Land Surface Temperature Images Research Articles

Gap-filling of land surface temperature in arid regions by combining Landsat 8 and 9 imageries

Abstract Land surface temperature (LST) is an important factor in land monitoring studies, but due to the presence of clouds, dust and sensor issues, there are missing values. The aims of this research are to determine the optimal parameters for the reconstruction of Landsat-LST images, required in many applications, by the harmonic analysis of time series algorithm (HANTS) and to investigate the possibility of improving LST reconstruction accuracy using Landsat 8 and 9 images simultaneously. For these aims, 91 Landsat 8 and 9 images with 100 m spatial resolution in 2022 and 2023 are employed, covering Yazd-Ardakan plain in Iran. Three methods are used for evaluation. In method one, a part of LST image is considered as a gap and is compared with the initial value after reconstruction. In method two, on a cloudy day and a cloudless day, surface temperature values are measured using thermometers at fifty points in plain lands, and the difference between gap-filled satellite measurements and ground measurements is calculated. In method three, all the reconstructed LST images are compared with the original images. In method one, the root mean square error (RMSE) of reconstructed LST reduces by 1.3°C when using the combined Landsat 8 and 9 images. In method two, RMSEs of reconstructed LST images are 6.1°C when using Landsat 8 and 5.4°C when using the combined Landsat 8 and 9. Method three shows that 41% of the study region has RMSE of less than 2°C when using only Landsat 8, while this value becomes 72% when combining Landsat 8 and 9. In general, the combined use of Landsat 8 and 9 LST images improves the accuracy of reconstruction using HANTS. The findings of this research are crucial for regional applications and remote monitoring of surface temperature in areas with limited weather stations.

Reconstructing daytime and nighttime MODIS land surface temperature in desert areas using multi-channel singular spectrum analysis

The availability of continuous spatiotemporal land surface temperature (LST) with high resolution is critical for many disciplines including hydrology, meteorology, ecology, and geology. Like other remote sensing data, satellite–based LST is also encountered with the cloud issue. In this research, over 5000 daytime and nighttime MODIS–LST images are utilized during 2014–2020 for Yazd–Ardakan plain in Yazd, Iran. The multi–channel singular spectrum analysis (MSSA) model is employed to reconstruct missing values due to dusts, clouds, and sensor defect. The selection of eigenvalues is based on the Monte Carlo test and the spectral analysis of eigenvalues. It is found that enlarging the window size has no effect on the number of significant components of the signal which account for the most variance of the data. However, data variance changes for all the three components. Employing two images per day, window sizes 60, 180, 360, and 720 are examined for reconstructing one year LST, where these selections are based on monthly, seasonal, semi-annual, and annual LST cycles, respectively. The results show that window size 60 had the least computational cost and the highest accuracy with RMSE (root mean square error) of 2.6 °C for the entire study region and 1.4 °C for a selected pixel. The gap–filling performance of MSSA is also compared with the one by the harmonic analysis of time series (HANTS) model, showing the superiority of MSSA with an improved RMSE of about 2.7 °C for the study region. In addition, daytime and nighttime LST series for different land covers are compared. Lastly, the maximum, minimum, and average LST for each day and night as well as average and standard deviation of LST images in the seven-year-long time series are also computed.

ISLAND: Interpolating Land Surface Temperature using land cover

Cloud occlusion is a common problem in the field of remote sensing, particularly for retrieving Land Surface Temperature (LST). Remote sensing thermal instruments onboard operational satellites are supposed to enable frequent and high-resolution observations over land; unfortunately, clouds adversely affect thermal signals by blocking outgoing longwave radiation emission from the Earth’s surface, interfering with the retrieved ground emission temperature. Such cloud contamination severely reduces the set of serviceable LST images for downstream applications, making it impractical to perform intricate time-series analysis of LST. In this paper, we introduce a novel method to remove cloud occlusions from Landsat 8 LST images. We call our method ISLAND, an acronym for Interpolating Land Surface Temperature using land cover. Our approach uses LST images from Landsat 8 (at 30m resolution with 16-day revisit cycles) and the NLCD land cover dataset. Inspired by Tobler’s first law of Geography, ISLAND predicts occluded LST through a set of spatio-temporal filters that perform distance-weighted spatio-temporal interpolation. A critical feature of ISLAND is that the filters are land cover-class aware, making it particularly advantageous in complex urban settings with heterogeneous land cover types and distributions. Through qualitative and quantitative analysis, we show that ISLAND achieves robust reconstruction performance across a variety of cloud occlusion and surface land cover conditions, and with a high spatio-temporal resolution. We provide a public dataset of 20 U.S. cities with pre-computed ISLAND LST outputs. Using several case studies, we demonstrate that ISLAND opens the door to a multitude of high-impact urban and environmental applications across the continental United States.

