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Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method
Abstract and Figures
Accurate inversion of land surface geo/biophysical variables from remote sensing data for earth observation applications is an essential and challenging topic for the global change research. Land surface temperature (LST) is one of the key parameters in the physics of earth surface processes from local to global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from the space. Landsat 8 Thermal Infrared Sensor (TIRS) is the newest thermal infrared sensor for the Landsat project, providing two adjacent thermal bands, which has a great benefit for the LST inversion. In this paper, we compared three different approaches for LST inversion from TIRS, including the radiative transfer equation-based method, the split-window algorithm and the single channel method. Four selected energy balance monitoring sites from the Surface Radiation Budget Network (SURFRAD) were used for validation, combining with the MODIS 8 day emissivity product. For the investigated sites and scenes, results show that the LST inverted from the radiative transfer equation-based method using band 10 has the highest accuracy with RMSE lower than 1 K, while the SW algorithm has moderate accuracy and the SC method has the lowest accuracy.
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... [21] developed a practical split-window algorithm to estimate LST from thermal infrared sensor (TIRS) aboard Landsat 8, obtaining LST with an accuracy better than 1.0 K; Maithani et al. [22] retrieved LST from Landsat thermal datasets using a single-channel algorithm for the Dehradun planning area situated in Uttarakhand (India); Yu et al. [23] compared three different approaches for LST inversion from TIRS, including the radiative transfer equation-based method, the split-window algorithm, and the singlechannel method. Their findings indicated that the LST obtained from the radiative transfer equation-based method, using Landsat band 10, has the highest accuracy with RMSE lower than 1.0 K, while the split-window algorithm has moderate accuracy and the singlechannel method has the lowest accuracy. ...
... To retrieve LST, the RTE approach was utilized since it is not dependent on average temperature and air humidity. Furthermore, according to Yu et al. [23], the RTE-based approach has the highest accuracy for LST retrieval using Landsat band 10. For the purpose of collecting the temperature of the region of interest (ROI), over a specific time period, two different types of Landsat images were considered. ...
Continuous and unplanned urbanization, combined with negative alterations in land use land cover (LULC), leads to a deterioration of the urban thermal environment and results in various adverse ecological effects. The changes in LULC and thermal characteristics have significant implications for the economy, climate patterns, and environmental sustainability. This study focuses on the Province of Naples in Italy, examining LULC changes and the Urban Thermal Field Variance Index (UTFVI) from 1990 to 2022, predicting their distributions for 2030. The main objectives of this research are the investigation of the future seasonal thermal characteristics of the study area by
characterizing land surface temperature (LST) through the UTFVI and analyzing LULC dynamics along with their correlation. To achieve this, Landsat 4-5 Thematic Mapper (TM) and Landsat 9
Operational Land Imager (OLI) imagery were utilized. LULC classification was performed using a supervised satellite image classification system, and the predictions were carried out using the cellular automata-artificial neural network (CA-ANN) algorithm. LST was calculated using the radiative transfer equation (RTE), and the same CA-ANN algorithm was employed to predict UTFVI for 2030. To investigate the multi-temporal correlation between LULC and UTFVI, a cross-tabulation
technique was employed. The study’s findings indicate that between 2022 and 2030, there will be a 9.4% increase in built-up and bare-land areas at the expense of the vegetation class. The strongest UTFVI zone during summer is predicted to remain stable from 2022 to 2030, while winter UTFVI shows substantial fluctuations with a 4.62% decrease in the none UTFVI zone and a corresponding increase in the strongest UTFVI zone for the same period. The results of this study reveal a concerning trend of outward expansion in the built-up area of the Province of Naples, with central northern regions experiencing the highest growth rate, predominantly at the expense of vegetation cover.
These predictions emphasize the urgent need for proactive measures to preserve and protect the diminishing vegetation cover, maintaining ecological balance, combating the urban heat island effect, and safeguarding biodiversity in the province.
... The air temperature corresponding to the remote sensing image was 18 • C (National Centre for Atmospheric Science Ibsen et al., 2021). Land surface temperature was estimated by the previously validated radiative transfer equation (Du et al., 2017;Masoudi and Tan, 2019;Qiu and Jia, 2020), which has the higher accuracy when compared to other algorithms, such as the mono-windows algorithm and the generalized single-channel algorithm (Yu et al., 2014). Land surface emissivity (ε) is an essential parameter for retrieving LST from thermal infrared remote sensing data. ...
