Land Surface Temperature Retrieval Research Articles

Satellite Time-Series Analysis for Thermal Anomaly Detection in the Naples Urban Area, Italy

Naples is the most densely populated Italian city (7744 inhabitants per km2). It is located in a particular geological context: the presence of Mt Vesuvius characterizes the eastern part, and the western part is characterized by the presence of the Phlegrean Fields, making Naples a high-geothermal-gradient region. This endogenous heat, combined with the anthropogenic heat due to intense urbanization, has defined Naples as an ideal location for Surface Urban Heat Island (SUHI) analysis. SUHI analysis was effectuated by acquiring the Land Surface Temperature (LST) over Naples municipality by processing Landsat 8 (L8) Thermal Infrared Sensor (TIRS) images in the 2013–2023 time series by employing Google Earth Engine (GEE). In GEE, two different approaches have been followed to analyze thermal images, starting from the Statistical Mono Window (SMW) algorithm, which computes the LST based on the brightness temperature (Tb), the emissivity value, and the atmospheric correction coefficients. The first one is used for the LST retrieval from daytime images; here, the emissivity component is derived using, firstly, the Normalized Difference Vegetation Index (NDVI) and then the Vegetation Cover Method (VCM), defining the Land Surface Emissivity (LSɛ), which considers solar radiation as the main source of energy. The second approach is used for the LST retrieval from nighttime images, where the emissivity is directly estimated from the Advance Spaceborne Thermal Emission Radiometer database (ASTER-GED), as, during nighttime without solar radiation, the main source of energy is the energy emitted by the Earth’s surface. From these two different algorithms, 123 usable daytime and nighttime LST images were downloaded from GEE and analyzed in Quantum GIS (QGIS). The results show that the SUHI is more concentrated in the eastern part, characterized by intense urbanization, as shown by the Corine Land Cover (CLC). At the same time, lower SUHI intensity is detected in the western part, defined by the Land Cover (LC) vegetated class. Also, in the analysis, we highlighted 40 spots (10 hotspots and 10 coldspots, both for daytime and nighttime collection) that present positive or negative temperature peaks for all the time series. Due to the huge amount of data, this work considered only the five representative spots that were most representative for SUHI analysis and determination of thermal anomalies in the urban environment.

Soil Moisture Profiles of Ecosystem Water Use Revealed With ECOSTRESS

AbstractWhile remote sensing has provided extensive insights into the global terrestrial water, carbon, and energy cycles, space‐based retrievals remain limited in observing the belowground influence of the full soil moisture (SM) profile on ecosystem function. We show that this gap can be addressed when coupling 70 m resolution ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station retrievals of land surface temperature (LST) with in‐situ SM profile measurements. These data sets together reveal that ecosystem water use decreases with depth with 93% of sites showing significant LST coupling with SM shallower than 20 cm while 34% of sites have interactions with SM deeper than 50 cm. Furthermore, the median depth of peak ecosystem water use is estimated to be 10 cm, though forests have more common peak interactions with deeper soil layers (50–100 cm) in 37% of cases. High spatial resolution remote sensing coupled with field‐level data can thus elucidate the role of belowground processes on land surface behavior.

Super-Resolution for Land Surface Temperature Retrieval Images via Cross-Scale Diffusion Model Using Reference Images

Geothermal resources are efficient, clean, and renewable energy sources. Using high-resolution images captured by remote sensing satellites for temperature retrieval and searching for geothermal anomaly areas is an efficient method. However, obtaining land surface temperature retrieval images requires multiple steps of calculation, which can result in a great loss of image information and resolution. Therefore, the super-resolution reconstruction of LST retrieval images is currently a challenge in geothermal resource exploration. Although the current super-resolution methods for LST retrieval images can appropriately restore image quality, the overall restoration of the surface temperature information in the region is still not ideal. We propose a cross-scale reference image super-resolution model based on a diffusion model using deep learning technology. First, we propose the Pre-Super-Resolution Network (PreNet), which can improve both indices and the visual effect of images. Second, to reduce the white noise in the super-resolution images, we propose the Cross-Scale Reference Image Attention Mechanism (CSRIAM). The introduction of this mechanism greatly reduces noise in the images and improves the overall image quality. Compared to previous methods, we improved both experimental indices such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), etc., and vision quality, and optimized the recovery of geothermal anomalies. Through our experimental results, we found that the CS-Diffusion model has a very strong ability to restore the image quality of the LST retrieval. After restoring its image quality, we can make a positive contribution to subsequent geothermal resource exploration.