Understanding the multifaceted influence of urbanization, spectral indices, and air pollutants on land surface temperature variability in Hyderabad, India

Hyderabad, similar to many cities worldwide, has experienced accelerated urbanization, leading to increased surface temperature and air pollution. The present study investigated the relationship between land surface temperature (LST) and various air pollutants, urbanization, surface indices, and meteorological parameters, which are associated with Sustainable Development Goals (SDGs), especially Goal 13 (Climate Action) and Goal 11 (Sustainable Cities and Communities). The effect of air pollution on LST across seasons was also examined for five years (2018–2022). LST images collected from MODIS satellite. Data on air pollutants and meteorological parameters were collected from air quality monitoring stations. The results showed that PM2.5 exceeded the WHO guidelines. A weak or moderate relationship was observed between LST and individual air pollutants. However, the association significantly improved when all air pollutants were considered simultaneously. The relationship improved even further when additional parameters such as Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), road line density (RLD), and global population density (GPD), were also included. The resulting relationship was very strong (R2 > 0.80) during the summer and winter seasons. The study identified that NH3 had a prominent influence on LST during winter, while PM2.5 and NH3 exhibited a higher influence during summer. The research findings emphasized that LST variations were influenced by both air pollutants and meteorological conditions, along with urbanization factors represented by various parameters and indices. The research underscores the importance of considering these findings in future studies. It suggests the need to explore efficient approaches to address the combined problem of rising LST and concentration of air pollutants in urban areas.

Prediction of surface urban heat island based on predicted consequences of urban sprawl using deep learning: A way forward for a sustainable environment

The present work aimed at the spatiotemporal analysis of Land Surface Temperature (LST) and several land-use land-cover spectral indices, namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Soil Adjusted Vegetation Index (SAVI), and Modified Normalized Difference Water Index (MNDWI) and to develop a Convolutional Neural Network (CNN) model to predict the LST and the indices for Surface Urban Heat Island (SUHI) evaluation based on the impervious surface area. The predictions were made only for LST and two indices (NDVI and NDBI) because of their strong correlation with LST. Landsat-8 satellite imageries acquired for 2013, 2015, 2017, 2019, and 2021 were utilized, and the Sialkot district, a swiftly urbanizing and advancing industrial city, was selected as a case study area. In the study, first, the indices (NDVI and NDBI) were predicted for 2019, 2021, and 2023 following a sequential procedure, and then based on the predicted indices, the LST for the mentioned years was predicted. Ultimately, to analyze the impact of impervious area on SUHI, impervious and SUHI were extracted for 2019, 2021, and 2023 using the predicted NDBI and LST, respectively, by employing Otsu's Thresholding technique. The predicted LST and indices for 2019 and 2021 were compared with the obtained 2019 and 2021 LST and indices through several statistical measures, such as the kappa index, to evaluate the performance of the CNN model. The kappa index of different kappa statistics for 2019 and 2021 varied between 0.81 and 0.96, which resembles several previous studies, thus indicating sufficient future predictions. SUHI evaluation was conducted based on the observed (2016) and simulated (2021) impervious area and normalized LST images. The results showed that the distribution of the SUHI was highly related to the impervious area. Suppose the urbanization rate in the study area continues at its current pace. In that case, this expansion will cause a dramatic increase in SUHI distribution unless decision-makers consider any proper urban planning model for the study area.

