A Method for Monitoring Iron and Steel Factory Economic Activity Based on Satellites

Thermal infrared (TIR) satellite images are generally employed to retrieve land surface temperature (LST) data in remote sensing. LST data have been widely used in evapotranspiration (ET) estimation based on satellite observations over broad regions, as well as the surface dryness associated with vegetation index. Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) can provide LST data with a 30-m spatial resolution. However, rapid changes in environmental factors, such as temperature, humidity, wind speed, and soil moisture, will affect the dynamics of ET. Therefore, ET estimation needs a high temporal resolution as well as a high spatial resolution for daily, diurnal, or even hourly analysis. A challenge with satellite observations is that higher-spatial-resolution sensors have a lower temporal resolution, and vice versa. Previous studies solved this limitation by developing a spatial and temporal adaptive reflectance fusion model (STARFM) for visible images. In this study, with the primary mechanism (thermal emission) of TIRS, surface emissivity is used in the proposed spatial and temporal adaptive emissivity fusion model (STAEFM) as a modification of the original STARFM for fusing TIR images instead of reflectance. For high a temporal resolution, the advanced Himawari imager (AHI) onboard the Himawari-8 satellite is explored. Thus, Landsat-like TIR images with a 10-minute temporal resolution can be synthesized by fusing TIR images of Himawari-8 AHI and Landsat-8 TIRS. The performance of the STAEFM to retrieve LST was compared with the STARFM and enhanced STARFM (ESTARFM) based on the similarity to the observed Landsat image and differences with air temperature. The peak signal-to-noise ratio (PSNR) value of the STAEFM image is more than 42 dB, while the values for STARFM and ESTARFM images are around 31 and 38 dB, respectively. The differences of LST and air temperature data collected from five meteorological stations are 1.53 °C to 4.93 °C, which are smaller compared with STARFM’s and ESATRFM’s. The examination of the case study showed reasonable results of hourly LST, dryness index, and ET retrieval, indicating significant potential for the proposed STAEFM to provide very-high-spatiotemporal-resolution (30 m every 10 min) TIR images for surface dryness and ET monitoring.

With the development of urbanization and industrialization, megacities have experienced more severe surface urban heat island (SUHI) effects. Land surface temperatures (LSTs) are retrieved; spatial distribution of temperature is characterized, and the relationship among temperatures or SUHIs and land-use and land cover (LULC) in Beijing City are discussed. The changing LSTs in Beijing, from 1990 to 2017, were calculated by a radiative transfer equation and mono-window algorithm. To estimate the effect of SUHI, Landsat-8 Thermal Infrared Sensor (TRIS) and Landsat-5 Thematic Mapper (TM) data were selected. There is an increasing trend toward high LSTs for different LULC types. The connection with building and vegetation density is analyzed. Results indicate that for every 1% increase in the density of buildings, the increase in amplitude of temperature in 2017 was twice as large as it was in 1995 for the study area. In terms of normalized difference vegetation index (NDVI) values, the decrease in amplitude of LST was 10 times that of the year 1995, where there is only a slight increase in the NDVI values of the area.

With the rapid development of new satellite thermal sensors and applications of land surface temperature (LST), research on finding effective algorithms to retrieve accurate LST from satellite thermal infrared (TIR) data is becoming more and more important. In this study, multiple algorithms for retrieving LST from Landsat-8 Thermal Infrared Sensor (TIRS) data are validated and intercompared in an extremely arid region, Northwest China. According to the validation and intercomparison, we find that the radiative transfer equation (RTE) based method with TIRS band 1 (10.60–11.19 µm) has the highest accuracy, while the single-channel (SC) method using TIRS band 2 (11.50–12.51 µm) yielded the lowest accuracy. The accuracies of split-window (SW) algorithms are slightly lower than the RTE based method. However, the SW algorithms have better applicability than the RTE based method. The most suitable SW algorithm for Landsat-8 TIRS data in the study area is recommended. This study will be beneficial for developing the LST product from Landsat-8 data for the study area.

