Thermal Infrared Sensor Research Articles

Landsat 9 Thermal Infrared Sensor 2 On-Orbit Calibration and Initial Performance

The Thermal Infrared Sensor 2 (TIRS-2) on Landsat 9 launched September 27, 2021 and underwent a variety of tests during its commissioning phase to establish its postlaunch performance. We report on the calibration updates performed to maintain its calibration and generate high quality imagery. This is done by transferring the SI-traceable pre-launch calibration to on-orbit while accounting for changes in the TIRS-2 response as detected through on-board calibrator observations. Additional empirical corrections were implemented to mitigate image striping observed on-orbit. The detector arrays were monitored through its commissioning phase to ensure that stable detectors were chosen for operations. TIRS-2 has demonstrated ~0.025% instability over its orbit, ~80 mK noise equivalent delta temperature, and an absolute radiometric uncertainty <1.4% in its nominal temperature range enabling a wide array of Earth science applications.

Retrieving Remotely Sensed Data Using Plank Algorithm in the Black Sea and Surrounding Seas (2008-2018)

The Black Sea is the world’s largest inner sea, its only connections with other seas being the Kerch Strait, which leads to the Azov Sea, and the Istanbul Strait, which connects it with the Mediterranean Sea. The main aim of this study processes to image analysis and applied algorithm, to retrieve remote SST data from satellite, to assess the estimated SSTs, to understand the characteristics of SST by presenting in the Black Sea and surrounding seas, to discuss the SSTs changes according to climate change impacts. In this study, Landsat 7 and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) Level-1 images of TIR bands are used to predict Sea Surface Temperature (SST) changes using the Plank algorithm in the Black Sea and surrounding seas between 2008 and 2018. The SST values were taken from 55 different stations chosen from the study area. The SST result presented the central months of the four hydrological seasons. The increasing temperature data were supported by the literature studies that alien invasive species entered the Black Sea. The climatic fluctuations obtained as a result of this study will increase the invasion level of non-native species and their invasion will continue into the Black Sea and surrounding seas.

A Physical-Based Framework for Estimating the Hourly All-Weather Land Surface Temperature by Synchronizing Geostationary Satellite Observations and Land Surface Model Simulations

The high-frequency all-weather land surface temperature (LST) product generated from the thermal-infrared (TIR) observations of the geostationary meteorological satellite is of great significance to study the diurnal variations in the LST and the land surface energy balance. However, the TIR sensor cannot penetrate the clouds and obtain the desired LST under cloudy conditions. In this study, we developed a physical-based framework for generating high-frequency (hourly) all-weather LST data by synchronizing geostationary satellite TIR observations and simulations of the land surface model (LSM). There are three parts in the developed framework. First, the clear-sky LST was retrieved from the Advanced Himawari Imager (AHI) onboard the geostationary satellite Himawari-8 using our newly developed temperature and emissivity separation algorithm. Second, the Advanced Microwave Scanning Radiometer 2 (AMSR2) observations were assimilated into the Noah land surface model with multiple parameterization options (Noah-MP) model to generate the all-weather LST. Finally, the retrieved clear-sky AHI LST and Noah-MP assimilated LST were fused using the Ensemble Kalman filter (EnKF) algorithm. In situ measurements from three networks were collected to evaluate the Noah-MP assimilated LST and EnKF fused LST. The bias/RMSE of the Noah-MP assimilated LST and EnKF fused LST were –0.16/3.01 K and 0.15/2.68 K, respectively, under all-weather conditions. Compared to the Noah-MP free-run LST, the absolute values of the bias were reduced by 0.64 K and 0.68 K for the Noah-MP assimilated LST and EnKF fused LST, while the RMSEs were reduced by 0.33 K and 0.65 K, respectively. In addition, the spatial distribution of EnKF fused LST was in good agreement with the retrieved clear-sky AHI LST. The proposed framework in this study was demonstrated to be capable of obtaining accurate high-frequency (hourly) all-weather LST data.

