Land Surface Temperature Algorithm Research Articles

Solar zenith angle-based calibration of Himawari-8 land surface temperature for correcting diurnal retrieval error characteristics

The geostationary Himawari-8 satellite offers a unique opportunity to monitor sub-daily thermal dynamics over Asia and Oceania, and several operational land surface temperature (LST) retrieval algorithms have been developed for this purpose. However, studies have reported inconsistency between LST data obtained from geostationary and polar-orbiting platforms, particularly for daytime LST, which can arise from variations in viewing geometries and inherent differences in sensor types and LST algorithms. Despite this, previous research has primarily focused on analysing the directionality of LST without thoroughly exploring systematic differences between platforms. Hence, we presented a Solar Zenith Angle-based Calibration (SZAC) method to harmonise the daytime component of a split-window retrieved Himawari-8 LST (referred to here as the baseline) with the MODerate-resolution Imaging Spectroradiometer (MODIS) LST. SZAC describes the spatial heterogeneity and magnitude of diurnal LST discrepancies from different platforms, which is anticipated to complement typical directionality analyses. We evaluated the harmonised LST data, referred to as the Australian National University LST with SZAC (ANUSZAC), against MODIS LST and the Visible Infrared Imaging Radiometer Suite (VIIRS) LST, as well as in-situ LST from the OzFlux network. Two peer Himawari-8 LST products from Chiba University and the Copernicus Global Land Service were also collected for comparison. The median daytime bias of ANUSZAC LST against Terra-MODIS LST, Aqua-MODIS LST and VIIRS LST was 1.52 K, 0.98 K and −0.63 K, respectively, which demonstrated improved performance compared to baseline (5.37 K, 4.85 K and 3.02 K, respectively) and Chiba LST (3.71 K, 2.90 K and 1.07 K, respectively). All four Himawari-8 LST products showed comparable performance of unbiased root mean squared error (ubRMSE), ranging from 2.47 to 3.07 K, compared to LST from polar-orbiting platforms. In the evaluation against in-situ LST, the mean values of bias (ubRMSE) of baseline, Chiba, Copernicus and ANUSZAC LST during daytime were 4.23 K (3.74 K), 2.16 K (3.62 K), 1.73 K (3.31 K) and 1.41 K (3.24 K), respectively, based on 171,289 hourly samples from 20 OzFlux sites across Australia between 01/Jan/2016 and 31/Dec/2020. In summary, the SZAC method offers a promising approach to enhance the reliability of geostationary LST retrievals by incorporating the spatiotemporal characteristics observed by accurate polar-orbiting LST data. SZAC can also reduce LST angular effects due to its capability of quantifying spatial inhomogeneity of surface heat dynamics. Furthermore, it is possible to implement SZAC using LST data acquired by geostationary satellites in other regions, e.g., Europe, Africa and Americas. This could improve our understanding of the error characteristics of coincident imageries from geostationary and polar-orbiting platforms, allowing for targeted refinements and global harmonisations to further enhance applicability.

INFLUENCE FACTORS OF LAND SURFACE TEMPERATURE INVERSION USING THERMAL INFRARED HYPERSPECTRAL REMOTE SENSING SATELLITES DATA

Abstract. Land surface temperature (LST) is widely used in research fields such as numerical forecasting, global circulation models, and regional climate models. For the remote sensing data from satellites with thermal infrared detection capability, the land surface temperature (LST), land surface emissivity (LSE), and atmospheric influence are mixed together. Using different assumptions and approximations for the radiative transfer equations and surface emissivity, various LST algorithms have been proposed. Among these algorithms, the split-window (SW) algorithm is currently most widely used. Besides, with the rapid development of machine learning, new ideas have been emerged for quantitative remote sensing inversion. For a hyperspectral remote sensing satellite with over 20 thermal infrared channels, machine learning methods such as random forest and artificial neural network can be selected to build an integrated separation and inversion algorithm for LST and LSE.In this paper, the influencing factors of LST inversion using thermal infrared hyperspectral satellites data is discussed, taking the SW algorithm and integrated machine learning algorithm as examples, and the contribution of these factors to the LST inversion error is analysed. We hope this paper could provide valuable reference for the design, index analysis and error calculation for remote sensing satellites with thermal infrared hyperspectral detection capability.

