Land Surface Temperature Retrieval Method Research Articles

Comparative assessment of the accuracies of daytime land surface temperature retrieval methods using Landsat 8 and MODIS imageries in Benin, West Africa

Comparative assessment of the accuracies of daytime land surface temperature retrieval methods using Landsat 8 and MODIS imageries in Benin, West Africa

A MODIS–Landsat cloud-based spatiotemporal downscaling algorithm to estimate land surface temperature

ABSTRACT Land Surface Temperature (LST) is a key variable in the energy and water balance between the surface and the atmosphere. LST is typically retrieved from remote sensing data because of the lack of sufficient flux towers for meteorological data collection, which makes local availability of LST scarce. Remote sensing can provide data for large areas, but the spatial resolution of the data may be coarser than the temporal resolution (e.g. a daily revisit interval for satellite-based Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors), or conversely, the spatial resolution may be coarser than the temporal resolution (e.g. Landsat satellite images obtained every 16 days). This tradeoff between spatial and temporal resolutions has motivated researchers to develop methods and algorithms to create image collections with both high spatial and temporal resolutions. In this study, we used a cloud-based downscaling algorithm to create LST images with high temporal and spatial resolution for the southern region of Brazil using Google Earth Engine (GEE). A comparison of the downscaling estimates with thermal Landsat images showed that 65% of the root mean squared errors (RMSE) were lower than 2 K, and 77% of the correlation coefficients (R) were greater than 0.70, while 84% of the bias values were between −2 and 2 K. The downscaling estimates were also validated using in situ measurements. Overall, a comparison between in situ measurements and different LST retrieval methods differed slightly in accuracy, with average RMSE values between 1.55 and 4.32 K, bias between −1.07 and 3.96 K, and correlation coefficients of nearly 0.90. These results demonstrate that cloud computing can be used to retrieve LST with high spatiotemporal resolution.

Retrieval of cloudy land surface temperature from AMSR2 brightness temperature, precipitable water vapour and cloud liquid water

ABSTRACT Land surface temperature (LST) is an important parameter in many studies including hydrology, meteorology, and ecology. In addition to thermal infrared remote sensing, passive microwave remote sensing is an effective alternative for LST retrieval, particularly under cloudy conditions. In this study, we analysed the impact of cloud on the transfer of microwave radiation in simulated database and presented a parameterization method to compensate the impact. On this basis, we put forward an improved method for LST retrieval from Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature at the 18.7 and 23.8 GHz channels with precipitable water vapour (PWV) and cloud liquid water (CLW) as supplementary input. The method is built on the basis of microwave radiative transfer equation and the assumption that 1) surface emissivity at two adjacent channels is linear and 2) the main factor causing brightness temperature difference at two channels is the absorption difference of PWV and CLW. The LST retrieval method was tested in simulated database, with root mean square error (RMSE) of 3.1 K. The method was then applied to AMSR2 brightness temperature and GFS reanalysis data to estimate global LST in 2019–2020, and the accuracy was validated using in situ LST measurements collected from 52 sites. The RMSE is approximately 4.7 K and 3.7 K for daytime and night-time, respectively. The improved LST retrieval method is promising in long-temporal and large-area applications.

Estimation of land surface temperature from AMSR2 microwave brightness temperature using machine learning methods

ABSTRACT Passive microwave has been used in land surface temperature (LST) inversion because of its all-weather availability. This paper proposed a LST retrieval method based on machine learning by integrating multiple datasets, including Advanced Microwave Scanning Radiometer 2 (AMSR2), Global Forecast System (GFS) reanalysis datasets, ERA5-land reanalysis products, Moderate Resolution Imaging Spectroradiometer (MODIS) products, Shuttle Radar Topography Mission (SRTM), and in situ measurements. Four algorithms, including linear regression (LR), random forests (RF), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), were explored and compared by ten-fold cross-validation. The best-performing LightGBM algorithm was applied to train the model, and day and night models were developed separately. The models were validated using in situ LST of training stations from 2017 to 2019. The day and night models root mean square error (RMSE) are 3.23 and 2.43 K, and that of bias are −0.24 and −0.36 K, respectively. The in situ LST from 2015 to 2019 that not used for model training were selected to further validate both day and night models, with RMSE = 5.42 and 2.91 K, and bias = −0.60 and 0.06 K, respectively. Validation results indicated that the temporal performance of the models is better than those of spatial performance and the night model performed better than the day model. Additionally, model performance in different seasons and land cover types demonstrated the robustness of the models over complicated surfaces. These results suggest that the LightGBM algorithm has good accuracy in LST estimation, making it possible to apply for the generation of LST at a global scale.

