Landsat-8 Thermal Research Articles

Land Surface Temperature Anomalies as Indicators of Urban Land Cover Change—A Study of Two Indian Cities

Rapid urbanisation over the years has led to the loss of natural land cover, thereby affecting Land Surface Temperature (LST) distribution in urban areas. This study aims to analyse LST anomalies (calculated as the deviation from the normal) over selected Indian cities and check if critical land cover changes can be identified. LST from Landsat Thermal Infrared (TIR) images acquired in March, April and May from 1988 to 2020 were used to estimate LST anomalies. Positive LST anomalies were observed mainly over barren and impervious areas; however, some areas showed a negative anomaly where the barren lands were converted to vegetated areas. The study has demonstrated that while some developed areas exhibit a positive anomaly indicative of significant changes or development, there are instances where the conversion of barren land to developed (i.e. built up) areas has resulted in a negative anomaly. Developed areas that are closer to the water creek or mangroves were associated with lower anomaly values indicating the cooling effect of the water body and vegetation. Conversely, the core urban areas generally exhibited higher LST values with positive anomalies indicating a warming effect. These findings can be used by city planners to identify hotspot areas and develop more effective strategies and policies to address the challenges of urban heat. They also highlight the regions that require infrastructural resources and policy changes to reduce the temperature.

R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC

In this paper, an image analysis framework is formulated for Landsat-8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) scenes using the R programming language. The libraries of R are shown to be effective in remote sensing data processing tasks, such as classification using k-means clustering and computing the Normalized Difference Vegetation Index (NDVI). The data are processed using an integration of the RStoolbox, terra, raster, rgdal and auxiliary packages of R. The proposed approach to image processing using R is designed to exploit the parameters of image bands as cues to detect land cover types and vegetation parameters corresponding to the spectral reflectance of the objects represented on the Earth’s surface. Our method is effective at processing the time series of the images taken at various periods to monitor the landscape dynamics in the middle part of the Congo River basin, Democratic Republic of the Congo (DRC). Whereas previous approaches primarily used Geographic Information System (GIS) software, we proposed to explicitly use the scripting methods for satellite image analysis by applying the extended functionality of R. The application of scripts for geospatial data is an effective and robust method compared with the traditional approaches due to its high automation and machine-based graphical processing. The algorithms of the R libraries are adjusted to spatial operations, such as projections and transformations, object topology, classification and map algebra. The data include Landsat-8 OLI-TIRS covering the three regions along the Congo river, Bumba, Basoko and Kisangani, for the years 2013, 2015 and 2022. We also validate the performance of graphical data handling for cartographic visualization using R libraries for visualising changes in land cover types by k-means clustering and calculation of the NDVI for vegetation analysis.

Remote Sensing-Based Prediction of Temporal Changes in Land Surface Temperature and Land Use-Land Cover (LULC) in Urban Environments

Pakistan has the highest rate of urbanization in South Asia. The climate change effects felt all over the world have become a priority for regulation agencies and governments at global and regional scales with respect assessing and mitigating the rising temperatures in urban areas. This study investigated the temporal variability in urban microclimate in terms of land surface temperature (LST) and its correlation with land use-land cover (LULC) change in Lahore city for prediction of future impact patterns of LST and LULC. The LST variability was determined using the Landsat Thermal Infrared Sensor (TIRS) and the land surface emissivity factor. The influence of LULC, using the normalized difference vegetation index (NDVI), the normalized difference building index (NDBI), and the normalized difference bareness index (NDBaI) on the variability LST was investigated applying Landsat Satellite data from 1992 to 2020. The pixel-level multivariate linear regression analysis was employed to compute urban LST and influence of LULC classes. Results revealed that an overall increase of 41.8% in built-up areas at the expense of 24%, 17.4%, and 0.4% decreases in vegetation, bare land, and water from 1992–2020, respectively. Comparison of LST obtained from the meteorological station and satellite images showed a significant coherence. An increase of 4.3 °C in temperature of built-up areas from 1992–2020 was observed. Based on LULC and LST trends, the same were predicted for 2025 and 2030, which revealed that LST may further increase up to 1.3 °C by 2030. These changes in LULC and LST in turn have detrimental effects on local as well as global climate, emphasizing the need to address the issue especially in developing countries like Pakistan.