Satellite land surface temperature for mapping the structurally controlled geothermal anomalies in the Cretaceous rift, South Egypt

ABSTRACT Exploration of new geothermal systems often requires regional-scale investigation. Satellite Land Surface Temperature (LST) products are essential in geothermal anomaly identification and mapping. In this study, seven thermal anomalies covering Egypt were observed using the Sentinel-3 LST images acquired in 2019 and confirmed using the 2020 LST images. In addition, the LST analysis reveals three new anomalies covering the southern part of Egypt (Kalabsha, Natash, and Wadi Allaqi), which are structurally controlled. Nuqra-Kharit-Natash is a good site for geothermal energy owing to the following reasons: 1) the high geothermal gradient beneath the Wadi Natash region (heat flow ~100 mW/m2); 2) the presence of many geothermal anomalies retrieved from the analyses of the Landsat-8 acquired in August 2018; 3) existence of surface geothermal manifestation at the western boundary of the basin includes warm springs and the tufa deposits. Intricate structural patterns, including NW-oriented normal faults, ENE-to-WNW-oriented right-lateral faults, and NNE-oriented sinistral strike-slip faults, characterise Wadi Natash. Wadi Natash LST anomaly shows a solid relation to fault trends. High values of LST are arranged along NW and NNE faults and recorded the peak at the intersection of two fault trends. Recent seismic activity along the NNE fault system enhances the permeability along this trend and its intersection with the NW-oriented fault.

Downscaling the MODIS land surface temperature using a trapezial concept applied to the MODIS and sentinel 2 images.

Downscaling methods are crucial for accessing high-resolution thermal data simultaneously. The DisTRAD model is commonly used for downscaling thermal images, but changes in soil moisture, such as those caused by irrigation operations, can lead to errors in the process. This study investigated the potential use of TOTRAM and OPTRAM models to reduce errors in LST downscaling in irrigated fields. Sentinel satellite imagery was utilised to enhance the resolution of MODIS Land Surface Temperature (LST) from 1000 to 20 m in the fields of Megsal and Hezarjolfa agro-industrial company in Qazvin province. Soil moisture was estimated using the OPTRAM model, and the results were compared with observational data. The findings indicated that on days with NDVI greater than 0.6, the R2 value exceeded 0.88 and the RMSE value was less than 0.06 cm3/cm3. Then, MODIS LST images were downscaled to 20 m using codes in Google Earth Engine (GEE). Evaluation was conducted using observational data from collected land surface temperature data for 36 points. Comparison of the downscaled LST data with observational data on days with irrigation revealed a decrease in MAE and RMSE error indices by approximately 0.4 and 1.2 degrees Celsius, respectively, in the OPTRAM-TPTRAM model compared to the DisTRAD model. Consequently, the OPTRAM-TOTRAM model generally outperforms the DisTRAD model in LST downscaling. Lastly, it is recommended to assess the TOTARM and OPTRAM models for downscaling MODIS sensor LST in other irrigated fields.

Analyzing Cooling Island Effect of Urban Parks in Zhengzhou City: A Study on Spatial Maximum and Spatial Accumulation Perspectives