Land Surface Temperature Retrieval From Landsat 8 Thermal Infrared Data Over Urban Areas Considering Geometry Effect: Method and Application

Accurate retrieval of land surface temperature (LST) over urban areas is of great significance for urban thermal environment monitoring. In previous studies, most of the urban LST retrieval methods were developed based on the assumption of a flat surface without considering the influence of urban 3-D geometry structure, which has a significant impact on the retrieval accuracy of LST over urban areas. In this study, a radiative transfer equation (RTE)-based single-channel method was developed to retrieve LST with urban geometry effect correction from the Landsat 8 thermal infrared (TIR) data in band 10. The increase in adjacent radiance from the surrounding pixels and the decrease in atmospheric downwelling radiance caused by urban geometry structure were taken into account in this method. Because it is difficult to directly validate the retrieval accuracy of LST over urban areas using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> LST measurements, the performance of the RTE-based LST retrieval method was evaluated via comparing brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the discrete anisotropic radiative transfer (DART) model and the urban RTE over three subregions. There is a good agreement between BT at the TOA simulated by the DART model and the urban RTE, with a root-mean-squared error (RMSE) of less than 0.25 K. The variations in LST retrieved with urban geometry effect correction over different local climate zones (LCZs) were analyzed. In general, built-up LCZs have relatively higher LST than land cover LCZs. The differences between LST retrieved without/with urban geometry effect correction over different LCZs are greater than 0.2 K. The largest average LST difference over built-up LCZs is approximately 0.9 K, whereas that over land cover LCZs is approximately 0.65 K. LST retrieved without/with urban geometry effect correction was used to calculate urban heat island intensity (UHII) in terms of the LCZ-based method. The results indicate that UHII calculated from LST with urban geometry effect correction is lower than that calculated from LST without urban geometry effect correction, with an average difference of approximately 0.5 K.

A Land Surface Temperature Retrieval Method for UAV Broadband Thermal Imager Data

Unmanned aerial vehicle (UAV) thermal infrared (TIR) remote sensing is an important way to obtain land surface temperature (LST) with high spatial and temporal resolutions. Due to wide spectral response function (SRF) ranges of UAV thermal imagers, currently available LST retrieval methods suitable for satellite sensors may induce significant uncertainty when applied to UAV sensors. Despite that some methods have been proposed to retrieve LST from UAV remote sensing, studies considering the adverse effect caused by the SRF ranges are still rare. Here, we present a so-called Temperature Retrieval for UAV Broadband thermal imager data (TRUB) method to retrieve LST from UAV broadband thermal imager data. TRUB’s core includes two parts: 1) a simple lookup table (LUT) algorithm for reducing the uncertainty induced by the wide SRF ranges; and 2) models suitable for UAV remote sensing for estimating the atmospheric parameters. Validation from the Heihe River Basin shows that the LST retrieved by TRUB, of which the root mean square error (RMSE) and mean bias error (MBE) is 1.71 and −0.02 K, respectively, is highly consistent with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> LST. TRUB is helpful to reduce the uncertainty caused by the wide SRF ranges of UAV thermal imagers and quantify the influence of atmosphere, thus can obtain UAV remote-sensing LST with better accuracy in large-area operating missions.

