Seasonal Land Surface Research Articles

Coupling effects of building-vegetation-land on seasonal land surface temperature on street-level: A study from a campus in Beijing

The land surface temperature (LST) of urban streets is significantly influenced by the surrounding urban environment. In the cold areas of northern China, this influence exhibits a seasonal pattern. Currently, in areas with noticeable seasonal variations, the coupling effects of urban elements on the annual stability of street LST remain unclear. This study developed a new metric, Normalized Winter-Summer Land Surface Temperature Difference, to measure the year-round LST stability of campus streets. Three key urban elements affecting street-level LST in winter and summer were identified: building, vegetation, and land. The influencing characteristics for winter and summer LST were quantified and ranked respectively. The study reveals that the green space ratio dominates summer LST, while fraction vegetation coverage, leaf area index and average building height dominate winter LST. We found that morphological characteristics of buildings and vegetation cannot fully explain the impact of the urban physical environment on street-level LST. For winter street LST studies, greater emphasis should be placed on non-morphological characteristics and land. Based on the impact mechanisms and critical thresholds of key features in winter and summer, this study establishes an interpretable spatial classification method. By assigning seasonal weights to influencing features, it provides comprehensive design strategies that consider both winter and summer features to enhance annual LST stability and climate resilience in urban spaces facing extreme meteorological conditions.

Impact of seasonal global land surface temperature (LST) change on gross primary production (GPP) in the early 21st century

Understanding the impact of global land surface temperature (LST) changes on gross primary production (GPP) is crucial for addressing global sustainability challenges effectively. This article explores the effects of winter and summer temperature variations on GPP from 2001 to 2020 on a global scale, employing a range of modeling techniques to investigate the complex relationship between temperature changes and GPP. The study employs various modeling approaches, including linear regression models, artificial neural networks, Random Forest (RF) models, and the XGBoost algorithm to examine both linear and non-linear relationships between global temperature changes and GPP. The results reveal that the RF and XGBoost models effectively capture the non-linear relationship between LST and GPP during both summer and winter, demonstrating a high level of statistical significance (P < 0.01) and achieving R2 values of 0.598. Furthermore, the study applies a Sen+MK trend analysis model to identify six distinct trend patterns in LST and GPP during both summer and winter seasons. In summer, the area exhibiting a non-significant increasing trend (NSI) for GPP covers 65,066,274.77 km2, whereas in winter, a strong significant decreasing trend (SSD) for GPP spans 64,537,108.31 km2. Notably, GPP patterns closely mirror those of LST, suggesting that rising high-temperature conditions during summer lead to reduced GPP, while increased low temperatures during winter promote GPP growth. Robust correlations between LST and GPP are observed under various trend conditions in both summer and winter, displaying strong statistical significance (P < 0.01), with SSD achieving a maximum R2 of 0.594. These findings contribute significantly to our comprehensive understanding of the dynamic relationship between LST and GPP and offer valuable insights for addressing the sustainable development of global climate change challenges.

XGBoost-Based Analysis of the Relationship Between Urban 2-D/3-D Morphology and Seasonal Gradient Land Surface Temperature

XGBoost-Based Analysis of the Relationship Between Urban 2-D/3-D Morphology and Seasonal Gradient Land Surface Temperature

Mechanism analysis of vegetation phenology in an urban agglomeration in an arid zone driven by seasonal land surface temperatures

In arid areas, vegetation is the habitat that sustains the life and survival of species, and it also plays a vital role in soil erosion prevention. This paper investigates the spatial aggregation patterns and trends of vegetation phenology (VP) and land surface temperature (LST) from 2002 to 2021. Additionally, the spatial Durbin model was used to analyze seasonal elements such as air temperature, evaporation, LST, precipitation, soil moisture, aerosol optical depth, and the impact of DEM on VP. Finally, explore the relationship between seasonal LST and urbanization and VP. The results show that in arid areas, the vegetation start of season time (SOS) showed an advancing trend, the vegetation end of season time (EOS) and length of season time (LOS) showed an extending trend, and the vegetation time of middle of season (MOS) tended to be stable. VP changes in oasis areas fluctuated significantly, while VP varies in non-oasis regions were stable. Under the scenario of spatial heterogeneity, LST is the most influential factor in VP. Spring LST has the greatest impact on SOS, winter LST on EOS and LOS. Urbanization advances SOS and extends EOS but has less impact on LOS. This study's results are essential for understanding the complex mechanism of VP changes in oases in arid regions and analyzing the distribution characteristics of VP under urbanization levels.