As a result of urbanization, cities worldwide are experiencing urban heat island (UHI) challenges. Urban parks, which are essential components of urban blue and green landscapes, typically have lower temperatures in providing outdoor comfort than their surroundings with impervious surfaces. This phenomenon, known as the park cooling island effect (PCIE), has been recognized as an effective approach to mitigate the negative effects of the UHI in the context of sustainable development of urban environment. To cope with the serious UHI challenge and to guide urban park planning and design for Zhengzhou City, which is one of the China’s new first-tier cities, 35 urban parks in the city were analyzed in this study. Remotely sensed land surface temperature (LST) and reflectance images by Landsat 9 and Sentinel-2 were selected as data sources. A cubic polynomial model that depicts the relationship between the LST and the distance from the park edge was first built for each park. Based on this model, the spatial maximum perspective metrics (including the park cooling area (PCA) and park cooling efficiency (PCE)) and the spatial accumulation perspective metrics (including park cooling intensity (PCI) and park cooling gradient (PCG)) were calculated to quantify the PCIE of each park. The 35 parks were divided into three groups using the hierarchical clustering method for further analysis. For each group, the metrics of the PCIE were statistically analyzed, and the main factors influencing the PCIE were identified by the Spearman correlation coefficient. The results indicate the following: (1) The 35 urban parks exhibit an obvious PCIE. The maximum cooling distance is 133.95 ± 41.93 m. The mean LST of the park is 3.01 ± 1.23 °C lower than that within the maximum cooling distance range. (2) The PCIE varies among different types of parks. Parks with large areas and covered by certain water bodies generally exhibit higher PCA, PCI, and PCG values. However, parks with small areas and mainly covered by vegetation show higher PCE values, which makes them more economical in exerting the PCIE. (3) Park area and landscape shape index (LSI) were positively correlated with PCA, PCI, and PCG. However, there is a threshold in the relationship between the park area and the PCI. A park area of approximately 19 ha can produce a higher PCI than a smaller one. In central urban areas with limited space, parks with small areas, complex shapes, and predominant vegetation coverage can be designed to achieve higher cooling efficiency.

Improved fusion model for generating hourly fine scale land surface temperature data under all-weather condition

Existing Land Surface Temperature (LST) fusion models encounter some challenges due to missing data, complex weather areas, and rapid land cover changes. To overcome these limitations, we proposed the Integrated SpatioTemporal Fusion Algorithm (ISFAT). ISFAT is developed based on contemporary fusion models but in addition incorporates data from partially contaminated LSTs using the masked weight function. This helps to predict fine-scale LST on prediction date while considering error resulting from landcover changes between the base and prediction date. This algorithm also factors in the calculation of model residuals, which are distributed back to the predicted fine-scale LST using the thin-plate spline function. The fine-scale LST on prediction can thereafter employed for predicting hourly fine-scale LST images by integrating a coarse resolution LST with hourly temporal resolution. Compared to contemporary LST fusion models, ISFAT demonstrates superior performance, with mean average differences of 0.1 K and 0.27 K over SADFAT and STITFM, respectively. Additionally, diurnal LST predictions from ISFAT compare well with air temperatures from automatic weather stations. Notably, on February 18, 2020, ISFAT effectively optimized fine-scale LST for Hong Kong, achieving an RMSE of 3.33 K, despite the limitation of cloud cover in the base date. The newly developed ISFAT could facilitate better LST retrieval over a large spatial coverage under different degrees of cloud contamination.

Spatio-Temporal Evolution of Olive Tree Water Status Using Land Surface Temperature and Vegetation Indices Derived from Landsat 5 and 8 Satellite Imagery in Southern Peru

Land surface temperature (LST) and its relationship with vegetation indices (VIs) have proven to be effective for monitoring water stress in large-scale crops. Therefore, the objective of this study is to find an appropriate VI to analyse the spatio-temporal evolution of olive water stress using LST images and VIs derived from Landsat 5 and 8 satellites in the semi-arid region of southern Peru. For this purpose, VIs (Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2) and Soil Adjusted Vegetation Index (SAVI)) and LST were calculated. The information was processed in Google Earth Engine (GEE) for the period 1985 to 2024, with an interval of every five years for the summer season. The triangle method was applied based on the LST-VIs scatterplot analysis, a tool that establishes wet and dry boundary conditions for the Temperature Vegetation Dryness Index (TVDI). The results indicated a better appreciation of olive orchard water stress over time, with an average of 39% drought (TVDINDVI and TVDISAVI), 24% severe drought (TVDINDVI) and 25% (TVDISAVI) of the total area, compared to TVDIEVI2, which showed 37% drought and 16% severe drought. It is concluded that TVDINDVI and TVDISAVI provide a better visualisation of the water stress map of the olive crop and offer a range of options to address current and future problems in water resource management in the olive sector in semi-arid areas of southern Peru.

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.