Land surface temperature downscaling in the karst mountain urban area considering the topographic characteristics

To obtain high-spatial-resolution land surface temperature (LST) in karst areas, it is necessary to select a downscale regression model with a better simulation effect and the scale factors that can best represent the topographic characteristics of karst mountainous areas. In Guiyang, a typical karst mountain city, two areas are selected as the study area, which is dominated by natural surface and construction land. Based on the data of Landsat-8 Thermal Infrared Sensor (TIRS), Sentinel-2, Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM), and meteorological stations, the scale factors representing bare land: bare soil index and topographic relief: mountain shadow (hillshade), relief degree of land surface (RDLS), solar incident angle, and sky view factor (SVF) are added on the basis of the conventional factors. At the same time, random forest (RF) and extreme gradient boosting (XGBoost) models are used to construct an LST downscaling method that is more suitable for karst mountain cities. After the above steps, the LST product with a spatial resolution of 10 m is finally estimated. The results show that, due to the characteristics of large elevation variation, fragmentation, and high heterogeneity of surface landscape in karst areas, digital elevation model (DEM), RDLS, and SVF factors need to be considered in the downscaling of surface temperature, and the contribution rates of these factors are all more than 6% in the model. In terms of accuracy evaluation of ground temperature, XGBoost model has the highest accuracy with an average absolute error of 1.67K, RF model has an average error of 1.90K, and thermal image sharpening has the worst accuracy with an average error of 2.41K. In terms of accuracy evaluation of ascending scale, the XGBoost model also shows higher accuracy and richer texture details. The research results can provide basic data for the acquisition of high-resolution LST and its intermediate parameters in this area and also provide a method reference for the reduction of high-resolution LST in similar areas.

Change Analysis of Urban Tree Canopy in Miami-Dade County

This study mapped and analyzed urban tree canopy change between 2014 and 2019 within the Urban Development Boundary of Miami-Dade County, Florida. The goal was to identify local areas of tree canopy gain or loss over this 5-year period. The comparison is based on land cover maps delineated from WorldView-2 satellite data applying a random forest classification algorithm, in combination with publicly available vector data of infrastructure (roads, railroads) and land use maps (water, cropland). Existing urban tree canopy (EUTC) was computed for census block groups and municipalities to compare tree canopy cover loss or gain to support strategic planning of equitable urban reforestation. For the entire study area, the percentage of EUTC did not change significantly between 2014 (19.9 ± 1.2%) and 2019 (20.1 ± 1.5%). However, some municipalities experienced changes in EUTC by over 10%. Comparison of Landsat-8 Thermal Infrared satellite imagery between both periods identified land cover change patterns that were associated with an increase or decrease in surface temperature. A significantly negative relationship between percentage of African American population and tree canopy in 2014 turned statistically insignificant in 2019, whereas the negative relationship with percentage of Hispanic population further strengthened in 2019 compared to 2014.

Effect of shadow on atmospheric and topographic processed NDSI values in Chenab basin, western Himalayas