Machine learning-based assessment and simulation of land use modification effects on seasonal and annual land surface temperature variations

Rapid urban sprawl adversely impacts the local climate and the ecosystem components. Islamabad, one of South Asia's green and environment-friendly capitals, has experienced major Land Use Land Cover (LULC) changes over the past three decades consequently, elevating the seasonal and annual Land Surface Temperature (LST) in planned and unplanned urban areas. The focus of this study was to quantify the fluctuations in LULC and LST in planned and unplanned urban areas using Landsat data and Machine Learning algorithms involving the Support Vector Machine (SVM) over the 1990–2020 data period. Moreover, hybrid Cellular Automata-Markov (CA-Markov) and Artificial Neural Network (ANN) models were employed to project the future changes in LULC and annual LST, respectively, for the years 2035 and 2050. The findings of the study reveal a distinct difference in seasonal and annual LST in planned and unplanned areas. Results showed an increase of ∼22 % in the built-up area but vegetation and bare soil decreased by ∼10 % and ∼12 %, respectively. Built-up land showed a maximum annual mean LST followed by bare-soil and vegetative surfaces. Seasonal analysis showed that summer months experience the highest LST, followed by spring, autumn and winter. Future projections revealed that the built-up areas (∼27 % in 2020) are likely to increase to ∼37 % and ∼50 %, and the areas under the highest annual mean LST class i.e., ≥28 °C are likely to increase to ∼19 % and ∼21 % in planned, and ∼38 % and ∼42 % in unplanned urban areas for the years 2035 and 2050, respectively. Planned areas have better temperature control with urban green spaces, and controlled infrastructure. The Capital Development Authority of Islamabad may be advised to control the expansion of built-up areas, grow urban forests, and thus mitigate the possible Urban Heat Island (UHI) effect.

Impacts of Urban Morphology on Seasonal Land Surface Temperatures: Comparing Grid- and Block-Based Approaches

Climate change is expected to result in increased occurrences of extreme weather events such as heat waves and cold spells. Urban planning responses are crucial for improving the capacity of cities and communities to deal with significant temperature variations across seasons. This study aims to investigate the relationship between urban temperature fluctuations and urban morphology throughout the four seasons. Through quadrant and statistical analyses, built-environment factors are identified that moderate or exacerbate seasonal land surface temperatures (LSTs). The focus is on Seoul, South Korea, as a case study, and seasonal LST values are calculated at both the grid (100 m × 100 m) and street block levels, incorporating factors such as vegetation density, land use patterns, albedo, two- and three-dimensional building forms, and gravity indices for large forests and water bodies. The quadrant analysis reveals a spatial segregation between areas demonstrating high LST adaptability (cooler summers and warmer winters) and those displaying LST vulnerability (hotter summers and colder winters), with significant differences in vegetation and building forms. Spatial regression analyses demonstrate that higher vegetation density and proximity to water bodies play key roles in moderating LSTs, leading to cooler summers and warmer winters. Building characteristics have a constant impact on LSTs across all seasons: horizontal expansion increases the LST, while vertical expansion reduces the LST. These findings are consistent for both grid- and block-level analyses. This study emphasizes the flexible role of the natural environment in moderating temperatures.

Amazon deforestation causes strong regional warming.

Tropical deforestation impacts the climate through complex land-atmosphere interactions causing local and regional warming. However, whilst the impacts of deforestation on local temperature are well understood, the regional (nonlocal) response is poorly quantified. Here, we used remote-sensed observations of forest loss and dry season land-surface temperature during the period 2001 to 2020 to demonstrate that deforestation of the Amazon caused strong warming at distances up to 100 km away from the forest loss. We apply a machine learning approach to show nonlocal warming due to forest loss at 2-100 km length scales increases the warming due to deforestation by more than a factor 4, from 0.16 K to 0.71 K for each 10-percentage points of forest loss. We estimate that rapid future deforestation under a strong inequality scenario could cause dry season warming of 0.96 K across Mato Grosso state in southern Brazil over the period 2020 to 2050. Reducing deforestation could reduce future warming caused by forest loss to 0.4 K. Our results demonstrate the contribution of tropical deforestation to regional climate warming and the potential for reduced deforestation to deliver regional climate adaptation and resilience with important implications for sustainable management of the Amazon.