A R T I C L E I N F O Keywords: Surface urban heat island Cooling effect Greenspace Canopy coverage Land surface temperature A B S T R A C T As climate warms, the impact of existing urban heat islands on the health of residents in towns and cities will worsen. A reduction in impervious in cities may help to reduce temperatures, but the relationship between tree canopy coverage and land surface temperature (LST) is not well characterised. Here, we quantified the summer LST of the temperate city of Leeds, UK using Landsat 8 TIRS remote sensing image and explored the spatial relationships between LST and impervious land cover, greenspace coverage, type of greenspace and canopy cover. We found a strong relationship between LST and canopy coverage across the built-up region of Leeds and use this relationship to project the impact of future canopy cover expansion. We found that of the nine main types of greenspaces in Leeds, private gardens occupied the greatest fraction of the total greenspace area and offered most potential for canopy cover expansion. Results suggest that a doubling of canopy coverage across the city, could reduce the mean LST by around 2.5 • C during the warmest summer months. Such a temperature reduction adds further weight to efforts by cities and countries globally to increase tree cover to both mitigate for and adapt to climate change.
... Land surface temperature retrieval In this study, LST was retrieved from the Landsat 8 OLI_TIRS image using the Radiative Transfer Equation (RTE) method. Compared with other methods, the RTE method has been proven to have higher accuracy (Yu et al. 2014). Firstly, the radiometric calibration and atmospheric correction were conducted to convert the digital numbers (DN) of the image to surface-leaving radiance, which were shown as follows: ...
ContextUrban green space (UGS) has been widely acknowledged to effectively reduce ambient temperature, and thus improving its cooling effect has become a hot topic in urban planning. Although the impact of UGS spatial pattern on land surface temperature (LST) has been investigated in previous studies, the differences caused by the heterogeneity of human activities within urban areas have not been fully understood.Objectives
This study attempted to explore different relationships between UGS spatial pattern and LST among various human activity dominated zones to effectively support urban heat mitigation.Methods
Taking the urban area within the third ring road of Changsha City as the study area, the random forest model was applied to evaluate the contribution of UGS spatial pattern on LST and partial dependence curves were generated to identify their nonlinear relationships among different urban functional zones (UFZs).ResultsIn terms of varied dominant influencing factors affecting LST, the area proportion of UGS was the most important factor in residential, industrial, campus, and public zones, which showed a negative impact on the variation of LST. The cohesion between UGS patches was also an important factor in residential zones, and the more aggregated the more conducive to cooling. UGS patch density interpreted the highest importance in commercial zones, which played a significantly negative role when less than a specific threshold. However, the influence of landscape shape complexity on LST was limited in five built-up functional zones.Conclusions
The impact of UGS spatial pattern on heat mitigation was quite different in various UFZs. The findings can provide targeted guidance for green space planning in regard to effectively mitigating the urban heat island effect.
... Next, the PV was calculated using Eq. 2 (Yu et al., 2014): ...
... However, several studies still proposed different split-window algorithms for this sensor [15][16][17]. In certain validation cases, the LST retrieval accuracy from the split-window algorithms was better than that of the single-channel algorithm [18]. Following the recommendations and results of the experiments carried out by foreign researchers, we used the single-channel algorithm in our work. ...
Climate change is creating an increase in temperatures, which is harming the quality of life of people all over the world, particularly those with minimal financial resources. While 30% of the world’s population is now vulnerable to extreme heat, estimates show that ratio will rise to 74% in the next 20 years, according to forecasts. Using the UrbClim climate model, this study examines the space-time variability of the heat stress index (HI) in different local climate zones (LCZs), as well as how heat wave conditions might affect this index based on land use and land cover. To that end, Seville, in Southern Spain, was investigated during the summer of 2017, when it had four heat waves. The following indices were considered for each urban sub-area: Normalized Difference Vegetation, Proportion Vegetation, Normalized Difference Built, and Urban Index. The goal is to conduct a statistical analysis of the link between the aforementioned elements and the heat stress index in order to recommend mitigation and resilience techniques. Our findings showed that compact and industrial LCZs (2, 3, and 10) are less resistant to HI than open and rural regions (5, 6, B, D, and G), which are more resistant to HI due to higher vegetation rates. The heat wave condition exacerbates the HI in all LCZs. As a result, initiatives such as enhancing open space, increasing green space, or using green roofs and façades might alleviate heat stress and improve people’s quality of life.