An extended SW-TES algorithm for land surface temperature and emissivity retrieval from ECOSTRESS thermal infrared data over urban areas

Land surface temperature (LST) over urban areas is an essential parameter in the monitoring of urban thermal environment. Urbanization has resulted in a more complex geometric structure of urban surface than natural surface. However, in the conventional LST retrieval algorithms, the impact of urban geometric structure is usually neglected. In this study, an extended algorithm that combines the split-window algorithm (SW) and the temperature and emissivity separation algorithm (TES) was proposed to retrieve LST and land surface emissivity (LSE) over urban areas simultaneously from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) thermal infrared (TIR) data. In the extended SW-TES algorithm with considering urban geometry effect, the brightness temperature at ground level was obtained using the SW algorithm, and the LST and LSE were retrieved simultaneously by using the TES algorithm. The results indicate that LST (LSE) retrieved by the extended SW-TES algorithm with urban geometry effect correction has generally lower (higher) values than that without urban geometry effect correction. The LST differences mostly vary from 0.3 K to 1.2 K, whereas the LSE differences are mainly concentrated between −0.01 and − 0.06. Furthermore, the root mean squared error (RMSE) between LST/LSE retrieved by the extended SW-TES algorithm with urban geometry effect correction and LST/LSE obtained from the ECOSTRESS level-2 LST/LSE product decrease with the increase of sky view factor (SVF). The largest RMSE is approximately 1.6 K for LST and 0.03 for LSE in the SVF of 0.1, which represents an urban pixel surrounded by high buildings within its adjacent pixels. There are no evident differences between LST/LSE retrieved by the extended SW-TES algorithm with urban geometry effect correction using five and three ECOSTRESS TIR bands. The LST differences are mainly concentrated between −0.2 K and 0.2 K, whereas the LSE differences mostly range from 0.001 to 0.004. The findings of this study indicate that it is necessary to consider urban geometry effect for accurate LST and LSE retrieval over urban areas.

Highly Efficient Security Level Implementation in Radiation-Tolerance FPGA Using a Combination of AES Algorithm and Hamming Code: LST-SW Case

Nowadays, Satellite remote sensing and especially earth observation satellites require improvement when implementing an algorithm using reconfigurable hardware in terms of efficiency, performance, and security level. Furthermore, the transmission of the computed data from the earth observation satellite to the ground station is subject to potential attacks, and data can be corrupted during transmission. The land surface Temperature algorithm is an essential factor that can be extracted from the earth observation satellite. In this paper, we proposed a secure land surface temperature implementation by combining the advanced encryption algorithm and Hamming code implemented on radiation-tolerant Virtex-4QV field programmable gate array (FPGA). The iterative looping technique was used to implement the Advanced Encryption Standard (AES) algorithm with the aim of achieving high throughput and high security levels. The results showed that the proposed hardware implementation consumes 3319 Slices and 2 BRAMs while achieving higher throughput of 1854.82 Mbps. Moreover, we have evaluated the efficiency of the suggested cryptosystem by exploiting security measurement analysis such as Histogram, correlation coefficient, entropy, number of pixels change rate (NPCR), and unified averaged changed intensity (UACI). The proposed cryptosystem results prove the efficiency in terms of hardware consumed and high-security level. This implementation may be utilized, proposed, and even configurable for any earth observation satellites, including nano- and pico-CubeSats since we employed radiation-tolerant FPGA.

Offset of MODIS land surface temperatures from in situ air temperatures in the upper Kaskawulsh Glacier region (St. Elias Mountains) indicates near-surface temperature inversions

Abstract. Remote sensing data are a crucial tool for monitoring climatological changes and glacier response in areas inaccessible for in situ measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product provides temperature data for remote glaciated areas where air temperature measurements from weather stations are sparse or absent, such as the St. Elias Mountains (Yukon, Canada). However, MODIS LSTs in the St. Elias Mountains have been found in prior studies to show an offset from available weather station measurements, the source of which is unknown. Here, we show that the MODIS offset likely results from the occurrence of near-surface temperature inversions rather than from the MODIS sensor’s large footprint size or from poorly constrained snow emissivity values used in LST calculations. We find that an offset in remote sensing temperatures is present not only in MODIS LST products but also in Advanced Spaceborne Thermal Emissions Radiometer (ASTER) and Landsat temperature products, both of which have a much smaller footprint (90–120 m) than MODIS (1 km). In all three datasets, the offset was most pronounced in the winter (mean offset >8 ∘C) and least pronounced in the spring and summer (mean offset <2 ∘C). We also find this enhanced seasonal offset in MODIS brightness temperatures, before the incorporation of snow surface emissivity into the LST calculation. Finally, we find the MODIS LST offset to be consistent in magnitude and seasonal distribution with modeled temperature inversions and to be most pronounced under conditions that facilitate near-surface inversions, namely low incoming solar radiation and wind speeds, at study sites Icefield Divide (60.68∘N, 139.78∘ W; 2,603 m a.s.l) and Eclipse Icefield (60.84∘ N, 139.84∘ W; 3017 m a.s.l.). Although these results do not preclude errors in the MODIS sensor or LST algorithm, they demonstrate that efforts to convert MODIS LSTs to an air temperature measurement should focus on understanding near-surface physical processes. In the absence of a conversion from surface to air temperature based on physical principles, we apply a statistical conversion, enabling the use of mean annual MODIS LSTs to qualitatively and quantitatively examine temperatures in the St. Elias Mountains and their relationship to melt and mass balance.