“Investigating and mapping day-night urban heat island and its driving factors using Sentinel/MODIS data and Google Earth Engine. Case study: Greater Cairo, Egypt”

Urban heat islands (UHI) represent one of the substantial human-induced challenges endangering urban livelihoods. UHI and climate change have significant interactions. Progressively rising warming tendencies are exacerbating higher temperatures in UHI regions. Using time-series data, we examined annual and seasonal day-night UHI intensity and its driving factors in Greater Cairo (GC), which is considered Egypt's megacity, between April 2021 and March 2022. Since Sentinel-1 synthetic aperture radar (SAR) proposes unique horizons for mapping urban areas, we used SAR data and the Random Forest (RF) algorithm for urban footprint extraction in the search region. Sentinel-3 SLSTR thermal images were utilized to derive annual and seasonal day-night land surface temperatures (LST) during the research period. A spatio-temporal assessment of tropospheric NO2, SO2, CO, and aerosol concentrations was performed via the Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI), MODIS, and Google Earth Engine (GEE) applications. The annual average UHI intensity was higher in Egypt's megacity during the nighttime (3.27 K) than during the daytime (0.50 K). This finding indicates that driving forces are boosting temperatures in urban regions at night. The seasonal average day-night UHI intensities have the highest values during the winter, followed by the summer. Significant influencing factors concerning UHI intensity varied between the daytime and nighttime. Moreover, NDBI, PV, NDVI, altitude, and wind speed were the driving factors that were influential during the day and at night. These results can be used to establish effective adaptation measures for combating the impacts of climatic change and UHI.

How urban parks and their surrounding buildings affect seasonal land surface temperature: A case study in Beijing, China

With rapid urbanization, urban areas face deteriorating thermal conditions and climate change challenges. Urban parks have effectively improved urban thermal environments. Several studies have explored the cooling effect of urban parks (PCEs) by adopting buffer analysis and have identified the dominant influencing factors. However, little attention has been paid to the impact of park age and the surrounding buildings on PCEs. Taking Beijing as an example, this study assessed the effects of urban age and surrounding buildings on PCEs across seasons. The main results showed that (1) the newly increasing parks with large areas were mainly distributed in the south and east of the Fifth Ring Road, and the area of the newly increasing parks within the Fourth Ring Road accounted for 16.70% of the total newly increasing parks, (2) the old parks had a lower mean land surface temperature (LST) than that of the newly increasing parks, with extent varying from 0.27° to 1.70°C across seasons, (3) in terms of the mean LST in the park, the old parks had stronger explanatory power than the newly increasing parks in summer (40% vs. 4%), and (4) the relationships between the surrounding buildings and cooling intensity were statistically significant, but with lower explanatory power (< 7%). These findings provide profound insights for improving urban thermal conditions through good maintenance of parks, extension of old parks, and management of surrounding buildings.

Evaluation of the Seasonal Thermal Environmental Benefits of Urban Green Space in the Core Areas of Urban Heat Island

The core areas of the urban heat island (CAUHI) are the most concentrated and closely associated with humans, and they are key to managing the urban heat island (UHI). It is widely acknowledged that one of the best ways to reduce the risk of UHI is the creation of urban green spaces (UGSs). However, most of the current studies are based on the grid or block scale to explore the impact of UGS on UHI. The key to mitigating the urban heat environment is to plan urban UGS rationally in the CAUHI and explore the thermal environmental benefits of UGS. This paper provides an assessment model for the thermal environmental advantages of UGS and uses ten UGS metrics as explanatory factors for seasonal land surface temperature (LST). It quantitatively evaluates the potential differences in landscape characteristics between LST and UGS under different seasons, as well as the seasonal impact on CAUHI. This study found the following: (1) The overall distribution pattern of CAUHI shows a characteristic of spreading from the central part to the surrounding area. Most of the extremely significant CAUHI is dispersed in the center and southeastern regions of the city, where there is a much greater density of impermeable surfaces and essentially no distribution of CAUHI on the natural surface represented by forest land and water bodies. (2) Except for the aggregation index (AI), correlation analysis revealed that other metrics were highly connected with LST. Among the metrics used in this study, the largest patch index (LPI) and landscape division index (DIVISION) had the highest significant correlation with LST. Patch density (PD) was strongly negatively correlated with LST, indicating that fragmented and complex UGS patches could promote vegetation cooling. (3) The green environmental benefit index (GEBI) results showed a significant degree of spatial and temporal variability in the extracted CAUHI. This study found higher GEBI values in the larger thermal patches and lower GEBI in the surrounding smaller patches. The highest mean GEBI was found in winter, at 0.6083, and the largest distribution of large high-value patches. This study revealed the geographical and temporal variability of UGS and CAUHI, and with the help of the constructed scientific evaluation model, it offered suggestions for the optimization of urban greenery.