Landsat-9, the next in the series of Landsat satellites, will have the same complement of two sensors as Landsat-8: The Operational Land Imager (OLI) that covers the reflective solar part of the spectrum in 9 spectral bands and the Thermal Infrared Sensor (TIRS) with two bands in the thermal infrared region. The main changes to the sensors for Landsat-9 will be to increase redundancy in the TIRS instrument, called TIRS-2, to bring it up to a five year design lifetime and fixes for anomalies observed on-orbit on Landsat-8 TIRS: Stray light and scene select mechanism encoder degradation. This work reports on the multi-pronged approach that will be used to ensure that stray light is reduced to required levels and properly characterized. Baffles to reduce stray light were designed and tested at several stages of sensor development. In parallel, optical modeling by NASA and independent teams was used to predict performance of the design changes to hold against test results as well as Landsat 8 TIRS on-orbit performance for model validation. A new subsystem-level test allows a large angular range to be tested to characterize out-of-field stray light that was not available during the first TIRS build. Combined, characterization results from modeling and ambient-, component-, subsystem-, and instrument-level testing will fully characterize TIRS-2 performance.

Satellite observations are widely used to investigate, monitor, and forecast volcanic activity. Spaceborne thermal infrared (TIR) measurements of high-temperature volcanic features improve our understanding of the underlying processes and our ability to identify reactivation of activity, forecast eruptions, and assess hazards. In particular, thermal changes, indicative of subtle pre-eruptive volcanic thermal activity, have been observed. Over the last several decades, different approaches have been explored to detect and estimate the temperature above background of these thermal anomalies. The most common approach relies on a spatial statistical analysis based on a scene by scene choice of the background temperature region. Satisfactory results have also been shown by using time series anomaly detection algorithms based on statistical profiling approach. Artificial intelligence (AI) is growing sharply in different remote sensing fields because of its capability to automatically learn patterns from the data. Here, we develop an AI approach to automatically detect volcanic thermal features by using spatiotemporal information from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), LANDSAT-8 Thermal Infrared Sensor (TIRS) and MODerate-resolution Imaging Spectroradiometer (MODIS) data acquired over several decades. Our goal is to exploit AI techniques using a combination of high temporal and high spatial resolution satellite data to improve thermal volcanic monitoring and detect very low-level anomalies caused by pre-eruptive activity. We use both low-spatial, high-temporal resolution MODIS data to detect hotter thermal features at short time scales; as well as high-spatial low-temporal resolution ASTER TIR and TIRS data to detect more subtle thermal changes otherwise missed by MODIS. Our analysis is conducted in Google Earth Engine (GEE), a cloud computing platform with fast access and processing of satellite data. The comparison with a new statistical algorithm is documented in a companion abstract in this session.

Kolkata, a major metropolitan area in India, is vulnerable to the Urban Heat Island (UHI) effect due to rapid urbanisation and industrialisation. The city’s geographical and climatic conditions further exacerbate this phenomenon, resulting in significant impacts on the urban climate and public health. The UHI effect reflects the influence of climate change and human-induced stress on the natural environment, resulting in adverse health outcomes and higher energy consumption. This study examines the role of urban design and planning in contributing to UHI formation. It highlights the importance of integrating green infrastructure and energy-efficient solutions to reduce its effects. Landsat-9 Level-2 Surface Reflectance and Thermal Infrared Sensor (TIRS) data were used to analyse UHI patterns and vegetation dynamics through indices such as the Normalised Difference Vegetation Index (NDVI), Normalised Difference Built-up Index (NDBI), Land Surface Temperature (LST), and a standardised UHI Z-score. LST was derived by converting the thermal infrared band to brightness temperature and applying emissivity correction. The results showed clear temperature differences between urban and rural areas, with certain regions experiencing strong UHI effects. The rise of the average temperature of 4.4 °C highlighted the need for improved urban planning and the use of green infrastructure to reduce the negative impacts of UHI. Recommendations are the construction of Green Mark commercial buildings, skyrise greenery, gardens, and national parks to reduce building-related environmental damage. Incorporating these measures into current planning and construction practices to support micro-climatic improvements in the Kolkata metropolitan city that contribute to its sustainable development.

Remote sensing technique often analyzes the thermal characteristics of any area. Our study focuses on estimating land surface temperature (LST) of Raipur City, emphasizing the urban heat island (UHI) and non-UHI inside the city boundary and the relationships of LST with four spectral indices (normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index). Mono-window algorithm is used as LST retrieval method on Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data, which needs spectral radiance and emissivity of TIRS bands. The entire study is performed on 11 multidate Landsat 8 OLI and TIRS images taken from four different seasons; premonsoon, monsoon, postmonsoon, and winter, in a single-year time period. The Landsat 8 data derived LST is validated significantly with Moderate Resolution Imaging Spectroradiometer (MOD11A1) data. The results show that the UHI zones are mainly developed along the northern and southern portions of the city. The common area of UHI for four different seasons is developed mainly in the northwestern parts of the city, and the value of LST in the common UHI area varies from 26.45°C to 36.51°C. Moreover, the strongest regression between LST and these spectral indices is observed in monsoon and postmonsoon seasons, whereas winter and premonsoon seasons revealed comparatively weak regression. The results also indicate that landscape heterogeneity reduces the reliability of the regression between LST with these spectral indices.

