Landsat-8 Thermal Infrared Sensor Research Articles

Land surface temperature downscaling in the karst mountain urban area considering the topographic characteristics

To obtain high-spatial-resolution land surface temperature (LST) in karst areas, it is necessary to select a downscale regression model with a better simulation effect and the scale factors that can best represent the topographic characteristics of karst mountainous areas. In Guiyang, a typical karst mountain city, two areas are selected as the study area, which is dominated by natural surface and construction land. Based on the data of Landsat-8 Thermal Infrared Sensor (TIRS), Sentinel-2, Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM), and meteorological stations, the scale factors representing bare land: bare soil index and topographic relief: mountain shadow (hillshade), relief degree of land surface (RDLS), solar incident angle, and sky view factor (SVF) are added on the basis of the conventional factors. At the same time, random forest (RF) and extreme gradient boosting (XGBoost) models are used to construct an LST downscaling method that is more suitable for karst mountain cities. After the above steps, the LST product with a spatial resolution of 10 m is finally estimated. The results show that, due to the characteristics of large elevation variation, fragmentation, and high heterogeneity of surface landscape in karst areas, digital elevation model (DEM), RDLS, and SVF factors need to be considered in the downscaling of surface temperature, and the contribution rates of these factors are all more than 6% in the model. In terms of accuracy evaluation of ground temperature, XGBoost model has the highest accuracy with an average absolute error of 1.67K, RF model has an average error of 1.90K, and thermal image sharpening has the worst accuracy with an average error of 2.41K. In terms of accuracy evaluation of ascending scale, the XGBoost model also shows higher accuracy and richer texture details. The research results can provide basic data for the acquisition of high-resolution LST and its intermediate parameters in this area and also provide a method reference for the reduction of high-resolution LST in similar areas.

Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem

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.

Integration of optical, SAR and DEM data for automated detection of debris-covered glaciers over the western Nyainqentanglha using a random forest classifier

The capability of optical images to detect clean-ice glaciers has been well-demonstrated. Debris-covered glaciers, are different from clean-ice glaciers and are important for research of glacier mass balance. However, the main challenges to detect debris-covered glaciers in alpine regions faced by many researchers are the spectral similarity between debris-covered glaciers and the rocks and soils on both sides, as well as shadows cast from mountains, clouds and seasonal snow in satellite images. This study aimed to develop an automatic algorithm (using a Random Forest classifier model) implemented in the western Nyainqentanglha to map debris-covered glaciers based on multi-source datasets such as Sentinel-1 Synthetic Aperture Radar data, Sentinel-2 Multispectral Instrument data, Landsat-8 Thermal Infrared Sensor and Digital Elevation Models. All data was split into training (70%) and testing datasets (30%). The main strength of this study is that our method overcomes most of the above-mentioned challenges and the great accuracy (Kappa coefficient: 0.96, overall accuracy: 97.21%) of the Random Forest classifier model represents a comprehensive success in identifying debris-covered glaciers, illustrating that if this method can be executed efficiently, it will bring benefits for glacier inventory management. Additionally, an analysis of the spatial characteristic of the mountain glaciers showed that the glacier area, elevation and slope of the Nyainqentanglha Mountains were closely related. Moreover, most glacier movement occurred in glaciers with an area of less than 1 km2 and greater than 10 km2, which had mean velocities of 0.141 m/day and 0.168 m/day, respectively, and glacier movement explained the uncertainty of glacier facies mapping.

Detecting heat-inducing urban built-up surface material with multi remote sensing datasets using reflectance and emission spectroscopy

