Spatiotemporal Surface Research Articles

Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery

The monitoring of impervious surfaces in urban areas using remote sensing with fine spatial and temporal resolutions is crucial for monitoring urban development and environmental changes in urban areas. Spatiotemporal super-resolution mapping (STSRM) fuses fine-spatial-coarse-temporal remote sensing data with coarse-spatial-fine-temporal data, allowing for urban impervious surface mapping at both fine-spatial and fine-temporal resolutions. The STSRM involves two main steps: unmixing the coarse-spatial-fine-temporal remote sensing data to class fraction images, and downscaling the fraction images to sub-pixel land cover maps. Yet, challenges exist in each step when applying STSRM in mapping impervious surfaces. First, the impervious surfaces have high spectral variability (i.e., high intra-class and low inter-class variability), which impacts the accurate extraction of sub-pixel scale impervious surface fractions. Second, downscaling the fraction images to sub-pixel land cover maps is an ill-posed problem and would bring great uncertainty and error in the predictions. This paper proposed a new Spatiotemporal Continuous Impervious Surface Mapping (STCISM) method to deal with these challenges in fusing Landsat and Google Earth imagery. The STCISM used the Multiple Endmember Spectral Mixture Analysis and the Fisher Discriminant Analysis to minimize the within-class variability and maximize the between-class variability to reduce the spectral unmixing uncertainty. In addition, the STCISM adopted a new temporal consistency check model to incorporate temporal contextual information to reduce the uncertainty in the time-series impervious surface prediction maps. Unlike the traditional temporal consistency check model that assumed the impervious-to-pervious conversion is unlikely to happen, the new model allowed the bidirectional conversions between pervious and impervious surfaces. The temporal consistency check was used as a post-procession method to correct the errors in the prediction maps. The proposed STCISM method was used to predict time-series impervious surface maps at 5 m resolution of Google Earth image at the Landsat frequency. The results showed that the proposed STCISM outperformed the STSRM model without using the temporal consistency check and the STSRM model using the temporal consistency check based on the unidirectional pervious-to-impervious surface conversion rule.

A rich catalog of C\u2013C bonded species formed in CO2 reduction on a plasmonic photocatalyst

The understanding and rational design of heterogeneous catalysts for complex reactions, such as CO2 reduction, requires knowledge of elementary steps and chemical species prevalent on the catalyst surface under operating conditions. Using in situ nanoscale surface-enhanced Raman scattering, we probe the surface of a Ag nanoparticle during plasmon-excitation-driven CO2 reduction in water. Enabled by the high spatiotemporal resolution and surface sensitivity of our method, we detect a rich array of C1–C4 species formed on the photocatalytically active surface. The abundance of multi-carbon compounds, such as butanol, suggests the favorability of kinetically challenging C–C coupling on the photoexcited Ag surface. Another advance of this work is the use of isotope labeling in nanoscale probing, which allows confirmation that detected species are the intermediates and products of the catalytic reaction rather than spurious contaminants. The surface chemical knowledge made accessible by our approach will inform the modeling and engineering of catalysts.

Estimation of spatio-temporal extreme distribution using a quantile factor model

This paper describes the estimation of the extreme spatio-temporal sea surface temperature data based on the quantile factor model implemented by the SNU multiscale team. The proposed method was developed for the EVA2019 Data Challenge. Various attempts have been conducted to use factor models in spatio-temporal data analysis to find hidden factors in high-dimensional data. Factor models represent high-dimensional data as a linear combination of several factors, and hence, can describe spatially and temporally correlated data in a simple form. Meanwhile, unlike ordinary factor models, there are asymmetric norm-based factor models, such as quantile factor models or expectile dynamic semiparametric factor models, that can help understand the quantitative behavior of data beyond their mean structure. For this purpose, we apply a quantile factor model to the data to obtain significant factors explaining the quantile response of the temperatures and find quantile estimates. We develop a new method for inference of quantiles of extremal levels by extrapolating quantile estimates from the factor model with extreme value theory. The proposed method provides better performance than the benchmark, gives some interpretable insights, and shows the potential to expand the factor model with various data.