Accuracy assessment of land surface temperature retrievals from remote sensing imagery: pixel-based, single and multi-channel methods

Land Surface Temperature (LST) measurements using remote sensing data are conducted using various methods, including the Single Channel Method (SCM) and the Multi-Channel Method (MCM). Emissivity, which significantly affects LST, is determined using methods such as the Correction-Based Emissivity Method (CBEM) and the Non-Linear Based Emissivity Method (NBEM). Given these factors, validating LST measurements is crucial for assessing the accuracy of data processing results. This study aims to compare LST data retrieved using SCM and MCM with various emissivity calculation methods and assess their accuracy compared to single measurements with an infrared thermometer, measurements using a grid plot, and measurements using a thermal camera with the assistance of an Unmanned Aerial Vehicle (UAV). The results show that SCMJM&S and SCMAC did not produce optimal accuracy (>2.5K) when using the emissivity estimates from CBEM and NBEM. In contrast, MCM that adopted MCMSko, MCMQin, and MCMMao generally produced high-accuracy LST with an average difference of <1 K from actual temperatures, specifically in shrub pixels. MCM with the NDVI threshold method (NDVITHM) showed excellent results in distinguishing between vegetation and non-vegetation objects. In conclusion, MCM provides more accurate LST than SCM. Nevertheless, selecting the appropriate method should consider heterogeneity, homogeneity, and regional physiography.

Research on Landsat 8 land surface temperature retrieval and spatial–temporal migration capabilities based on random forest model

Land surface temperature (LST) plays a crucial role in characterizing land surface processes and the energy balance. Accurate monitoring of spatial–temporal variations in LST holds great significance for global and regional climate change research. However, with the increasing of multi-source remote sensing data, it has become a challenging task to construct an LST model that reduces dependence on auxiliary data (such as land surface emissivity (LSE) and atmospheric water vapor content (WVC) synchronized with satellite observations. This study proposed an LST retrieval model based on random forest model (RFLSTR). The results of 10-fold cross-validation showed that the RFLSTR model had high modelling accuracy, the coefficient of determination (R2) was 0.99, and the root mean square error (RMSE) and mean absolute error (MAE) were lower than 2 K. Landsat 8 images from the Beijing area and South Korea, were utilized to assess the spatial–temporal migration performance of the RFLSTR model. Additionally, Landsat 9 images from South Korea were employed to analyse the sensor migration performance of the RFLSTR model. In conclusion, the findings highlight that the proposed RFLSTR model achieve high retrieval accuracy without relying on auxiliary data (such as LSE and WVC) synchronized with satellite observations. Moreover, the RFLSTR model exhibits robust spatial–temporal capabilities, enhancing the efficiency of LST retrieval and facilitating the utilization of satellite data.

Multi-scale remote sensing retrieval model for land surface emissivity and its impact on land surface temperature estimation

ABSTRACT As a fundamental parameter of the surface radiation process, land surface emissivity (LSE) is the most direct factor affecting the land surface temperature (LST) retrieval accuracy. Based on the measured data from 2017 to 2023, the characteristics of LSE changes were analysed, different timescale LSE retrieval models were developed, and the impact of LSE on pixel scale observational LST acquisition was evaluated by using unmanned aerial vehicle (UAV) remote sensing images. The results show that with changes in atmospheric environmental conditions, the LSE changes are relatively less pronounced at 3.99 µm and 4.02 µm in the mid-infrared band and at 10.8 µm and 11.8 µm in the thermal infrared band than in the other bands. At the timescale above day, the LSE is significant correlation with the normalized differential vegetation index (NDVI). Vegetation is the main parameter that dominates the LSE change under heterogeneous underlying surfaces. On a daily timescale, LSE exhibits typical diurnal variation characteristics, and the amplitude of diurnal variation is influenced mainly by shallow soil water and heat conditions. The developed LSE model based on Fengyun-4A (FY-4A) satellite remote-sensing data can effectively reproduce the daily variation characteristics of LST and avoid the ‘jumping’ phenomenon that may occur in the NDVI threshold method. The root-mean-square error (RMSE) between the retrieved LST and in situ data is reduced to 3.78°C, and the retrieval accuracy is better than that of the published LST product. When using UAVs to obtain pixel-scale LSTs, dry bare soil is more sensitive to LSE. When the LSE increases from 0.95 to 1.0, the average LST obtained by the UAV deviates by approximately 1.0°C. This study preliminarily provides some spatiotemporal variation patterns of LSE and lays a foundation for the later-modified LST retrieval algorithms.