Vegetation-induced asymmetric diurnal land surface temperatures changes across global climate zones

Unprecedented global vegetation greening during past decades is well known to affect annual and seasonal land surface temperatures (LST). However, the impact of observed vegetation cover change on diurnal LST across global climatic zones is not well understood. Using global climatic time-series datasets, we investigated the long-term growing season daytime and nighttime LST changes globally and explored associated dominant contributors including vegetation and climate factors including air temperature, precipitation, and solar radiation. Results revealed asymmetric growing season mean daytime and nighttime LST warming (0.16 °C/10a and 0.30 °C/10a, respectively) globally from 2003 to 2020, as a result, the diurnal LST range (DLSTR) declined at 0.14 °C/10a. The sensitivity analysis indicated the LST response to changes in LAI, precipitation, and SSRD mainly concentrated during daytime instead of nighttime, however, which showed comparable sensitivities for air temperature. Combining the sensitivities results and the observed LAI and climate trends, we found rising air temperature contributes to 0.24 ± 0.11 °C/10a global daytime LST warming and 0.16 ± 0.07 °C/10a nighttime LST warming, turns to be the dominant contributor to the LST changes. Increased LAI cooled global daytime LST (−0.068 ± 0.096 °C/10a) while warmed nighttime LST (0.064 ± 0.046 °C/10a); hence LAI dominates declines in DLSTR trends (−0.12 ± 0.08 °C/10a), despite some day-night process variations across climate zones. In Boreal regions, reduced DLSTR was due to nighttime warming from LAI increases. In other climatic zones, daytime cooling, and DLSTR decline, was induced by increased LAI. Biophysically, the pathway from air temperature heats the surface through sensible heat and increased downward longwave radiation during day and night, while the pathway from LAI cools the surface by enhancing energy redistribution into latent heat rather than sensible heat during the daytime. These empirical findings of diverse asymmetric responses could help calibrate and improve biophysical models of diurnal surface temperature feedback in response to vegetation cover changes in different climate zones.

How does 2D and 3D of urban morphology affect the seasonal land surface temperature in Island City? A block-scale perspective

The global climate warming caused by urbanization has significantly affected the urban environment. Whilst land surface temperature (LST) is an important factor reflecting urban temperature, previous research on LST mostly focused on two-dimensional (2D) factors and rarely mentioned about the role of three-dimensional (3D) factors, particularly the LST variation characteristics of island cities. Therefore, this study examined the seasonal variation characteristics of urban LST by analyzing the impact of 2D and 3D urban morphology factors of different urban block types on LST in Xiamen Island. The main results are as follows. First, compact low layer (CL), a block type with a higher density of low-rise buildings, has a higher LST in any season. Under the same block density (BD), the higher the block average height (BH), the lower the LST. Second, among the 2D urban morphology factors, normalized difference vegetation index (NDVI) was the main factor for cities to reduce urban LST, especially in summer, while normalized difference built-up index (NDBI) was the opposite. Different from land cities, we found a positive correlation between modified normalized difference water body index (MNDWI) and LST in autumn and winter. Third, in the 3D urban morphology factors, sky view factor (SVF) was significantly positively correlated with LST, while building fluctuation (BF) was negatively correlated. The higher the SVF, the worse the radiation shielding effect between buildings. On the contrary, the higher the BF, the higher the building undulation, and the better the building radiation shielding. These findings should provide some quantitative insights for the future construction and planning of island cities, which can be used to improve the thermal environment of island cities and support the sustainable development of cities.