The influence of system heterogeneity on peat-surface temperature dynamics

Temperatures at the soil–atmosphere interface influence ecosystem function by driving nonlinear terrestrial biogeochemical, ecohydrological, and micrometeorological processes. Whilst climate, soil and vegetation controls on spatially average ecosystem temperatures are recognised, how interacting and heterogeneous ecosystem layers create spatio-temporal complex thermal ecosystems has not been determined. Such thermal hot spots and hot moments may underpin the capability of ecosystems to support biological and biogeochemical diversity and control the likelihood of tipping points in system-regulating feedbacks being locally exceeded. This is of notable importance in peatlands, where soil temperatures control the storage of their associated globally important carbon stocks. Here, through the application of high spatio-temporal resolution surface temperature data and peat thermal modelling, we assess the impact of system heterogeneity (spatio-temporal impact of the following system layers: tree, shrubs, microtopography, groundcover species and sub-surface ice) on surface temperature regimes. We show (a) that peat-surface thermal hotspot intensity and longevity is linked to system heterogeneity and (b) that not all system layers have an equal influence over the peat-surface thermal regime and extreme temperatures; thermal heterogeneity increases up to a maximum of five layers of heterogeneity and decreases thereafter. The results crucially demonstrate that such changes in the spatio-temporal thermal dynamics and extremes may occur without significant changes in median temperatures. This is important to the conceptual understanding of peatland responses and ecosystem resilience to disturbance. It emphasises the need to determine the potential for transitions in magnitude, longevity and locality of small-scale thermal extremes to induce functional transitions that propagate through given ecosystems, and to characterise the impact of such small-scale spatio-temporal complexity on ecosystem scale biogeochemical and ecohydrological function.

Estimating Daily Actual Evapotranspiration at a Landsat-Like Scale Utilizing Simulated and Remote Sensing Surface Temperature

Actual evapotranspiration (ET) with high spatiotemporal resolution is very important for the research on agricultural water resource management and the water cycle processes, and it is helpful to realize precision agriculture and smart agriculture, and provides critical references for agricultural layout planning. Due to the impact of the clouds, weather environment, and the orbital period of optical satellite, there are difficulties in providing daily remote sensing data that are not contaminated by clouds for estimating daily ET with high spatial-temporal resolution. By improving the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), this manuscript proposes the method to fuse high temporal and low spatial resolution Weather Research and Forecasting (WRF) model surface skin temperature (TSK) with the low temporal and high spatial resolution remote sensing surface temperature for obtaining high spatiotemporal resolution daily surface temperature to be used in the estimation of the high spatial resolution daily ET (ET_WRFHR). The distinction of this study from the previous literatures can be summarized as the novel application of the fusion of WRF-simulated TSK and remote sensing surface temperature, giving full play to the availability of model surface skin temperature data at any time and region, making up for the shortcomings of the remote sensing data, and combining the high spatial resolution of remote sensing data to obtain ET with high spatial (Landsat-like scale) and temporal (daily) resolution. The ET_WRFHR were cross-validated and quantitatively verified with MODIS ET products (MOD16) and observations (ET_Obs) from eddy covariance system. Results showed that ET_WRFHR not only better reflects the difference and dynamic evolution process of ET for different land types but also better identifies the details of various fine geographical objects. It also represented a high correlation with the ET_Obs by the R2 amount reaching 0.9186. Besides, the RMSE and BIAS between ET_WRFHR and the ET_Obs are obtained as 0.77 mm/d and −0.08 mm/d respectively. High R2, as well as the small RMSE and BIAS amounts, indicate that ET_WRFHR has achieved a very good performance.

Spatial resolution and object segmentation efficiency constrain grouping effects in attentive tracking

Previous studies of multiple object tracking suggested that spatiotemporal features (e.g., speed, direction, location) and surface features (e.g., color, shape, size) can guide perceptual grouping. However, it is still unclear how target-distractor distinctiveness and target-background similarity affect grouping effects in attentive tracking. To address these two questions, three experiments have been carried out in the current study. In Experiment 1, we manipulated the target-distractor distinctiveness and found that tracking performance logarithmically improved when the target-distractor distinctiveness linearly increased. In Experiment 2a and 2b, we varied the target-background similarity and found that too high or too low target-background similarity damaged the tracking performance, while only the middle target-background similarity resulted in the best tracking performance. These findings reveal that not only target-distractor distinctiveness but also target-background similarity plays a vital role in guiding the attention of perceptual grouping in attentive tracking. The guidance induced by target-distractor distinctiveness is constrained by the spatial resolution, while the guidance induced by target-background similarity is constrained by the efficiency of object segmentation. Additionally, our results showed that tracking capacity varied with the target-distractor distinctiveness and the target-background similarity, even though the number of targets being tracked was fixed. It suggests that there may be a trade-off between the difficulty of tracking and the number of targets that can be tracked. Thus, tracking capacity is more likely to be limited by the flexible attention resources rather than the number of fixed slots.

