Spatio-Temporal Statistical Sequential Analysis for Temperature Change Detection in Satellite Imagery

High-resolution evapotranspiration (ET) maps can assist demand-based irrigation management. Development of high-resolution daily ET maps requires high-resolution land surface temperature (LST) images. Earth-observing satellite sensors such as the Landsat 5 Thematic Mapper (TM) and MODerate resolution Imaging Spectroradiometer (MODIS) provide thermal images that are coarser than simultaneously acquired visible and near-infrared images. In this study, we evaluated the TsHARP downscaling technique for its capability to downscale coarser LST images using finer resolution normalized difference vegetation index (NDVI) data. The TsHARP technique was implemented to downscale seven coarser scale (240, 360, 480, 600, 720, 840, and 960 m) synthetic images to a 120 m LST image. The TsHARP was also evaluated for downscaling a coarser 960 m LST image to 240 m to mimic MODIS datasets. Comparison between observed 120 m LST images and 120 m LST images downscaled from coarser 240, 360, 480, 600, 720, 840, and 960 m images yielded root mean square errors of 1.0, 1.3, 1.5, 1.6, 1.7, 1.8, and 1.9°C, respectively. This indicates that the TsHARP method can be used for downscaling coarser (960 m) MODIS-based LST images using finer Landsat (120 m) or MODIS (240 m)-derived NDVI images. However, the TsSHARP method should be evaluated further with real datasets before using it for an operational ET remote sensing program for irrigation scheduling purposes.

Land surface temperature (LST) images retrieved from the thermal infrared (TIR) band data of Moderate Resolution Imaging Spectroradiometer (MODIS) have much lower spatial resolution than the MODIS visible and near-infrared (VNIR) band data. The coarse pixel scale of MODIS LST images (1000 m under nadir) have limited their capability in applying to many studies required high spatial resolution in comparison of the MODIS VNIR band data with pixel scale of 250–500 m. In this paper we intend to develop an efficient approach for pixel decomposition to increase the spatial resolution of MODIS LST image using the VNIR band data as assistance. The unique feature of this approach is to maintain the thermal radiance of parent pixels in the MODIS LST image unchanged after they are decomposed into the sub-pixels in the resulted image. There are two important steps in the decomposition: initial temperature estimation and final temperature determination. Therefore the approach can be termed double-step pixel decomposition (DSPD). Both steps involve a series of procedures to achieve the final result of decomposed LST image, including classification of the surface patterns, establishment of LST change with normalized difference of vegetation index (NDVI) and building index (NDBI), reversion of LST into thermal radiance through Planck equation, and computation of weights for the sub-pixels of the resulted image. Since the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with much higher spatial resolution than MODIS data was on-board the same platform (Terra) as MODIS for Earth observation, an experiment had been done in the study to validate the accuracy and efficiency of our approach for pixel decomposition. The ASTER LST image was used as the reference to compare with the decomposed LST image. The result showed that the spatial distribution of the decomposed LST image was very similar to that of the ASTER LST image with a root mean square error (RMSE) of 2.7 K for entire image. Comparison with the evaluation DisTrad (E-DisTrad) and re-sampling methods for pixel decomposition also indicate that our DSPD has the lowest RMSE in all cases, including urban region, water bodies, and natural terrain. The obvious increase in spatial resolution remarkably uplifts the capability of the coarse MODIS LST images in highlighting the details of LST variation. Therefore it can be concluded that, in spite of complicated procedures, the proposed DSPD approach provides an alternative to improve the spatial resolution of MODIS LST image hence expand its applicability to the real world.

Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval

Thermal unmixing based downscaling for fine resolution diurnal land surface temperature analysis

Impacts of urban biophysical composition on land surface temperature in urban heat island clusters

ISLAND: Interpolating Land Surface Temperature using land cover

The limitation of thermal satellite images at finer spatial resolution (FR) led to increased demand for developing various downscaling techniques for the generation of FR image of land surface temperature (LST) to enhance the information content. Thus, the major concern of the analysis is the thermal sharpening of MODIS-LST in various urban regions by establishing the correlation of LST with various spectral indices (SI). Various regression techniques using combination of SI were applied for the thermal sharpening of LST from MODIS data over four different Indian cities with different climate zones i.e. Bikaner, Vadodara, Hyderabad and Varanasi. The LST image from MODIS sensors at spatial resolution of 930 m was disaggregated to 100 m and accuracy was determined by comparing with the LST image of Landsat-8-TIRS at 100 m. The best combination of indices found to include both vegetation and built-up/soil indices for thermal sharpening of MODIS-LST. Further, the variation of the best combination for different cities indicates its dependence on the present land cover. The correlation coefficients (R) between the downscaled MODIS-LST image and the reference-Landsat image were found to be 0.84, 0.76, 0.83 and 0.92, whereas the RMSE values were found to be 1.27, 1.02, 0.73 and 0.62 for Bikaner, Vadodara, Hyderabad and Varanasi, respectively. The RMSE values were found remarkably below the standard deviation of each reference LST image. Therefore, the downscaling approach adopted in this study showed high potential for accurate LST mapping at FR in various urban areas.

Urban heat island (UHI) attenuation is an essential aspect for maintaining environmental sustainability at a local, regional, and global scale. Although impervious surfaces (IS) and green spaces have been confirmed to have a dominant effect on the spatial differentiation of the urban land surface temperature (LST), comprehensive temporal and quantitative analysis of their combined effects on LST and surface urban heat island intensity (SUHII) changes is still partly lacking. This study took the plain area of Beijing, China as an example. Here, rapid urbanization and a large-scale afforestation project have caused distinct IS and vegetation cover changes within a small range of years. Based on 8 scenes of Landsat 5 TM/7ETM/8OLI images (30 m × 30 m spatial resolution), 920 scenes of EOS-Aqua-MODIS LST images (1 km × 1 km spatial resolution), and other data/information collected by different approaches, this study characterized the interrelationship of the impervious surface area (ISA) dynamic, forest cover increase, and LST and SUHII changes in Beijing’s plain area during 2009–2018. An innovative controlled regression analysis and scenario prediction method was used to identify the contribution of ISA change and afforestation to SUHII changes. The results showed that percent ISA and forest cover increased by 6.6 and 10.0, respectively, during 2009–2018. SUHIIs had significant rising tendencies during the decade, according to the time division of warm season days (summer days included) and cold season nights (winter nights included). LST changes during warm season days responded positively to a regionalized ISA increase and negatively to a regionalized forest cover increase. However, during cold season nights, LST changes responded negatively to a slight regionalized ISA increase, but positively to an extensive regionalized ISA increase, and LST variations responded negatively to a regionalized forest cover increase. The effect of vegetation cooling was weaker than ISA warming on warm season days, but the effect of vegetation cooling was similar to that of ISA during cold season nights. When it was assumed that LST variations were only caused by the combined effects of ISA changes and the planting project, it was found that 82.9% of the SUHII rise on warm season days (and 73.6% on summer days) was induced by the planting project, while 80.6% of the SUHII increase during cold season nights (and 78.9% during winter nights) was caused by ISA change. The study presents novel insights on UHI alleviation concerning IS and green space planning, e.g., the importance of the joint planning of IS and green spaces, season-oriented UHI mitigation, and considering the thresholds of regional IS expansion in relation to LST changes.

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.

Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation

Background and Objectives: The outlines a study that investigates the variations in land surface temperature (LST) and land use in Phrae Province, Thailand, during two distinct climate events: The El Niño event in 2020 and the La Niña event in 2024. The study highlights the impact of climate change, including the increasing frequency of these events, on the environment and agriculture. It emphasizes the importance of geospatial technology in monitoring these changes and utilizing satellite imagery to understand land temperature trends for effective disaster prevention planning. The study begins by acknowledging the role of climate change, both natural and human-induced, in contributing to global warming and exacerbating the frequency of El Niño and La Niña events. These climate phenomena lead to severe weather changes that directly impact sectors such as agriculture, often resulting in natural disasters. The objective of this research is twofold: 1) Compare Land Surface Temperature (LST) Variations: The study aims to analyze and compare the variations in LST during two distinct periods, the El Niño event of 2020 and the La Niña event of 2024, within Phrae Province.2) Examine the Relationship between LST and Land Use Types: It seeks to explore how different types of land use—such as forest land, agricultural land, and urban areas—affect LST during these events. Methodology: The methodology involves the use of Landsat 8 satellite imagery from both 2020 and 2024 to gather the data needed for the analysis. The images were processed in the following steps: 1) Composite Band Images: Landsat imagery was used to create composite band images, a process that allows the extraction of relevant data for analysis. 2) Supervised Classification: The images were classified using the Maximum Likelihood Classification algorithm. Training areas were designated to guide the classification, which aimed to categorize land use into three primary types: forest land, agricultural land, and urban/built-up land. 3) Land Surface Temperature (LST) Analysis: The next step was converting the satellite image’s Digital Number (DN) values into radiance, which were then translated into brightness temperature. To adjust for the surface emissivity, the brightness temperature was further refined to provide an accurate measure of LST. 4) Categorizing LST by Land Use Type: After deriving the LST values, the study categorized the temperature ranges according to the different land use types, helping to identify temperature patterns related to land use changes. Main Results: The study presents several key findings related to both land use and LST: 1) land use classification (2020 vs. 2024): In 2020, forest land made up 66.3% of Phrae Province’s total land area, followed by agricultural land (25.7%) and urban/built-up areas (8%). By 2024, the land use pattern had changed slightly. Forest land decreased to 63.5%, while agricultural land increased to 26.9%, and urban/built-up land grew to 9.6%. 2) Land Surface Temperature (LST) Data (2020 and 2024) showed that in 2020, the highest recorded temperature was 44.2°C in March, and the lowest was 23.8°C in August. While, in 2024, the highest recorded temperature was 43.7°C in April, and the lowest was 24.2°C in August. The Monthly average temperature comparison between two events expressed that in 2020, the highest and lowest average temperature found in March (34°C) and August (10°C), respectively. While, in 2024, it was found in April (34°C) and August (10.8°C), respectively. The LST varied among land use types. In 2020, the average LST for forest areas was lowest (21.5°C) and followed by urban/built-up land (32.3°C) and agricultural land (36.2°C), respectively. While, in 2024, it still be found as the same trend as in 2022 but the forest areas shown increased LST (24.4°C), while agricultural land and urban/built-up areas experienced a slight decrease, 34.5°C and 32.3°C, respectively. Conclusion: This study concludes that land surface temperature (LST) variations are significantly influenced by the El Niño and La Niña climate phenomena, with the effects modulated by land use type. Key findings reveal that: (1) During the El Niño event in 2020, agricultural and urban/built-up areas recorded elevated LSTs. This is likely attributable to the intensified heat and drier conditions commonly associated with El Niño episodes. (2) In contrast, during the La Niña event in 2024, forested areas exhibited comparatively higher LSTs than agricultural and urban zones. Although La Niña typically brings cooler and wetter weather, it can also produce localized temperature anomalies, particularly in dense forest regions with limited capacity for heat dissipation. The study underscores the crucial role of geospatial technologies in tracking and analyzing climate-induced variations in land use and surface temperatures. Such insights are essential for local governments and policymakers to design informed, spatially targeted strategies for disaster risk reduction and climate adaptation. This is especially pertinent in agriculturally reliant regions such as Phrae Province, where climate variability poses direct risks to livelihoods and food security. Moreover, the findings contribute to the expanding literature on climate-land interactions, emphasizing the complexity of climate impacts across different ecosystems. The application of satellite data in this context proves to be a powerful tool for environmental monitoring, enabling more accurate and timely assessments of climatic effects. Ultimately, this research advocates for enhanced integration of geospatial analysis in climate resilience planning, ensuring that both human and natural systems are better equipped to navigate future environmental challenges.

