Radiometric modeling of mechanical draft cooling towers to assist in the extraction of their absolute temperature from remote thermal imagery

Determination of the internal temperature of a mechanical draft cooling tower (MDCT) from remotely-sensed thermal imagery is important for many applications that provide input to energy-related process models. The problem of determining the temperature of an MDCT is unique due to the geometry of the tower and due to the exhausted water vapor plume. The radiance leaving the tower is dependent on the optical and thermal properties of the tower materials (i.e., emissivity, BRDF, temperature, etc.) as well as the internal geometry of the tower. The tower radiance is then propagated through the exhaust plume and through the atmosphere to arrive at the sensor. The expelled effluent from the tower consists of a warm plume with a higher water vapor concentration than the ambient atmosphere. Given that a thermal image has been atmospherically compensated, the remaining sources of error in extracted tower temperature due to the exhausted plume and the tower geometry must be accounted for. A temperature correction factor due to these error sources is derived through the use of three-dimensional radiometric modeling. A range of values for each important parameter are modeled to create a target space (i.e., look-up table) that predicts the internal MDCT temperature for every combination of parameter values. The look-up table provides data for the creation of a fast-running parameterized model. This model, along with user knowledge of the scene, provides a means to convert the image-derived apparent temperature into the estimated absolute temperature of an MDCT

Derivation and Validation of the Stray Light Correction Algorithm for the Thermal Infrared Sensor Onboard Landsat 8

It has been known and documented that the Thermal Infrared Sensor (TIRS) on-board Landsat 8 suffers from a significant stray light problem (Reuter et al., 2015; Montanaro et al., 2014a). The issue appears both as a non-uniform banding artifact across Earth scenes and as a varying absolute radiometric calibration error. A correction algorithm proposed by Montanaro et al. (2015) demonstrated great potential towards removing most of the stray light effects from TIRS image data. It has since been refined and will be implemented operationally into the Landsat Product Generation System in early 2017. The algorithm is trained using near-coincident thermal data (i.e., Terra/MODIS) to develop per-detector functional relationships between incident out-of-field radiance and additional (stray light) signal on the TIRS detectors. Once trained, the functional relationships are used to estimate and remove the stray light signal on a per-detector basis from a scene of interest. The details of the operational stray light correction algorithm are presented here along with validation studies that demonstrate the effectiveness of the algorithm in removing the stray light artifacts over a stressing range of Landsat/TIRS scene conditions. Results show that the magnitude of the banding artifact is reduced by half on average over the current (uncorrected) product and that the absolute radiometric error is reduced to approximately 0.5% in both spectral bands on average (well below the 2% requirement). All studies presented here indicate that the implementation of the stray light algorithm will lead to greatly improved performance of the TIRS instrument, for both spectral bands

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 imager

Simulation of Image Performance Characteristics of the Landsat Data Continuity Mission (LDCM) Thermal Infrared Sensor (TIRS)

The next Landsat satellite, which is scheduled for launch in early 2013, will carry two instruments: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). Significant design changes over previous Landsat instruments have been made to these sensors to potentially enhance the quality of Landsat image data. TIRS, which is the focus of this study, is a dual-band instrument that uses a push-broom style architecture to collect data. To help understand the impact of design trades during instrument build, an effort was initiated to model TIRS imagery. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool was used to produce synthetic “on-orbit” TIRS data with detailed radiometric, geometric, and digital image characteristics. This work presents several studies that used DIRSIG simulated TIRS data to test the impact of engineering performance data on image quality in an effort to determine if the image data meet specifications or, in the event that they do not, to determine if the resulting image data are still acceptable

Deep learning-assisted high-throughput analysis of freeze-fracture replica images applied to glutamate receptors and calcium channels at hippocampal synapses

The molecular anatomy of synapses defines their characteristics in transmission and plasticity. Precise measurements of the number and distribution of synaptic proteins are important for our understanding of synapse heterogeneity within and between brain regions. Freeze–fracture replica immunogold electron microscopy enables us to analyze them quantitatively on a two-dimensional membrane surface. Here, we introduce Darea software, which utilizes deep learning for analysis of replica images and demonstrate its usefulness for quick measurements of the pre- and postsynaptic areas, density and distribution of gold particles at synapses in a reproducible manner. We used Darea for comparing glutamate receptor and calcium channel distributions between hippocampal CA3-CA1 spine synapses on apical and basal dendrites, which differ in signaling pathways involved in synaptic plasticity. We found that apical synapses express a higher density of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors and a stronger increase of AMPA receptors with synaptic size, while basal synapses show a larger increase in N-methyl-D-aspartate (NMDA) receptors with size. Interestingly, AMPA and NMDA receptors are segregated within postsynaptic sites and negatively correlated in density among both apical and basal synapses. In the presynaptic sites, Cav2.1 voltage-gated calcium channels show similar densities in apical and basal synapses with distributions consistent with an exclusion zone model of calcium channel-release site topography