Modeling Daily Temperatures Via a Phenology-Based Annual Temperature Cycle Model

High spatiotemporal resolution land surface temperature (LST) plays an important role in various environment applications. However, the limitation of thermal infrared sensors and the effect of clouds and other atmospheric conditions result in discontinuous daily thermal observations of the Moderate Resolution Imaging Spectroradiometer (MODIS). Annual temperature cycle (ATC) models can help to supply daily continuous LSTs via limited observations, but these ATC models seldom consider the disturbance of weather conditions or the land cover change. On the other hand, spatial interpolation techniques also limit in implementation when available data in one day or several days are not able to obtain enough spatiotemporal information for LST reconstruction. The objective of this study is to propose a phenology-based ATC model (termed PATC), which takes the phenology change and local weather change into account, to reconstruct daily unscanned LSTs at an annual scale. Daily MODIS LSTs collected in 2015 were utilized to analyze the performance of PATC compared with other ATC models. Results show that PATC improved the accuracy by 1.6 and 0.5 K compared to the classic ATC model in the daytime and nighttime, respectively. Compared to the enhanced ATC model, PATC also shows better performance with higher accuracies, especially during the growing season of vegetation in the daytime. Future research may focus on an incorporation with Landsat observations and diurnal temperature cycle models to implement LST reconstruction at a diurnal scale.

Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks.

Spatiotemporal (4D) cortical surface atlas during infancy plays an important role for surface-based visualization, normalization and analysis of the dynamic early brain development. Conventional atlas construction methods typically rely on classical group-wise registration on sub-populations and ignore longitudinal constraints, thus having three main issues: 1) constructing templates at discrete time points; 2) resulting in longitudinal inconsistency among different age's atlases; and 3) taking extremely long runtime. To address these issues, in this paper, we propose a fast unsupervised learning-based surface atlas construction framework incorporating longitudinal constraints to enforce the within-subject temporal correspondence in the atlas space. To well handle the difficulties of learning large deformations, we propose a multi-level multimodal spherical registration network to perform cortical surface registration in a coarse-to-fine manner. Thus, only small deformations need to be estimated at each resolution level using the registration network, which further improves registration accuracy and atlas quality. Our constructed 4D infant cortical surface atlas based on 625 longitudinal scans from 291 infants is temporally continuous, in contrast to the state-of-the-art UNC 4D Infant Surface Atlas, which only provides the atlases at a few discrete sparse time points. By evaluating the intra- and inter-subject spatial normalization accuracy after alignment onto the atlas, our atlas demonstrates more detailed and fine-grained cortical patterns, thus leading to higher accuracy in surface registration.

Estimation of Land Surface Albedo from MODIS and VIIRS Data: A Multi-Sensor Strategy Based on the Direct Estimation Algorithm and Statistical-Based Temporal Filter

Land surface albedo is an important variable for Earth’s radiation and energy budget. Over the past decades, many surface albedo products have been derived from a variety of remote sensing data. However, the estimation accuracy, temporal resolution, and temporal continuity of these datasets still need to be improved. We developed a multi-sensor strategy (MSS) based on the direct-estimation algorithm (DEA) and Statistical-Based Temporal Filter (STF) to improve the quality of land surface albedo datasets. The moderate-resolution imaging spectroradiometer (MODIS) data onboard Terra and Aqua and the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi-National Polar-orbiting Partnership (NPP) were used as multi-sensor data. The MCD43A3 product and in situ measurements from the Surface Radiation Budget Network (SURFRAD) and FLUXNET sites were employed for validation and comparison. The results showed that the proposed MSS method significantly improved the temporal continuity and estimation accuracy during the snow-covered period, which was more consistent with the measurements of SURFRAD (R = 0.9498, root mean square error (RMSE) = 0.0387, and bias = −0.0017) and FLUXNET (R = 0.9421, RMSE = 0.0330, and bias = 0.0002) sites. Moreover, this is a promising method to generate long-term, spatiotemporal continuous land surface albedo datasets with high temporal resolution.

