Thermal Infrared Sensor Bands Research Articles

An Estimation of the Land Surface Temperature, Derived from the Landsat Satellite, for the Major Cities in Sindh Province, Pakistan

Thermal infrared remote sensing data are used to make estimates of the land surface temperature (LST) by recording the radiant energy emitted by the surface of the Earth. Satellite data and image processing software also allow for LST estimation. Since its launch, two thermal infrared bands aboard the Landsat satellite have been used to continuously observe Earth, providing data for the estimation of LST and the normalized difference vegetation index (NDVI). Due to the significant uncertainty in data from both Landsat 5 thematic mapper (TM) thermal band 6, which has a wavelength of 10.40–12.50 m, and Landsat 8 thermal infrared sensor (TIRS) Band 11, as indicated by USGS calibration notifications, it was advised to use TIRS Band 10 data as a single spectral band for LST estimation. For LST estimation from Landsat 5 and Landsat 8, the mono-window (MW) approach was used with TM and TIRS Bands 6, 10, and data with a resolution of 120 and 100 m. (Path-152 and Row-40, 41, 42, and 43). The emission coefficient was calculated using the operational land imager (OLI) Bands 4 and 5 (30 m resolution) and the normalized difference vegetation index (NDVI) proportion of vegetation method. Based on the results, the LST was higher in the arid regions, whereas the NDVI was higher in the less arid parts. Also, the LST findings were compared to the air temperature data, both data were found to be consistent with one another. The approach of MW algorithm could be a useful tool for estimating LST from TM data acquired from Landsat 5 and Landsat 8 TIRS Bands 6 and 10.

Assessment of spatio-temporal direction of impervious surface area surface temperature in Pretoria, South Africa

Over the years, rapid urban growth has led to the conversion of natural lands into large man-made landscapes due to enhanced political and economic growth. This study assessed the spatio-temporal change characteristics of impervious surface area (ISA) expansion using its surface temperature (LST) at selected administrative subplace units (i.e., local region scale). ISA was estimated for 1995, 2005 and 2015 from Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) images using a Random Forest (RF) algorithm. The spatio-temporal trends of ISA were assessed using an optimal analytical scale to aggregate ISA LST coupled with weighted standard deviational ellipse (SDE) method. The ISA was quantified with high predictive accuracy (i.e., AUROC = 0.8572 for 1995, AUROC = 0.8709 for 2005, AUROC = 0.8949 for 2015) using RF classifier. More than 70% of the selected administrative subplaces in Pretoria experienced an increase in growth rate (415.59%) between 1995 and 2015. LST computations from the Landsat TIRS bands yielded good results (RMSE = ∼1.44OC, 1.40OC, ∼0.86OC) for 1995, 2005 and 2015 respectively. Based on the hexagon polygon grid (90x90), the aggregated ISA surface temperature weighted SDE analysis results indicated ISA expansion in different directions at the selected administrative subplace units. Our findings can represent useful information for policymakers in evaluating urban development trends in Pretoria, City of Tshwane (COT).

Landsat 8 Thermal Infrared Sensor Scene Select Mechanism Open Loop Operations

The Landsat 8 (L8) spacecraft and its two instruments, the operational land imager (OLI) and thermal infrared sensor (TIRS), have been consistently characterized and calibrated since its launch in February 2013. These performance metrics and calibration updates are determined through the U.S. Geological Survey (USGS) Landsat image assessment system (IAS), which has been performing this function since its launch. The TIRS on-orbit geometric calibration procedures include TIRS-to-OLI alignment, TIRS sensor chip assembly (SCA) alignment, and TIRS band alignment. In December 2014, the TIRS instrument experienced an anomalous condition related to the instrument’s ability to accurately measure the location of the scene select mechanism (SSM). The SSM is a rotating mirror that allows the instrument’s field of view to be pointed at the Earth, for normal imaging, or at either deep space or an onboard black body, for radiometric calibration purposes. This anomalous condition in the SSM’s position sensor made it necessary to implement a new mode of operation for this mirror, termed mode-0. Mode-0 involves operating the mirror in an open-loop control state during normal mission operations when acquiring Earth data. Closed-loop mode-4 is needed for directing the mirror towards the radiometric calibration targets and is used approximately once every two weeks to collect radiometric calibration data. Mode-0 is used for most operational imaging because it does not require SSM encoder data, thereby allowing the SSM encoder electronics to remain unpowered most of the time, reducing its use throughout the lifetime of the TIRS instrument, thus helping to preserve its nominal behavior during it use. This paper discusses the geometric calibration of the SSM mirror during its current normal mode-0 set of image operations, as its open-loop control allows the mirror to drift over time in its uncontrolled state and its effects on products. The results shown in this paper demonstrate that the ability to have ongoing updates to the modeling of the TIRS SSM mirror model, in both an automated fashion and with a set of more manual operations, allows accuracy that approaches mode-4 results within days from the start of a mode-0 event.