The present study was undertaken in the Chenab basin, western Himalayas to explore the effect of shadow on atmospherically and topographically corrected normalized difference snow index (NDSI) values. The research was conducted using the Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) datasets of two time periods i.e., February 2015 and February 2018. Our results suggested that total snow cover area (SCA) computed using NDSI from raw satellite images, under estimated SCA (viz. 10,938.66 and 7739.97 Km2); relative to atmospherically corrected (AC) (14,819.01 and 11,199.7 Km2) and topographically corrected (TC) (14,882.07 and 11,188.18 Km2) images during both the time periods (i.e., 2015 and 2018, respectively). This is mainly due to the non-identification of snow pixels in the shadow regions of raw imageries. Maximum SCA lies in the higher NDSI range in case of AC and TC images, unlike raw image where maximum SCA lies in lower NDSI range for both sunlit and shadow test sites. In the sunlit and shadow terrains, minimum NDSI values were found to be higher for TC images as compared to AC images, except few observations which exhibited similar values for both AC and TC images. In case of maximum NDSI values, most of the observations exhibited similar values for both AC and TC images, except few observations which depicted higher maximum NDSI value in AC images as compared to TC images in both the terrains. This observation ascertains the narrow range of NDSI values for TC images as compared to AC images, which could be attributed to reduced background reflection and low atmospheric scattering in the TC images. Comparison among shadow and sunlit snow terrains, reveals low values of minimum and maximum NDSI in the shadow site, which is ascertained by significant (p < 0.001) t-values of means difference between shadow and sunlit terrains. Moreover, snow surface temperature (SST) values computed for both the time periods, reveal low SST values for shadow sites (t-values −5.1 and − 10.57 for minimum SST, and − 9.1 and − 12.61 for maximum SST for the years 2015 and 2018, respectively) as compared to sunlit sites, and this observation validates that minimum amount of solar radiation reaches the shadow sites. Thus, it is concluded that AC and TC are helpful for delineation of snow pixels under shadow, but shadow has a significant impact on the spectral reflectance of snow, even in the AC and TC images. The influence of shadow should be taken into account for accurate estimation of physical properties and broadband albedo of the snow.

Assessing the impact of spatio-temporal land cover changes on land surface temperature using satellite data in Beas Valley, Himachal Pradesh, India

Here, the impact of spatio-temporal Land Use Land Cover Changes (LULC) on the variations of Land Surface Temperature (LST) has been estimated using Landsat 7 ETM+, Landsat 8 TIRS/OLI and Sentinel-2 data for Beas basin of Kullu district, Indian Himalayan Region (IHR). Also, the relationship of various remote sensing indices such as NDVI, NDSI, NDBI and NDMI with LST were established using coefficient of correlation to monitor the role of natural ecosystem alteration on LST. LST was calculated using Single Window algorithm (SW) for eight major land cover categories extracted from Landsat-7 ETM+ and Landsat-8 (Path-147 and Row-38) Thermal Infrared Sensor (TIRS) data for October, 2000 and October, 2020 respectively. LULC change detection revealed that there was a huge increase in agricultural land including orchard expansion of 123 % during the year 2020 than that of 2000. Also there’s a sharp increase of 40.63 % in settlement areas which includes the tourism activities. These are the significant factors for the changes in LST. Further, it shows that negative correlation with the strong correlation coefficients of R2 = 0.7072 and R2 = 0.5642 between NDSI and LST in Pre-winter season of 2000 and that of 2020 respectively. Whereas, the correlation between NDVI and LST showed positive correlation with the coefficient of R2 = 0.2577 in 2000, which increased to R2 = 0.5959 in 2020. This positive relationship highlighted the fact that an increase in LST provides favourable conditions for the vegetation growth in the valley during the pre-winter season 2000 and 2020 respectively. The main outcome of the paper, we believe, will be helpful in analysing the dynamics of land cover changes and sustainable environmental planning in the Beas Valley, which is crucial for livelihood sustainability of the people residing in the already fragile IHR.

Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem

Evapotranspiration (ET) is key to assess crop water balance and optimize water-use efficiency. To attain sustainability in cropping systems, especially in semi-arid ecosystems, it is necessary to improve methodologies of ET estimation. A method to predict ET is by using land surface temperature (LST) from remote sensing data and applying the Operational Simplified Surface Energy Balance Model (SSEBop). However, to date, LST information from Landsat-8 Thermal Infrared Sensor (TIRS) has a coarser resolution (100 m) and longer revisit time than Sentinel-2, which does not have a thermal infrared sensor, which compromises its use in ET models as SSEBop. Therefore, in the present study we set out to use Sentinel-2 data at a higher spatial-temporal resolution (10 m) to predict ET. Three models were trained using TIRS’ images as training data (100 m) and later used to predict LST at 10 m in the western section of the Copiapó Valley (Chile). The models were built on cubist (Cub) and random forest (RF) algorithms, and a sinusoidal model (Sin). The predicted LSTs were compared with three meteorological stations located in olives, vineyards, and pomegranate orchards. RMSE values for the prediction of LST at 10 m were 7.09 K, 3.91 K, and 3.4 K in Cub, RF, and Sin, respectively. ET estimation from LST in spatial-temporal relation showed that RF was the best overall performance (R2 = 0.710) when contrasted with Landsat, followed by the Sin model (R2 = 0.707). Nonetheless, the Sin model had the lowest RMSE (0.45 mm d−1) and showed the best performance at predicting orchards’ ET. In our discussion, we argue that a simplistic sinusoidal model built on NDVI presents advantages over RF and Cub, which are constrained to the spatial relation of predictors at different study areas. Our study shows how it is possible to downscale Landsat-8 TIRS’ images from 100 m to 10 m to predict ET.