Evolution of seasonal land surface temperature trend in pond-breeding newt (Neurergus derjugini) in western Iran and eastern Iraq

BackgroundTemperature, as one of the effective environmental stimuli in many aspects of species life and ecosystems, can affect amphibians in many ways. Knowing and predicting temperature change and its possible effects on the habitat suitability and movements of amphibians have led many researchers to use climate change scenarios and species distribution models (SDMs). One of the important remote-sensing products that received less attention of conservation biologists is the land surface temperature (LST). Due to the small difference between LST and air temperature, this component can be used to investigate and monitor the daily and seasonal changes of habitats. This study aims to investigate the seasonal trend of LST in the habitat suitability and connectivity of the critically endangered newt (Neurergus derjugini) in its small distribution range, using the MODIS LST time series (2003 to 2021) and with the help of SDMs, Mann–Kendall (MK) and Pettitt non-parametric tests.ResultsIn the last decade, the increasing trend of LST versus its decreasing trends is obvious. Based on MK and Pettitt tests, in the winter and spring, with the decrease in latitude of 35.45° and increase in longitude of 46.14°, the core populations which are located in the southeast have experienced an increase in temperature. Considering the period time of breeding and overwintering, the continuity of winter and spring can be effective on the survival of adult newts as well as larvae in the microclimate. Linkages with the highest current flow between core populations in the winter and summer are the most likely to be vulnerable. At the level of habitat, the increase in LST is proportional to the trend of thermal landscape changes, and all seasons have had an increase in LST, but in winter and summer, the largest area of the habitat has been involved. By continuing the current trend, many high-altitude southern habitats in Iran will be endangered, and the species will be at risk of local extinction.ConclusionThe increasing trend of temperature in all seasons such as winter will affect many adaptations of the species and these effects are mostly evident in the southern parts of its distribution range therefore, captive breeding and reintroduction are recommended for the populations of these areas.

PhenoC++: An open-source tool for retrieving vegetation phenology from satellite remote sensing data

Satellite-retrieved vegetation phenology has great potential for application in characterizing seasonal and annual land surface dynamics. However, obtaining regional-scale vegetation phenology from satellite remote sensing data often requires extensive data processing and computation, which makes the accurate and rapid retrieval of regional-scale phenology a challenge. To retrieve vegetation phenology from satellite remote sensing data, we developed an open-source tool called phenoC++, which uses parallel technology in C++. phenoC++ includes six common algorithms: amplitude threshold (AT), first-order derivative (FOD), second-order derivative (SOD), third-order derivative (TOD), relative change rate (RCR), and curvature change rate (CCR). We implemented the proposed phenoC++ and evaluated its performance on a site scale with PhenoCam-observed phenology metrics. The result shows that SOS derived from MODIS images by phenoC++ with six methods (i.e., AT, FOD, SOD, RCR, TOD, and CCR) obtained r-values of 0.75, 0.76, 0.75, 0.76, 0.64, and 0.67, and RMSE values of 21.36, 20.41, 22.38, 19.11, 33.56, and 32.14, respectively. Satellite-retrieved EOS by phenoC++ with six methods obtained r-values of 0.58, 0.59, 0.57, 0.56, 0.36, and 0.40, and RMSE values of 52.43, 46.68, 55.13, 49.46, 71.13, and 69.34, respectively. Using PhenoCam-observed phenology as a baseline, SOS retrieved by phenoC++ was superior to MCD12Q2, while EOS retrieved by phenoC++ was slightly inferior to that of MCD12Q2. Moreover, compared with MCD12Q2 on a regional scale, phenoC++-retrieved vegetation phenology yields more effective pixels. The innovative features of phenoC++ are 1) integrating six algorithms for retrieving SOS and EOS; 2) quickly processing data on a large scale with simple input startup parameters; 3) outputting phenology metrics in GeoTIFF format image, which is more convenient to use with other geospatial data. phenoC++ could aid in investigating and addressing large-scale phenology problems of the ecological environment.