This paper describes the application of Sentinel-1 TOPS (Terrain Observation with Progressive Scans), the latest generation of SAR satellite imagery, to detect displacement of the Merapi volcano due to the May–June 2018 eruption. Deformation was detected by measuring the vertical displacement of the surface topography around the eruption centre. The Interferometric Synthetic Aperture Radar (InSAR) technique was used to measure the vertical displacement. Furthermore, several Landsat-8 Thermal Infra Red Sensor (TIRS) imageries were used to confirm that the displacement was generated by the volcanic eruption. The increasing temperature of the crater was the main parameter derived using the Landsat-8 TIRS, in order to determine the increase in volcanic activity. To understand this phenomenon, we used Landsat-8 TIRS acquisition dates before, during and after the eruption. The results show that the eruption in the May–June 2018 period led to a small negative vertical displacement. This vertical displacement occurred in the peak of volcano range from -0.260 to -0.063 m. The crater, centre of eruption and upper slope of the volcano experienced negative vertical displacement. The results of the analysis from Landsat-8 TIRS in the form of an increase in temperature during the 2018 eruption confirmed that the displacement detected by Sentinel-1 TOPS SAR was due to the impact of volcanic activity. Based on the results of this analysis, it can be seen that the integration of SAR and thermal optical data can be very useful in understanding whether deformation is certain to have been caused by volcanic activity.

This paper described the application of Landsat-8 TIRS (Thermal Infra Red Sensor) to analyze the surface temperature changes of the crater region of Agung volcano during early of 2013 until late of 2019. Agung volcano is an active stratovolcano located in Bali island. We processed the brightness temperature from channel-10 of Landsat-8 TIRS during early of 2013 – late of 2019 and analyzed the changes. The results of this research showed that the eruptions that occurred during 2017 - 2018 have indicated highly increasing in the surface temperature of the crater region. The surface temperature changes of the crater region, which can be detected from Landsat-8 TIRS data, can be used as a precursor of an eruption.

The Landsat-8 Thermal Infrared Sensor (TIRS) has been acquiring two-band thermal infrared images of the Earth’s surface since 2013. The calibration of the two-band system has been monitored using the on-board calibrator and validated with vicarious calibration performed by NASA/JPL and RIT since launch. Soon after launch, it was discovered that the instrument had a significant stray light effect that was affecting the radiometric calibration. The stray light was corrected in the processing system in 2017. Since then, it has become apparent that there was an additional radiometric error, based on the vicarious calibration results. With a failure within the primary electronic system and subsequent switch to the redundant electronic system, the TIRS instrument effectively has two separate calibration regimes. The vicarious calibration found a statistically significant calibration error, primarily a constant over time, in Band 11 on the primary electronics (Feb 11, 2013 through March 5, 2015) of about -0.6K at 300K. The calibration error in Band 10 was smaller though still statistically significant at about 0.2K at 300K. On the redundant side (March 5, 2015 to present), the calibration error is more signal dependent than time dependent. Both bands are affected, with Band 10 having an error between 1K and -0.4K (between 273-320K) and Band 11 having an error between 0.8K and -1.44K (between 273-320K). This calibration error will be corrected within the USGS Landsat Product Generation System with the release of Landsat Collection-2 products. The Collection-2 release also includes a correction to the relative radiometric calibration of TIRS data. Striping as a result of poor detector-to-detector normalization has been increasing in the imagery since launch. The TIRS relative radiometric calibration will be updated based on internal calibrator data to remove the stripes on a quarterly basis. The visible stripes are generally at 0.1-0.2% level, though there are some detectors in each band that have changed by 1% or more. The Collection-2 release will result in much more uniform TIRS images.

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.

This paper proposes a practical split-window algorithm (SWA) for retrieving land surface temperature (LST) from Landsat-8 Thermal Infrared Sensor (TIRS) data. This SWA has a universal applicability and a set of parameters that can be applied when retrieving LSTs year-round. The atmospheric transmittance and the land surface emissivity (LSE), the essential SWA input parameters, of the Landsat-8 TIRS data are determined in this paper. We also analysed the error sensitivity of these SWA input parameters. The accuracy evaluation of the proposed SWA in this paper was conducted using the software MODTRAN 4.0. The root mean square error (RMSE) of the simulated LST using the mid-latitude summer atmospheric profile is 0.51 K, improving on the result of 0.93 K from Rozenstein (2014). Among the 90 simulated data points, the maximum absolute error is 0.99 °C, and the minimum absolute error is 0.02 °C. Under the Tropical model and 1976 US standard atmospheric conditions, the RMSE of the LST errors are 0.70 K and 0.63 K, respectively. The accuracy results indicate that the SWA provides an LST retrieval method that features not only high accuracy but also a certain universality. Additionally, the SWA was applied to retrieve the LST of an urban area using two Landsat-8 images. The SWA presented in this paper should promote the application of Landsat-8 data in the study of environmental evolution.