This study aims to evaluate the spatial and spectral characteristics of dominant heat-inducing urban built-up surface materials (UBSM) in Kalaburagi, Karnataka State (India) using multi remote sensing datasets and field/lab instruments. A total of 16 UBSM were collected at different locations of the study area and spectra were generated using ASD Field spec 3 Spectro radiometer operated at VNIR and SWIR regions (0.35 μm to 2.5 μm). The collected spectra were then resampled with the spectral range of 0.3–2.5 μm and used for classifying dimensionally reduced AVIRIS-NG (Airborne Visible and Infrared Imaging Spectrometer – Next Generation) dataset through Spectral Angle Mapper (SAM) classifier for understanding spatial and spectral characteristics of heat inducing UBSM in the study area. The classification shows that concrete, basalt stones and asbestos are the dominant UBSM in the study area and its spectral reflectance are comparatively low in the VNIR and SWIR regions. The land surface temperature (LST) of the study area was retrieved from the Landsat-8 TIRS (Thermal Infrared Sensor), and compared with the ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) LST data product and Fluke Infrared thermometer measurements operated at LWIR (8–14 μm). All these LST estimation and comparison processes were synchronized at the most possible manner (±7 days). Additionally, emissivity of collected UBSM were determined using FTIR (Fourier Transform Infrared Radiometer) emissivity in the laboratory. The amalgamation of results shows that barren land (mostly basaltic), basalt stones and concrete surfaces cover dominantly the high LST region of the study area. The FTIR measurements reveal that, among 16 dominant UBSM, basalt stones had recorded high spectral radiance (0.0309 Wm−1μm−1sr−1) and emissivity (0.98) followed by concrete (emissivity = 0.94). Whereas marble had the lowest spectral radiance (0.0246 Wm−1μm−1sr−1) and emissivity (0.49). The results agree with the SWIR reflection spectra (where basalt stones, concrete and barren rocks shows lower reflectance) and principle of Wien's displacement law (where radiance spectra display series of peaks that corresponding to the temperature measurements). Besides proposing a newer methodology for understanding spatial and spectral distribution of dominant heat inducing urban built-up surface materials, the results offer baseline data for studying microclimatic variations in the study area.

Comparing different space-borne sensors and methods for the retrieval of land surface temperature

The importance of land surface temperature (LST) is increasingly recognized, and various methodologies have been proposed for the retrieval of LST using space-borne thermal infrared data. However, the selection of LST retrieval from Thermal Infrared Sensor (TIRS) of Landsat-8 based on different methods and the readily available MODIS LST products is still a challenging topic for local and global environmental studies. In this study, the potential of three different methods for retrieving LST using Landsat-8 TIRS data, including Radiative Transfer Equation (RTE), Single Channel (SC), and Split Window (SW) method in comparison with MODIS MOD11A1 LST product was evaluated. For accuracy assessment, 0 cm ground surface temperature (LSTGST) data was used. Our results almost showed same accuracy for RTEB10 with RMSE = 0.35 °C, followed by MODIS with RMSE = 0.36 °C, and SCB10 with RMSE = 0.38 °C. Secondly, SCmean (Mean of B10 and B11), and RTEmean (Mean of B10 and B11) generate nearly the same accuracy with RMSE = 0.53 °C, and RMSE = 0.54 °C, respectively. The other methods viz., SCB11, RTEB11, and SW method slightly showed lower accuracy with RMSE = 0.87 °C, RMSE = 0.88 °C, and RMSE = 0.91 °C, respectively. We found all the methods highly accurate and can be used successfully by climatologists, environmentalists, hydrologists, and urban planners concerning planning, and monitoring of the ever-increasing LST at local and global scale studies.

Predicting Aedes aegypti infestation using landscape and thermal features

Identifying Aedes aegypti breeding hotspots in urban areas is crucial for the design of effective vector control strategies. Remote sensing techniques offer valuable tools for mapping habitat suitability. In this study, we evaluated the association between urban landscape, thermal features, and mosquito infestations. Entomological surveys were conducted between 2016 and 2019 in Vila Toninho, a neighborhood of São José do Rio Preto, São Paulo, Brazil, in which the numbers of adult female Ae. aegypti were recorded monthly and grouped by season for three years. We used data from 2016 to 2018 to build the model and data from summer of 2019 to validate it. WorldView-3 satellite images were used to extract land cover classes, and land surface temperature data were obtained using the Landsat-8 Thermal Infrared Sensor (TIRS). A multilevel negative binomial model was fitted to the data, which showed that the winter season has the greatest influence on decreases in mosquito abundance. Green areas and pavements were negatively associated, and a higher cover of asbestos roofs and exposed soil was positively associated with the presence of adult females. These features are related to socio-economic factors but also provide favorable breeding conditions for mosquitos. The application of remote sensing technologies has significant potential for optimizing vector control strategies, future mosquito suppression, and outbreak prediction.