Relationship between urban spatial form and seasonal land surface temperature under different grid scales

The urban heat island (UHI) effect is intensifying with increasing urbanization. As an important representation of the UHI effect and the urban thermal environment, it is critical to investigate the importance of evaluating urban spatial form (USF) indicators on land surface temperature (LST) to alleviate urban thermal environment problems. Therefore, in this study, we evaluated the importance of USF indicators from three perspectives: landscape pattern, building morphology, and social development, on LST at 10 grid scales in the main area of Xi'an City, using the XGBoost model. The results showed that: (1) LST was similar in spring and autumn, but significantly lower in winter. The distribution of high-temperature areas in the four seasons has its own characteristics, but the low-temperature areas are mainly distributed in the water bodies and parks in the north and south of the middle of the study area, which are less affected by the seasons. (2) Mean architecture height (MAH) is a seasonal stable factor with a cooling effect. The relationship between patch density (PD), landscape shape index (LSI), Shannon's diversity index (SHDI), contagion index (CONTAG), people density (POD), floor area ratio (FAR) and LST varies with seasons. The correlation between building density (BD) and LST is complex. (3) The degree of influence of the USF indicators on seasonal LST could be ranked in the following order: building morphology > landscape pattern > social development. (4) The appropriate size to study the relationship between USF and seasonal LST is 60 m. The highest contribution of USF to LST is building morphology, three indicators affect 43%–55% of LST. The findings of this study provide useful information for urban land-use planning and building layout, to mitigate the UHI effect.

Investigating the effects of urban morphological factors on seasonal land surface temperature in a “Furnace city” from a block perspective

• The effects of urban block form zone on the seasonal LSTs were explored. • The correlations between LST and urban morphological factors vary with seasons. • Blocks with higher building density and lower building height had higher LST. • Different contributions of urban morphological factors to seasonal LSTs were compared. • More attention should be paid to building form metrics and surface biophysical parameters in urban planning. A growing number of studies have examined the impact of urban morphological factors on surface urban heat islands (SUHI). However, less attention is paid to investigating the comprehensive effect of urban morphological factors on land surface temperature (LST) across seasons, particularly at the block scale. In this study, we investigated 385 blocks in the central part of Fuzhou city, China. Twelve urban morphological factors in four categories were calculated from multi-source data, and the random forest regression (RFR) method was employed to calculate the relative importance of urban morphological factors from a block perspective. Our results confirmed that the blocks with high-density low-rise buildings had the highest LST and distribution index. The correlations between LST and urban morphological factors varied seasonally. The normalized difference vegetation index yielded a positive correlation with LST in winter. The RFR model revealed that twelve urban morphological factors could explain 52–57.8% of LST variation. More importantly, the building form metrics and surface biophysical parameters are the two essential categories affecting LST, contributing >70% of the total relative contribution to LST. These findings provide useful insights to ameliorate the SUHI effect and have substantial implications for urban ecological sustainability from a block perspective.

Impacts of eco-environmental quality, spatial configuration, and landscape connectivity of urban vegetation patterns on seasonal land surface temperature in Harare metropolitan city, Zimbabwe

ABSTRACT The study examined the impact of eco-environmental quality conditions, spatialconfigurations and landscape connectivity of urban vegetation on seasonal land surfacetemperature (LST) in Harare, Zimbabwe between May and October 2018. The resultsshowed that densely built-up areas with sparse vegetation experienced extremelypoor eco-environmental conditions. Clustered and highly connected were morebeneficial in decreasing LST. These findings haveimportant urban and landscape planning implications regarding how thespatial configuration and landscape connectivity patterns of urban vegetationcan be optimized to mitigate UrbanHeat Island (UHI) effects and to improve the thermal comfort conditions in rapidlyurbanizing cities.

The role of blue green infrastructure in the urban thermal environment across seasons and local climate zones in East Africa

Rapid urbanisation and climate change are two major trends in Africa in need of further investigation. In this paper, the urban thermal environment and vegetation abundance in four East African cities (Khartoum, Addis Ababa, Kampala and Dar es Salaam) were characterised, providing new insights into the role and potentials of blue green infrastructure in differing climate regions. The Local Climate Zone (LCZ) framework was employed to detect the seasonal Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) derived from Landsat-8 data. Significant LST differences between LCZs in dry and rainy seasons were confirmed using a Welch's T test. The LCZs were found to offer potentially new approaches to investigating issues pertaining to urban heating in data-scarce regions. Greater surface urban heat island (SUHI) intensity during the rainy season was apparent in Khartoum and Addis Ababa, emphasising the importance of seasonality in urban thermal studies. Spatial correlations between EVI and LST within each LCZ were analysed through Moran's I and further illustrated the complex relationships of vegetation and thermal behaviour in urban areas. Despite these complexities, urban blue green infrastructure was found to moderate the SUHI, with different types of intervention required across different LCZs.