A Comprehensive Clear-Sky Database for the Development of Land Surface Temperature Algorithms

Land surface temperature is linked to a wide range of surface processes. Given the increased development of earth observation systems, a large effort has been put into advancing land surface temperature retrieval algorithms from remote sensors. Due to the very limited number of reliable in situ observations matching the spatial scales of satellite observations, algorithm development relies on synthetic databases, which then constitute a crucial part of algorithm development. Here we provide a database of atmospheric profiles and respective surface conditions that can be used to train and verify algorithms for land surface temperature retrieval, including machine learning techniques. The database was built from ERA5 data resampled through a dissimilarity criterion applied to the temperature and specific humidity profiles. This criterion aims to obtain regular distributions of these variables, ensuring a good representation of all atmospheric conditions. The corresponding vertical profiles of ozone and relevant surface and vertically integrated variables are also included in the dataset. Information on the surface conditions (i.e., temperature and emissivity) was complemented with data from a wide array of satellite products, enabling a more realistic surface representation. The dataset is freely available online at Zenodo.

Assessment of Urban Heat Island and Spatiotemporal Landscape Transformation In Three Cities Of Sindh, Pakistan

Urban Heat Island is considered one of the main causes of urbanization. It impacts the overall livability of a city. It is mainly due to the biophysical changes of the land surface due to urbanization. This study is based on three cities of Sindh namely: Hyderabad, Sukkur and Larkana. To study the UHI effects, a Land Surface Temperature algorithm is used. Land use land cover changes are identified by using Maximum likelihood classification. This study reveals that there is a major change in urban development in Hyderabad and Sukkur that is (82 to 97) Km2 and (18 to 25.7) Km2, respectively. While, in Larkana, minimal urban development is observed (33 to 34.6) Km2. Similarly, massive changes in vegetation are also observed in Sukkur and Larkana from 1990 to 2020 that are (90 to 161) Km2 and (82 to 331) Km2, respectively. Increase in vegetation is majorly due to the agricultural activities mainly occurring in different seasons. This study confirms that the LST has a strong negative correlation with NDVI because with increasing vegetation the LST is reduced. The findings also reveal that the major source of increased surface temperature is not only urban areas but the bare lands. The study finds that the skin temperature of the ground drops at least 3-5 degrees in the areas having a water body or any vegetation. This research highlights the importance of the inclusion of green strategies in better and effective urban planning to maintain a healthy urban environment.

PKULAST-An Extendable Model for Land Surface Temperature Retrieval From Thermal Infrared Remote Sensing Data

Land surface temperature (LST) is one of important parameter in the earth energy and water budget. Recently, open-source modules or packages are emerging as all-in-one solutions to speed up the rapid development and critical application of LST from thermal infrared (TIR) remote sensing data. In this study, we developed a python package, dubbed Peking University LAnd Surface Temperature (PKULAST) model to address the retrieval of LST from TIR remote sensing data with different LST retrieval algorithms, such as Single Channel algorithm, Split Window algorithm, and the Temperature and Emissivity Separation algorithm. Extendable data structures in the model covers common conceptual models used in LST algorithms implementation, such as spectral response functions, surface spectral libraries (ASTER, ECOSTRESS etc.), atmospheric profiles (NCEP, ERA5, MERRA-2, etc.), making it a convenience to construct the processing chain of developing new LST retrieval algorithms and generating LST products. The ASTER imageries are tested to assess the performance of the PKULAST to derive accurate LST. The LST result is then evaluated using long-term ground-based longwave radiation observations collected at seven sites from 2000 to 2021 under cloud-free scenes, and the result shows that the PKULAST model can obtain LST with an uncertainty of 2.7 K in bias, and 4.4 K in RMSE. With the help of predefined extendable data structures and workflows for different sensors, atmospheric profile libraries and surface spectral libraries, new algorithms for retrieving LST can be developed easily. The PKULAST promises to be a tool that seamlessly interoperates with developing LST retrieval algorithms and generating LST scientific products.

