Land Surface Temperature Images Research Articles

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.

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.

A Spatial Downscaling Approach for Land Surface Temperature by Considering Descriptor Weight

Acquiring the satellite land surface temperature (LST) with high spatiotemporal resolutions is pressing in the land surface biophysical process. However, most current LST products hardly satisfy this requirement. LST Downscaling provides an effective way to solve this issue by introducing driving factors, but existing methods usually ignore the weights of descriptors. In this letter, based on the Geographically Weighted Regression (GWR) and Random Forest (RF), a new downscaling method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., WGWR) considering the weights of LST descriptors is proposed. To examine the performance of WGWR, the 100-m Landsat-8 TIRS and Terra ASTER LSTs are aggregated to 1000 m as the simulated coarse LSTs, and then the coarse LSTs are downscaled to 100 m using WGWR, RF, and GWR. Meanwhile, the original 100-m LSTs are used as validation references. Results indicate that the proposed WGWR outperforms RF and GWR: for RF (GWR), the RMSEs can be reduced by 0.34 K (0.26 K) in Zhangye and 0.22 K (0.1 K) in Beijing. Compared to RF and GWR, WGWR also yields better image quality: the downscaled LST images have neither obvious smoothing effect nor boundary effect and maintain the details of the image at high spatial resolution. Validation based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> LST indicates that the downscaled LST based on WGWR has better agreement with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> LST, and the RMSE is reduced by 0.57 K. The proposed WGWR contributes to obtain high spatio-temporal resolution LSTs and promote hydrological, meteorological, and ecological studies.

Filling Then Spatio-Temporal Fusion for All-Sky MODIS Land Surface Temperature Generation

The thermal infrared band of the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra/Aqua satellite can provide daily, 1 km land surface temperature (LST) observations. However, due to the influence of cloud contamination, spatial gaps are common in the LST product, restricting its application greatly at the regional scale. In this article, to deal with the challenge of large gaps (especially complete data loss) in MODIS LST for local monitoring, a filling then spatio-temporal fusion (FSTF) method is proposed, which utilizes another type of product with all-sky coverage, but coarser spatial resolution (i.e., the 7 km China Land Data Assimilation System (CLDAS) LST product). Due to the great temporal heterogeneity of LST, temporally closer auxiliary MODIS LST images are considered to be preferable choices for spatio-temporal fusion of CLDAS and MODIS LST time-series. However, such data are always abandoned inappropriately in conventional spatio-temporal fusion if they contain gaps. Accordingly, pregap filling is performed in FSTF to make fuller use of the valid information in temporally close MODIS LST images with small gaps. Through evaluation in both the spatial and temporal domains for three regions in China, FSTF was found to be more accurate in reconstructing MODIS LST images than the original spatio-temporal fusion methods. FSTF, thus, has great potential for updating the current MODIS LST product at the global scale.

Reconstructing NDVI and land surface temperature for cloud cover pixels of Landsat-8 images for assessing vegetation health index in the Northeast region of Thailand.

Critical applications of satellite data products include monitoring vegetation dynamics and assessing vegetation health conditions. Some indicators like normalized difference vegetation index (NDVI) and land surface temperature (LST) are used to assess the status of vegetation growth and health. But one of the major problems with passive remote sensing satellite data products is cloud and shadow cover that leads to data gaps in the images. The present study proposes temporal aggregation of images over a short time span and developing short span harmonic analysis of time series (SS-HANTS) and pixel-wise multiple linear regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI datasets. The calculated root mean square (RMSE) values were ranging from 0.03 to 0.11 and 1.50 to 2.98°C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) computed from cloud retrieved continuous NDVI and LST images at province level shows that most of the western provinces have healthy vegetation condition than other provinces in the northeast of Thailand.