A normal form for synchronous land surface temperature and emissivity retrieval using deep learning coupled physical and statistical methods

The innovative normal form, named deep learning-couple-physical and statistical methods (DL-C-PS), has been developed for synchronously retrieving land surface temperature and emissivity (LST&E) from Japan's geostationary meteorological satellite Himawari-8 carrying the Advanced Himawari Imager (AHI). First, geophysical logical reasoning (GLR) and expert knowledge were used to formulate radiative transfer equations (RTEs). Then, a hybrid approach integrating physical and statistical methods was employed to derive the solution, with deep learning (DL) optimizing the solution process. Three band combination schemes were designed to assess the effectiveness of the DL-C-PS normal form. Simulation data from the MODerate spectral resolution atmospheric TRANsmittance mode (MODTRAN) yielded promising results during validation. Root mean square error (RMSE) values were below 1 K for LST and below 0.008 for LSE when using band combinations of four thermal infrared (TIR) bands or at least three TIR bands combined with water vapor information. Cross-validation and in situ validation showed consistent findings with simulation validation. Compared to MODIS LST&E products (MYD21), in most cases, the RMSE values for LST&E were approximately 2 K and less than 0.015 during daytime, and below 1.3 K and 0.017 during night, respectively. Validated against in situ observations, nighttime RMSE values for LST were approximately 1.5 K with correlation coefficient (R) values better than 0.93. The higher RMSE observed in daytime compared to nighttime can be attributed to the influence of the sun's illumination angle on satellite scanning imaging. Overall, this research presented a normal form for multi-parameter estimation by leveraging the optimal calculation of DL and incorporating physical interpretations.

High-resolution (1 km) all-sky net radiation over Europe enabled by the merging of land surface temperature retrievals from geostationary and polar-orbiting satellites

Abstract. Surface net radiation (SNR) is a vital input for many land surface and hydrological models. However, most of the current remote sensing datasets of SNR come mostly at coarse resolutions or have large gaps due to cloud cover that hinder their use as input in models. Here, we present a downscaled and continuous daily SNR product across Europe for 2018–2019. Long-wave outgoing radiation is computed from a merged land surface temperature (LST) product in combination with Meteosat Second Generation emissivity data. The merged LST product is based on all-sky LST retrievals from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the geostationary Meteosat Second Generation (MSG) satellite and clear-sky LST retrievals from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the polar-orbiting Sentinel-3A satellite. This approach makes use of the medium spatial (approx. 5–7 km) but high temporal (30 min) resolution, gap-free data from MSG along with the low temporal (2–3 d) but high spatial (1 km) resolution of the Sentinel-3 LST retrievals. The resulting 1 km and daily LST dataset is based on an hourly merging of both datasets through bias correction and Kalman filter assimilation. Short-wave outgoing radiation is computed from the incoming short-wave radiation from MSG and the downscaled albedo using 1 km PROBA-V data. MSG incoming short-wave and long-wave radiation and the outgoing radiation components at 1 km spatial resolution are used together to compute the final daily SNR dataset in a consistent manner. Validation results indicate an improvement of the mean squared error by ca. 7 % with an increase in spatial detail compared to the original MSG product. The resulting pan-European SNR dataset, as well as the merged LST product, can be used for hydrological modelling and as input to models dedicated to estimating evaporation and surface turbulent heat fluxes and will be regularly updated in the future. The datasets can be downloaded from https://doi.org/10.5281/zenodo.8332222 (Rains, 2023a) and https://doi.org/10.5281/zenodo.8332128 (Rains, 2023b).

