Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration

Launched in February 2013, the Landsat-8 carries on-board the Thermal Infrared Sensor (TIRS), a two-band thermal pushbroom imager, to maintain the thermal imaging capability of the Landsat program. The TIRS bands are centered at roughly 10.9 and 12 micrometers (Bands 10 and 11 respectively). They have 100 m spatial resolution and image coincidently with the Operational Land Imager (OLI), also on-board Landsat-8. The TIRS instrument has an internal calibration system consisting of a variable temperature blackbody and a special viewport with which it can see deep space; a two point calibration can be performed twice an orbit. Immediately after launch, a rigorous vicarious calibration program was started to validate the absolute calibration of the system. The two vicarious calibration teams, NASA/Jet Propulsion Laboratory (JPL) and the Rochester Institute of Technology (RIT), both make use of buoys deployed on large water bodies as the primary monitoring technique. RIT took advantage of cross-calibration opportunity soon after launch when Landsat-8 and Landsat-7 were imaging the same targets within a few minutes of each other to perform a validation of the absolute calibration. Terra MODIS is also being used for regular monitoring of the TIRS absolute calibration. The buoy initial results showed a large error in both bands, 0.29 and 0.51 W/sq msrmicrometers or -2.1 K and -4.4 K at 300 K in Band 10 and 11 respectively, where TIRS data was too hot. A calibration update was recommended for both bands to correct for a bias error and was implemented on 3 February 2014 in the USGS/EROS processing system, but the residual variability is still larger than desired for both bands (0.12 and 0.2 W/sq msrmicrometers or 0.87 and 1.67 K at 300 K). Additional work has uncovered the source of the calibration error: out-of-field stray light. While analysis continues to characterize the stray light contribution, the vicarious calibration work proceeds. The additional data have not changed the statistical assessment but indicate that the correction (particularly in band 11) is probably only valid for a subset of data. While the stray light effect is small enough in Band 10 to make the data useful across a wide array of applications, the effect in Band 11 is larger and the vicarious results suggest that Band 11 data should not be used where absolute calibration is required

Calibration of the Thermal Infrared Sensor on the Landsat Data Continuity Mission

The Landsat series of satellites provides the longest running continuous data set of moderate-spatial-resolution imagery beginning with the launch of Landsat 1 in 1972 and continuing with the 1999 launch of Landsat 7 and current operation of Landsats 5 and 7. The Landsat Data Continuity Mission (LDCM) will continue this program into a fourth decade providing data that are keys to understanding changes in land-use changes and resource management. LDCM consists of a two-sensor platform comprised of the Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS). A description of the applications and design of the TIRS instrument is given as well as the plans for calibration and characterization. Included are early results from preflight calibration and a description of the inflight validation

Landsat-8 Sensor Characterization and Calibration

Landsat-8 was launched on 11 February 2013 with two new Earth Imaging sensors to provide a continued data record with the previous Landsats. For Landsat-8, pushbroom technology was adopted, and the reflective bands and thermal bands were split into two instruments. The Operational Land Imager (OLI) is the reflective band sensor and the Thermal Infrared Sensor (TIRS), the thermal. In addition to these fundamental changes, bands were added, spectral bandpasses were refined, dynamic range and data quantization were improved, and numerous other enhancements were implemented. As in previous Landsat missions, the National Aeronautics and Space Administration (NASA) and United States Geological Survey (USGS) cooperated in the development, launch and operation of the Landsat- 8 mission. One key aspect of this cooperation was in the characterization and calibration of the instruments and their data. This Special Issue documents the efforts of the joint USGS and NASA calibration team and affiliates to characterize the new sensors and their data for the benefit of the scientific and application users of the Landsat archive. A key scientific use of Landsat data is to assess changes in the land-use and land cover of the Earth's surface over the now 43-year record. In order to perform these analyses and avoid confusing sensor changes with Earth surface changes, a solid understanding of the sensors' performance, consistent geolocation and radiometry are essential. Particularly with the significant changes in the Landsat-8 sensors relative to previous Landsat missions, this characterization becomes all the more important