An Operational Land Surface Temperature Retrieval Methodology for Chinese Second-Generation Huanjing Disaster Monitoring Satellite Data

The Chinese second-generationHuanjing disaster monitoring satellite (HJ-2A) was launched on September 27, 2020, and underutilized due to the lack of accurate operational methodologies for land surface temperature (LST) retrieval.In this article, an operational LST retrieval method is proposed to retrieve LSTs from HJ-2A thermal infrared observations. The LST retrieval methodology involves two main steps. The land surface emissivities (LSEs) over all land cover types are obtained with the improved normalized difference vegetation index-based threshold method, and then the LST is retrieved operationally from the adjacent infrared bands.The algorithm coefficients for LST retrieval are from regression analysis of radiative transfer simulations, and LSTs could be retrieved based on thermal images without any additional auxiliary data. The simulation results demonstrated that the root-mean-square errors (RMSEs) of LST retrieval were less than 2.4 K in all subranges, and the minimum RMSE for the two emissivity groups (high- (low-) emissivity group) was 0.16 K (0.20 K) and appeared in the tractable subrange with water vapor content (WVC) varying from 0 to 1.5 g&#x002F;cm² and view zenith angle (VZA) being 0&#x00B0;. Furthermore, an error analysis was performed, the results showed that the LSE, NE&#x0394;T, and atmospheric water vapor uncertainty of 1&#x0025;, 0.2 K, and 20&#x0025; caused the LST retrieval errors with 0.88&#x2013;1.21 K (0.84&#x2013;1.19 K), 0.1 K (0.09 K), and 0.006 K (0.008 K) for the high- (low-) emissivity group, respectively, with WVC&#x220A;[0&#x2013;1.5] g&#x002F;cm² and VZA &#x003D; 0&#x00B0;. Finally, the retrieved LSTs were applied to seven images of the Wuhai, Geermu, Dunhuang, and Baotou sites from January to March and cross validated by the moderate resolution imaging spectroradiometer (MODIS) LST products. From the cross-validated results, it can be found that the RMSEs of the retrieved LSTs and the MODIS LST products were between 2.3 and 3.7 K, and the mean RMSE value was 2.89 K.

A method for land surface temperature retrieval based on model-data-knowledge-driven and deep learning

Most algorithms for land surface temperature (LST) retrieval depend on acquiring prior knowledge. To overcome this drawback, we propose a novel LST retrieval method based on model-data-knowledge-driven and deep learning, called the MDK-DL method. Based on the expert knowledge and radiation transfer model, we deduce LST retrieval mechanism and determine the best combination of the thermal infrared (TIR) bands of the sensor. Then, we use the radiation transfer model simulation and reliable satellite-ground data to establish a training and test database, and finally use the deep learning neural network for optimal computation. Three typical high-, medium- and low-spatial-resolution TIR remote sensing datasets (from Gaofen, the Moderate Resolution Imaging Spectroradiometer (MODIS), and Fengyun) are used for theoretical simulation and application analysis. The simulation shows that the minimum mean absolute error (MAE) is less than 0.1 K (standard deviation: 0.04 K; correlation coefficient: 1.000) at a small viewing direction (<7.5°) and less than 0.8 K at a large viewing direction (<65°). The in situ validation shows that the minimum MAE obtained by the optimal band combination is approximately 1 K (root mean square error (RMSE) = 1.12 K; coefficient of determination (R2) = 0.902). The retrieval accuracy is improved by increasing the number of TIR bands in the atmospheric window, and adding accurate atmospheric water vapor information produces better results. In general, four TIR bands in the atmospheric window bands are sufficient to retrieve the LST with high accuracy. Likewise, three TIR bands plus atmospheric water vapor information are sufficient for the retrieval requirements. All analyses indicate that our method is feasible and reliably accurate and can also be used to help design the instrument band to retrieve the LST with high precision.