Generating Daily Land Surface Temperature Downscaling Data Based on Sentinel-3 Images

The land surface temperature (LST) images obtained by thermal infrared remote sensing sensors are of great significance for numerous fields of research. However, the low spatial resolution is a drawback of LST images. Downscaling is an effective way to solve this problem. The traditional downscaling methods, however, have various drawbacks, including their low temporal and spectral resolutions, difficult processes, numerous errors, and single downscaling factor. They also rely on two or more separate satellite platforms. These drawbacks can be partially compensated for by the Sentinel-3 satellite’s ability to acquire LST and multispectral images simultaneously. This paper proposes a downscaling model based on Sentinel-3 satellite and ASTER GDEM images—D-DisTrad—and compares the effects of the D-DisTrad model with DisTrad model and TsHARP model over four sites and four seasons. The mean bias (MB) range of the D-DisTrad model is −0.001–0.017 K, the mean absolute error (MAE) range is 0.103–0.891 K, and the root mean square error (RMSE) range is 0.220–1.235 K. The Pearson correlation coefficient (PCC) and R2 ranges are 0.938–0.994 and 0.889–0.989, respectively. The D-DisTrad model has the smallest error, the highest correlation, and the best visual effect, and can eliminate some “mosaic” effects in the original image. This paper shows that the D-DisTrad model can improve the spatial resolution and visual effects of LST images while maintaining high temporal resolution, and discusses the influence of the terrain and land cover on LST data.

A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data

High temporal resolution and spatially complete (seamless) land surface temperature (LST) play a crucial role in numerous geoscientific aspects. This paper proposes a data fusion method for producing hourly seamless LST from Himawari-8 Advanced Himawari Imager (AHI) data. First, the high-quality hourly clear-sky LST was retrieved from AHI data by an improved temperature and emissivity separation algorithm; then, the hourly spatially complete China Land Data Assimilation System (CLDAS) LST was calibrated by a bias correction method. Finally, the strengths of the retrieved AHI LST and bias-corrected CLDAS LST were combined by the multiresolution Kalman filter (MKF) algorithm to generate hourly seamless LST at different spatial scales. Validation results showed the bias and root mean square error (RMSE) of the fused LST at a finer scale (0.02°) were −0.65 K and 3.38 K under cloudy sky conditions, the values were −0.55 K and 3.03 K for all sky conditions, respectively. The bias and RMSE of the fused LST at the coarse scale (0.06°) are −0.46 K and 3.11 K, respectively. This accuracy is comparable to the accuracy of all-weather LST derived by various methods reported in the published literature. In addition, we obtained the consistent LST images across different scales. The seamless finer LST data over East Asia can not only reflect the spatial distribution characteristics of LST during different seasons, but also exactly present the diurnal variation of the LST. With the proposed method, we have produced a 0.02° seamless LST dataset from 2016 through 2021 that is freely available at the National Tibetan Plateau Data Center. It is the first time that we can obtain the hourly seamless LST data from AHI.

Reconstruction of land surface temperature under cloudy conditions from Landsat 8 data using annual temperature cycle model