The purpose of this study was to analyze the trend of change in land use land cover (LULC) and land surface temperature (LST) in Mirpur and its surrounding area over the last 30years using Landsat satellite images and remote sensing indices, and to develop relationships between LULC types and LST, as well as to analyze their impact on local warming. Using this analyzed data, a further projection of LULC and LST change over the next two decades was made. From 1989 to 2019, 5-year intervals of Landsat 4-5 TM and Landsat 8 OLI images were utilized to track the relationship between LULC changes and LST. The modeled LST was validated with MODIS-derived LST within the study area. Cellular automata-based artificial neural network (CA-ANN) algorithm was used to model the LULC and LST maps for the year 2039. The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) were analyzed to determine their link with LST. The relation between LST and LULC types indicates that built-up area raises LST by substituting non-evaporating surfaces for natural vegetation. The average surface temperature was increasing steadily for the last 30years. For the year 2019, it was determined that roughly 86% of total land area has been converted to built-up area and that 89% of land area had an LST greater than 28°C. According to the study, if the current trend continues, 72% of the Mirpur area is predicted to see temperatures near 32°C in 2039. Additionally, LST had a significant positive association with NDBI and a negative correlation with NDVI. The overall accuracy of LULC was greater than 90%, with a kappa coefficient of 0.83. The study may assist urban planners and environmental engineers in comprehending and recommending effective policy measures and plans to mitigate the consequences of LULC.

When we feel sad or depressed, our face invariably “drops”. Conversely, when we try to cheer someone up, we might tell them “keep your smile up”, so presupposing that modifying the configuration of their facial muscles will enhance their mood. A crucial assumption that underpins this hypothesis is that mental states are shaped by information originating from the peripheral neuromotor system — a view operationalised as the Facial Feedback Hypothesis. We used botulinum toxin (BoNT-A) injected over the frown area to temporarily paralyse muscles necessary to express anger. Using a pre-post treatment design, we presented participants with gradually changing videos of a face morphing from neutral to full-blown expressions of either anger or happiness and asked them to press a button as soon as they had detected any change in the display. Results indicate that while all participants (control and BoNT-A) improved their reaction times from pre-test to post-test, the BoNT-A group did not when detecting anger in the post-test. We surmise that frown paralysis disadvantaged participants in their ability to improve the detection of anger. Our finding suggests that facial feedback causally affects perceptual awareness of changes in emotion, as well as people’s ability to use perceptual information to learn.

Evapotranspiration (ET) plays a significant role in monitoring and forecasting drought. High spatiotemporal resolution ET data enables precise decision-making in agricultural management and drought assessment. The synergy of daily Sentinel-3 Land Surface Temperature (LST) and Sentinel-2 multispectral images offers opportunities for more accurate ET estimation. In this study, we estimate and assess ET of Busia County, Kenya using Earth observation data, machine learning, and Two-Source Energy Balance (TSEB) model. A random forest model is employed with an input of Sentinel-2 spectral indices to downscale Sentinel-3 1-km LST images to 20-m. An optimized random forest model produced 20-m LST with accuracies 0.88, 0.82, and 0.55 for R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, RMSE, and MAE. The finer resolution of LST and ERA5 meteorological data serve as inputs for TSEB, estimating daily actual ET at 20-m. The final ET product (20m, Sentinel 3 timesteps) is compared with WaPOR and Gleam products to evaluate the derived ET products.

Detection of land use and land cover change and land surface temperature in English Bazar urban centre