The Utility of Simpler Spatial Disaggregation Models for Retrieving Land Surface Temperature at High Spatiotemporal Resolutions

The thermal infrared (TIR) sensors aboard geostationary satellites provide multi-temporal land surface temperature (LST) observations for characterizing the diurnal temperature cycle (DTC) at a coarse spatial resolution. This study aims to test if simpler spatial disaggregation models developed to improve the spatial resolution of LST data can be applied across the temporal domain to obtain LST at relatively higher spatial resolution and to capture the DTC. LST from geostationary satellites Kalpana-1 and INSAT-3D were downscaled from 8- and 5-km spatial resolutions respectively to 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) data acquired during its daytime overpass. Two disaggregation models, DisTrad and NL-DisTrad and a data fusion model spatiotemporal integrated fusion model (STITFM) were selected for the purpose. The downscaled LSTs were compared against the MODIS LST observations from Terra nighttime and Aqua daytime overpasses in addition to in situ data at four sites in India. The results indicated that the DisTrad model (RMSE: 3.61 K, R²: 0.95) was able to capture the spatiotemporal patterns with an accuracy comparable to that of the STITFM model (RMSE: 3.22 K, R²: 0.96). However, the NL-DisTrad performed inconsistently, leading to higher errors.

A Simulation-Based Error Budget of the TES Method for the Design of the Spectral Configuration of the Micro-Bolometer-Based MISTIGRI Thermal Infrared Sensor

In preparation of the micro-bolometer-based MIcro Satellite for Thermal Infrared GRound surface Imaging (MISTIGRI) mission, we study the error budget of the Temperature-Emissivity Separation (TES) method using several spectral configurations that differ in channel numbers, locations, and widths. The error budget quantifies the contribution of 1) the TES underlying assumption about emissivity spectral contrast, 2) the errors on atmospheric corrections, and 3) the instrumental noise. When dealing with atmospheric corrections, we consider errors in atmospheric temperature, water vapor content, and concentrations of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> . To that end, we design an end-to-end simulator of MISTIGRI measurements in order to simulate the radiative and biophysical quantities involved in the data processing. We conduct numerous simulations over a wide range of realistic setups that include cavity effect, i.e., radiance trapping within vegetation canopy. In the case of micro-bolometer-based sensing, the current study highlights that atmospheric and instrumental noises have similar impacts on the TES retrievals, with resulting errors twice as large as those due to the TES intrinsic assumption about spectral contrast, where the latter contributes to the TES error budget within the [0.005–0.009] interval for emissivity, and within the [0.3–0.4 K] interval for land surface temperature (LST). Also, we show that retrieval performance of surface temperature is very similar across all considered MISTIGRI spectral configurations, with RMSE variation within 0.2 K. Eventually, our study permits us to select a 4-channels spectral configuration as the most suited for the MISTIGRI instrument, notably because it enables a moderately better capture of the emissivity contrast than a 3-channels one.

UAV Thermal Images for Water Presence Detection in a Mediterranean Headwater Catchment

As Mediterranean streams are highly dynamic, reconstructing space–time water presence in such systems is particularly important for understanding the expansion and contraction phases of the flowing network and the related hydro–ecological processes. Unmanned aerial vehicles (UAVs) can support such monitoring when wide or inaccessible areas are investigated. In this study, an innovative method for water presence detection in the river network based on UAV thermal infrared remote sensing (TIR) images supported by RGB images is evaluated using data gathered in a representative catchment located in Southern Italy. Fourteen flights were performed at different times of the day in three periods, namely, October 2019, February 2020, and July 2020, at two different heights leading to ground sample distances (GSD) of 2 cm and 5 cm. A simple methodology that relies on the analysis of raw data without any calibration is proposed. The method is based on the identification of the thermal signature of water and other land surface elements targeted by the TIR sensor using specific control matrices in the image. Regardless of the GSD, the proposed methodology allows active stream identification under weather conditions that favor sufficient drying and heating of the surrounding bare soil and vegetation. In the surveys performed, ideal conditions for unambiguous water detection in the river network were found with air–water thermal differences higher than 5 °C and accumulated reference evapotranspiration before the survey time of at least 2.4 mm. Such conditions were not found during cold season surveys, which provided many false water pixel detections, even though allowing the extraction of useful information. The results achieved led to the definition of tailored strategies for flight scheduling with different levels of complexity, the simplest of them based on choosing early afternoon as the survey time. Overall, the method proved to be effective, at the same time allowing simplified monitoring with only TIR and RGB images, avoiding any photogrammetric processes, and minimizing postprocessing efforts.