With the rapid urban infrastructure expansion in China, numerous cities have been suffering from the negative effects of urban warming and ensuing social and environmental challenges. Accurate acquisition of long-term dynamics in urban land surface temperature (LST) helps to the development of sustainable urban management actions. In this paper, based on the Landsat and MODIS long-term observations from 2000 to 2020 covering Hefei City, the capital of Anhui Province of China, four spatio-temporal fusion algorithms including the spatial and temporal adaptive reflectance fusion model (STARFM), enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), flexible spatio-temporal data fusion (FSDAF) model and spatial and temporal nonlocal filter-based fusion model (STNLFFM) were implemented and compared, followed by the extraction of the spatial distribution characteristics and evolution trend of urban summer average LST. Next, the relationships between LST grading zones and different land cover (LC) types were explored, and the 2030 urban LST was predicted by using the patch-generating land use simulation (PLUS) coupled with Markov chain model. The results showed that STARFM was the best spatio-temporal fusion algorithm here. From 2000 to 2020, the high temperature zone expanded from 638.81 km
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to 886.31 km
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, and there was still a continuous trend of enhancing high temperature zone and built-up area in Hefei during 2020-2030, indicating that the urban heat island effect will intensify continuously. This study provides a feasible and comprehensive approach to monitoring urban surface temperature dynamics. These findings contribute a scientific insight for local governments to formulate urban land management plans.
In this paper, the authors introduce the theory base of split-window algorithm and make a derivation for the split-window algorithm. As to the characters of the MODIS bands, the authors make a simplification for the Planck function. For the two main parameters of the split algorithm, the authors obtain them from the same image. Finally the authors retrieve the LST of Huanbohai region by using MODIS data and get the spatial distribution of LST in this region and make some analysis for the retrieval results.
The high temporal resolution of data acquisition by geostationary satellites and their capability to resolve the diurnal cycle allows for the retrieval of a valuable source of information about geophysical parameters. In this paper, we implement a Kalman filter approach to apply temporal con-straints on the retrieval of surface emissivity and temperature from radiance measurements made from geostationary plat-forms. Although we consider a case study in which we ap-ply a strictly temporal constraint alone, the methodology will be presented in its general four-dimensional, i.e., space-time, setting. The case study we consider is the retrieval of emis-sivity and surface temperature from SEVIRI (Spinning En-hanced Visible and Infrared Imager) observations over a tar-get area encompassing the Iberian Peninsula and northwest-ern Africa. The retrievals are then compared with in situ data and other similar satellite products. Our findings show that the Kalman filter strategy can simultaneously retrieve sur-face emissivity and temperature with an accuracy of ± 0.005 and ±0.2 K, respectively.
This paper presents the procedure and results of the radiance-based validation approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) Land-Surface Temperature (LST) product. Surface emissivity spectra were retrieved by a sun-shadow method from surface-leaving radiance spectra measured with a thermal infrared (TIR) spectroradiometer in the 3.5-14 µm spectral region under sunshine and sun-shadow conditions. By using the measured surface emissivity spectrum and atmospheric profiles obtained by radiosonde balloons, and the LST values at validation sites in the V5 MODIS level-2 LST products, radiative transfer simulations were made with the MODTRAN4 code to calculate the top-of-atmosphere (TOA) radiance values in MODIS band 31 (L31). By adjusting the LST input values in the simulations to match the calculated L31 values to the MODIS measured radiance (MOD L31) values, MOD L31 inverted LSTs can be obtained. The MODIS LST product was validated by comparison to the values of the MOD L31 inverted LSTs. This approach compares well with the conventional temperature-based approach. The results of the radiance-based validation indicate that the accuracy of the MODIS LST product is better than 1 K in most cases, including lake, vegetation and soil sites in clear-sky conditions. The errors in the split-window retrieved LSTs may be larger in bare soil sites and highly heterogeneous sites due to large uncertainties in surface emissivities. The results of the radiance-based validation also reveal the weakness of the split-window algorithm used for the generation of the MODIS LST product in two situations: one in cases where LSTs are larger than the air temperature at the surface level (T s-air) by more than 16 K and the columnar water vapour (cwv) is larger than 1.5 cm, and another in cases under the influence of thin cirrus clouds or heavy aerosol loadings. These two situations were not considered in the development of the current MODIS LST algorithm.