Filling gaps in cloudy Landsat LST product by spatial-temporal fusion of multi-scale data

Land surface temperature (LST) is an important factor in studies of surface energy fluxes between the Earth's surface and atmosphere. The Landsat LST product has been applied widely due to its fine spatial resolution and high data quality. Frequent cloud coverage, however, results in different degrees of gaps in the Landsat LST images, restricting greatly their application in practical cases requiring continuous LST time-series. Gap filling is an important technique used to reconstruct the missing data in the Landsat LST images. Using only temporally neighboring data from the same sensor, however, current gap filling methods struggle to deal with large spatial gaps and large temporal changes in LST. To solve these challenging issues, in this paper, multi-scale data (i.e., the 2 km, all-sky, hourly GOES LST product) were proposed for gap filling of Landsat LST images, and a spatial-temporal random forest (STRF) method was developed. The GOES LST product can provide effective information on LST in the gaps of the target Landsat LST data, with great potential to decreasing the uncertainty in gap filling when large LST change exists or the gap size is large. Through experiments in six regions of the USA, the proposed STRF method was shown to be more accurate than three typical gap filling and spatio-temporal fusion methods, and particularly advantageous for cases involving large temporal changes or large spatial gaps in LST. Specifically, STRF produces an average root mean square error (RMSE) of 1.6444 K and average correlation coefficient (CC) of 0.8653 for the six regions, which are 0.1133 K smaller and 0.0176 larger than the most competitive benchmark method (i.e., the spatial random forest (SRF) method). Conceptually, by spatial-temporal fusion of multi-scale data in gap filling, STRF provides a new paradigm for both spatial-temporal fusion and gap filling. Furthermore, the proposed STRF has the potential to be applied to various gap filling tasks in other situations.

A stepwise unmixing model to address the scale gap issue present in downscaling of geostationary meteorological satellite surface temperature images

Land surface temperature (LST) is a critical parameter that drives the response of a variety of ecosystems to environmental and climatic changes. The geostationary satellite brings unique opportunities to monitor the LST at a hemispheric scale with temporal resolutions of up to 5 min. However, the ultra-coarse spatial resolutions ranging from 2 km to 5 km limit its application at local spatial scales. Downscaling the geostationary satellite LST image with the high-resolution low-Earth-orbit satellite images is a cost-effective way to circumvent this dilemma. Yet, the big gap between the observation scales of these satellite data poses a challenge for accurate downscaling. To address this problem, we proposed a stepwise temperature unmixing (TUM) model called ‘UnmixGO’, which downscales hourly LST images of Geostationary Operational Environmental Satellites (GOES-R) from 2 km to 100 m resolution. The spatially adaptive endmembers and the constrained solution space of the TUM model keep the errors in downscaled LSTs from being over-amplified in the stepwise data treatment. We validated the algorithm in six experimental areas and at five flux tower sites across the contiguous United States, revealing that UnmixGO outperformed conventional methods in accuracy by 0.49 K and 1.11 K on average for downscaling the simulated and real GOES-R LST images, respectively. Furthermore, the technical framework employed by UnmixGO is compatible with multi-source satellite images, enhancing the added value of our study in a future where various remote sensing data is increasingly accessible.

Soil organic carbon stocks in native forest of Argentina: a useful surrogate for mitigation and conservation planning under climate variability