Analytical study of seasonal variability in land surface temperature with normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index

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.

Comparison of Three Methods for Estimating Land Surface Temperature from Landsat 8-TIRS Sensor Data

After Landsat 8 was launched in 2013, it was observed that for Thermal Infrared sensor (TIRS) bands, radiance from outside of an instrument’s field-of-view produced a non-uniform ghost signal across the focal plane that varied depending on the out-of-scene content (i.e., the stray light effect). A new stray light correction algorithm (SLCA) is currently operational and has been implemented into the United States Geological Survey (USGS) ground system since February 2017. The SLCA has also been applied to reprocess historical Landsat 8 scenes. After approximately two years of SLCA implementation, more land surface temperature (LST) validation studies are required to check the effect of correction in the estimation of LST from different retrieval algorithms. For this purpose, three different LST estimation method algorithms (i.e., the radiative transfer equation (RTE), single-channel algorithm (SCA), and split-window algorithm (SWA)) have been assessed. The study site is located on the campus of the University of Balearic Islands on the island of Mallorca (Spain) in the western Mediterranean Sea. The site is considered a heterogeneous area that is composed of different types of surfaces, such as buildings, asphalt roads, farming areas, sloped terrains, orange fields, almond trees, lawns, and some natural vegetation regions. Data from 21 scenes, which were acquired by the Landsat 8-TIRS sensor and extracted from a 100 × 100 m2 pixel, were used to retrieve the LST with different algorithms; then, they were compared with in situ LST measurements from a broadband thermal infrared radiometer located on the same Landsat 8 pixel. The results show good performances of the three methods, with the SWA showing the lowest observed RMSE (within 1.6–2 K), whereas the SCA applied to the TIRS band 10 (10 µm) was also appropriate, with a RMSE ranging within 2.0–2.3 K. The LST estimates using the RTE algorithm display the highest observed RMSE values (within 2.0–3.6 K) of all of the compared methods, but with an almost unbiased value of −0.1 K for the case of techniques applied to band 10 data. The SWAs are the preferred method to estimate the LST in our study area. However, further validation studies around the world are required.

Split-Window Algorithm for Retrieval of Land Surface Temperature Using Landsat 8 Thermal Infrared Data

Land surface temperature (LST) plays a vital role in global climate change, radiation budgets, heat balance, vegetation, snowmelt, glacier hydrology, and geo-biophysical processes. It is, therefore, essential to determine LST precisely over large areas. With advancements in remote sensing, LST can now be estimated. In this study, a critical appraisal of various LST inversion algorithms is presented. These algorithms include mono-window (MW), split-window (SW), dual-angle (DA), single-channel (SC), and Sabmao method. The main objective is to derive an SW algorithm to retrieve LST from Landsat 8 satellite data and demonstrate the application to the Beas River basin in India. Located within the Himalayan range, the study area is characterized by heterogeneity and rugged terrain with areas covered by snow and glacier. The satellite imagery is a product of the Optical Land Imager (OLI) with a spatial resolution of 30 m and a thermal infrared sensor (TIRS) having a spatial resolution of 100 m. The SW algorithm requires spectral radiance and emissivity from two bands of the TIRS as input for the estimation of LST. The spectral radiance has been estimated using the TIRS bands 10 and 11. The normalized difference vegetation index (NDVI) and the threshold technique of OLI bands (2 to 5) have been used to derive the emissivity. The estimates of LST from the TIRS and OLI bands using the SW algorithm are found to be accurate and close to the in situ air temperature measurements and the LST values obtained from the MW algorithm. Results obtained show that the values of LST are high in the barren/rocky areas and low in the snow/glacier areas. The study reveals that the LST estimates from SW and MW algorithms are linearly transferable with negligible loss of accuracy. The LST estimates from the SW algorithm differs at most by up to 5 °C with the measured air temperature.

Mapping finer‐resolution land surface emissivity using Landsat images in China

AbstractLand surface emissivity is a crucial parameter for obtaining the land surface temperature and estimating the land surface energy budget from remote sensing data. The current emissivity products always have a coarser spatial resolution than the products from the visible and near‐infrared data. This study focused on the generation of an emissivity product at a spatial resolution of 30 m using a new global land cover product called Finer‐Resolution Observation and Monitoring of Global Land Cover and Landsat images. Summer‐average emissivity products in four narrowbands (Landsat 5/Thematic Mapper Band 6, Landsat 7/Enhanced Thematic Mapper Plus Band 6, and Landsat 8 Thermal Infrared Sensor bands 1 and 2) and two broadbands (3–14 μm and 8–13.5 μm) were produced in China. Results illustrated that the narrowband emissivities ranged from 0.95 to 0.99, whereas the broadband emissivities ranged from 0.93 to 0.99 in the study area. Intercomparisons in different places showed that the new emissivity was close to Advanced Spaceborne Thermal Emission and Reflection Radiometer emissivity with a difference of about 0.015 for narrowband emissivity and about 0.02 for broadband emissivity on a regional scale. For application purposes, the emissivities were released in the Worldwide Reference System 2 and geographic coordinate systems with several spatial resolutions resampled from its original scale of 30 m.