A Physics-Based Method for Retrieving Land Surface Emissivities from FengYun-3D Microwave Radiation Imager Data

The passive microwave land surface emissivity (MLSE) plays a crucial role in retrieving various land surface and atmospheric parameters and in Numerical Weather Prediction models. The retrieval accuracy of MLSE depends on many factors, including the consistency of the input data acquisition time. The FengYun-3D (FY-3D) polar-orbiting meteorological satellite, equipped with passive microwave and infrared bands, offers time-consistent data crucial for MLSE retrieval. This study proposes a physics-based MLSE retrieval algorithm using all the input data from the FY-3D satellite. Based on the retrieved MLSE, the spatial distribution of the MLSE is closely correlated with the land cover types and topography. Lower emissivities prevailed over barren or sparsely vegetated regions, river basins, and coastal areas. Higher emissivities dominated densely vegetated regions and mountainous areas. Moderate emissivities dominated grasslands and croplands. Lower-frequency channels showed larger emissivity differences with different polarizations than those of higher-frequency channels in barren or sparsely vegetated regions. The MLSE across densely vegetated land areas, mountainous areas, and deserts showed small seasonal variations. However, woody savannas, grasslands, croplands, and seasonal snow-covered areas showed noticeable seasonal variations. For most land cover types, the differences between vertically and horizontally polarized emissivities remained relatively constant across seasons. However, certain grasslands in eastern Inner Mongolia and southern Mongolia showed clear seasonal variations. It is very difficult to verify the MLSE on a large scale. Consequently, the possible error sources in the retrieved MLSE were analyzed, including the brightness temperature errors (correlation coefficient ranging from 0.92 to 0.99) and the retrieved land surface temperature errors (Root Mean Square Error was 3.34 K and relation coefficient was 0.958).

Land Surface Temperature and Urban Heat Island: A Case Study of Faridabad City using RS and GIS techniques

Land use and land surface temperature both plays a crucial role in the global climate change studies, as a little fluctuation in these, can bring about a dramatic impact on the regional as well as global environment. With rapid urbanization many regions across the world are altering the existing land use/land cover (LULC), which is significantly raising the land surface temperature (LST). The present study aims to estimate the land use change and land surface temperature and what changes does both bring about in a span of 20 years over Faridabad MC. Faridabad being a part of Delhi NCR has seen rapid growth of population in last two decades which has severely impacted its Land cover. Landsat imagery of 2002, 2013 and 2022 has been used for estimating both the LULC and LST of the city. Supervised classification with Maximum likelihood Classification (MLC) which is basically a machine learning based algorithm has been adopted for LULC classification and Mono window algorithm has been used for the retrieval of LST. UTFVI (Urban Thermal Field Variance Index) has also been calculated which showed different Urban heat islands across the city. The results reveal a rapid increase in the built-up area and reduction in the vegetation and agricultural land. Similarly, Land surface temperature was highest in highly dense built-up areas as well in the barren area of Faridabad, compared to this LST was comparatively lower over the agriculture and vegetation and least of water bodies. There were several UHI hotspots over the city area, mostly located in the densely populated and industrial parts of city.

Simultaneous Retrieval of Land Surface Temperature and Soil Moisture Using Multichannel Passive Microwave Data

Simultaneous Retrieval of Land Surface Temperature and Soil Moisture Using Multichannel Passive Microwave Data

Comparison of nighttime land surface temperature retrieval using mid-infrared and thermal infrared remote sensing data under different atmospheric water vapor conditions

Comparison of nighttime land surface temperature retrieval using mid-infrared and thermal infrared remote sensing data under different atmospheric water vapor conditions

Comparative analysis of two parameter-dependent split window algorithms for the land surface temperature retrieval using MODIS TIR observations