Spatially characterizing land surface deformation and permafrost active layer thickness for Donnelly installation of Alaska using DInSAR and MODIS data

The degradation of permafrost in the Earth's cold regions due to climate warming and human activities is of increasing concern because of its impact on ecosystems and land management, thus, monitoring the degradation becomes critical. This study aimed to demonstrate a method in which Sentinel-1 DInSAR and MODIS data were combined to estimate and map seasonal and long-term land surface deformation (subsidence/uplifting) and permafrost active layer thickness (ALT) in Donnelly Training Area (DTA) in Alaska. The coherence (similarity) between the paired SAR images was analyzed for their applicability. A sensitivity analysis and accuracy assessment of the estimates were conducted to account for their quality. Results showed that the estimated seasonal subsidence mainly occurring in June and July was in the range of 0 to −0.43 m, while the estimated uplifting mainly happening from September to May of next year was as great as +0.34 m. Moreover, the estimated long-term (2015 to 2018) subsidence and uplifting were mostly distributed in the southern and northern parts of DTA, respectively. The spatially variable time delays led to the ALT estimates greater along the east river and in the west and south parts, and smaller in the north parts. At the significance level of 0.05, the coherence estimates of the paired images were significantly different from zero and compared with the referenced predictions from a widely used annual prediction model, the average residual of the ALT estimates did not significantly differ from zero at the significance level of 0.05. The spatial distributions of the uncertainties for the seasonal surface deformation estimates were similar to those of the input uncertainties from the estimates of model coefficients, the phase change, the modelling error, and the image pairs. Thus, the DInSAR image based method coupled with MODIS data offered the potential of mapping and monitoring the dynamics of permafrost environment for the cold regions such as DTA in which collecting field observations is difficult and costly. This study also enhanced understanding spatiotemporal variability of permafrost deformation in DTA and provided guidelines for developing a near real-time monitoring system of the permafrost environment.

A novel regression method for harmonic analysis of time series

Harmonic analysis of time series is an important technique to reveal seasonal land surface dynamics using remote sensing information. However, frequency selection in the harmonic analysis is often difficult because high-frequency components are useful for delineating seasonal dynamics but sensitive to noise and gaps in time series. On the other hand, it is challenging to obtain temporally continuous satellite data with high quality because of atmospheric contamination. We developed a novel regression method named Harmonic Adaptive Penalty Operator (HAPO) for harmonic analysis of unevenly distributed time series. We introduced a new penalty function to minimize unexpected fluctuations in the model, which can substantially reduce the overfitting issue of regression in time series with temporal gaps. Specifically, the new penalty function minimizes the length of the model curve and the value range difference between the model and time series observations. We compared HAPO with three widely used regression methods (OLS: Ordinary Least Squares; LASSO: Least Absolute Shrinkage and Selection Operator; and Ridge) with different scenarios using Landsat time series data across the United States. First, we evaluated methods using Landsat surface reflectance time series within a single year. HAPO showed small and consistent monthly Root Mean Square Deviation (RMSD) values, in which most of the time RMSD values of predicted reflectance were less than 0.04. More importantly, HAPO showed consistent and less bias given varying density and irregularity of time series. Second, we evaluated methods using multi-year time series and the result suggested that HAPO was a better predictor of relatively short time series (less than4 years) with steady small RMSD values. When a longer time series (≥4 years) was used, all four methods disclosed similar RMSD values, but HAPO outperformed other three methods when there were temporal gaps. Last, we preliminarily tested how regression methods affected change detection and classification accuracy. HAPO showed the highest change detection accuracy of all tests in terms of F1 score when using the change threshold of 0.9999. In classification, HAPO produced the highest accuracy for short time series segments (one- or two-year time series). In contrast, all methods reached similar accuracy for 5-year time series. These results suggest that for areas that have large seasonal observation gaps or for time series that have less than 4 years records, HAPO can provide more consistent and accurate analytical results than other regression methods for harmonic analysis of time series.