Comparison among the natural radioactivity levels, the radiogenic heat production, and the land surface temperature in arid environments: A case study of the El Gilf El Kiber area, Egypt

The El Gilf El Kiber area is one of the most promising areas for urban growth and reclamation projects in the southwest of Egypt. In the study area, the lithological units vary in age from Precambrian (mainly granite) to Quaternary (mainly sand sediments). The analysis of airborne magnetic and gamma-ray spectrometry data was incorporated into that of the remote sensing data (Landsat-8). The integrated analysis was used to detect the lithological distributions and to delineate the subsurface basement rocks that control the radioactivity in the study area. It was also used to determine the relationship between radiogenic heat production (RHP) and land surface temperature (LST) that was retrieved from the Landsat-8 thermal infrared sensor (TIRS) data. The output of the principal component analysis (PCA) of Landsat-8 Operational Land Imager (OLI) data is a false-color map (PC1, PC2, and PC3 as red, green, and blue, respectively) displaying the various lithological units in the study area. The LST map, calculated from Landsat-8 TIRS data, shows that the surface temperatures range from approximately 41 to 53 °C. The highest concentrations of eU, eTh, and K in the area were 25 ppm, 121 ppm, and 7.8%, respectively. The RHP values ranged from 1.1 μW/m3 to 15.1 μW/m3. These relatively high anomalies are associated with basement outcrops and the sedimentary rocks overlying shallow basement rocks. The total horizontal derivative (THD) of the magnetic data delineates the horizontal extension of the basement rocks. The Curie point depth (CPD), estimated from magnetic data, ranges from 35.5 km to 41.0 km. The results also exhibited geothermal gradients ranging from 16.10 °C/km to 14.14 °C/km, and heat flow ranges from 35.4 mW/m2 to 40.8 mW/m2. It was evident after comparing that the RHP and LST maps exhibited the same features as the gamma-ray spectrometric maps. Moreover, the areas around the basement rocks exhibited higher radioactivity, RHP, and temperature than the basement rocks themselves.

Surface temperature changes of the crater of Agung Volcano from Landsat-8 TIRS during 2017-2018 eruption

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.

Modifying an Image Fusion Approach for High Spatiotemporal LST Retrieval in Surface Dryness and Evapotranspiration Estimations

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.

Downscaling Land Surface Temperature Using Multiscale Geographically Weighted Regression Over Heterogeneous Landscapes in Wuhan, China

The deficiency of fine-resolution satellite-derived land surface temperature (LST) has impeded the meticulous exploration of the urban thermal environment at micro spatial scales. Although the LST downscaling methods are well-documented, the scale dependence between LST and environmental factors has been rarely considered in the regression establishment. This article proposes a new method (MGWRK) coupling multiscale geographically weighted regression (MGWR) and area-to-point kriging (ATPK) to generate fine-resolution LST maps over heterogeneous landscapes. Technically, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and albedo are selected using random forests (RF) as scale factors. First, the spatial and temporal robustness of MGWRK is evaluated by downscaling aggregated Landsat-8 Thermal Infrared Sensor images (1000-m) into finer resolutions of 500, 400, 300, 200, and 100-m in different seasons (spring, summer, and winter). Results reveal that MGWRK is superior to GWR and DisTrad methods for spatial details improvement (SSIM > 0.93) and LST fidelity ( ${\boldsymbol{R}^2}$ > 0.91) under all scales and in the seasons. Second, MGWRK is utilized to downscale the synchronous 1000-m Moderate Resolution Imaging Spectroradiometer (MODIS) LST map into 100-m. The quantitative and qualitative result substantiates that MGWRK obtains ideal performance when applied on MODIS data with few LST distortion ( ${\boldsymbol{R}^2}\; = \;0.915$ , RMSE = 1.607 K) and obvious spatial information enrichment (SSIM = 0.903).

DETECTING DEFORMATION DUE TO THE 2018 MERAPI VOLCANO ERUPTION USING INTERFEROMETRIC SYNTHETIC APERTURE RADAR (INSAR) FROM SENTINEL-1 TOPS

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.

Characterization of Combined Effects of Urban Built-Up and Vegetated Areas on Long-Term Urban Heat Islands in Beijing

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.