A New Strategy for Comparison of Land Surface Temperature Retrieval Methods with Landsat Remote Sensing Images Considering Regional Consistency

Landsat remote sensing images are widely used in fields such as land surface temperature retrieval, urban expansion, and urban heat islands, due to their high spatial resolution and the availability of long time series data. Long-term land surface temperature (LST) change analysis usually requires comprehensive utilization of remote sensing data from different sensors, such as Landsat 7, Landsat 8. A LST retrieval algorithm with generalization for Landsat thermal infrared image can effectively improve the reliability of long-term analysis using multi-source images. In order to evaluate the performances of different LST retrieval methods for Landsat images, a new strategy based on regional consistency was proposed in this paper so that different LST retrieval algorithms can be compared with each other without utilizing reference data from ground observation. The general hypothesis is that there is a significant positive correlation between the obtained Landsat 7 and Landsat 8 LST products with adjacent imaging time, similar imaging environment for the identical area. Firstly, the Landsat 7 and Landsat 8 image pairs from Shenzhen were selected under aforementioned constraints. Secondly, four representative LST retrieval methods, radiative transfer equation method (RTEM), image-based method (IBM), mono-window algorithm (MWA) and single-channel algorithm (SCA) were used to generate the LST products from the Landsat 7 and Landsat 8 image pairs respectively. Lastly, the correlation between the Landsat 7 and Landsat 8 LST products from different methods can be calculated as different indexes, including the goodness of fit, Pearson correlation coefficient and Euclidean distance. It is convincing that the optimal LST retrieval method should exhibit a higher regional consistency between the Landsat 7 and Landsat 8 LST product pair given the certain area. The experimental results show that the radiative transfer equation method generates the highest correlation and it is considered as a suitable option for long-term LST research with multi-source Landsat datasets.

Comparison of the accuracy of daytime land surface temperature retrieval methods using Landsat 8 images in arid regions

Land surface temperature (LST) is a significant parameter in surface and atmospheric systems which can be used to describe the physical processes of energy and water exchange. There are several ways to retrieve land surface temperature using satellite imagery. The purpose of the current study is to compare the accuracy of different LST retrieval algorithms, including 11 types of split-window algorithms, two mono-window algorithms, and three single-channel algorithms. The validation of LST retrieval algorithms was done using temperature-based and cross-validation methods. In this study, 20 images from Landsat 8 satellite related to 2017 and 2018 were used to retrieve LST in different ways. The temperatures obtained from different methods were compared in one day, and the average of LST of the different methods was about 5 ℃ different, which indicates the importance of the existing validation methods. Using Sentinel-2 image classification, 20 points in homogeneous areas with pure pixels were selected to measure the LST using a thermometer for land validation. Results showed that in a split window algorithm which was applied on the water vapor image, the RMSE error has been reduced by 2 ℃. In cross-validation, MODIS LST images were used, and RMSE temporal image was prepared, and classified into five groups including less than 2, 2–3, 3–4 and more than 5 ℃ .Comparing the area of each of these groups in different methods showed that MW2 and SW1 methods have less accuracy than the other methods. The SW9 algorithm can retrieve temperature with an error of less than 2 ℃ in 80% of the area. The results of cross-validation showed that the SC2 algorithm had the highest accuracy among the algorithms that use a thermal band for LST retrieval with a RMSE spatial of 5.3 °C and the SW10 and SW11 have shown the highest accuracy among the algorithms that use two thermal bands with RMSE less than 5 ℃. In general, the algorithms that use atmospheric transmittance coefficients and water vapor as inputs are more accurate than the others.

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.

Land Surface Temperature Retrieval from Passive Microwave Satellite Observations: State-of-the-Art and Future Directions

Land surface temperature (LST) is an important variable in the physics of land–surface processes controlling the heat and water fluxes over the interface between the Earth’s surface and the atmosphere. Space-borne remote sensing provides the only feasible way for acquiring high-precision LST at temporal and spatial domain over the entire globe. Passive microwave (PMW) satellite observations have the capability to penetrate through clouds and can provide data under both clear and cloud conditions. Nonetheless, compared with thermal infrared data, PMW data suffer from lower spatial resolution and LST retrieval accuracy. Various methods for estimating LST from PMW satellite observations were proposed in the past few decades. This paper provides an extensive overview of these methods. We first present the theoretical basis for retrieving LST from PMW observations and then review the existing LST retrieval methods. These methods are mainly categorized into four types, i.e., empirical methods, semi-empirical methods, physically-based methods, and neural network methods. Advantages, limitations, and assumptions associated with each method are discussed. Prospects for future development to improve the performance of LST retrieval methods from PMW satellite observations are also recommended.