MODIS Land Surface Temperature (LST) product is extensively used in agricultural studies like crop health assessment, soil moisture estimation, irrigation management, land use land cover change, air-temperature retrieval and crop water stress detection. Numerous studies have used Split Window (SW) algorithms to retrieve LST from MODIS TIR bands. Among them, some utilize Sensor View Angle Dependent (SVAD) or Columnar Water Vapor Dependent (CWVD) SW algorithms. Present study aims to make use of SVAD and CWVD SW algorithms and compare them to evaluate the LST retrieval accuracy over various land surface type. Theoretical accuracy assessment of the CWVD and SVAD algorithms demonstrates a good accuracy with the RMSE of 1.09K and 1.42K, respectively. The experimental retrieval of LST achieves exceptionally good accuracy, with a RMSE of 1.45K in the CWVD algorithm and 1.80K in the SVAD algorithm, particularly in heterogeneous regions. In homogeneous regions, the RMSE values are 1.14K in CWVD and 1.10K in SVAD. Both algorithms exhibit satisfactory accuracy; nevertheless, the application of these algorithms may vary in agricultural contexts. Based on the obtained results and the inclusion of required parameters, we have arrived at a conclusion regarding the superior performance of the SVAD compared to the CWVD for LST retrieval.

Evaluating Landsat-9 TIRS-2 calibrations and land surface temperature retrievals against ground measurements using multi-instrument spatial and temporal sampling along transects

High-resolution TIR data are essential for a wide range of studies, e.g., in environmental monitoring, urban effects, water stress, agricultural productivity, and land/water resources. Landsat series provides high-resolution TIR data, but data accuracy must be ensured to contribute to these fields. After the calibration problems of Landsat 8 (L8) TIRS due to a stray light effect, efforts were made in the design of Landsat 9 (L9) TIRS-2 to mitigate this effect. In any case, field calibration and validation activities are necessary to evaluate the accuracy of recently launched sensors. These activities require high-quality ground TIR radiance data that are representative at satellite spatial resolution and that quantify spatial–temporal fluctuations at the experimental sites concurrently to satellite overpasses. However, these requirements are not always accomplished in the literature.This paper provides vicarious calibrations of the L9 TIRS-2 data with accurate ground measurements acquired using multi-instrument spatial and temporal sampling in a rice paddy area from December 2021 to August 2023, in order to contribute to the calibration and validation of L9 TIRS-data.In March 1, 2023 L9 TIRS-2 scenes started to be reprocessed and calibration was updated. Both the original calibration and that after reprocessing were evaluated. Close results were obtained for band 10 (b10). When comparing satellite brightness temperatures and those determined from in situ LSTs, systematic differences of −0.3 K were shown, with root-mean-square differences (RMSDs) of 0.5 K. Results for band 11 showed a bias of −0.1 K for the original data and of 0.2 K after the reprocessing, with RMSDs of 0.6 K.The L2 LST product generated operationally from b10 data was also evaluated, together with alternative single-channel (SC) algorithms. A bias (RMSD) of 0.4 K (1.1 K) was obtained for the product. However, negative biases were shown both with the radiative transfer equation used on b10 radiances and the alternative SC algorithm.Finally, we validated six types of split-window (SW) algorithms applied to L9 TIRS-2 data before and after the reprocessing. A systematic difference of about −0.6 K between SW LST biases when using the reprocessed and original data was observed. In any case, for most of the algorithms with original and reprocessed data, LST biases and standard deviations were within the recommended threshold of 1 K, which proves the good performance of L9 TIRS-2 data to monitor LST at high spatial resolution.

Remote Sensing-Based Outdoor Thermal Comfort Assessment in Local Climate Zones in the Rural–Urban Continuum of eThekwini Municipality, South Africa