Integration of optical, SAR and DEM data for automated detection of debris-covered glaciers over the western Nyainqentanglha using a random forest classifier

The capability of optical images to detect clean-ice glaciers has been well-demonstrated. Debris-covered glaciers, are different from clean-ice glaciers and are important for research of glacier mass balance. However, the main challenges to detect debris-covered glaciers in alpine regions faced by many researchers are the spectral similarity between debris-covered glaciers and the rocks and soils on both sides, as well as shadows cast from mountains, clouds and seasonal snow in satellite images. This study aimed to develop an automatic algorithm (using a Random Forest classifier model) implemented in the western Nyainqentanglha to map debris-covered glaciers based on multi-source datasets such as Sentinel-1 Synthetic Aperture Radar data, Sentinel-2 Multispectral Instrument data, Landsat-8 Thermal Infrared Sensor and Digital Elevation Models. All data was split into training (70%) and testing datasets (30%). The main strength of this study is that our method overcomes most of the above-mentioned challenges and the great accuracy (Kappa coefficient: 0.96, overall accuracy: 97.21%) of the Random Forest classifier model represents a comprehensive success in identifying debris-covered glaciers, illustrating that if this method can be executed efficiently, it will bring benefits for glacier inventory management. Additionally, an analysis of the spatial characteristic of the mountain glaciers showed that the glacier area, elevation and slope of the Nyainqentanglha Mountains were closely related. Moreover, most glacier movement occurred in glaciers with an area of less than 1 km2 and greater than 10 km2, which had mean velocities of 0.141 m/day and 0.168 m/day, respectively, and glacier movement explained the uncertainty of glacier facies mapping.

Spatio-Temporal Assessment of Surface Moisture and Evapotranspiration Variability Using Remote Sensing Techniques

The relationship between the physic features of the Earth’s surface and its temperature has been significantly investigated for further soil moisture assessment. In this study, the spatiotemporal impacts of surface properties on land surface temperature (LST) were examined by using Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) and meteorological data. The significant distinctions were observed during a crop growing season through the contrast in the correlation between different multi-spectral satellite indices and LST, in which the highest correlation of −0.65 was found when the Normalized Difference Latent heat Index (NDLI) was used. A new index, named as Temperature-soil Moisture Dryness Index (TMDI), is accordingly proposed to assess surface moisture and evapotranspiration (ET) variability. It is based on a triangle space where NDLI is set as a reference basis for examining surface water availability and the variation of LST is an indicator as a consequence of the cooling effect by ET. TMDI was evaluated against ET derived from the commonly-used model, namely Surface Energy Balance Algorithm for Land (SEBAL), as well as compared to the performance of Temperature Vegetation Dryness Index (TVDI). This study was conducted over five-time points for the 2014 winter crop growing season in southern Taiwan. Results indicated that TMDI exhibits significant sensitivity to surface moisture fluctuation by showing a strong correlation with SEBAL-derived ET with the highest correlation of −0.89 was found on 19 October. Moreover, TMDI revealed its superiority over TVDI in the response to a rapidly changing surface moisture due to water supply before the investigated time. It is suggested that TMDI is a proper and sensitive indicator to characterize the surface moisture and ET rate. Further exploitation of the usefulness of the TMDI in a variety of applications would be interesting.