An improved mask-RCNN algorithm for UAV TIR video stream target detection

In emergency rescue operations, unmanned aerial vehicles (UAVs) equipped with thermal infrared (TIR) sensors are essential to obtain ground information during nighttime operations. However, existing target detection algorithms mainly consider the detection accuracy but not the processing speed and storage space requirements. Furthermore, current neural network target detection algorithms primarily focus on conventional RGB images and are not optimized for UAV-based TIR video stream data. This paper proposes an improved Mask-RCNN algorithm for target detection in UAV TIR video streams to address current research deficiencies. First, MobileNetV3, which is used to process RGB images, is applied to process TIR data for outdoor emergency rescue operations, significantly increasing the time efficiency of the algorithm. Second, prior knowledge such as the projection model of the airborne camera and the target temperature characteristics in the UAV TIR video stream is utilized to filter the detecting results instead of pre-detection temperature masks. Compared with the original Mask-RCNN algorithm, the improved algorithm increases the processing speed, reduces the storage space requirements, and provides detection performances equal or slightly superior to that before the improvement.

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).

Optimized algorithm for evapotranspiration retrieval via remote sensing

Many algorithms for surface energy balance (SEB) based on remote sensing (RS) have been advanced to determine evapotranspiration (ET). These algorithms were developed for specific conditions (e.g., sensors, land use, and crop management) in which functions and empirical parameters within its algorithms concur with those conditions. Therefore, this study aims to develop a SEB-RS algorithm for retrieving ET adjusted to in situ observations. The study was conducted in two experimental fields in Brazil with the crops Jatropha curcas, maize, soybean, and sugarcane. We used multispectral images from the orbital sensors, Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) coupled in Landsat 8 satellite and from the terrestrial sensor, Altum, on board of an unmanned aerial vehicle. The proposed algorithm termed as Ground-truthed Surface Energy Balance (GT-SEB) is based on physical formulation of SEB-RS algorithms, where two extra computational processes using in situ ET observations were proposed for originating the new algorithm. The first additional process for optimizing the automatic “anchor” pixels selection and another for algorithm parameters optimization. Thus, both processes aim to reduce the difference between the observed ET and estimated by GT-SEB. Being assessed for both orbital (OLI/TIRS) and suborbital (Altum) sensors, the GT-SEB yielded excellent results (root-mean-square-error, RMSE, ≤ 0.48 mm and modified Kling-Gupta efficiency, KGE, ≥ 0.92). In addition to GT-SEB being an optimized algorithm, it uses a classic parameterization of SEB-RS algorithms, providing efficiency and scalability for other remote sensors, climates, and surfaces.

The Effects of Thermal-Spatial Behaviours of Land Covers on Urban Heat Islands in Semi-Arid Climates

In recent decades, unsustainable urban development stemming from uncontrolled changes in land cover and the accumulation of population and activities have given rise to adverse environmental consequences, such as the formation of urban heat islands (UHIs) and changes in urban microclimates. The formation and intensity of UHIs can be influenced not only by the type of land cover, but also by other factors, such as the spatial patterns of thermal clusters (e.g., dimensions, contiguity, and integration). By emphasising the differences between semi-arid and cold-and-humid climates in terms of the thermal-spatial behaviours of various types of land cover in these climates, this paper aims to assess the behavioural patterns of thermal clusters in Tehran, Iran. To this end, the relationship between the land surface temperature (LST) and the types of land cover is first demonstrated using combined multispectral satellite images taken by Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS) of the Landsat8 and MODIS, and Sentinel satellites to determine LST and land cover. The effects of different behavioural patterns of thermal clusters on the formation of daytime urban heat islands are then analysed through spatial cross-correlation analysis. Lastly, the thermal behaviours of each cluster are separately examined to reveal how their spatial patterns, such as contiguity, affect the intensity and formation of UHI, with the assumption that each point in a contiguous surface may exhibit different thermal behaviours, depending on its distance from the edge or centre. The results of this study show that the daytime UHIs do not occur in the central parts of Tehran, and instead they are created in the surrounding layer, which mostly consists of barren cover. This finding contrasts with previous research conducted regarding cities located in cold-and-humid climates. Our research also finds that the more compact the hot and cool clusters are, the more contiguous they become, which leads to an increase in UHIs. The results suggest that for every 100 pix/km2 increase, the cluster temperature increases by approximately 0.7–1 °C. Additionally, placing cool clusters near or in combination with hot clusters interrupts the effect of the hot clusters, leading to a significant temperature reduction. The paper concludes with recommendations for potential sustainable and context-based solutions to UHI problems in semi-arid climates that relate to the determination of the optimal contiguity distance and land use integration patterns for thermal clusters.