A New Material-Oriented TES for Land Surface Temperature and SUHI Retrieval in Urban Areas: Case Study over Madrid in the Framework of the Future TRISHNA Mission

The monitoring of the Land Surface Temperature (LST) by remote sensing in urban areas is of great interest to study the Surface Urban Heat Island (SUHI) effect. Thus, it is one of the goals of the future spaceborne mission TRISHNA, which will carry a thermal radiometer onboard with four bands at a 60-m spatial resolution, acquiring daytime and nighttime. In this study, TRISHNA-like data are simulated from Airborne Hyperspectral Scanner (AHS) data over the Madrid urban area at 4-m resolution. To retrieve the LST, the Temperature and Emissivity Separation (TES) algorithm is applied with four spectral bands considering two main original approaches compared with the classical TES algorithm. First, calibration and validation datasets with a large number of artificial materials are considered (called urban-oriented database), contrary to most of the previous studies that do not use a large number of artificial material spectra during the calibration step, thus impacting the LST retrieval over these materials. This approach produces one TES algorithm with one empirical relationship, called 1MMD TES. Second, two empirical relationships are used, one for the artificial materials and the other for the natural ones. These relationships are defined thanks to two calibration datasets (artificial-surface-oriented database and natural-surface-oriented database, respectively), one containing mainly artificial materials and the other mainly natural ones. Finally, in order to use two empirical relationships, a ground cover classification map is given to the TES algorithm to separate artificial pixels from natural ones. This approach produces one material-oriented TES algorithm with two empirical relationships, called 2MMD TES. In order to perform a complete comparison of these two addenda in the TES algorithm and their impact on the LST retrieval, both AHS and TRISHNA spatial resolutions are studied, i.e., 4-m and 60-m resolutions, respectively. Relative to the calibration of the TES algorithm, we conclude that (1) the urban-oriented database is more representative of the urban areas than previous databases from the state-of-the-art, and (2) using two databases (artificial-surface-oriented and natural-surface-oriented) instead of one prevents the overestimation of the LST over natural materials and the underestimation over artificial ones. Thus, for both studied spatial resolutions (AHS and TRISHNA), we find that the 2MMD TES outperforms the 1MMD TES. This difference is especially important for artificial materials, corroborating the above conclusion. Furthermore, the comparison with ground measurements shows that, on 4-m spatial resolution images, the 2MMD TES outperforms both the 1MMD TES and the TES from the state-of-the-art used in this study. Finally, we conclude that the 2MMD TES method, with only four spectral bands, better retrieves the LST over artificial and natural materials and that the future TRISHNA sensor is suited for the monitoring of the LST over urban areas and the SUHI effect.

Accuracy Evaluation and Parameter Analysis of Land Surface Temperature Inversion Algorithm for Landsat-8 Data

Many researchers have developed a variety of land surface temperature (LST) inversion algorithms based on satellite data. The main LST inversion algorithms include Radiative Transfer Equation (RTE), Single Channel (SC) algorithm, Mono Window (MW) algorithm, and Split Window (SW) algorithm. In this study, nine LST inversion algorithms were designed using Landsat-8 data and meteorological station data to test the inversion efficiency of different algorithms in different seasons and different locations. The results show that the error of various LST inversion algorithms will increase with the rise of LST. R2 of the inversion results of each LST algorithm and the measured data are all greater than 0.73°C in winter and about 0.5°C in the other seasons. By analyzing the stability of various algorithms inside and outside the city, it is found that the stability of each LST inversion algorithm inside the city is better than that outside the city. For the same surface features, the inversion temperature inside the city is 3–5°C higher than that outside the city. In addition, the sensitivity of various inversion algorithms to parameters was also analyzed. The influence of atmospheric transmittance on RTE, SC, and MW inversion algorithms is in logarithmic form. The effect of emissivity on each algorithm is linear. The influence of NDVI on the algorithms is mainly through the estimation of surface emissivity parameters to affect the inversion results. The effect of ascending radiation on SC (LST4 and LST5) is linear and on RTE (LST1 and LST2) is logarithmic. The effect of downslope radiation on SC and RTE is linear. The influence of atmospheric water vapor content on SW (LST7) is nonlinear.