Land surface temperature (LST) is an important parameter in the processes of energy exchange and water cycle between the land surface and the atmosphere. The impact of cloud cover leads to spatially incomplete of thermal infrared (TIR)-based LST products, which seriously hinders the applications of LST products in various fields. Several methods have been developed to reconstruct LST under cloudy conditions in previous studies, but there is a lack of an effective method for the reconstruction of cloudy LST at the spatial resolution of Landsat pixel (30 m). In this study, a novel method was proposed to reconstruct LST under cloudy conditions from Landsat 8 data. The LST reconstruction method includes four main steps: (1) identification of cloud-free, cloud-shadow, cloud-obscured, and cloud-covered pixels by integrating the Fmask method with a cloud-shape matching method; (2) calculation of annual temperature cycle (ATC)-based reference LST by fitting an ATC model to all available Landsat 8 LST product during 2013-2020; (3) estimation of LST residual from spatially adjacent similar pixels; and (4) estimation of reconstructed LST in terms of the sum of ATC-based reference LST and LST residual. The performance of the LST reconstruction method was evaluated using Landsat 8 LST images under clear-sky conditions as reference data. The root mean squared error (RMSE) between reconstructed LST and Landsat 8 reference LST ranges from 0.9 K to 2.5 K. The LST reconstruction method was further applied to reconstruct actual Landsat 8 LST images under cloudy conditions. Compared with original Landsat 8 LST images, the spatial distribution of reconstructed LST images is more complete. The pattern of reconstructed LST images reflects the spatial variability of LST well. The accuracy of the LST reconstruction method was validated against in situ LST measurements at six SURFRAD (Surface Radiation Budget Network) sites. The overall bias and RMSE between reconstructed LST and in situ LST at all sites are approximately −0.3 K and 3.5 K, respectively. The LST reconstruction method has great potentials to improve the applications of Landsat LST product in urban thermal environment monitoring and crop water stress monitoring.

Cross-Comparison between Sun-Synchronized and Geostationary Satellite-Derived Land Surface Temperature: A Case Study in Hong Kong

Harmonization of satellite imagery provides a good opportunity for studying land surface temperature (LST) as well as the urban heat island effect. However, it is challenging to use the harmonized data for the study of LST due to the systematic bias between the LSTs from different satellites, which is highly influenced by sensor differences and the compatibility of LST retrieval algorithms. To fill this research gap, this study proposes the comparison of different LST images retrieved from various satellites that focus on Hong Kong, China, by applying diverse retrieval algorithms. LST images generated from Landsat-8 using the mono-window algorithm (MWAL8) and split-window algorithm (SWAL8) would be compared with the LST estimations from Sentinel-3 SLSTR and Himawari-8 using the split-window algorithm (SWAS3 and SWAH8). Intercomparison will also be performed through segregated groups of different land use classes both during the daytime and nighttime. Results indicate that there is a significant difference among the quantitative distribution of the LST data generated from these three satellites, with average bias of up to −1.80 K when SWAH8 was compared with MWAL8, despite having similar spatial patterns of the LST images. The findings also suggest that retrieval algorithms and the dominant land use class in the study area would affect the accuracy of image-fusion techniques. The results from the day and nighttime comparisons revealed that there is a significant difference between day and nighttime LSTs, with nighttime LSTs from different satellite sensors more consistent than the daytime LSTs. This emphasizes the need to incorporate as much night-time LST data as available when predicting or optimizing fine-scale LSTs in the nighttime, so as to minimize the bias. The framework designed by this study will serve as a guideline towards efficient spatial optimization and harmonized use of LSTs when utilizing different satellite images associated with an array of land covers and at different times of the day.

Assessment of the TsHARP method for spatial downscaling of land surface temperature over urban regions

Nowadays, acquiring high spatial and temporal land surface temperature (LST) is one of the significant factors in urban climate studies. This study aims to assess the sharpening thermal imagery (TsHARP) method, which is developed for determining the LST in urban regions. Whereas the original TsHARP method correlates the Normalized Difference Vegetation Index (NDVI) with the LST, the current study seeks to evaluate the performance of the TsHARP method by correlating between the LST and NDVI over urban areas. Additionally, it is intended to investigate the relationship between the impervious surfaces (IS) and LST. At this point, the Advanced Spaceborne Thermal Emission and Reflection Radiometer ASTER image of the year 2003 was used to retrieve the IS as well as the LST of the Kuala Lumpur city, Malaysia. To retrieve the main LST, IS and NDVI, ASTER Level1B data were used. On the other hand, ASTER land surface temperature products (AST08) was utilized to evaluate the downscaled LST (DLST) image. With selection of the ~1/4 of the pixels with lowest coefficient of variation (CV), the LST has more correlation with the IS in comparison with the NDVI, while the correlation between the IS and LST was increased in this study. The IS index illustrates the most optimal correlation for the urban LST (R2 = 0.59). Furthermore, the TsHARPIS-based method depicted a good correlation between the DLST and the ASTER LST product (RMSE = 1.5 °C, R2 = 0.83) compared to the NDVI (RMSE = 4 °C, R2 = 0.73). Consequently, it is found that the application of TsHARPIS-based method to ASTER remote sensing data is a robust/low-cost approach for spatial downscaling of the LST over urban regions.