A Fusion of Feature-Oriented Principal Components of Multispectral Data to Map Granite Exposures of Pakistan

Despite low spatial resolutions, thermal infrared bands (TIRs) are generally more suitable for mineral mapping due to fundamental tones and high penetration in vegetated areas compared to shortwave infrared (SWIR) bands. However, the weak overtone combinations of SWIR bands for minerals can be compensated by fusing SWIR-bearing data (Sentinel-2 and Landsat-8) with other multispectral data containing fundamental tones from TIR bands. In this paper, marble in a granitic complex in Mardan District (Khyber Pakhtunkhwa) in Pakistan is discriminated by fusing feature-oriented principal component selection (FPCS) obtained from the ASTER, Landsat-8 Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS) and Sentinel-2 MSI data. Cloud computing from Google Earth Engine (GEE) was used to apply FPCS before and after the decorrelation stretching of Landsat-8, ASTER, and Sentinel-2 MSI data containing five (5) bands in the Landsat-8 OLI and TIRS and six (6) bands each in the ASTER and Sentinel-2 MSI datasets, resulting in 34 components (i.e., 2 × 17 components). A weighted linear combination of selected three components was used to map granite and marble. The samples collected during field visits and petrographic analysis confirmed the remote sensing results by revealing the region’s precise contact and extent of marble and granite rock types. The experimental results reflected the theoretical advantages of the proposed approach compared with the conventional stacking of band data for PCA-based fusion. The proposed methodology was also applied to delineate granite deposits in Karoonjhar Mountains, Nagarparker (Sindh province) and the Kotah Dome, Malakand (Khyber Pakhtunkhwa Province) in Pakistan. The paper presents a cost-effective methodology by the fusion of FPCS components for granite/marble mapping during mineral resource estimation. The importance of SWIR-bearing components in fusion represents minor minerals present in granite that could be used to model the engineering properties of the rock mass.

Accuracy assessment for Landsat 8 thermal bands in measuring sea surface temperature over Kuwait and North West Arabian Gulf

Studying physical oceanography is one of the important fields of remote sensing applications. Previously, the thermal mapping of seas and oceans relied on primitive methods, such as the use of sensors installed on buoys, extracting contour lines, and deriving the values from the confluence of contour lines. Today's remote sensing provides more advanced methods for extracting sea surface temperature (SST) values for all bodies of water as a continuous raster model, through thermal sensors installed on satellites designated to monitor and observe the Earth. The Landsat program has facilitated a quantum leap by providing its data free for the public. What has become increasingly important is the inclusion, in Landsat 8, of a thermal band on the TIRS sensor through which SST can be extracted with a spatial resolution of 100 m2. In this article, the accuracy of the two thermal bands (band 10 and 11) of Landsat 8 was validated in estimating the SST of Kuwaiti and Northwest Arabian Gulf waters, through the use of 62 thermal images and 66 ground-truthing points (GTPs) taken from the field in the period from July 2013 to March 2020. This was achieved through a function provided by the ENVI 5.3 software - “brightness temperature” - to derive the surface temperature. The accuracy of Landsat 8 to monitor the SST of Kuwait and north-west Arabian Gulf waters was validated by calculating the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The accuracy of the thermal band 10 was ± 2.03 degrees (7.9%), while the accuracy of the thermal band 11 was ± 3.13 degrees (13.7%). Therefore, this study demonstrated that the thermal band 10 of Landsat 8 is more accurate than the thermal band 11 in monitoring the SST of Kuwaiti and north-west Arabian Gulf waters, with a difference of ± 1.1 degrees (5.8%).