DETECTION OF FOREST FIRE, SMOKE SOURCE LOCATIONS IN KALIMANTAN DURING THE DRY SEASON FOR THE YEAR 2015 USING LANDSAT 8 FROM THE THRESHOLD OF BRIGHTNESS TEMPERATURE ALGORITHM

Almost every dry season, there are large forest/land fires in several regions in Indonesia, especially in Kalimantan and Sumatra in the dry season of August to September 2015 a forest fire in 6 provinces namely West Kalimantan, Central Kalimantan, South Kalimantan, Riau, Jambi, and South Sumatra. Even some parties proposed that the Government of Indonesia declares them as a national disaster. The low-resolution remote sensing data have been widely used for monitoring the occurrence of forest/land fires (hotspots), and mapping of burnt scars. The hotspot detection was done by utilizing the data of NOAA-AVHRR and MODIS data which have a lower spatial resolution (1 km). In order to increase the level of detail and accuracy of product information, this research is done by using Landsat 8 TIRS (Thermal Infrared Sensor) band which has a greater spatial resolution of 100 m. The purpose of this research is to find and to determine the threshold value of the brightness temperature of the TIRS data to identify the source of fire smoke. The data used is the Landsat 8 of several parts of Borneo during the period of 24 August to 18 September 2015 recorded by the LAPAN's receiving station. Landsat - 8 TIRS band was converted into brightness temperature in degrees Celsius, then dots in a region that is considered the source of the smoke if the temperature of each pixel in the region > 43oC, and given the attributes with the highest temperatures of the pixels in the region. The source of the smoke was obtained through visual interpretation of the objects in the multispectral Natural Color Composite (NCC) and True Color Composite (TCC) images. Analysis of errors (commission error) is obtained by comparing the temperature detected by TIRS band with a visual appearance of the source of the smoke. The result of the experiment showed that there were detected 9 scenes with high temperatures over 43oC from the 27 scenes Kalimantan Landsat 8 data, which include 153 sites. The accuracy (commission error) of identification results using temperature ≥ 51°C is 0%, temperature ≥ 47°C is 10%, and temperature ≥ 43°C is 30.5%.

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.

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

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 R<sup>2</sup> = 0.95 and RMSE = 0.24.

SOME ASPECTS OF SATELLITE IMAGERY INTEGRATION FROM EROS B AND LANDSAT 8

The Landsat 8 satellite which was launched in 2013 is a next generation of the Landsat remote sensing satellites series. It is equipped with two new sensors: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). What distinguishes this satellite from the previous is four new bands (coastal aerosol, cirrus and two thermal infrared TIRS bands). Similar to its antecedent, Landsat 8 records electromagnetic radiation in a panchromatic band at a range of 0.5‐0.9 μm with a spatial resolution equal to 15 m. In the paper, multispectral imagery integration capabilities of Landsat 8 with data from the new high resolution panchromatic EROS B satellite are analyzed. The range of panchromatic band for EROS B is 0.4‐0.9 μm and spatial resolution is 0.7 m. Research relied on improving the spatial resolution of natural color band combinations (bands: 4,3,2) and of desired false color band composition of Landsat 8 satellite imagery. For this purpose, six algorithms have been tested: Brovey’s, Mulitplicative, PCA, IHS, Ehler's, HPF. On the basis of the visual assessment, it was concluded that the best results of multispectral and panchromatic image integration, regardless land cover, are obtained for the multiplicative method. These conclusions were confirmed by statistical analysis using correlation coefficient, ERGAS and R-RMSE indicators.

SOME ASPECTS OF SATELLITE IMAGERY INTEGRATION FROM EROS B AND LANDSAT 8

Abstract. The Landsat 8 satellite which was launched in 2013 is a next generation of the Landsat remote sensing satellites series. It is equipped with two new sensors: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). What distinguishes this satellite from the previous is four new bands (coastal aerosol, cirrus and two thermal infrared TIRS bands). Similar to its antecedent, Landsat 8 records electromagnetic radiation in a panchromatic band at a range of 0.5‐0.9 μm with a spatial resolution equal to 15 m. In the paper, multispectral imagery integration capabilities of Landsat 8 with data from the new high resolution panchromatic EROS B satellite are analyzed. The range of panchromatic band for EROS B is 0.4‐0.9 μm and spatial resolution is 0.7 m. Research relied on improving the spatial resolution of natural color band combinations (bands: 4,3,2) and of desired false color band composition of Landsat 8 satellite imagery. For this purpose, six algorithms have been tested: Brovey’s, Mulitplicative, PCA, IHS, Ehler's, HPF. On the basis of the visual assessment, it was concluded that the best results of multispectral and panchromatic image integration, regardless land cover, are obtained for the multiplicative method. These conclusions were confirmed by statistical analysis using correlation coefficient, ERGAS and R-RMSE indicators.