A comparison of four land surface temperature retrieval method using TERRA-ASTER satellite images in the semi-arid region of Saudi Arabia

Land surface temperature is a significant source of energy budget and climate information, contributing to various environmental and biophysical processes. This research includes comparing the LSTs retrieved from the ASTER sensor using the Reference Channel method, the Emissivity Normalization method (NOR), TES method and Retrieving LSE by taking the proportion of vegetation cover coupled with NDVI and integrates it into the TES algorithm. The results of derived LST from the four algorithms compare with MODIS data of 7 control points having thermally homogenous sites. The analysis showed that the four algorithms are suitable for LST retrieval, whereby the proposed emissivity-derived NDVI algorithms exhibited the highest degree of accuracy (RMSE 0.145), and the NOR had the least accuracy (RMSE 0.403). The analysis shows that emissivity derived NDVI TES method more reliant on the upwelling, downwelling and transmittance and will achieve the best results compared to the other three algorithms.

New land surface temperature retrieval algorithm for heavy aerosol loading during nighttime from Gaofen-5 satellite data.

Land surface temperature (LST) is a key variable used for studies of the water cycles and energy budgets of land-atmosphere interfaces. The Chinese Gaofen-5 (GF5) satellite, with an onboard visual and infrared multispectral imager (VIMS), is the only satellite that can capture the earth's thermal infrared information for use in the national high-resolution earth observation project of China; it can observe the earth surface at a high spatial resolution of 40 m in four thermal infrared channels and two mid-infrared channels. This article selects the optimum spectral channel combination for reducing the aerosol effect on LST retrieval with the aid of simulated data, and a new four-channel LST retrieval method from GF5 infrared data under heavy dust aerosol during nighttime is proposed. The results show that the channel combination of channels 7, 8, 9, and 10 (denoted as CC1) performed better than the combination of channels 7, 8, 11, and 12 (denoted as CC2). The root mean square errors (RMSEs) between the actual and estimated LST were 0.28 K for the CC1 group with an aerosol optical thickness (AOD) of 0.1 and 1.94 K for the CC1 group with an AOD of 1.0. The RMSEs for CC2 were 0.28 K for the group with an AOD of 0.1 and 2.54 K for the other group with an AOD of 1.0. Moreover, an error analysis for the proposed method was performed in terms of the noise equivalent temperature difference (NEΔT), the uncertainties of land surface emissivity (LSE), water vapor content (WVC) and AOD. The results show that the LST errors caused by an LSE uncertainty of 0.01, a NEΔT of 0.2 K, a WVC uncertainty of 20%, an AOD uncertainty of 0.1 were 0.31 ∼ 1.01 K, 0.4 ∼ 2.0 K, within 0.6 K, and within 0.3 K for CC1 and 0.32 ∼ 3.08 K, 0.4 ∼ 1.7 K, within 0.7 K, and within 0.3 K for CC2, respectively.

Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

Land Surface Temperature (LST) is an important parameter for many scientific disciplines since it affects the interaction between the land and the atmosphere. Many LST retrieval algorithms based on remotely sensed images have been introduced so far, where the Land Surface Emissivity (LSE) is one of the main factors affecting the accuracy of the LST estimation. The aim of this study is to evaluate the performance of LST retrieval methods using different LSE models and data of old and current Landsat missions. Mono Window Algorithm (MWA), Radiative Transfer Equation (RTE) method, Single Channel Algorithm (SCA) and Split Window Algorithm (SWA) were assessed as LST retrieval methods processing data of Landsat missions (Landsat 5, 7 and 8) over rural pixels. Considering the LSE models introduced in the literature, different Normalized Difference Vegetation Index (NDVI)-based LSE models were investigated in this study. Specifically, three LSE models were considered for the LST estimation from Landsat 5 Thematic Mapper (TM) and seven Enhanced Thematic Mapper Plus (ETM+), and six for Landsat 8. For the accurate evaluation of the estimated LST, in-situ LST data were obtained from the Surface Radiation Budget Network (SURFRAD) stations. In total, forty-five daytime Landsat images; fifteen images for each Landsat mission, acquired in the Spring-Summer-Autumn period in the mid-latitude region in the Northern Hemisphere were acquired over five SURFRAD rural sites. After determining the best LSE model for the study case, firstly, the LST retrieval accuracy was evaluated considering the sensor type: when using Landsat 5 TM, 7 ETM+, and 8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) data separately, RTE, MWA, and MWA presented the best results, respectively. Then, the performance was evaluated independently of the sensor types. In this case, all LST methods provided satisfying results, with MWA having a slightly better accuracy with a Root Mean Square Error (RMSE) equals to 2.39 K and a lower bias error. In addition, the spatio-temporal and seasonal analyses indicated that RTE and SCA presented similar results regardless of the season, while MWA differed from RTE and SCA for all seasons, especially in summer. To efficiently perform this work, an ArcGIS toolbox, including all the methods and models analyzed here, was implemented and provided as a user facility for the LST retrieval from Landsat data.

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

Remote sensing technique often analyzes the thermal characteristics of any area. Our study focuses on estimating land surface temperature (LST) of Raipur City, emphasizing the urban heat island (UHI) and non-UHI inside the city boundary and the relationships of LST with four spectral indices (normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index). Mono-window algorithm is used as LST retrieval method on Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data, which needs spectral radiance and emissivity of TIRS bands. The entire study is performed on 11 multidate Landsat 8 OLI and TIRS images taken from four different seasons; premonsoon, monsoon, postmonsoon, and winter, in a single-year time period. The Landsat 8 data derived LST is validated significantly with Moderate Resolution Imaging Spectroradiometer (MOD11A1) data. The results show that the UHI zones are mainly developed along the northern and southern portions of the city. The common area of UHI for four different seasons is developed mainly in the northwestern parts of the city, and the value of LST in the common UHI area varies from 26.45°C to 36.51°C. Moreover, the strongest regression between LST and these spectral indices is observed in monsoon and postmonsoon seasons, whereas winter and premonsoon seasons revealed comparatively weak regression. The results also indicate that landscape heterogeneity reduces the reliability of the regression between LST with these spectral indices.

Using Landsat-8 Data in Preliminary Exploration for Geothermal Resources

To characterize geothermal potential areas, conventional surface exploration activities involve field surveys, gathering geothermal information from locals and review of any existing geothermal literature. This is not only time consuming and costly but also unreliable for inaccessible geothermal potential areas. Thus, this study explores the cost-effectiveness and powerful tools of satellite remote sensing in preliminary land surface characterization for expansive geothermal exploration. The main approach entailed the use of free-access Landsat-8 and atmospheric data to retrieve land surface temperature (LST) using split-window and single channel algorithm, analysis of retrieved surface products, validation using in-situ ground temperature data, and finally delineation of surface thermal anomalies associated with geothermal features. Gilgil district and Baringo County in Kenya made the study areas. The former is a known and confirmed geothermal area while the latter is only a geothermal prospect. The two areas sit on the central section of the Kenyan rift; geothermal belt, and combined form a suitable case study for preliminary exploration using Landsat-8 data. The main objective of the study was to demonstrate the use of satellite remote sensing data to identify surface thermal anomalies associated with geothermal features as a cost-effective geothermal exploration support tool. Identify the best LST retrieval method between split window and single channel method using Landsat 8 data, and finally employ the better retrieval method to characterize geothermal prospect area and suggest targets for further investigations. Results showed that free-access satellite remote sensing imagery can conveniently be used to identify and map surface thermal anomalies associated with geothermal features and thus can be employed to complement the main geothermal exploration studies namely geological, geochemical and geophysical. Further, single channel method had better LST retrieval results compared to split-window method when using Landsat-8 data