Assessing of Land Surface Temperature at the Seulawah Agam Volcano Area using the Landsat Series Imagery

Seulawah Agam, a stratovolcano located in the Aceh province, Indonesia, has not erupted for a long decade after the last eruption in 1839. Thermal infrared remote sensing has been used to determine the land surface temperature (LST) of the volcano area. However, the application of remotely sensed thermal imagery in identifying the LST of the Seulawah Agam volcano, as a precursor of geothermal energy and eruption hazard, has not been completely monitored. The volcano locates relatively close to residential areas, which is a challenging approach to apply thermal bands in determining geothermal identities. In this research, we assess the LST and vegetation index for the detection of the thermal activity of the mountain. These characteristics were retrieved from Landsat 7 ETM+ and Landsat 8 TIRS/OLI imageries, acquired on 23 April 2004 and 16 March 2015 over the Seulawah Agam area, respectively. The normalized difference vegetation index (NDVI) threshold method for emissivity retrieval and split-window algorithm for land surface temperature (LST) were utilized. The results show that the vegetation index changes moderately over the geothermal area, especially at the residential area and western side of the volcano which is in line with a fault structure of the Seulimeum segment. We calculated the LST from the thermal bands of Landsat images 2004 and 2015 with approximate results are 28 – 35 °C. The spatial distribution of surface temperatures at the mountain derived from the classified image 2015 varies considerably compared with the classified image 2004. The surface temperature and vegetation index changes indicate a thermal activity at the Seulawah Agam volcano. It can be concluded that the Landsat 7 ETM+ and Landsat 8 TIRS/OLI imageries are potentially used to study the thermal status of the Seulawah Agam geothermal area.

Sensitivity Analysis and Validation of Daytime and Nighttime Land Surface Temperature Retrievals from Landsat 8 Using Different Algorithms and Emissivity Models

Land Surface Temperature (LST) is a substantial element indicating the relationship between the atmosphere and the land. This study aims to examine the efficiency of different LST algorithms, namely, Single Channel Algorithm (SCA), Mono Window Algorithm (MWA), and Radiative Transfer Equation (RTE), using both daytime and nighttime Landsat 8 data and in-situ measurements. Although many researchers conducted validation studies of daytime LST retrieved from Landsat 8 data, none of them considered nighttime LST retrieval and validation because of the lack of Land Surface Emissivity (LSE) data in the nighttime. Thus, in this paper, we propose using a daytime LSE image, whose acquisition is close to nighttime Thermal Infrared (TIR) data (the difference ranges from one day to four days), as an input in the algorithm for the nighttime LST retrieval. In addition to evaluating the three LST methods, we also investigated the effect of six Normalized Difference Vegetation Index (NDVI)-based LSE models in this study. Furthermore, sensitivity analyses were carried out for both in-situ measurements and LST methods for satellite data. Simultaneous ground-based LST measurements were collected from Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) stations, located at different rural environments of the United States. Concerning the in-situ sensitivity results, the effect on LST of the uncertainty of the downwelling and upwelling radiance was almost identical in daytime and nighttime. Instead, the uncertainty effect of the broadband emissivity in the nighttime was half of the daytime. Concerning the satellite observations, the sensitivity of the LST methods to LSE proved that the variation of the LST error was smaller than daytime. The accuracy of the LST retrieval methods for daytime Landsat 8 data varied between 2.17 K Root Mean Square Error (RMSE) and 5.47 K RMSE considering all LST methods and LSE models. MWA with two different LSE models presented the best results for the daytime. Concerning the nighttime accuracy of the LST retrieval, the RMSE value ranged from 0.94 K to 3.34 K. SCA showed the best results, but MWA and RTE also provided very high accuracy. Compared to daytime, all LST retrieval methods applied to nighttime data provided highly accurate results with the different LSE models and a lower bias with respect to in-situ measurements.