ESTIMATION OF LAND SURFACE TEMPERATURE OF HIGH RANGE MOUNTAIN LANDSCAPE OF DEVIKULAM TALUK USING LANDSAT 8 DATA

Land Surface Temperature (LST) can be calculated through a thermal infrared remote sensing data. It can record the apparent temperature of the Earth’s surface by measuring the radiant energy exiting its surface. With the advent of satellite images and digital image processing software, now it is possible to calculate LST. In this study, LST for Devikulam taluk was derived using Single Window algorithm (SW) with the use of Landsat-8 (Path-144 and Row-53) Thermal Infrared Sensor (TIRS) data of 100m resolution. Emissivity was derived with the help of NDVI proportion of vegetation technique for which Operational Land Imager (OLI) bands 4 and 5 (30 m resolution) were used. The results showed that surface temperature was high in the barren regions whereas it was low in the thick vegetation cover regions. As the SW algorithm uses both OLI and TIRS bands, the LST derived using them were more reliable and accurate.

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

The successful launch of the Landsat 8 satellite with two thermal infrared bands on February 11, 2013, for continuous Earth observation provided another opportunity for remote sensing of land surface temperature (LST). However, calibration notices issued by the United States Geological Survey (USGS) indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. In this study, we presented an improved mono-window (IMW) algorithm for LST retrieval from the Landsat 8 TIRS Band 10 data. Three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature) were required for the IMW algorithm to retrieve LST. A new method was proposed to estimate the parameter of effective mean atmospheric temperature from local meteorological data. The other two essential parameters could be both estimated through the so-called land cover approach. Sensitivity analysis conducted for the IMW algorithm revealed that the possible error in estimating the required atmospheric water vapor content has the most significant impact on the probable LST estimation error. Under moderate errors in both water vapor content and ground emissivity, the algorithm had an accuracy of ~1.4 K for LST retrieval. Validation of the IMW algorithm using the simulated datasets for various situations indicated that the LST difference between the retrieved and the simulated ones was 0.67 K on average, with an RMSE of 0.43 K. Comparison of our IMW algorithm with the single-channel (SC) algorithm for three main atmosphere profiles indicated that the average error and RMSE of the IMW algorithm were −0.05 K and 0.84 K, respectively, which were less than the −2.86 K and 1.05 K of the SC algorithm. Application of the IMW algorithm to Nanjing and its vicinity in east China resulted in a reasonable LST estimation for the region. Spatial variation of the extremely hot weather, a frequently-occurring phenomenon of an abnormal heat flux process in summer along the Yangtze River Basin, had been thoroughly analyzed. This successful application suggested that the IMW algorithm presented in the study could be used as an efficient method for LST retrieval from the Landsat 8 TIRS Band 10 data.

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 μm (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/m2·sr·μm 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/m2·sr·μm 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.

ESTIMATION OF LAND SURFACE TEMPERATURE OF DINDIGUL DISTRICT USING LANDSAT 8 DATA

Land Surface Temperature (LST) is an important phenomenon in global climate change. As the green house gases in the atmosphere increases, the LST will also increase. This will result in melting of glaciers and ice sheets and affects the vegetation of that area. Its impact will be more in the monsoon areas, because the rainfall is unpredictable, failure of monsoon and there will be heavy down pour of rainfall. LST can be estimated through many algorithms viz., Split-Window (SW), Dual-Angle (DA), Single-Channel (SC), Sobrino and Mao. With the advent of satellite images and digital image processing software, now it is possible to calculate LST. In this study, LST for Dindigul District, Tamil Nadu, India, was derived using SW algorithm with the use of Landsat 8 Optical Land Imager (OLI) of 30 m resolution and Thermal Infrared Sensor (TIR) data of 100 m resolution. SW algorithm needs spectral radiance and emissivity of two TIR bands as input for deriving LST. The spectral radiance was estimated using TIR bands 10 and 11. Emissivity was derived with the help of NDVI threshold technique for which OLI bands 2, 3, 4 and 5 were used. The output revealed that LST was high in the barren regions whereas it was low in the hilly regions because of vegetative cover. As the SW algorithm uses both the TIR bands (10 and 11) and OLI bands 2, 3, 4 and 5, the LST generated using them were more reliable and accurate.