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

Landsat Program

Landsat initiated the revolution in moderate resolution Earth remote sensing in the 1970s. With seven successful missions over 40+ years, Landsat has documented - and continues to document - the global Earth land surface and its evolution. The Landsat missions and sensors have evolved along with the technology from a demonstration project in the analog world of visual interpretation to an operational mission in the digital world, with incremental improvements along the way in terms of spectral, spatial, radiometric and geometric performance as well as acquisition strategy, data availability, and products

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

Sensitivity of evapotranspiration retrievals from the METRIC processing algorithm to improved radiometric resolution of Landsat 8 thermal data and to calibration bias in Landsat 7 and 8 surface temperature

We made an assessment on the use of 12-bit resolution of Landsat 8 (L8) on evapotranspiration (ET) retrievals via the METRIC process as compared to using 8-bit resolution imagery of previous Landsat missions. METRIC (Mapping Evapotranspiration at high Resolution using Internalized Calibration) is an ET retrieval system commonly used in water and water rights management where the surface energy balance process is coupled with an extreme- end point calibration process to remove most impacts of systematic bias in remotely sensed inputs. We degraded L8 thermal images by grouping sequential digital numbers to reduce the apparent numerical resolution and then recomputed ET using METRIC and compared to nondegraded ET products. The use of 8-bit thermal data did not substantially impair the accuracy of ET retrievals derived from METRIC, as compared to the use of 12-bit thermal data. The largest error introduced into ET was \u3c1%. We also compared ET retrieved from images processed during the L8 and Landsat 7 (L7) March 2013 underfly to assess differences in ET caused by differences in signal to noise ratio (SNR) and scaling of the two systems. We evaluated the impact of bias in land surface temperature (LST) retrievals on ET determination using the CIMEC calibration approach (Calibration using Inverse Modeling using Extreme Member Calibration) employed in METRIC by introducing globally systematic biases into LST retrievals from L7 and L8 and comparing to ET from non-biased retrievals. The impacts of the introduction of both additive and multiplicative biases into surface temperature on ET were small for the three regions of the US studied, and for both L7 and L8 satellite systems. An independent study showed that METRIC-produced ET compared to within 3% of measured ET for the California site. The study assessed the impact of the February 2014 recalibration of L8 thermal data that caused a 3 K downward shift in LST estimation and changed reflectance values by about 0.7%. We found that the use of the recalibrated LST and shortwave data sets in METRIC did not change the accuracy of ET retrievals due to the automatic compensation for systematic biases employed by METRIC

In-Flight Calibration of the Thermal Infrared Sensor (TIRS) on the Landsat Data Continuity Mission

Describe in-flight calibration for the Thermal Infrared Sensor (TIRS) (1) Overview of TIRS (2) On-orbit radiometric calibration (2a) Onboard calibrator (2b) Terrestrial sites (3) On-orbit geometric and spatial calibratio