Urbanization effect on spatiotemporal thermal patterns and changes in Hangzhou (China)

Urban heat island (UHI) shows thermal intensification in urban areas compared to surrounding areas. Land surface temperature (LST) has been widely applied in assessing UHI. However, due to limitations of the LST retrieval method, few researchers attempted to track the long-term dynamics of UHI and their responses to progressive urbanization process based on Landsat series data. In this study, we improved the retrieval method to extract the average UHI intensity of Hangzhou city core in 1990–2010, and explored the dynamics of UHI composition and configuration in response to urbanization. Google Earth Engine (GEE) was applied to calculate the average UHI intensity in the study area. Hangzhou city core was heating up in study period with extensive conversions from neutral and cool area to heated area. The expansion of heated area initially radiated from the original urban core in linear configuration, and subsequently expanded to infill areas away from the core by replacing neutral and cool area. The significant differences in average UHI intensity between different land use and land cover (LULC) types can be observed: urban land had the highest value whereas forest and water body registered the lowest. The expansion of urban land at the expense of natural land had exceeded a threshold to induce spillover of heat energy from the former to the latter. The findings can inform nature-based solutions to optimize the thermal environment and livability of fast-growing subtropical cities.

A mixture emissivity analysis method for urban land surface temperature retrieval from Landsat 8 data

Land surface temperature (LST) retrieval from satellite imagery is one of the most practical ways to consistently monitor urban thermal environment. Given the heterogeneous nature of urban landscape, an implicit assumption should be considered in remotely sensed LST determinations that a mixed urban land cover aggregation is the combination of its constituent components. Currently, the common LST retrieval method which utilize emissivity measures estimated by NDVI threshold method (NDVITHM), including mono window (MW), single channel (SC), and split window algorithms (SW), does not take into account heterogeneity of pixels. While in this study, a new approach, the mixture analysis of emissivity (MAoE), is proposed to calculate temperature by estimating pixel emissivity from mixed land cover classes. We conduct a comparison of six approaches by the combinations of three LST retrieval algorithms with NDVITHM and MAoE respectively. The differences among strategies are characterized and analyzed by comparing LST estimates from Landsat 8 thermal images. The LST gradients derived from transect analysis are found consistently similar for combinations of two LST algorithms (MW and SC) and the two emissivity estimation algorithms (MAoE and NDVITHM). LSTs derived from SW algorithms using band 10 have the highest mean values, while the SC algorithms have moderate mean values and the MW algorithms have the lowest values. Standard deviations of estimated LST from MAoE are smaller compared with NDVITHM methods, SC retrieval algorithm with MAoE has the smallest standard deviation, and NDVITHM temperature estimation could be more impacted by different land use land cover types.

Land Surface Temperature Retrieval From FY-3C/VIRR Data and Its Cross-Validation With Terra/MODIS

Accurate inversion of land surface temperature (LST) from remote sensing data is an essential and challenging topic for earth observation applications. This paper successfully retrieves the LST from FY-3C/VIRR data with split-window method. With the simulated data, the algorithm coefficients are acquired with root mean square errors lower than 1.0 K for all subranges when view zenith angle (VZA) < 30° and the water vapor content (WVC) < 4.25 g/cm2 , as well as those in which the VZA < 30° and the LST < 307.5 K. In addition, a detailed sensitivity analysis is carried out. The analysis result indicates that the total LST uncertainty caused by the standard error of the algorithm, the uncertainties of land surface emissivity and WVC, and the instrument noise would be 1.22 K and 0.94 K for dry and wet atmosphere, respectively. Furthermore, LST retrieval method is applied to the visible and infrared radiometer measurements over the study area covering the geographical latitude of 31.671°N to 44.211°N and longitude of 10.739°W to 1.898°E, and the derived LST is cross-validated with Terra/MODIS LST product. The preliminary validation result shows that the split-window method determines the LST within 2.0 K for vegetation and soil areas.