Spatial-temporal analysis of changes in land-cover and land surface temperature (LST) within Universiti Putra Malaysia campus area

The urban forest is one of the typical vegetation-covered space in an urban area. The existence and development of the urban forest commonly found within the university campus site. The ecological and environmental characteristics in a university campus site usually resemble and similar to the urban area. The physical environment of the campus area is gradually changing due to development and improvement purposes, and it is impacting the land-cover on the surface. Changing the physical element of the surface will affect the absorption of the sun radiation, and further, it is changing the dynamic of the micro-climatic condition or temperature within the surrounding area. The primary focus of the research is to observe the impact of land-cover change on the land surface temperature (LST) within the campus area. Universiti Putra Malaysia is one of the largest campuses located in the urban area of Serdang district, state of Selangor. The dynamic changes in this site observed through multi-temporal Landsat 8 OLI-TIRS level 1 imageries. Datasets from 2013 to 2015 were collected to employ spatial-temporal analysis on land-cover changes, as well as the LST algorithm to monitor the surface temperature. The result shows that there was an increasing surface temperature during 2013-2015, the maximum LST in September 2015 was found at 32.17 °C, it is higher than the maximum LST in September 2013 (28.84 °C). The result shows that there was an increasing surface temperature during 2013-2015, which corresponds with the inclining built-up area. The research concluded that changes in land-cover would affect the spatial-temporal land surface temperature at University Putra Malaysia.

Spatiotemporal analysis land surface temperature in relation to earthquake occurrence around the cimandiri fault

Cimandiri Fault is one of the faults that have high tectonic activity. Several earthquake events around the Cimandiri Fault have caused impacts and losses on humans. Efforts to detect earthquake events need to be done as disaster mitigation. Detection of earthquake events in this study by looking at deviations in surface temperature. This study was conducted to investigate the relationship between changes in land surface temperature (LST) in relation to the earthquake that occurred on 7 July 2018 (ML = 4.73) and 11 October 2018 (ML = 3.77). Landsat 8 satellite imagery are used in this study to get the value of the land surface temperature using the LST algorithm. To find out the deviation that occurred researchers used a statistical test x ± σ with a 66% confidence level and compared with the average LST for five years. The results of the study are an increase in land surface temperature due to the earthquake occurrence on July 7, 2011, an increase in surface temperature of land was 6.56 oC on the day before the earthquake, and October 11, 2011, there was an increase of 7.02°C the day before the earthquake.

A Simple Method for Converting 1-km Resolution Daily Clear-Sky LST into Real LST

Land-surface temperature (LST) plays a key role in the physical processes of surface energy and water balance from local through global scales. The widely used one kilometre resolution daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product has missing values due to the influence of clouds. Therefore, a large number of clear-sky LST reconstruction methods have been developed to obtain spatially continuous LST datasets. However, the clear-sky LST is a theoretical value that is often an overestimate of the real value. In fact, the real LST (also known as cloudy-sky LST) is more necessary and more widely used. The existing cloudy-sky LST algorithms are usually somewhat complicated, and the accuracy needs to be improved. It is necessary to convert the clear-sky LST obtained by the currently better-developed methods into cloudy-sky LST. We took the clear-sky LST, cloud-cover duration, downward shortwave radiation, albedo and normalized difference vegetation index (NDVI) as five independent variables and the real LST at the ground stations as the dependent variable to perform multiple linear regression. The mean absolute error (MAE) of the cloudy-sky LST retrieved by this method ranged from 3.5–3.9 K. We further analyzed different cases of the method, and the results suggested that this method has good flexibility. When we chose fewer independent variables, different clear-sky algorithms, or different regression tools, we also achieved good results. In addition, the method calculation process was relatively simple and can be applied to other research areas. This study preliminarily explored the influencing factors of the real LST and can provide a possible option for researchers who want to obtain cloudy-sky LST through clear-sky LST, that is, a convenient conversion method. This article lays the foundation for subsequent research in various fields that require real LST.

Estimation of Land Surface Temperature in an Agricultural Region of Bangladesh from Landsat 8: Intercomparison of Four Algorithms.