Integrating Gravimetry Data With Thermal Infra‐Red Data From Satellites to Improve Efficiency of Operational Irrigation Advisory in South Asia

AbstractThe rapid decline of groundwater resources in South Asia due to excessive irrigation during dry season requires awareness of optimal on‐field water requirements. Such information is currently provided on farmer cellphones through an operational Irrigation Advisory System (IAS). To minimize the cost of sending such irrigation advisory texts to farmers while maximizing impact of IAS on groundwater sustainability, we integrated Gravity Recovery and Climate Experiment (GRACE) data with Landsat Thermal Infrared (TIR) Imagery to target regions in greater need of the IAS service. We demonstrated the concept of an improved IAS over eight irrigation districts of the Ganges and Indus basins. The Surface Energy Balance Algorithm for Land (SEBAL) was used to monitor on‐field water consumption (evapotranspiration‐ET) over cropped areas using Landsat TIR data at plot‐scale spatial resolution. Comparison of SEBAL ET with crop water demand from Penman‐Monteith (FAO56) technique quantified the extent of overirrigation at the plot scale and provided a tangible pathway to microtarget the IAS service only to farmers with the largest groundwater use footprint, thereby improving the impact of the IAS service further. Our results suggested that an operational IAS that integrates GRACE and Landsat TIR data on average can save about 85% (80 million m3) of groundwater per dry season for irrigation districts of Northern India and 87% (or 150 million m3) per year for irrigation districts of Eastern Pakistan.

Retrieval of land surface temperature, normalized difference moisture index, normalized difference water index of the Ravi basin using Landsat data

Land surface temperature (LST) is an important parameter for the biosphere, cryosphere, and climate change studies. In this study, we estimate LST, NDMI, and NDWI over the Ravi basin, India, and parts of Pakistan, using Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data. The study develops an ERDAS IMAGINE image processing method by using the LANDSAT 8 band 3(Green), band 4(Red), band 5(NIR), band 6(SWIR 1), and band 10 (TIR) data for determining the various spectral indices. The LST results show that most of the areas experienced extreme anomalies ranging from −350Cand 36 ​°C. The normalized difference moisture index (NDMI) value ranges from 0.685 to - 0.154. The normalized difference water index (NDWI) value ranges from 0.146 to - 0.444. Further, the LST result validated with in situ temperature observations at six locations in the study area, providing excellent correlation.

Predicting Aedes aegypti infestation using landscape and thermal features

Identifying Aedes aegypti breeding hotspots in urban areas is crucial for the design of effective vector control strategies. Remote sensing techniques offer valuable tools for mapping habitat suitability. In this study, we evaluated the association between urban landscape, thermal features, and mosquito infestations. Entomological surveys were conducted between 2016 and 2019 in Vila Toninho, a neighborhood of São José do Rio Preto, São Paulo, Brazil, in which the numbers of adult female Ae. aegypti were recorded monthly and grouped by season for three years. We used data from 2016 to 2018 to build the model and data from summer of 2019 to validate it. WorldView-3 satellite images were used to extract land cover classes, and land surface temperature data were obtained using the Landsat-8 Thermal Infrared Sensor (TIRS). A multilevel negative binomial model was fitted to the data, which showed that the winter season has the greatest influence on decreases in mosquito abundance. Green areas and pavements were negatively associated, and a higher cover of asbestos roofs and exposed soil was positively associated with the presence of adult females. These features are related to socio-economic factors but also provide favorable breeding conditions for mosquitos. The application of remote sensing technologies has significant potential for optimizing vector control strategies, future mosquito suppression, and outbreak prediction.