Rogue waves associated with resonant slow sloshing waves spontaneously excited in wind-driven water wave turbulence

Rogue wave events (RWEs), rare uncertainly emerging localized events with extreme amplitudes, widely exist in various nonlinear wave media. Past laboratory studies on RWEs of water surface waves have mainly focused on mechanically excited waves but to a lesser extent on waves solely excited by wind, which also have a large number of degrees of freedom. In this work, we experimentally demonstrate the laboratory observation of RWEs in a wind-driven water surface wave system after the transition to the steady wave turbulent state by increasing averaged wind speed. The spatiotemporal water surface height evolution is measured using diffusive light photography. It is found that RWEs uncertainly emerge in traveling bursts with high frequency large-amplitude fluctuations and irregular burst amplitude. The associated spontaneous slow periodic wave is identified as the resonant slow sloshing wave selected by the water tank with a finite length and spontaneously excited under turbulence. The surface wind speed modulation by the oscillating surface height of the sloshing wave in the small fetch region is proposed as the source for the excitations of sequential large amplitude bursts. The burst envelope shape deterioration through further turbulent wind and water wave interactions as bursts propagate downstream causes the non-Gaussian water surface height histogram with a highly stretched tail, contributed by the uncertain generation of RWEs in irregular bursts in the middle fetch region.

STEREO-PHOTOGRAMMETRIC MEASUREMENT OF SPATIO-TEMPORAL VELOCITY FIELDS AT LANGE GLACIER, KING GEORGE ISLAND

Abstract. A network consisting of six cameras was set up on both sides of Lange Glacier on King George Island, Antarctica, for a period of two years to monitor changes in the glacier’s motion behaviour. The cameras were observing spatio-temporal glacier surface velocity vector fields as well as the position of the glacier front. Velocity field information was obtained from image sequences by applying subpixel accuracy photogrammetric image sequence analysis techniques. Georeferencing in a superordinate coordinate system was performed via integrated photogrammetric-geodetic network adjustment. As a result, velocity fields were determined with maximum glacier surface velocities in the order of 1.5 meter per day. The results of terrestrial camera image sequence processing can be used as validation and calibration reference for satellite image based glacier velocity dynamics calculations. Moreover, the very high temporal resolution of the image sequences taken at 20 minute time intervals can also be used to analyse highly dynamic processes.

Identifying watershed-scale spatiotemporal groundwater and surface water mixing function in the Yiluo River, Middle of China.

Researching the watershed-scale spatiotemporal groundwater and surface water mixing function, groundwater recharge heights and age can provide important information to watershed water sources management and water pollution controlling. In this study, 20 groundwater samples, 6 precipitation samples, and 40 surface water samples in the Yiluo River watershed in middle China were collected, and the mixing function between groundwater and surface water, recharge height, and age of groundwater was studied by using hydrochemistry (Cl, DO, EC, pH), radioactive isotopes (14C), and stable isotopes (13C, 18O, 15N). The results showed that river and shallow groundwater had frequent interaction. Evaporation function played a major role in the groundwater recharging process. Groundwater age displayed spatial and seasonal variations, and the age ranged from 660 to 2800years (dry season) and 560 to 1800years (wet season) in downstream and midstream, while the age range from 12,025 to 143,600years (dry season) and 10,560 to 12,650years (wet season) in upstream. The average recharge height of downstream and midstream both in dry and wet seasons is about 576.00 to 908.70m. While in upstream, both, in the dry and wet seasons, ranged from 1285 to 2680m. The mixing function between groundwater and surface water displayed spatiotemporal variety. These results of this study can provide useful information for the watershed-scale water resources management.

Imaging how thermal capillary waves and anisotropic interfacial stiffness shape nanoparticle supracrystals

Development of the surface morphology and shape of crystalline nanostructures governs the functionality of various materials, ranging from phonon transport to biocompatibility. However, the kinetic pathways, following which such development occurs, have been largely unexplored due to the lack of real-space imaging at single particle resolution. Here, we use colloidal nanoparticles assembling into supracrystals as a model system, and pinpoint the key role of surface fluctuation in shaping supracrystals. Utilizing liquid-phase transmission electron microscopy, we map the spatiotemporal surface profiles of supracrystals, which follow a capillary wave theory. Based on this theory, we measure otherwise elusive interfacial properties such as interfacial stiffness and mobility, the former of which demonstrates a remarkable dependence on the exposed facet of the supracrystal. The facet of lower surface energy is favored, consistent with the Wulff construction rule. Our imaging–analysis framework can be applicable to other phenomena, such as electrodeposition, nucleation, and membrane deformation.

