LDCMs OLI instrument exhibits the potential to be a useful tool for the continuous monitoring of coastal and inland water resources. To actually achieve the potential demonstrated in this study, ongoing work focuses on atmospherically compensating simulated OLI data.

An Analysis of the Side Slither On-Orbit Calibration Technique Using the DIRSIG Model

Pushbroom-style imaging systems exhibit several advantages over line scanners when used on space-borne platforms as they typically achieve higher signal-to-noise and reduce the need for moving parts. Pushbroom sensors contain thousands of detectors, each having a unique radiometric response, which will inevitably lead to streaking and banding in the raw data. To take full advantage of the potential exhibited by pushbroom sensors, a relative radiometric correction must be performed to eliminate pixel-to-pixel non-uniformities in the raw data. Side slither is an on-orbit calibration technique where a 90-degree yaw maneuver is performed over an invariant site to flatten the data. While this technique has been utilized with moderate success for the QuickBird satellite [1] and the RapidEye constellation [2], further analysis is required to enable its implementation for the Landsat 8 sensors, which have a 15-degree field-of-view and a 0.5% pixel-to-pixel uniformity requirement. This work uses the DIRSIG model to analyze the side slither maneuver as applicable to the Landsat sensor. A description of favorable sites, how to adjust the maneuver to compensate for the curvature of “linear” arrays, how to efficiently process the data, and an analysis to assess the quality of the side slither data, are presented.

Chasing the TIRS ghosts: calibrating the Landsat 8 thermal bands

The Thermal Infrared Sensor (TIRS) on board Landsat 8 has exhibited a number of anomalous characteristics that have made it difficult to calibrate. These anomalies include differences in the radiometric appearance across the blackbody pre- and post-launch, variations in the cross calibration ratios between detectors that overlap on adjacent arrays (resulting in banding) and bias errors in the absolute calibration that can change spatially/temporally. Several updates to the TIRS calibration procedures were made in the months after launch to attempt to mitigate the impact of these anomalies on flat fielding (cosmetic removal of banding and striping) and mean level bias correction. As a result, banding and striping variations have been reduced but not eliminated and residual bias errors in band 10 should be less than 2 degrees for most targets but can be significantly more in some cases and are often larger in band 11. These corrections have all been essentially ad hoc without understanding or properly accounting for the source of the anomalies, which were, at the time unknown. This paper addresses the procedures that have been undertaken to; better characterize the nature of these anomalies, attempt to identify the source(s) of the anomalies, quantify the phenomenon responsible for them, and develop correction procedures to more effectively remove the impacts on the radiometric products. Our current understanding points to all of the anomalies being the result of internal reflections of energy from outside the target detector’s field-of-view, and often outside the telescope field-of-view, onto the target detector. This paper discusses how various members of the Landsat calibration team discovered the clues that led to how; these “ghosts” were identified, they are now being characterized, and their impact can hopefully eventually be corrected. This includes use of lunar scans to generate initial maps of influence regions, use of long path overlap ratios to explore sources of change and use of variations in bias calculated from truth sites to quantify influences from the surround on absolute bias errors.