Mapping spatiotemporal variability of the urban heat island across an urban gradient in Worcester, Massachusetts using in-situ Thermochrons and Landsat-8 Thermal Infrared Sensor (TIRS) data

ABSTRACT Landsat-8 derived Land Surface Temperature (LST) is used to measure Surface Urban Heat Island (SUHI) patterns and intensity in Worcester, MA, USA. Additionally, near-surface air temperature variability is measured using in-situ sensors to further contextualize the urban-to-rural land-cover driven thermal patterns in the study area. Despite the widespread applicability of thermal data, many SUHI studies do not compare LST with in-situ information. Comparisons between satellite-based and in-situ measurements of land surface temperature (LST) are important for establishing confidence in the utility of remotely sensed information to monitor and help improve the lived experience in cities. The objective of this study is to determine the capability of Landsat-8 Thermal Infrared Scanner (TIRS) to measure fine-scale temperature variation in a moderately sized urban area with a mixture of land-cover types. Ground-Level Temperature (GLT) was measured at 13 sites using iButton® Thermochron® temperature sensors from 13 June 2013 to 28 October 2014. Landsat-derived LST was compared to in-situ GLT using 30 Landsat-8 TIRS images for the commensurate time period. Sites with 1) eastern solar exposure; 2) low tree canopy coverage; and 3) proximity to impervious surfaces have higher annual temperature and greater offset from Landsat-derived LST. Sites with more than 47% tree canopy coverage have a more consistent LST and GLT relationship, (e.g. MAE < 3.74°C), show lower annual variability (e.g. r2 > 0.85), and also experienced low LST variability over the time series (e.g. coefficient of variation 0.007). Results indicate that site characteristics and land-cover type affect the offset between in-situ air temperature measurements and satellite-derived LST.

Use of Remote Sensing in Comprehending the Influence of Urban Landscape’s Composition and Configuration on Land Surface Temperature at Neighbourhood Scale

The spatial composition and configuration of land use land cover (LULC) in the urban landscape impact the land surface temperature (LST). In this study, we assessed such impacts at the neighbourhood level of the City of Edmonton. In doing so, we employed Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS) satellite images to derive LULC and LST maps, respectively. We used three classification methods, such as ISODATA, random forest, and indices-based, for mapping LULC classes including built-up, water, and green. We obtained the highest overall accuracy of 98.53 and 97.90% with a kappa value of 0.96 and 0.92 in the indices-based method for the 2018 and 2015 LULC maps, respectively. Besides, we estimated the LST map from the brightness temperature using a single-channel algorithm. Our analysis showed that the highest contributors to LST were the industrial (303.51 K in 2018 and 295.99 K in 2015) and residential (303.47 K in 2018 and 296.56 K in 2015) neighbourhoods, and the lowest contributor was the riverine/creek (298.77 K in 2018 and 292.89 K in 2015) during the 2018 late summer and 2015 early spring seasons. We also found that the residential neighbourhoods exhibited higher LST in comparison with the industrial with the same LULC composition. The result was also supported by our surface albedo analysis, where industrial and residential neighbourhoods were giving higher and lower albedo values, respectively. This indicated that the rooftop materials played further role in impacting the LST. In addition, our spatial autocorrelation (local Moran’s I) and proximity (near distance) analyses revealed that the structural configurations would additionally play an important role in contributing to the LST in the neighbourhoods. For example, the cluster pattern with a small gap of minimum 2.4 m between structures in the residential neighbourhoods were showing higher LST in compared with the sparse pattern, with large gaps between structures in the industrial areas. The wide passages for wind flow through the large gaps would be responsible for cooling the LST in the industrial neighbourhoods. The outcomes of this study would help planners in planning and designing urban neighbourhoods, and policymakers and stakeholders in developing strategies to balance surface energy and mitigate local warming.

Variable Urbanization Warming Effects across Metropolitans of China and Relevant Driving Factors