Reflectance-based Calibration and Validation of the Landsat Satellite Archive

The primary objective of this project was to consistently calibrate the entire Landsat series to a common reflectance scale by performing cross-calibration corrections from Landsat-8 OLI to Landsat- 1 MSS. A consistent radiance-based calibration was already performed from Landsat-8 OLI through Landsat-1 MSS using bright targets and dark targets. The MSS radiance-based calibration results showed an uncertainty of about ±5%. Typically to convert from radiance to reflectance a solar model is used. Unfortunately, there are numerous solar models, all with various levels of accuracies. It was also seen that there is a data format inconsistency for different types of MSS data that impact the radiometric uncertainty of the products when compared to Landsat-8 OLI data. One of the advances Landsat-8 OLI has over to earlier missions is a solar model independent reflectance calibration. Hence, to reduce these uncertainties and remove the dependency on the solar model, direct reflectance-based calibration was performed for all previous missions using Landsat-8 OLI as the “standard”. A consistent cross-calibration of all Landsat sensors was achieved using coincident/near-coincident scene pairs. The work started from cross-calibration of Landsat-8 OLI to Landsat-7 ETM+ and continued through Landsat-1 MSS. Due to the fact each Landsat sensor measures slightly different parts of the electromagnetic spectrum, a spectral band adjustment factor (SBAF) was computed and used prior to the cross-calibration. To determine the significance of the bias derived from cross-calibration, a t-test was performed with a null hypothesis that the bias equals zero at a confidence interval of 95%. From the final calibration equations, it was found that for band 5 of Landsat-1 bias is significant. The effectiveness of these cross-calibration results is discussed by showing a significant improvement in the observed inconsistencies in the absolute calibration of all Landsat sensors for both bright and dark targets. The results show a significant improvement in reflectance calibration, and an overall uncertainty of less than ±3%

Evaluation of landsat-8 thermal bands to monitor land surface temperature

Ponencia presentada en: IX Congreso de la Asociación Española de Climatología celebrado en Almería entre el 28 y el 30 de octubre de 2014.[ES]El nuevo Thermal Infrared Sensor (TIRS) a bordo del Landsat-8 (L8) dispone de dos bandas térmicas, 10 (11.60-11.19 μm) y 11 (11.50-12.51 μm), con una resolución espacial de 100m, con el fin de proporcionar temperaturas de la superficie terrestre (LST) de una manera más precisa que su predecesor Landsat-7 ETM+. El L8 fue lanzado en febrero de 2013, comenzando su adquisición operativa a mediados de abril. Los primeros estudios realizados por el equipo de calibración de L8 mostraron errores sistemáticos significativos para el TIRS, y en febrero de 2014 el archivo de imágenes L8 TIRS fue reprocesado para corregir dichos errores. En este estudio, con el fin de comprobar la calibración del L8 TIRS, realizamos medidas de campo en una zona llana y térmicamente homogénea dedicada al cultivo del arroz. A partir de estas medidas de LST simulamos las radiancias y temperaturas de brillo a nivel del satélite y las comparamos con los datos TIRS. Tal y como apuntaba el equipo de L8, nuestros resultados muestran una sobreestimación para la banda 11. Sin embargo, el recalibrado aplicado por dicho equipo para ambas bandas ha resultado no ser satisfactorio en nuestra zona experimental, ya que proponen sustraer errores sistemáticos mayores a los requeridos.[EN]The new Landsat-8 (L8) Thermal Infrared Sensor (TIRS) has two thermal bands, 10 (11.60- 11.19 μm) and 11 (11.50-12.51 μm) at 100-m spatial resolution, aimed to provide more accurate Land Surface Temperatures (LST) than Landsat-7 ETM+. L8 was launched on February 2013, and operational acquisitions started in middle April 2013. The first studies by the L8 Calibration Team showed significant TIRS temperature offsets, and in February 2014 the L8 TIRS archive was reprocessed to remove these offsets. In this study, ground LST measurements were performed in a flat and thermally homogeneous area of rice-crop fields for checking the calibration of the L8 TIRS bands. At-sensor radiances and brightness temperatures were simulated from ground-measured LSTs and compared with TIRS values. A significant overestimation was observed for band 11, in agreement with the L8 Calibration Team results. However, their recalibration was shown unsatisfactory in our test site for both bands, since they proposed subtracting higher offsets than required.This study was supported by the Spanish Ministerio de Economía y Competitividad (projects CGL2010-16364, CGL2010-17577/CLI, CGL2011-13579-E, CGL2011-30433 and GRACCIE Consolider-Ingenio 2010; and Dr. Niclòs "Ramón y Cajal" Research Contract) and Generalitat Valenciana (PROMETEO/2009/006 and PROMETEO/2009/086 projects)