Remote sensing of land surface temperature (LST) from the thermal band data of Landsat Thematic Mapper (TM) still remains unused in comparison with the extensive studies of its visible and near-infrared (NIR) bands for various applications. The brightness temperature can be computed from the digital number (DN) of TM6 data using the equation provided by the National Aeronautics and Space Administration (NASA). However, a proper algorithm for retrieving LST from the only one thermal band of the sensor still remains unavailable due to many difficulties in the atmospheric correction. Based on thermal radiance transfer equation, an attempt has been made in the paper to develop a mono-window algorithm for retrieving LST from Landsat TM6 data. Three parameters are required for the algorithm: emissivity, transmittance and effective mean atmospheric temperature. Method about determination of atmospheric transmittance is given in the paper through the simulation of atmospheric conditions with LOWTRAN 7 program. A practicable approach of estimating effective mean atmospheric temperature from local meteorological observation is also proposed in the paper when the in situ atmospheric profile data is unavailable at the satellite pass, which is generally the case in the real world especially for the images in the past. Sensitivity analysis of the algorithm indicates that the possible error of ground emissivity, which is difficult to estimate, has relatively insignificant impact on the probable LST estimation error i T, which is sensible to the possible error of transmittance i 6 and mean atmospheric temperature i T a . Validation of the simulated data for various situations of seven typical atmospheres indicates that the algorithm is able to provide an accurate LST retrieval from TM6 data. The LST difference between the retrieved and the simulated ones is less than 0.4°C for most situations. Application of the algorithm to the sand dunes across the Israel-Egypt border results in a reasonable LST estimation of the region. Based on this LST estimation, spatial variation of the interesting thermal phenomenon has been analysed for comparison of LST difference across the border. The result shows that the Israeli side does have significantly higher surface temperature in spite of its denser vegetation cover than the Egyptian side where bare sand is prevalent.
Advanced Remote Sensing is an application-based reference that provides a single source of mathematical concepts necessary for remote sensing data gathering and assimilation. It presents state-of-the-art techniques for estimating land surface variables from a variety of data types, including optical sensors such as RADAR and LIDAR. Scientists in a number of different fields including geography, geology, atmospheric science, environmental science, planetary science and ecology will have access to critically-important data extraction techniques and their virtually unlimited applications. While rigorous enough for the most experienced of scientists, the techniques are well designed and integrated, making the book's content intuitive, clearly presented, and practical in its implementation. Comprehensive overview of various practical methods and algorithms Detailed description of the principles and procedures of the state-of-the-art algorithms Real-world case studies open several chapters More than 500 full-color figures and tables Edited by top remote sensing experts with contributions from authors across the geosciences.
There is only one cause of failure in the proportional hazard model for identification of important prognostic factors, in which the covariates are assumed to be independent of time, that is, the event or failure is allowed to occur only once for each person, and there is no correlation among failure times of different persons. However, in practice, failure may be due to more than one event or cause, the same event or failure may recur during a follow-up study, and the event or failure time observed may be from related persons in a family or from the same person at different times. In this chapter we discuss several models for these situations. The first two models are extensions of the proportional hazards model to handle time-dependent covariates and to perform stratified analysis. Other models introduced in this chapter are for multiple causes of failure, recurrent events, and related observations. The chapter concludes with a problem solving section.
The importance of land surface temperature (LST) retrieved from high to medium spatial resolution remote sensing data for many environmental studies, particularly the applications related to water resources management over agricultural sites, was a key factor for the final decision of including a thermal infrared (TIR) instrument on board the Landsat Data Continuity Mission or Landsat-8. This new TIR sensor (TIRS) includes two TIR bands in the atmospheric window between 10 and 12 $muhbox{m}$, thus allowing the application of split-window (SW) algorithms in addition to single-channel (SC) algorithms or direct inversions of the radiative transfer equation used in previous sensors on board the Landsat platforms, with only one TIR band. In this letter, we propose SC and SW algorithms to be applied to Landsat-8 TIRS data for LST retrieval. Algorithms were tested with simulated data obtained from forward simulations using atmospheric profile databases and emissivity spectra extracted from spectral libraries. Results show mean errors typically below 1.5 K for both SC and SW algorithms, with slightly better results for the SW algorithm than for the SC algorithm with increasing atmospheric water vapor contents.