Urbanization is mainly characterized by the expansion of impervious surface (IS) and hence modifies hydrothermal properties of the urbanized areas. This process results in rising land surface temperature (LST) of the urbanized regions, i.e., urban heat island (UHI). Previous studies mainly focused on relations between LST and IS over individual city. However, because of the spatial heterogeneity of UHI from individual cities to urban agglomerations and the influence of relevant differences in climate background across urban agglomerations, the spatial-temporal scale independence of the IS-LST relationship still needs further investigation. In this case, based on Landsat-8 Operational Land Imager and Thermal Infrared Sensor (Landsat 8 OLI/TIRS) remote sensing image and multi-source remote sensing data, we extracted IS using VrNIR-BI (Visible red and NIR-based built-up Index) and calculated IS density across three major urban agglomerations across eastern China, i.e., the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) to investigate the IS-LST relations on different spatial and temporal scales and clarify the driving factors of LST. We find varying warming effects of IS on LST in diurnal and seasonal sense at different time scales. Specifically, the IS has stronger impacts on increase of LST during daytime than during nighttime and stronger impacts on increase of LST during summer than during winter. On different spatial scales, more significant enhancing effects of IS on LST can be observed across individual city than urban agglomerations. The Pearson correlation coefficient (r) between IS and LST at the individual urbanized region can be as high as 0.94, indicating that IS can well reflect LST changes within individual urbanized region. However, relationships between IS and LST indicate nonlinear effects of IS on LST. Because of differences in spatial scales, latitudes, and local climates, we depicted piecewise linear relations between IS and LST across BTH when the IS density was above 10% to 17%. Meanwhile, linear relations still stand between IS density and LST across YRD and PRD. Besides, the differences in the IS-LST relations across urban agglomeration indicate more significant enhancing effects of IS on LST across PRD than YRD and BTH. These findings help to enhance human understanding of the warming effects of urbanization or UHI at different spatial and temporal scales and is of scientific and practical merits for scientific urban planning.

Modifying an Image Fusion Approach for High Spatiotemporal LST Retrieval in Surface Dryness and Evapotranspiration Estimations

Thermal infrared (TIR) satellite images are generally employed to retrieve land surface temperature (LST) data in remote sensing. LST data have been widely used in evapotranspiration (ET) estimation based on satellite observations over broad regions, as well as the surface dryness associated with vegetation index. Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) can provide LST data with a 30-m spatial resolution. However, rapid changes in environmental factors, such as temperature, humidity, wind speed, and soil moisture, will affect the dynamics of ET. Therefore, ET estimation needs a high temporal resolution as well as a high spatial resolution for daily, diurnal, or even hourly analysis. A challenge with satellite observations is that higher-spatial-resolution sensors have a lower temporal resolution, and vice versa. Previous studies solved this limitation by developing a spatial and temporal adaptive reflectance fusion model (STARFM) for visible images. In this study, with the primary mechanism (thermal emission) of TIRS, surface emissivity is used in the proposed spatial and temporal adaptive emissivity fusion model (STAEFM) as a modification of the original STARFM for fusing TIR images instead of reflectance. For high a temporal resolution, the advanced Himawari imager (AHI) onboard the Himawari-8 satellite is explored. Thus, Landsat-like TIR images with a 10-minute temporal resolution can be synthesized by fusing TIR images of Himawari-8 AHI and Landsat-8 TIRS. The performance of the STAEFM to retrieve LST was compared with the STARFM and enhanced STARFM (ESTARFM) based on the similarity to the observed Landsat image and differences with air temperature. The peak signal-to-noise ratio (PSNR) value of the STAEFM image is more than 42 dB, while the values for STARFM and ESTARFM images are around 31 and 38 dB, respectively. The differences of LST and air temperature data collected from five meteorological stations are 1.53 °C to 4.93 °C, which are smaller compared with STARFM’s and ESATRFM’s. The examination of the case study showed reasonable results of hourly LST, dryness index, and ET retrieval, indicating significant potential for the proposed STAEFM to provide very-high-spatiotemporal-resolution (30 m every 10 min) TIR images for surface dryness and ET monitoring.

Cross-Comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) Derived Vegetation Indices in a Mediterranean Environment