Due to the need to continuously monitor and understand the thermal environment and its socioeconomic implications, this study used remotely sensed data to analyze thermal comfort variation in LCZs, including along the rural to urban gradient of the eThekwini Municipality in KwaZulu-Natal province of South Africa. LCZs were mapped using multi-temporal and multi-spectral Landsat 8 and Landsat 9 data using the approach by World Urban Database and Access Portal Tools (WUDAPT), while thermal data were used to retrieve land surface temperatures (LSTs). Data for training classification of LCZs and accuracy assessment were digitized from GoogleEarth guided by knowledge gained and data collected during a field survey in March 2022 as well as pre-existing maps. LCZs were mapped using the random forest classifier in SAGA GIS software while a single channel algorithm based on band 10 was used to compute LST for different scenes. The LSTs were adjusted and further used to derive thermal comfort based on the Universal Thermal Comfort Index (UTCI) categories as an indicator for outdoor thermal comfort on the extremely low- and extremely high-temperature periods in the cool and hot seasons, respectively. LCZs were mapped with high accuracy (overall accuracy of 90.1% and kappa of 0.88) while inter-class separability was high (&gt;1.5) for all LCZ pairs. Built-up LCZs dominate the eastern parts of the municipality, signifying the influence of the sea on development within the area. Average LST was coolest in the dense forest, open low-rise and water LCZs in the cool and hot seasons, respectively. The compact high-rise LCZ was the warmest in both the hot (36 °C) and the cool (23 °C) seasons. The sea sands were among coolest regions in both seasons due to their high water content, attributed to their high water table and close proximity to the ocean. There was no thermal stress during the cool season, while most areas recorded moderate to strong heat stress in the hot season. Some areas in the densely built-up LCZs recorded very strong heat stress in the hot season. The findings suggest that policies and strategies should enhance heat mitigation capacities in strong-heat-stress areas during the hot season. Municipal authorities and citizens must work together to build strategies to minimize temperature extremes and associated socioeconomic pressures. Urban development policies, plans and strategies should consider implications on the thermal environment as well as the value of conservation of LCZs with high-heat mitigation value such as dense forests and expansion of built-up LCZs with low-heat absorption levels such as open low-rise. The study was based mainly on remotely sensed temperatures with some ground data used to validate results, which may limit the assessment. Overall, the study provides insights towards achievement of global sustainable and climate-smart development targets.

Retrieving land surface temperatures from IASI hyperspectral thermal infrared data using an AFNO-transformer model.

An adaptive Fourier neural operator (AFNO)-transformer model was developed to retrieve land surface temperature (LST) data from infrared atmospheric sounding interferometer (IASI) observations. A weight selection scheme based on linearization of the radiative transfer equation was proposed to solve the hyperspectral data channel redundancy problem. The IASI brightness temperatures and Advanced Very High Resolution Radiometer onboard MetOp (AVHRR/MetOp) LST product were selected to construct the training and test datasets. The AFNO-transformer performed effective token mixing through self-attention and effectively solved the global convolution problem in the Fourier domain, which can better learn complex nonlinear equations and achieve time-series forecasting. The root mean square error indicated that the LST in Eastern Spain and North Africa could be retrieved with an error of less than 2.5 K compared with the AVHRR/MetOp LST product. Moreover, the validation results from other time period data showed that the retrieval accuracy of this model can be less than 3 K. The proposed model provides a novel approach for hyperspectral LST retrieval.

Satellite-Based Optimization and Planning of Urban Ventilation Corridors for a Healthy Microclimate Environment

Urban ventilation corridors (UVCs) have the potential to effectively mitigate urban heat islands and air pollution. Shanghai, a densely populated city located in eastern China, is among the hottest cities in the country and requires urgent measures in order to enhance its ventilation system. This study introduces a novel approach that integrates land surface temperature retrieval, PM2.5 concentration retrieval, and wind field simulation to design UVCs at the city level. Through remote sensing data inversion of land surface temperature (LST) and PM2.5 concentration, the study identifies the action spaces and compensation spaces for UVCs. The Weather Research and Forecasting (WRF) model, coupled with the multilayer urban scheme Building Effect Parameterization (BEP) model, is employed to numerically simulate and analyze the wind field. Based on the identification of thirty high-temperature zones and high PM2.5 concentration zones as action spaces, and twenty-two low-temperature zones and low PM2.5 concentration zones as compensation spaces in Shanghai, the study constructs seven first-class ventilation corridors and nine secondary ventilation corridors according to local circulation patterns. Unlike previous UVC research, this study assesses the cleanliness of cold air, which is a common oversight in UVC planning. Ignoring the assessment of cold air cleanliness can result in less effective UVCs in improving urban air quality and even exacerbate air pollution in the central city. Therefore, this study serves as a crucial contribution by rectifying this significant deficiency. It not only provides a fresh perspective and methodology for urban-scale ventilation corridor planning but also contributes to enhancing the urban microclimate by mitigating the effects of urban heat islands and reducing air pollution, ultimately creating a livable and comfortable environment for urban residents.