An algorithm for the retrieval of precipitable water vapour (PWV) from Landsat-8 thermal infrared sensor (TIRS) data over land area has been developed in this paper. This method is based on the split-window covariance-variance ratio (SWCVR) theory and introduces normalized difference vegetation index (NDVI) to improve PWV retrieval from the relatively high-resolution thermal infrared data. Validation of the method is performed with meteorological data and Moderate Resolution Imaging Spectroradiometer (MODIS) total column PWV product (MOD05), and comparisons between NDVI-based SWCVR method and previous SWCVR method are employed. The root mean square error (RMSE) between PWV retrieved and that provided by meteorological data is 0.39 and 0.57 g cm−2 respectively for the proposed and previous method. The RMSE is 0.55 and 0.69 g cm−2 respectively for the proposed and previous method as validated with MOD05. It is concluded that the proposed method for retrieval of PWV from Landsat-8 TIRS data attained a better accuracy than the previous SWCVR method. PWV obtained from the proposed method is of great value as an input for land surface temperature retrieval and atmospheric correction for the Landsat-8 data.

Evapotranspiration (ET) is key to assess crop water balance and optimize water-use efficiency. To attain sustainability in cropping systems, especially in semi-arid ecosystems, it is necessary to improve methodologies of ET estimation. A method to predict ET is by using land surface temperature (LST) from remote sensing data and applying the Operational Simplified Surface Energy Balance Model (SSEBop). However, to date, LST information from Landsat-8 Thermal Infrared Sensor (TIRS) has a coarser resolution (100 m) and longer revisit time than Sentinel-2, which does not have a thermal infrared sensor, which compromises its use in ET models as SSEBop. Therefore, in the present study we set out to use Sentinel-2 data at a higher spatial-temporal resolution (10 m) to predict ET. Three models were trained using TIRS’ images as training data (100 m) and later used to predict LST at 10 m in the western section of the Copiapó Valley (Chile). The models were built on cubist (Cub) and random forest (RF) algorithms, and a sinusoidal model (Sin). The predicted LSTs were compared with three meteorological stations located in olives, vineyards, and pomegranate orchards. RMSE values for the prediction of LST at 10 m were 7.09 K, 3.91 K, and 3.4 K in Cub, RF, and Sin, respectively. ET estimation from LST in spatial-temporal relation showed that RF was the best overall performance (R2 = 0.710) when contrasted with Landsat, followed by the Sin model (R2 = 0.707). Nonetheless, the Sin model had the lowest RMSE (0.45 mm d−1) and showed the best performance at predicting orchards’ ET. In our discussion, we argue that a simplistic sinusoidal model built on NDVI presents advantages over RF and Cub, which are constrained to the spatial relation of predictors at different study areas. Our study shows how it is possible to downscale Landsat-8 TIRS’ images from 100 m to 10 m to predict ET.

The Landsat-8 Thermal Infrared Sensor (TIRS) has been acquiring two-band thermal infrared images of the Earth’s surface since 2013. The calibration of the two-band system has been monitored using the on-board calibrator and validated with vicarious calibration performed by NASA/Jet Propulsion Laboratory and Rochester Institute of Technology since launch. An update to the radiometric calibration was introduced into the Collection-2 processing system in late 2020 to correct for signal-dependent and time-dependent calibration errors. In November 2020, the Landsat-8 spacecraft experienced two safeholds and, while TIRS seemingly recovered nominally, there were slowly developing changes as a result. By December 2020, the TIRS Band 11 responsivity had decreased by as much as 2%. It was determined that a contaminant has been slowly depositing on a component in the optical path since the safehold and continues as of this writing. As of late June 2021, the responsivity is still decreasing in both spectral bands; Band 11 band-average responsivity has dropped by 3.7% and Band 10 band-average responsivity has dropped by 2.0%, though the decrease in responsivity is not uniform across the focal plane. Since March 2021, the TIRS products have been processed with calibration gains that account for the changing responsivity.