Estimation of turbulent heat flux over leads using satellite thermal images

Abstract. Sea ice leads are an important feature in pack ice in the Arctic. Even covered by thin ice, leads can still serve as prime windows for heat exchange between the atmosphere and the ocean, especially in the winter. Lead geometry and distribution in the Arctic have been studied using optical and microwave remote sensing data, but turbulent heat flux over leads has only been measured on-site during a few special expeditions. In this study, we derive turbulent heat flux through leads at different scales using a combination of surface temperature and lead distribution from remote sensing images and meteorological parameters from a reanalysis dataset. First, ice surface temperature (IST) was calculated from Landsat-8 Thermal Infrared Sensor (TIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) thermal images using a split-window algorithm; then, lead pixels were segmented from colder ice. Heat flux over leads was estimated using two empirical models: bulk aerodynamic formulae and a fetch-limited model with lead width from Landsat-8. Results show that even though the lead area from MODIS is a little larger, the length of leads is underestimated by 72.9 % in MODIS data compared to TIRS data due to the inability to resolve small leads. Heat flux estimated from Landsat-8 TIRS data using bulk formulae is 56.70 % larger than that from MODIS data. When the fetch-limited model was applied, turbulent heat flux calculated from TIRS data is 32.34 % higher than that from bulk formulae. In both cases, small leads accounted for more than a quarter of total heat flux over leads, mainly due to the large area, though the heat flux estimated using the fetch-limited model is 41.39 % larger. A greater contribution from small leads can be expected with larger air–ocean temperature differences and stronger winds.

Heat distribution modeling of the subsurface of Wapsalit geothermal area based on land surface temperature from Landsat-8 thermal infrared sensor imagery

In this research, models of heat distribution of the subsurface of the Wapsalit geothermal area were built, which their structures were known before, using finite different method. Rock thermal diffusivity was used as the model parameter, which controlled the heat flow. The result showed that the heat flow was adjusted the model parameters effectively. Land surface temperature (LST) as the result of the model was compared to LST from Landsat-8 Thermal Infrared Sensor Imagery and produced absolute error 6.8% and 3.6% for cross-section 1 and 2, respectively. This error percentage confirmed that the model was successfully depicted the actual heat distribution of the subsurface of the study area.

Land Surface Temperature Retrieval from LANDSAT-8 Thermal Infrared Sensor Data and Validation with Infrared Thermometer Camera

This paper illustrates a proposed method for the retrieval of land surface temperature (LST) from the two thermal bands of the LANDSAT-8 data. LANDSAT-8, the latest satellite from Landsat series, launched on 11 February 2013, using LANDSAT-8 Operational Line Imager and Thermal Infrared Sensor (OLI & TIRS) satellite data. LANDSAT-8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12 bits. In this search a trial has been made to estimate LST over Al-Hashimiya district, south of Babylon province, middle of Iraq. Two dates images acquired on 2nd &18th of March 2018 to retrieve LST and compare them with ground truth data from infrared thermometer camera (all the measurements contacted with target by using type-k thermocouple) at the same time of images capture. The results showed that the rivers had a higher LST which is different to the other land cover types, of less than 3.47 C ◦, and the LST different for vegetation and residential area were less than 0.4 C ◦ with correlation coefficient of the two bands 10 and 11 Rbnad10= 0.70, Rband11 = 0.89 respectively, for the imaged acquired on the 2nd of march 2018 and Rband10= 0.70 and Rband11 = 0.72 on the 18th of march 2018. These results confirm that the proposed approach is effective for the retrieval of LST from the LANDSAT-8 Thermal bands, and the IR thermometer camera data which is an effective way to validate and improve the performance of LST retrieval. Generally the results show that the closer measurement taken from the scene center time, a better quality to classify the land cover. The purpose of this study is to assess the use of LANDSAT-8 data to specify temperature differences in land cover and compare the relationship between land surface temperature and land cover types.