Landsat 9 Thermal Infrared Sensor 2 Subsystem-Level Spectral Test Results

Results from the Thermal Infrared Sensor 2 (TIRS-2) prelaunch spectral characterization at telescope and detector subsystem level are presented. The derived relative spectral response (RSR) shape is expected to be very similar to the instrument-level spectral response and provides an initial estimate of the RSR and its differences to the component-level RSR measurements. Such differences were observed at TIRS- 1 and are likely a result of angular dependence of the spectral response of the detector. The subsystem RSR measurements also provide an opportunity for a preliminary assessment of the spectral requirements. Final requirements verification will be performed at future thermal vacuum environmental testing with the fully assembled TIRS-2 instrument

LANDSAT 9 Thermal Infrared Sensor 2 Characterization Plan Overview

Landsat 9 will continue the Landsat data record into its fifth decade with a near-copy build of Landsat 8 with launch scheduled for December 2020. The two instruments on Landsat 9 are Thermal Infrared Sensor-2 (TIRS-2) and Operational Land Imager-2 (OLI-2). TIRS-2 is a two-channel pushbroom imager with a 15-degree field of view that will have a 16-day measurement cadence from its nominal 705-km orbit altitude. Its carefully developed instrument performance requirements and associated characterization plan will result in stable and well-understood science-quality imagery that will be used for environmental, economic and legal applications. This paper will present a summary of the plan for TIRS-2 prelaunch characterization at the component, subsystem, and instrument level

Spectral Analysis of the Primary Flight Focal Plane Arrays for the Thermal Infrared Sensor

Thermal Infrared Sensor (TIRS) is a (1) New longwave infrared (10 - 12 micron) sensor for the Landsat Data Continuity Mission, (2) 185 km ground swath; 100 meter pixel size on ground, (3) Pushbroom sensor configuration. Issue of Calibration are: (1) Single detector -- only one calibration, (2) Multiple detectors - unique calibration for each detector -- leads to pixel-to-pixel artifacts. Objectives are: (1) Predict extent of residual striping when viewing a uniform blackbody target through various atmospheres, (2) Determine how different spectral shapes affect the derived surface temperature in a realistic synthetic scene

Landsat 9 TIRS-2 Performance Results Based on Subsystem-Level Testing

Landsat 9 is the next in the series of Landsat satellites and has a complement of two pushbroom imagers: Operational Land Imager-2 (OLI-2) that samples the solar reflective spectrum with nine channels and Thermal Infrared Sensor-2 (TIRS-2) samples the thermal infrared spectrum with two channels. The first builds of these sensors, OLI and TIRS, were launched on Landsat 8 in 2013 and Landsat 9 is expected to launch in December 2020. TIRS-2 is designed and built to continue the Landsat data record and satisfy the needs of the remote sensing community. There are two sets of requirements considered for planning the component, subsystem and instrument level tests for TIRS-2: performance requirements and Special Calibration Test Requirements (SCTR). The performance requirements specify key spectral, spatial, radiometric, and operational parameters of TIRS-2 while the SCTRs specify parameters of how the instrument is tested. Several requirements can only be verified at the instrument level, but many performance metrics can be assessed earlier in prelaunch testing at the subsystem level. A test program called TIRS Imaging Performance and Cryoshell Evaluation (TIPCE) was developed to characterize TIRS-2 spectral, spatial, and scattered-light rejection performance at the telescope and detector subsystem level. There were three thermal vacuum campaigns in TIPCE that occurred from November 2017 to March 2018. This work shows results of TIPCE data analysis which provide confidence that key requirements will be met at instrument level with a few minor waivers. A full complement of performance testing will be done at the TIRS-2 instrument level for final verification in late 2018 through Spring 2019

Landsat-8 On-Orbit and Landsat-9 Pre-Launch Sensor Radiometric Characterization

Topics: Landsat 8, 9 Instrument Overview -Operational Land Imager (OLI) -Thermal Infrared Sensor (TIRS); Landsat-8 Instrument Status and On-orbit Radiometric Performance Characterization; Landsat-9 Instrument Status and Pre-launch Radiometric Performance Characterization