Correlation analysis of Land Surface Temperature (LST) measurement using DJI Mavic Enterprise Dual Thermal and Landsat 8 Satellite Imagery (case study: Surabaya City)

Landsat 8 Satellite Imagery (Landsat Data Continuity Mission, LDCM) is a satellite product made by Orbital Science Corporation, which launched with The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) instruments as the latest features. One of the Thermal Infrared Sensor (TIRS) instruments is called Band 10, that provide temperature information on the earth’s surface. As many research conduct the temperature comparison between satellite imagery analysis and land cover temperature has been come with positive correlation for both of the variable. As to prove the temperature relationship, it is necessary to validate the actual temperature values on the earth’s surface by conduct the temperature survey in the area using the temperature measurement tools. One of the tools is DJI Mavic Enterprise Dual Thermal camera as the camera that capable to take samples data of particular objects categories that included urban areas, waters, vegetation, open land, settlements, and industrial factories. Using the satellite imagery’s temperature data and the land cover temperature data survey, comparing and accuration assessment are needed to see how close the value of both variable. The data processing carried out that both of the data have a positive correlation as the relationship, which have a Pearson correlation value of 0.892 and sig. (2-tailed) at the number 0.000000068. This correlation value showed that the relationship between both data is acceptable as the both data can represent each other to conduct any research. However, as the satellite imagery contains 29,85% of cloud cover, the temperature obtained lower in the Landsat 8 satellite image rather than the actual temperature on the earth’s surface.

Validation of Landsat 8 high resolution Sea Surface Temperature using surfers

Nearshore coastal waters are highly dynamic in both space and time. They can be difficult to sample using conventional methods due to their shallow depth, tidal variability, and the presence of strong currents and breaking waves. High resolution satellite sensors can be used to provide synoptic views of Surface Temperature (ST), but the performance of such ST products in the nearshore zone is poorly understood. Close to the shoreline, the ST pixels can be influenced by mixed composition of water and land, as a result of the sensor’s spatial resolution. This can cause thermal adjacency effects due to the highly different diurnal temperature cycles of water bodies and land. Previously, temperature data collected during surfing sessions has been proposed for validation of moderate resolution (1 km pixel size) satellite ST products. In this paper we use surfing temperature data to validate three high resolution (100 m resampled to 30 m pixel size) ST products derived from the Thermal InfraRed Sensor (TIRS) on board Landsat 8 (L8). ST was derived from Collection 1 and 2 Level 1 data (C1L1 and C2L1) using the Thermal Atmospheric Correction Tool (TACT), and was obtained from the standard Collection 2 Level 2 product (USGS C2L2). This study represents one of the first evaluations of the new C2 products, both L1 and L2, released by USGS at the end of 2020. Using automated matchup and image quality control, 88 matchups between L8/TIRS and surfers were identified, distributed across the North-Western semihemisphere. The unbiased Root Mean Squared Difference (uRMSD) between satellite and in situ measurements was generally ¡ 2 K, with warm biases (Mean Average Difference, MAD) of 1.7 K (USGS C2L2), 1.3 K (TACT C1L1) and 0.8 K (TACT C2L1). Large interquartile ranges of ST in 5 × 5 satellite pixels around the matchup location were found for several images, especially for the summer matchups around the Californian coast. By filtering on target stability the number of matchups reduced to 31, which halved the uRMSD across the three methods (to around 1.1K), MAD were much lower, i.e. 1.1 K (USGS C2L2), 0.6 K (TACT C1L1), and 0.2 K (TACT C2L1). The larger biases of the C2L2 product compared to TACT C2L1 are caused as a result of: (1) a lower emissivity value for water targets used in USGS C2L2, and (2) differences in atmospheric parameter retrieval, mainly from differences in upwelling atmospheric radiance and lower atmospheric transmittance retrieved by USGS C2L2. Additionally, tiling artefacts are present in the C2L2 product, which originate from a coarser atmospheric correction process. Overall, the L8/TIRS derived ST product compares well with in situ measurements made while surfing, and we found the best performing ST product for nearshore coastal waters to be the Collection 2 Level 1 data processed with TACT.