Land Surface Temperature Retrieval from LANDSAT-8 Ther-mal Infrared Sensor Data and Validation with Infrared Ther-mometer Camera

This paper illustrates a proposed method for the retrieval of land surface temperature (LST) from the two thermal bands of the LAND-SAT-8 data. LANDSAT-8, the latest satellite from Landsat series, launched on 11 February 2013, using LANDSAT-8 Operational Line Imager and Thermal Infrared Sensor (OLI & TIRS) satellite data. LANDSAT-8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12 bits. In this search a trial has been made to estimate LST over Al-Hashimiya district, south of Babylon province, middle of Iraq. Two dates images acquired on 2nd &18th of March 2018 to retrieve LST and compare them with ground truth data from infrared thermometer camera (all the meas-urements contacted with target by using type-k thermocouple) at the same time of images capture. The results showed that the rivers had a higher LST which is different to the other land cover types, of less than 3.47 C ◦, and the LST different for vegetation and residential area were less than 0.4 C ◦ with correlation coefficient of the two bands 10 and 11 Rbnad10= 0.70, Rband11 = 0.89 respectively, for the im-aged acquired on the 2nd of march 2018 and Rband10= 0.70 and Rband11 = 0.72 on the 18th of march 2018. These results confirm that the proposed approach is effective for the retrieval of LST from the LANDSAT-8 Thermal bands, and the IR thermometer camera data which is an effective way to validate and improve the performance of LST retrieval. Generally the results show that the closer measure-ment taken from the scene center time, a better quality to classify the land cover. The purpose of this study is to assess the use of LAND-SAT-8 data to specify temperature differences in land cover and compare the relationship between land surface temperature and land cover types.

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

The Chinese government has promulgated a de-capacity policy for economic growth and environmental sustainability, especially for the iron and steel industry. With these policies, this study aimed to monitor the economic activities and evaluate the production conditions of an iron and steel factory based on satellites via Landsat-8 Thermal Infrared Sensor (TIRS) data and high-resolution images from January 2013 to October 2017, and propel next economic adjustment and environmental protection. Our methods included the construction of a heat island intensity index for an iron and steel factory (ISHII), a heat island radio index for an iron and steel factory (ISHRI) and a dense classifying approach to monitor the spatiotemporal changes of the internal heat field of an iron and steel factory. Additionally, we used GF-2 and Google Earth images to identify the main production area, detect facility changes to a factory that alters its heat field and verify the accuracy of thermal analysis in a specific time span. Finally, these methods were used together to evaluate economic activity. Based on five iron and steel factories in the Beijing-Tianjin-Hebei region, when the ISHII curve is higher than the seasonal changes in a time series, production is normal; otherwise, there is a shut-down or cut-back. In the spatial pattern analyses, the ISHRI is large in normal production and decreases when cut-back or shut-down occurs. The density classifying images and high-resolution images give powerful evidence to the above-mentioned results. Finally, three types of economic activities of normal production, shut-down or cut-back were monitored for these samples. The study provides a new perspective and method for monitoring the economic activity of an iron and steel factory and provides supports for sustainable development in China.

Characterization of thermal environment over heterogeneous surface of National Capital Region (NCR), India using LANDSAT-8 sensor for regional planning studies

Land surface temperature (LST) is an essential indicator of the Earth's surface energy budget. It is essential for urban micro-climate change studies and acts as a controlling variable in climatic models. The present study, LST retrieves with the improved mono-window algorithm from the Landsat-8 Thermal Infrared Sensor Band 10. The proportion of vegetation cover coupled with Normalized Difference Vegetation Index (NDVI) used for the estimation of emissivity. The derived emissivity and surface temperature over different ground features have compared with field observation as well as those in the literature and found in an accepted range (error of around 1–2%). Herein, results show that the spatial distribution of surface temperature was significantly affected by landscape characteristics. The built-up and commercial/industrial areas display higher surface temperature in comparison with surrounding other vegetated lands. Whereas, the cooling effect towards the surrounding urban built-up area are found increasing in the vegetated area, and inside the green zones. A strong correlation is observed between the LST with NDVI over different land cover and land use (LCLU) classes. The Geoinformation technique provides a practical-based theoretical model of the land characterization of an urban thermal environment which assists in regional planning and decision-making process.