Landsat 8 is the most recent generation of Landsat satellite missions that provides remote sensing imagery for earth observation. The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, together with Landsat-8 Operational Land Imager (OLI) and Thermal Infrared sensor (TIRS) represent fundamental tools for earth observation due to the optimal combination of the radiometric and geometric images resolution provided by these sensors. However, there are substantial differences between the information provided by Landsat 7 and Landsat 8. In order to perform a multi-temporal analysis, a cross-comparison between image from different Landsat satellites is required. The present study is based on the evaluation of specific intercalibration functions for the standardization of main vegetation indices calculated from the two Landsat generation images, with respect to main land use types. The NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), LSWI (Land Surface Water Index), NBR (Normalized Burn Ratio), VIgreen (Green Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and EVI (Enhanced Vegetation Index) have been derived from August 2017 ETM+ and OLI images (path: 188; row: 32) for the study area (Basilicata Region, located in the southern part of Italy) selected as a highly representative of Mediterranean environment. Main results show slight differences in the values of average reflectance for each band: OLI shows higher values in the near-infrared (NIR) wavelength for all the land use types, while in the short-wave infrared (SWIR) the ETM+ shows higher reflectance values. High correlation coefficients between different indices (in particular NDVI and NDWI) show that ETM+ and OLI can be used as complementary data. The best correlation in terms of cross-comparison was found for NDVI, NDWI, SAVI, and EVI indices; while according to land use classes, statistically significant differences were found for almost all the considered indices calculated with the two sensors.

Downscaling Land Surface Temperature Using Multiscale Geographically Weighted Regression Over Heterogeneous Landscapes in Wuhan, China

The deficiency of fine-resolution satellite-derived land surface temperature (LST) has impeded the meticulous exploration of the urban thermal environment at micro spatial scales. Although the LST downscaling methods are well-documented, the scale dependence between LST and environmental factors has been rarely considered in the regression establishment. This article proposes a new method (MGWRK) coupling multiscale geographically weighted regression (MGWR) and area-to-point kriging (ATPK) to generate fine-resolution LST maps over heterogeneous landscapes. Technically, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and albedo are selected using random forests (RF) as scale factors. First, the spatial and temporal robustness of MGWRK is evaluated by downscaling aggregated Landsat-8 Thermal Infrared Sensor images (1000-m) into finer resolutions of 500, 400, 300, 200, and 100-m in different seasons (spring, summer, and winter). Results reveal that MGWRK is superior to GWR and DisTrad methods for spatial details improvement (SSIM > 0.93) and LST fidelity ( ${\boldsymbol{R}^2}$ > 0.91) under all scales and in the seasons. Second, MGWRK is utilized to downscale the synchronous 1000-m Moderate Resolution Imaging Spectroradiometer (MODIS) LST map into 100-m. The quantitative and qualitative result substantiates that MGWRK obtains ideal performance when applied on MODIS data with few LST distortion ( ${\boldsymbol{R}^2}\; = \;0.915$ , RMSE = 1.607 K) and obvious spatial information enrichment (SSIM = 0.903).

DETECTING DEFORMATION DUE TO THE 2018 MERAPI VOLCANO ERUPTION USING INTERFEROMETRIC SYNTHETIC APERTURE RADAR (INSAR) FROM SENTINEL-1 TOPS

This paper describes the application of Sentinel-1 TOPS (Terrain Observation with Progressive Scans), the latest generation of SAR satellite imagery, to detect displacement of the Merapi volcano due to the May–June 2018 eruption. Deformation was detected by measuring the vertical displacement of the surface topography around the eruption centre. The Interferometric Synthetic Aperture Radar (InSAR) technique was used to measure the vertical displacement. Furthermore, several Landsat-8 Thermal Infra Red Sensor (TIRS) imageries were used to confirm that the displacement was generated by the volcanic eruption. The increasing temperature of the crater was the main parameter derived using the Landsat-8 TIRS, in order to determine the increase in volcanic activity. To understand this phenomenon, we used Landsat-8 TIRS acquisition dates before, during and after the eruption. The results show that the eruption in the May–June 2018 period led to a small negative vertical displacement. This vertical displacement occurred in the peak of volcano range from -0.260 to -0.063 m. The crater, centre of eruption and upper slope of the volcano experienced negative vertical displacement. The results of the analysis from Landsat-8 TIRS in the form of an increase in temperature during the 2018 eruption confirmed that the displacement detected by Sentinel-1 TOPS SAR was due to the impact of volcanic activity. Based on the results of this analysis, it can be seen that the integration of SAR and thermal optical data can be very useful in understanding whether deformation is certain to have been caused by volcanic activity.