A Survey of Strategies, and Procedures for Detecting Drone Utilizing Sound, Radar, Thermal Infrared Sensor and Video Applications

With the exponential rise in the use of drones anywhere anytime, malicious use by outlaws is increasing as well. This calls for protective, detective, preventive measures to counter these attacks. This paper aims to review literature on drone detection and classification that utilizes a myriad of modalities ranging from using thermal infrared sensors to radar detections. In addition, there is a summary of a detailed discussion on drone classification along with recent progress and development in drone detection using machine learning, all of which is performed in an attempt to identify means to thwart such attacks. Furthermore, some future research directions, related to this new field of study, are discussed.

Landsat 8 Data as a Source of High Resolution Sea Surface Temperature Maps in the Baltic Sea

Sea surface temperature (SST) is a key hydrological variable which can be monitored via satellite. One source of thermal data with a spatial resolution high enough to study sub-mesoscale processes in coastal waters may be the Landsat mission. The Thermal Infrared Sensor on board Landsat 8 collects data in two bands, which allows for the use of the well-known nonlinear split-window formula to estimate SST (NLSST) using top-of-the-atmosphere (TOA) brightness temperature. To calibrate its coefficients a significant number of matchup points are required, representing a wide range of atmospheric conditions. In this study over 1200 granules of satellite data and 12 time series of in situ measurements from buoys and platforms operating in the Baltic Sea over a period of more than 6 years were used to select matchup points, derive NLSST coefficients and evaluate the results. To filter out pixels contaminated by clouds, ice or land influences, the IdePix algorithm was used with Quality Assessment Band and additional test of the adjacent pixels. Various combinations of flags were tested. The results show that the NLSST coefficients derived previously for coastal areas, characterised by a more humid atmosphere, might overestimate low SST values. Formulas derived for the Baltic Sea produced biases close to 0 °C and RMSEs in the range of 0.49–0.52 °C.

The Estimation of Iron Oxide Content in Soil based on Landsat 8 OLI TIRS Imagery in Wetland Areas

Wetland areas are volatile and have high iron content. In this study, through a remote sensing approach, especially using Landsat Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery, we discussed the method to estimate the presence of iron oxide in the wetlands of South Kalimantan in 2018, 2019, and 2020. Interpretation of the Landsat OLI TIRS was employed in April 2018, August 2018, February 2019, August 2019, March 2020, and August 2020. The band ratio method was used to determine the distribution of samples in this study. The results of the iron oxide index from the image were performed regression and correlation analysis with field measurement and laboratory test results to validate the oxide index values. The results showed that the iron oxide index value in the dry season was higher than in the rainy season. Iron oxide index value in open land was higher than in vegetation cover. The wetland was in dry condition during the dry season, making it easier to detect iron oxide values. Vegetation cover could reduce the iron oxide index value on the soil surface so that the iron oxide value was more easily identified in open land. The results of linear regression testing for the wet season sample obtained a coefficient of determination R² = 0.413, while the results of linear regression testing for the dry season sample obtained a coefficient of determination R² = 0.667. Thus, the Landsat image has strong enough to estimate the iron oxide index in the wetland area of Kalimantan.

Surface Albedo and Temperature Models for Surface Energy Balance Fluxes and Evapotranspiration Using SEBAL and Landsat 8 over Cerrado-Pantanal, Brazil

The determination of the surface energy balance fluxes (SEBFs) and evapotranspiration () is fundamental in environmental studies involving the effects of land use change on the water requirement of crops. SEBFs and have been estimated by remote sensing techniques, but with the operation of new sensors, some variables need to be parameterized to improve their accuracy. Thus, the objective of this study is to evaluate the performance of algorithms used to calculate surface albedo and surface temperature on the estimation of SEBFs and in the Cerrado-Pantanal transition region of Mato Grosso, Brazil. Surface reflectance images of the Operational Land Imager (OLI) and brightness temperature () of the Thermal Infrared Sensor (TIRS) of the Landsat 8, and surface reflectance images of the MODIS MOD09A1 product from 2013 to 2016 were combined to estimate SEBF and by the surface energy balance algorithm for land (SEBAL), which were validated with measurements from two flux towers. The surface temperature () was recovered by different models from the and by parameters calculated in the atmospheric correction parameter calculator (ATMCORR). A model of surface albedo () with surface reflectance OLI Landsat 8 developed in this study performed better than the conventional model () SEBFs and in the Cerrado-Pantanal transition region estimated with combined with and performed better than estimates with . Among all the evaluated combinations, SEBAL performed better when combining with the model developed in this study and the surface temperature recovered by the Barsi model (). This demonstrates the importance of an model based on surface reflectance and atmospheric surface temperature correction in estimating SEBFs and ET by SEBAL.