Modeling seasonal surface temperature variations in secondary tropical dry forests

Secondary tropical dry forests (TDFs) provide important ecosystem services such as carbon sequestration, biodiversity conservation, and nutrient cycle regulation. However, their biogeophysical processes at the canopy-atmosphere interface remain unknown, limiting our understanding of how this endangered ecosystem influences, and responds to the ongoing global warming. To facilitate future development of conservation policies, this study characterized the seasonal land surface temperature (LST) behavior of three successional stages (early, intermediate, and late) of a TDF, at the Santa Rosa National Park (SRNP), Costa Rica. A total of 38 Landsat-8 Thermal Infrared Sensor (TIRS) data and the Surface Reflectance (SR) product were utilized to model LST time series from July 2013 to July 2016 using a radiative transfer equation (RTE) algorithm. We further related the LST time series to seven vegetation indices which reflect different properties of TDFs, and soil moisture data obtained from a Wireless Sensor Network (WSN). Results showed that the LST in the dry season was 15–20K higher than in the wet season at SRNP. We found that the early successional stages were about 6–8K warmer than the intermediate successional stages and were 9–10K warmer than the late successional stages in the middle of the dry season; meanwhile, a minimum LST difference (0–1K) was observed at the end of the wet season. Leaf phenology and canopy architecture explained most LST variations in both dry and wet seasons. However, our analysis revealed that it is precipitation that ultimately determines the LST variations through both biogeochemical (leaf phenology) and biogeophysical processes (evapotranspiration) of the plants. Results of this study could help physiological modeling studies in secondary TDFs.

FEASIBILITY STUDY OF LANDSAT-8 IMAGERY FOR RETRIEVING SEA SURFACE TEMPERATURE (CASE STUDY PERSIAN GULF)

Abstract. Sea surface temperature (SST) is one of the critical parameters in marine meteorology and oceanography. The SST datasets are incorporated as conditions for ocean and atmosphere models. The SST needs to be investigated for various scientific phenomenon such as salinity, potential fishing zone, sea level rise, upwelling, eddies, cyclone predictions. On the other hands, high spatial resolution SST maps can illustrate eddies and sea surface currents. Also, near real time producing of SST map is suitable for weather forecasting and fishery applications. Therefore satellite remote sensing with wide coverage of data acquisition capability can use as real time tools for producing SST dataset. Satellite sensor such as AVHRR, MODIS and SeaWIFS are capable of extracting brightness values at different thermal spectral bands. These brightness temperatures are the sole input for the SST retrieval algorithms. Recently, Landsat-8 successfully launched and accessible with two instruments on-board: (1) the Operational Land Imager (OLI) with nine spectral bands in the visual, near infrared, and the shortwave infrared spectral regions; and (2) the Thermal Infrared Sensor (TIRS) with two spectral bands in the long wavelength infrared. The two TIRS bands were selected to enable the atmospheric correction of the thermal data using a split window algorithm (SWA). The TIRS instrument is one of the major payloads aboard this satellite which can observe the sea surface by using the split-window thermal infrared channels (CH10: 10.6 μm to 11.2 μm; CH11: 11.5 μm to 12.5 μm) at a resolution of 30 m. The TIRS sensors have three main advantages comparing with other previous sensors. First, the TIRS has two thermal bands in the atmospheric window that provide a new SST retrieval opportunity using the widely used split-window (SW) algorithm rather than the single channel method. Second, the spectral filters of TIRS two bands present narrower bandwidth than that of the thermal band on board on previous Landsat sensors. Third, TIRS is one of the best space born and high spatial resolution with 30 m. in this regards, Landsat-8 can use the Split-Window (SW) algorithm for retrieving SST dataset. Although several SWs have been developed to use with other sensors, some adaptations are required in order to implement them for the TIRS spectral bands. Therefore, the objective of this paper is to develop a SW, adapted for use with Landsat-8 TIRS data, along with its accuracy assessment. In this research, that has been done for modelling SST using thermal Landsat 8-imagery of the Persian Gulf. Therefore, by incorporating contemporary in situ data and SST map estimated from other sensors like MODIS, we examine our proposed method with coefficient of determination (R2) and root mean square error (RMSE) on check point to model SST retrieval for Landsat-8 imagery. Extracted results for implementing different SW's clearly shows superiority of utilized method by R2 = 0.95 and RMSE = 0.24.