Investigating the Effect of Lockdown During COVID-19 on Land Surface Temperature: Study of Dehradun City, India

Urban environment imposes challenges due to its dynamics and thermodynamic characteristics of the built environment. The present study aims to study the effect of lockdown during COVID-19 on the spatio-temporal land surface temperature (LST) patterns in Dehradun city. The TIRS sensor data of 14 April 2020 (post-lockdown), 28 April 2019, 25 April 2018 and 08 May 2017 were downloaded, and LST was retrieved using radiative transfer equation. The wardwise change in LST, urban hot spots and thermal comfort was studied as a function of built-up density. It was observed that there was an overall decrease in LST values in Dehradun city in post-COVID lockdown period. Wards with high built-up density had minimum decrease in LST; on the contrary, wards with large proportion of open spaces and having low, medium built-up density had the maximum decrease in LST. Hot spot analysis was carried out using Getis Ord GI* statistic, and the level of thermal comfort was found using the urban thermal field variance index. It was observed that there was an increase in number of hot spots accompanied by a decrease in thermal comfort level post-lockdown. The methodology proposed in the present study can be applied to other Indian cities which exhibit similar growth patterns and will provide a tool for rational decision making.

Construction of Spatiotemporal Infant Cortical Surface Functional Templates.

Infant cortical surface templates play an essential role in spatial normalization of cortical surfaces across individuals in pediatric neuroimaging analysis. However, existing infant surface templates have two major limitations in functional MRI analysis. First, they are constructed by co-registration of cortical surfaces based on structural attributes, which cannot lead to accurate functional alignment, due to the highly variable relationship between cortical folds and functions. Second, they are constructed by simply averaging co-registered cortical attributes, which is sensitive to registration errors and leads to blurred attribute patterns on templates, thus deteriorating the accuracy in spatial normalization. Therefore, the construction of infant cortical functional templates encoding sharp functional architectures is critical for infant fMRI analysis. To this end, we construct the first set of spatiotemporal infant cortical surface functional templates using Wasserstein barycenter and a state-of-the-art functional feature, namely the gradient density of functional connectivity. To address the first issue, we leverage functional gradient density to drive surface registration to improve inter-individual functional correspondences. To address the second issue, we compute templates based on the Wasserstein barycenter of functional gradient density maps across individuals. The motivation is that Wasserstein barycenter represents a meaningful mean under the Wasserstein distance metric, which takes into account the alignment of local spatial distribution of cortical attributes and thus is robust to registration errors, leading to sharp and detailed patterns on templates. Experiments on a dataset with 207 fMRI scans between 0 and 2 years of age show the validity and accuracy of our constructed infant cortical functional templates.

Chitosan‐Based, Biocompatible, Solution Processable Films for In Vivo Localization of Neural Interface Devices

AbstractChitosan (CS) is a biocompatible, inexpensive organic polymer that is increasingly used in neural tissue applications. However, its intrinsic fluorescence has not yet been leveraged to facilitate localization of neural interface devices, a key procedure to ensure accurate analysis of neurophysiological signals. A process is developed to enable control of mechanical and chemical properties of CS‐based composites, generating freestanding films that are stable in aqueous environments and exhibit concentration‐dependent fluorescence intensity. The shape and location of CS‐coated probes are reliably visualized in vitro and in vivo using fluorescence microscopy. Furthermore, CS neural probe marking is fully compatible with classical immunohistochemical and histological techniques, enabling localization of high spatiotemporal resolution surface electrocorticography arrays in adult rats and mouse pups. CS composites have the potential to simplify and streamline experimental procedures required to efficiently acquire, localize, and interpret neurophysiological data.

The Importance of Systematic Spatial Variability in the Surface Heat Flux of a Large Lake: A Multiannual Analysis for Lake Geneva

AbstractThe spatiotemporal surface heat flux (SurHF) distribution over Lake Geneva, the largest lake in Western Europe, was estimated for a 7‐year period (2008–2014). Data sources included hourly maps of over‐the‐lake assimilated meteorological data from a validated numerical weather model and lake surface water temperature (LSWT) from satellite imagery. A set of bulk algorithms, previously optimized and calibrated at two locations in Lake Geneva, was used. Results indicate a systematic long‐term average spatial range of >40 Wm‐2 between different parts of the lake and little year‐to‐year variability. This variability is mainly due to topographically induced wind sheltering over parts of the lake, which in turn produces spatial variability in the sensible and latent heat fluxes. These results are supported by a systematic spatial heat content variability obtained from long‐term temperature profile measurements in the lake. During spring, a lower SurHF spatial range was evident. Unlike other seasons, the spring spatial variability of air‐water temperature differences and, to a lesser extent, the global radiation variability resulting from sheltering by the mountainous topography were the main drivers of the SurHF spatial variability. Analysis of the atmospheric thermal boundary layer showed stable conditions from March to early June and unstable conditions for the rest of the year. This regime change can explain the low SurHF spatial variability observed during spring. The results emphasize that spatial variability in meteorological and LSWT patterns, and consequently in the spatiotemporal SurHF data, should be considered when assessing the time evolution of the heat budget of large lakes.