The presence of two thermal bands in Landsat 8 brings the opportunity to use either one or both of these bands to retrieve Land Surface Temperature (LST). In order to compare the performances of existing algorithms, we used four methods to retrieve LST from Landsat 8 and made an intercomparison among them. Apart from the direct use of the Radiative Transfer Equation (RTE), Single-Channel Algorithm and two Split-Window Algorithms were used taking an agricultural region in Bangladesh as the study area. The LSTs retrieved in the four methods were validated in two ways: first, an indirect validation against reference LST, which was obtained in the Atmospheric and Topographic CORection (ATCOR) software module; second, cross-validation with Terra MODerate Resolution Imaging Spectroradiometer (MODIS) daily LSTs that were obtained from the Application for Extracting and Exploring Analysis Ready Samples (AEEARS) online tool. Due to the absence of LST-monitoring radiosounding instruments surrounding the study area, in situ LSTs were not available; hence, validation of satellite retrieved LSTs against in situ LSTs was not performed. The atmospheric parameters necessary for the RTE-based method, as well as for other methods, were calculated from the National Centers for Environmental Prediction (NCEP) database using an online atmospheric correction calculator with MODerate resolution atmospheric TRANsmission (MODTRAN) codes. Root-mean-squared-error (RMSE) against reference LST, as well as mean bias error against both reference and MODIS daily LSTs, was used to interpret the relative accuracy of LST results. All four methods were found to result in acceptable LST products, leaving atmospheric water vapor content (w) as the important determinant for the precision result. Considering a set of several Landsat 8 images of different dates, Jiménez-Muñoz et al.’s (2014) Split-Window algorithm was found to result in the lowest mean RMSE of . Du et al.’s (2015) Split-Window algorithm resulted in mean RMSE of . The RTE-based direct method and the Single-Channel algorithm provided the mean RMSE of and , respectively. For Du et al.’s algorithm, the w range of to g cm−2 was considered, whereas for the other three methods, w values as retrieved from the NCEP database were considered for corresponding images. Land surface emissivity was retrieved through the Normalized Difference Vegetation Index (NDVI)-threshold method. This intercomparison study provides an LST retrieval methodology for Landsat 8 that involves four algorithms. It proves that (i) better LST results can be obtained using both thermal bands of Landsat 8; (ii) the NCEP database can be used to determine atmospheric parameters using the online calculator; (iii) MODIS daily LSTs from AEEARS can be used efficiently in cross-validation and intercomparison of Landsat 8 LST algorithms; and (iv) when in situ LST data are not available, the ATCOR-derived LSTs can be used for indirect verification and intercomparison of Landsat 8 LST algorithms.

A New Approach for Surface Urban Heat Island Monitoring Based on Machine Learning Algorithm and Spatiotemporal Fusion Model

Land surface temperature (LST) is an important indicator for assessing the surface urban heat island (SUHI) effect. This paper presents a novel approach to derive LST estimates by integrating machine learning algorithm and spatiotemporal fusion model at high spatial and temporal resolution. The spatial resolutions of Landsat TM and Landsat 8 LST data were first downscaled using random forest (RF) algorithm from 120 m and 100 m, respectively, to 30 m. The resultant LST data were fused with MODerate-resolution Imaging Spectroradiometer (MODIS) LST data, by means of the Flexible Spatiotemporal Data Fusion method (FSDAF), in order to generate high spatiotemporal resolution summer daytime LST data covering the center of Chengdu city in China. The proposed new method was used to estimate the spatiotemporal variations of the summer daytime SUHI from 2009 to 2018 over Chengdu city. Results show that: (1) RF performs way better than the classical downscaling algorithm-thermal sharpening algorithm (TsHARP) for LST, and produces higher accuracy for different land covers; (2) the fused high spatiotemporal resolution summer daytime LST values were evaluated with in situ LST obtained from Chengdu Meteorological Office and the final validation results indicated that the proposed method, in generating LST dataset, can provide more details of urban thermal environment and produce higher accuracy than the traditional FSDAF; (3) significantly increasing trends of summer daytime SUHI intensity (SUHII) in the study area were observed. SUHII increased from 2.78 °C in 2009 to 4.04 °C in 2018. The highest and lowest summer daytime LST estimates were recorded over impervious surface area (ISA) and waters, respectively.

Comparison of the MuSyQ and MODIS Collection 6 Land Surface Temperature Products Over Barren Surfaces in the Heihe River Basin, China