BackgroundThe nationally determined contribution (NDC) presented by Argentina within the framework of the Paris Agreement is aligned with the decisions made in the context of the United Nations Framework Convention on Climate Change (UNFCCC) on the reduction of emissions derived from deforestation and forest degradation, as well as forest carbon conservation (REDD+). In addition, climate change constitutes one of the greatest threats to forest biodiversity and ecosystem services. However, the soil organic carbon (SOC) stocks of native forests have not been incorporated into the Forest Reference Emission Levels calculations and for conservation planning under climate variability due to a lack of information. The objectives of this study were: (i) to model SOC stocks to 30 cm of native forests at a national scale using climatic, topographic and vegetation as predictor variables, and (ii) to relate SOC stocks with spatial–temporal remotely sensed indices to determine biodiversity conservation concerns due to threats from high inter-annual climate variability.MethodsWe used 1040 forest soil samples (0–30 cm) to generate spatially explicit estimates of SOC native forests in Argentina at a spatial resolution of approximately 200 m. We selected 52 potential predictive environmental covariates, which represent key factors for the spatial distribution of SOC. All covariate maps were uploaded to the Google Earth Engine cloud-based computing platform for subsequent modelling. To determine the biodiversity threats from high inter-annual climate variability, we employed the spatial–temporal satellite-derived indices based on Enhanced Vegetation Index (EVI) and land surface temperature (LST) images from Landsat imagery.ResultsSOC model (0–30 cm depth) prediction accounted for 69% of the variation of this soil property across the whole native forest coverage in Argentina. Total mean SOC stock reached 2.81 Pg C (2.71–2.84 Pg C with a probability of 90%) for a total area of 460,790 km2, where Chaco forests represented 58.4% of total SOC stored, followed by Andean Patagonian forests (16.7%) and Espinal forests (10.0%). SOC stock model was fitted as a function of regional climate, which greatly influenced forest ecosystems, including precipitation (annual mean precipitation and precipitation of warmest quarter) and temperature (day land surface temperature, seasonality, maximum temperature of warmest month, month of maximum temperature, night land surface temperature, and monthly minimum temperature). Biodiversity was influenced by the SOC levels and the forest regions.ConclusionsIn the framework of the Kyoto Protocol and REDD+, information derived in the present work from the estimate of SOC in native forests can be incorporated into the annual National Inventory Report of Argentina to assist forest management proposals. It also gives insight into how native forests can be more resilient to reduce the impact of biodiversity loss.

Investigation of land surface temperature heterogeneity in municipal landfills by satellite images

With the increasing population and urbanization, the amount of municipal solid waste (MSW) is increasing day by day. As a result, problems such as odor, fire, and intense biogas formation originate from landfills. In order to detect and solve these problems, landfills should be monitored regularly. Geographic Information Systems (GIS) and Remote Sensing offer fast and practical solutions for the regular monitoring of landfills compared to field studies. In this study, Kömürcüoda landfill on the Anatolian side of Istanbul is monitored throughout 2022 with open source Landsat8/9 and Sentinel-2 satellite images. In this context, the surface temperature heterogeneity of the landfill was mapped by generating Land Surface Temperature (LST) images for the landfill from the Landsat thermal band. Points with statistically significant high - low LST values were determined with Hot Spot Analysis. The average annual LST for 2022 was calculated as 25.5 °C. It was observed that LST had the highest values during the summer season and the lowest values during the winter season. Additionally, it has been determined that there are persistent hot spots and cold spots in the landfill. This study presents a simple methodology using open source satellite data to monitor LST and detect LST abnormalities on landfills.

Spatial enhancement of Landsat-9 land surface temperature imagery by Fourier transformation-based panchromatic fusion

ABSTRACT Landsat-9 Panchromatic (PAN) band images are 7 times finer than land surface temperature (LST) photos of the Thermal Infrared (TIR) band. PAN bands have superior image resolution, consistency, and less ambiguity than TIR bands due to their smaller pixel sizes. Image fusion enhances images by combining data from several sources to make them better. Image fusion methods cannot combine PAN and TIR bands. This research proposes Fourier Transformation-based fusion (FTBF) to merge PAN and TIR band data to spatially enhance Landsat-9 LST images from 100 m to 15 m resolution. Fourier transformation integrates frequency domain filtering and spatial matching in FTBF. In-situ infrared thermometers data loggers verified temperature and picture quality parameters for FTBF algorithm fused image thermal points. Comparing downscaled LST with ground truth points yielded an RMSE of 0.18 and a correlation of 0.93. Eight qualitative and quantitative characteristics reveal that FTBF fusion methods improve TIR picture spatial resolution and preserve original LST data thermal attributes. LST-Pan fusion can detect surface temperature change for land-use change, fire detection, forest fire, agricultural analysis, crop management, and flood mapping at finer scales.

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.