A New Multigrid 3D-VAR Optimization Method for Bottom Friction Using HF Radar Current Observation

This paper proposes a new method for data assimilation of the surface radial current observed by High Frequency ground wave radar and optimization of the bottom friction coefficient. In this method, the shallow water wave equation is introduced into the cost function of the multigrid three-dimensional variation data assimilation method as the weak constraint term, the surface current and the bottom friction coefficient are defined as the analytical variables, and the high spatiotemporal resolution surface radial flow observed by the high-frequency ground wave radar is used to optimize the surface current and bottom friction coefficient. This method can effectively consider the spatiotemporal correlation of radar data and extract multiscale information from surface radial flow data from long waves to short waves. Introducing the shallow water wave equation into the cost function as a weak constraint condition can adjust both the momentum and mass fields simultaneously to obtain more reasonable analysis information. The optimized bottom friction coefficient is introduced into the regional ocean numerical model to carry out numerical experiments. The test results show that the bottom friction coefficient obtained by this method can effectively improve the accuracy of the numerical simulation of sea surface height in the offshore area and reduce the simulation error.

Physically constrained spatiotemporal modeling: generating clear-sky constructions of land surface temperature from sparse, remotely sensed satellite data

Satellite remote-sensing is used to collect important atmospheric and geophysical data at various spatial resolutions, providing insight into spatiotemporal surface and climate variability globally. These observations are often plagued with missing spatial and temporal information of Earth's surface due to (1) cloud cover at the time of a satellite passing and (2) infrequent passing of polar-orbiting satellites. While many methods are available to model missing data in space and time, in the case of land surface temperature (LST) from thermal infrared remote sensing, these approaches generally ignore the temporal pattern called the ‘diurnal cycle’ which physically constrains temperatures to peak in the early afternoon and reach a minimum at sunrise. In order to infill an LST dataset, we parameterize the diurnal cycle into a functional form with unknown spatiotemporal parameters. Using multiresolution spatial basis functions, we estimate these parameters from sparse satellite observations to reconstruct an LST field with continuous spatial and temporal distributions. These estimations may then be used to better inform scientists of spatiotemporal thermal patterns over relatively complex domains. The methodology is demonstrated using data collected by MODIS on NASA's Aqua and Terra satellites over both Houston, TX and Phoenix, AZ USA.

Urban heat island intensity and its mitigation strategies in the fast-growing urban area

Climate change especially rising temperature in the urban areas has become a major focus of attention worldwide because of the impacts having on human beings, biodiversity, and urban ecosystem. Time series Landsat (TM and ETM+) satellite data products have been employed in this study to quantify the spatiotemporal Land Surface Temperature (LST) and Urban Heat Island (UHI) intensity for the year of 2000, 2005, 2010 and 2015, respectively. Biophysical characteristics of the city have been assessed through Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Bareness Index (NDBaI). The thermal behavior of the city varied distinctly. Seasonal LST and biophysical composition of the city has been analyzed to explore the temperature and greenness sensitivity across the city region. The per capita electricity consumption of the city was positively correlated with the surface LST for both summer and autumn/spring season. A relative brightness temperature approach was employed to examine the nature of UHI across the city. It is evident from the observation that the temperature is very high within the city core as well as certain surrounding areas of the city, especially on the southern side. The temperature is comparatively lower on the western side of the city than the eastern region. Certain peripheral regions, however, show a higher temperature. This can be due to the development taking place in the outer areas of the city and destruction of vegetation in the outlying parts of the city. Studied NDVI indicates that vegetation in the city is not balanced. It is high in the western part which maybe because of the locations of different academic institutions, botanical gardens, seminary hills, agricultural land, etc. Whereas, the eastern part is devoid of vegetation. Also, the areas in the periphery, especially near the airport and Ambajhari Lake, has very low vegetation. The bareness is also high in the peripheral regions. Result also shows that street-based heat intensity mitigation helps for urban planning.