In this study, to improve the accuracy of land surface temperature (LST) products over barren surfaces, we present an operational algorithm to retrieve the LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) thermal infrared data using physically retrieved emissivity products. The LST algorithm involved two steps. First, the emissivity in the two MODIS split-window (SW) channels was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity data set. Then, the LST was retrieved using a modified generalized SW algorithm. This algorithm was implemented in the MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product system. The MuSyQ MODIS LST product and the Collection 6 MODIS LST product (MxD11_L2) were compared and validated using ground measurements collected from four barren surface sites in Northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment from June 2012 to December 2015. In total, 2268 and 2715 clear-sky samples were used in the validation for Terra and Aqua, respectively. The evaluation results indicate that the MuSyQ LST products provide better accuracy than the C6 MxD11 product during both daytime and nighttime at all four sites. For the daytime results, the LST is underestimated by the C6 MxD11 products at all four sites, with a mean bias of -1.78 and -2.86 K and a mean root-mean-square error (RMSE) of 3.16 and 3.94 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are within 1 K, with a mean bias of -0.26 and -1.03 K and a mean RMSE of 2.45 and 2.71 K for Terra and Aqua, respectively. For the nighttime results, the LST is also underestimated by the C6 MxD11 products at all four sites, with a mean bias of -1.60 and -1.26 K and a mean RMSE of 1.93 and 1.60 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are 0.16 and 0.58 K and the mean RMSEs are 1.12 and 1.25 K for Terra and Aqua, respectively. The results indicate that the underestimation of the C6 MxD11 LST product at all four sites mainly results from the overestimation of the emissivities in MODIS bands 31 and 32. This study demonstrates that physically retrieved emissivity products are a useful source for LST retrieval over barren surfaces and can be used to improve the accuracy of global LST products.

Spatial Model of Micro Climate Assessment and Recommendation of Mitigation In Semarang City With Remote Sensing Technology

The city of Semarang is one of the 10 major cities in Indonesia that are developing towards the metropolis. Urban infrastructure develops, resulting in the use of vegetation land to be converted into build up land. The increasingly hot climate of the city will disrupt the comfort of residents. Urban design and planning should consider land use change. Good planning will have a very good effect on the city’s climate system. This study aims to examine the phenomenon of micro-climate change in the city with the algorithm of the Temperature Heat Index (Leisure Index). The assessment starts from the Land Surface Temperature algorithm, from here model of Urban Heat Island (UHI) can be done. The distribution of UHI phenomena is derived from Land Surface Temperature. The UHI mitigation recommendation for Semarang City is based on land cover. The method used is the Remote Sensing approach through some spatial data extraction from satellite imagery. Data processing is done by using multi-temporal data of Landsat 8 satellite imagery. The result is presented in thematic map of LST, UHI, and Mitigation model map. Mitigation that can be done is the physical modification of the building, like the use of material with high albedo, the application of green wall, green roof, greening parking lots, and the addition of vegetation around the building and along the road. Besides, it is also necessary to monitor the change of land function and its suitability with the Urban Land use Plan of Semarang City.

The effect of thermal radiation from surrounding terrain on glacier surface temperatures retrieved from remote sensing data: A case study from Qiyi Glacier, China

Large-scale spatiotemporal variations in land surface temperature (LST) can be derived from satellite observations, particularly for remote high-elevated mountain glaciers where in situ observations are scarce. Our previous LST retrieval study on Qiyi Glacier of China employed a generalized single-channel algorithm and Landsat thermal infrared data, but a clear LST summertime overestimation was observed, particularly on the glacier tongue. Here we extend our previous study by calculating the thermal radiance on Qiyi Glacier that is emitted from the surrounding terrain, and incorporating this thermal radiation term into two generalized single-channel LST algorithms to analyze its influence on the errors of glacier surface temperatures retrieved from remote sensing data. In addition, we also discuss other possible causes of this overestimation in LST retrieval. The key findings of our study are as follows. (1) The surrounding terrain provides nonnegligible summertime thermal radiation to the glacier in the Landsat7 band 6 spectrum range (10.4–12.5 μm) that is 2.6 times larger than the atmospheric down-welling thermal radiance on the glacier tongue. The retrieved LSTs yield an overestimation of up to 0.28 K when the effect of thermal radiation from the surrounding slopes is not considered. (2) The average errors due to the surrounding topographic thermal radiation and the cubic convolution resampling method (CC method) employed in the Landsat7 thermal infrared data processing are only 0.07 K and − 0.01 K, respectively. These are much smaller than the average LST errors (1.79 K), indicating that the neither the surrounding topographic thermal radiation nor the CC method are primary factors in the retrieved LST overestimation. (3) The sole dependence of the generalized single-channel algorithm on the atmospheric water vapor content may therefore be the most likely cause of this overestimation, as high-altitude regions generally possess low atmospheric water contents. Radiative transfer models (e.g., MODTRAN) may provide a solution to this problem by simulating the influence of other atmospheric constituents on LST retrieval under conditions of low water vapor, which can then indicate any additional input variables that should be incorporated into the LST algorithm.