Analysis of Surface Temperature in Buru District Using Cloud Computing on Google Earth Engine

Monitoring land surface temperature in Buru Regency using Google Earth Engine cloud computing-based geospatial technology can help in understanding global climate and weather change, as well as provide important information for scientists, governments, and non-governmental organizations in making decisions related to climate change mitigation and natural disaster management. This research aims to analyze land surface temperature in Buru Regency using MODIS satellite image data based on the cloud computing google earth engine. This research uses Moderate Resolution Imaging Spectroradiometer (MODIS) Terra Land Surface Temperature and Emissivity 8-Day Global image data analyzed on Google Earth Engine. The results show that the lowest land surface temperature value in Buru Regency is 12, 7438ᵒ C, and the highest value is 31, 9582ᵒ C. The area that has a land surface temperature (LST) in the very high class has an area of 96,604.46 ha or 19.90%, the LST area in the high class is 139,606.47 ha or 28.76%, the LST area in the medium class is 140,853.38 ha or 29.02%, the LST area in the low class is 79,896.56 ha or 16.46% and the LST area in the very low class is 28,458.57 ha or 5.86%. The land surface temperature analysis in Buru Regency can provide important information for the local government in making policies and planning for sustainable regional development.

Investigation of the Efficiency of Satellite-Derived LST Data for Mapping the Meteorological Parameters in Istanbul

Land surface temperature (LST) is an essential parameter for studying environmental and ecological processes and climate change at various scales. It is also valuable for studies of evapotranspiration, soil moisture conditions, surface energy balance, and urban heat islands. Since meteorological station data can provide a limited number of point data, satellite images that provide high temporal and spatial resolution LST data in large areas are needed to be used in all these applications. In this study, the usage of satellite-derived LST images was investigated in comparison with meteorological station data measurements in Istanbul, which has heterogeneous urban structures. LST data were obtained from Landsat 5 TM, Landsat 8 OLI/TIRS, and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images using the Google Earth Engine (GEE) cloud platform. The linear correlation analysis performed between Landsat LST and MODIS LST images gave a high correlation (r = 0.88). In the correlation analysis, hourly air temperature and soil temperature meteorology station data provided by the State Meteorological Service and LST values obtained from images taken from Landsat TM/TIRS and Terra MODIS were used. The correlations between air temperatures and Landsat LST ranged from 0.47–0.95 for 1987–2017 to 0.44–0.80 for MODIS LST for 2000–2017. The correlations between 5 cm soil temperatures and Landsat LST ranged from 0.76–0.93 for 2009–2017 to 0.22–0.61 for MODIS LST 2000–2017. In addition, linear regression models produced with meteorological parameters and LST values were applied to 2022 LST maps to show the spatial distribution of these parameters, and then, accuracy analyses were made.

LANDSAT 8 LST Pan sharpening using novel principal component based downscaling model

The panchromatic (PAN) band is accessible at a much higher spatial resolution than the thermal infrared (TIR) band in remote sensing satellites. To mitigate this issue, current research proposes a fusion method based on principal components of PAN and TIR band pairs to enhance the spatial resolution of Land surface temperature (LST) images. In this research, LANDSAT-8 LST images of 100 m resolution have been downscaled to 15 m resolution by the Novel principal component (NPC) based downscaling model. The downscaled LST image's thermal values have been cross-verified by in situ thermal points collected by the thermal data logger and image quality indices. The results have shown the root mean square error (RMSE) of 0.51–0.92 for downscaled LST image from 100m to 15m resolution. The correlation between downscaled LST and ground truth points is between 0.54 and 0.83. The proposed NPC-based LST-PAN combination technique collects spatial details of the Earth's surface from PAN data as well as indulges thermal information of TIR image. The proposed fusion technique has upgraded the spatial goal and peculiarity of TIR images data set. The higher spatial resolution of thermal images can increase the level of precision in various remote sensing applications. These improved data sets can be used in a variety of applications, including urban heat island mapping, agricultural crop monitoring, forest fire detection, and hydrological modeling, to name a few. The increased spatial resolution provided by NPC fusion results in more accurate and consistent temperature readings, leading to improved decision-making and management in these and other applications.