LS8pyCalc: semi-automated Python calculator to retrieve land surface temperature, cross verified through in situ and MODIS datasets

LS8pyCalc: semi-automated Python calculator to retrieve land surface temperature, cross verified through in situ and MODIS datasets

Improving HJ-1B/IRS LST Retrieval of the Generalized Single-Channel Algorithm with Refined ERA5 Atmospheric Profile Database

Land surface temperature (LST) is a fundamental variable of environmental monitoring and surface equilibrium. Although the HJ-1B infrared scanner (IRS) has accumulated many observations, further application of HJ-1B/IRS is limited by the lack of LST products. This study refined the ERA5 atmospheric profile database, instead of the widely used traditional TIGR atmospheric profile database, and simulated the coefficients of the generalized single-channel (GSCs) algorithms to improve LST retrieval. GSCs can be divided into the GSCw and GSCwT algorithms, depending on whether the input is atmospheric water vapor content (WVC) or in situ near-surface air temperature and WVC. Land surface emissivity (LSE) was obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) and vegetation/snow cover products. Then, the retrieved LSTs were evaluated using the LSTs from the RTE algorithm, TIGRw/TIGRwT profiles, and in situ near-surface air temperature from the HiWATER experiment in China from 2012 to 2014. The bias (root mean square error (RMSE)) values are displayed as ERA5wT &lt; RTE &lt; ERA5w &lt; TIGRwT &lt; TIGRw. The accuracy of ERA5wT, with a bias (RMSE) of 0.02 K (2.30 K), is higher than that of RTE, with a bias (RMSE) of 0.74 K (2.47 K). The accuracy of RTE is preferable to that of ERA5w, with a bias (RMSE) of 0.89 K (2.48 K), followed by TIGRwT, with a bias (RMSE) of −1.18 K (2.50 K), and then, TIGRw, with a bias (RMSE) of 1.60 K (2.77 K). In summary, the accuracy of LST obtained by GSC from the refined ERA5 atmospheric profiles is higher than that obtained from the TIGR profiles. The accuracy of LST obtained by GSCwT is greater than that obtained by GSCw. The accuracy of LST obtained using in situ near-surface air temperature is higher than that obtained using ERA5 air temperature. The accuracy of LSEASTER is slightly better than that of LSEMOD21. The aforementioned conclusions can provide scientific support to generate HJ-1B/IRS LST products.

Assessment of Land Surface Temperature Variations and Implications of Land Use/Land Cover Changes: A Case of Malappuram Urban Agglomeration Region, Kerala, India

Urbanization is taking place faster, and urban air temperatures are gradually rising in all cities across the world. Uncontrolled and unplanned urbanization leads to constant environmental threats and can alter local and regional climates. According to the survey published by Economist Intelligence Unit, in India, Kerala's Malappuram district ranks first among the fastest-growing urban areas globally, with a 44.05% growth rate. Hence, the present study aims to identify the hotspot regions of extreme heat within the Malappuram urban agglomeration region and suggest strategies for its improvement. The split-window algorithm retrieved land surface temperature (LST) for 1991, 1998, 2014, and 2020 using Landsat 5 ETM and Landsat 8 satellite imageries. A rising trend in LST has been detected in the last 30 years, and the mean value has increased by 1.70°C within the region. Among the selected hotspots, an LST increase of 1.84°C was observed for those areas with the highest increase in urban density with decreased vegetation. The increasing impact of urbanization and the subsequent change in land use patterns at the cost of greenery have caused a substantial effect on the local climate. Accordingly, planning and policy directions are proposed for the local government that can help provide awareness to the people through the effective implementation of mitigation measures.