Assessing and modeling diurnal temperature buffering and evapotranspiration dynamics in forest restoration using ECOSTRESS thermal imaging

Restoration of temperate forests alters the diurnal dynamics of land surface temperature (LST) and evapotranspiration (ET) through increases in biomass, diversity, and complexity. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is the first space-based thermal imaging instrument that allows for a study of diurnal LST and ET dynamics at a moderate spatial resolution (70 × 70 m).We quantified, compared, and modeled the LST and ET dynamics of two groups of forest restoration sites (43 sites in total, between 2 and 75 ha) in Southern Ontario, Canada that were initially restored from mainly agriculture between 2007 and 2019. We included all useable LST (n = 29) and ET (n = 9) ECOSTRESS image products from a full growing season in 2020 (June 1st to September 30th). Forest restoration sites were compared by age and against agriculture (pre-restoration state), mature forest (post-restoration state), and suburban residential sites (competing land use).The ability of forest restoration sites to buffer temperatures was highest in the afternoon, around 14:00 local time (EDT). As predicted, restoration sites were significantly cooler (x̄ 4.4–7.4 °C) than residential and agricultural areas and significantly warmer (x̄1.6–2.9 °C) compared to mature reference forest sites at both groups of sites. Relative diurnal (24-h) LST variability of forest and restoration sites was also significantly lower (0.9–2.9 °C) compared to agriculture and residential sites (3.3–5.2 °C). Daytime LST decreased significantly by 0.1 °C (3.1%), per year since restoration for one of the groups of sites relative to nearby mature reference forest sites as per a linear mixed effects regression model. It would take these sites ~30 years to reach the same buffering as mature forests. In characterizing ET dynamics of a subset of sites, we found that more recently restored sites had a statistically significant higher overall ET than older ones and that daytime relative instantaneous ET decreased with years since restoration. The variation explained by the ET model was however low.Our study provides insights into how diurnal forest ecosystem energy conversion and storage dynamics changes over time after restoration. These diurnal thermal dynamics impact wildlife habitat as well as human wellbeing. The change in thermal buffering over time could be used to assess the pace and trajectory of restoration by managers. The thermal buffering provided by restored forest can also be quantified as an ecosystem service. As such, the study demonstrates the utility and also limitations of novel thermal remote sensing methods, using free and publicly available imagery from ECOSTRESS.

Estimating land surface temperature to study land use land cover influence on the temperature in a city with a dry arid climate

Turning arid regions or forests into urban areas (Urbanization) is affecting the global temperature. This effect is intensifying due to uncontrolled urban growth. To understand the influence of land use land cover (LULC) on land surface temperature (LST) in dry arid climate cities like Sharjah, UAE, 2 Landsat 8 images were used to retrieve the LST, one in summer and one in winter by using the radiation correction equation in google earth engine. Supervised classification of a high-resolution image from worldview-3 will produce the LULC map. In addition, hourly ground temperature data (GT) is retrieved from 6 field stations to validate LST and find the correlation if it exists. The LST images were then compared to the different LULC, to find relationships between the two. This study showed that barren soil experienced the highest LST when compared to vegetation and urban areas, and vegetation areas experienced the lowest temperatures compared to all other LULC, finally, urbanization that was combined with green lands can have a positive impact on the temperature in cities with dry arid climates. This study proves that understanding the effects of urbanization on temperature accurately will aid greatly in the future planning of cities.