Spatial Variability and Temporal Heterogeneity of Surface Urban Heat Island Patterns and the Suitability of Local Climate Zones for Land Surface Temperature Characterization

This study investigated monthly variations of surface urban heat island intensity (SUHII) and the applicability of the local climate zones (LCZ) scheme for land surface temperature (LST) differentiation within three spatial contexts, including urban, rural and their combination, in Shenyang, China, a city with a monsoon-influenced humid continental climate. The monthly SUHII and LST of Shenyang were obtained through 12 LST images, with one in each month (within the period between 2018 and 2020), retrieved from the Thermal InfraRed Sensor (TIRS) 10 in Landsat 8 based on a split window algorithm. Non-parametric analysis of Kruskal-Wallis H test and a multiple pairwise comparison were adopted to investigate the monthly LST differentiations with LCZs. Overall, the SUHII and the applicability of the LCZ scheme exhibited spatiotemporal variations. July and August were the two months when Shenyang underwent strong heat island effects. Shenyang underwent a longer period of cool than heat island effects, occurring from November to May. June and October were the transition months of cool–heat and heat–cool island phenomena, respectively. The SUHII analysis was dependent on the definition of urban and rural boundaries, where a smaller rural buffering zone resulted in a weaker SUHI or surface urban cool island (SUCI) phenomenon and a larger urban area corresponded to a weaker SUHI or SUCI phenomenon as well. The LST of LCZs did not follow a fixed order, where in July and August, the LCZ-10 (Heavy industry) had the highest mean LST, followed by LCZ-2 (Compact midrise) and then LCZ-7 (Lightweight low-rise). In comparison, LCZ-7, LCZ-8 (Large low-rise) and LCZ-9 (Sparsely built) had the highest LST from October to May. The LST of LCZs varied with urban and rural contexts, where LCZ-7, LCZ-8 and LCZ -10 were the three built LCZs that had the highest LST within urban context, while LCZ-2, LCZ-3 (Compact low-rise), LCZ-8, LCZ-9 and LCZ-10 were the five built LCZs that had the highest LST within rural context. The suitability of the LCZ scheme for temperature differentiation varied with the month, where from July to October, the LCZ scheme had the strongest capability and in May, it had the weakest capability. Urban context also made a difference to the suitability, where compared with the whole study area (the combination of urban and rural areas), the suitability of built LCZs in either urban or rural contexts weakened. Moreover, the built LCZs had a higher level of suitability in an urban context compared with a rural context, while the land-cover LCZs within rural had a higher level of suitability.

Measuring UHI using Landsat 8 OLI and TIRS data with NDVI and NDBI in Municipality of Prishtina

The study is aimed at investigating the phenomenon of the Surface Urban Heat Island (SUHI) over the municipality of Prishtina. The SUHI was investigated based on the relationship between Land Surface Temperature (LST) estimated from Landsat 8 Thermal Infrared Sensor (TIRS) band with Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) from Landsat 8 Operational Land Imager (OLI) bands using Geographic Information System (GIS). To understand this relationship, a regression analysis was performed. Regression analysis in both cases showed high relationships between LST, NDVI and NDBI. LST relationships with NDVI showed a strong negative correlation having an R2 value of 0.7638 highlighting the extraordinary role of vegetation towards reducing the SUHI effect while LST relationships with NDBI showed a strong positive correlation having an R2 value of 0.8038 highlighting the role that built-up areas have in strengthening the SUHI effect. Built-up areas and bare surfaces are responsible for generating the SUHI effect while vegetation and water bodies minimize this effect by creating freshness. The maps in which the SUHI phenomenon are identified, are extremely important and should be paid great attention by the city leaders themselves. This should be done in order for urban planning policies to go to those areas where such a harmful phenomenon occurs in order for the lives of citizens to be as healthy as possible.