Land Surface Temperature Data Research Articles

Exploring seasonal diurnal surface temperature variation in cities based on ECOSTRESS data: A local climate zone perspective.

High urban temperatures affect city livability and may be harmful for inhabitants. Analyzing spatial and temporal differences in surface temperature and the thermal impact of urban morphological heterogeneity can promote strategies to improve the insulation of the urban thermal environment. Therefore, we analyzed the diurnal variation of land surface temperature (LST) and seasonal differences in the Fifth Ring Road area of Beijing from the perspective of the Local Climate Zone (LCZ) using latest ECOSTRESS data. We used ECOSTRESS LST data with a resolution of 70 m to accurately interpret the effects of urban morphology on the local climate. The study area was dominated by the LCZ9 type (sparse low-rise buildings) and natural LCZ types, such as LCZA/B (woodland), LCZD (grassland), and LCZG (water body), mainly including park landscapes. There were significant differences in LST observed in different seasons as well as day and night. During daytime, LST was ranked as follows: summer > spring > autumn > winter. During night-time, it was ranked as follows: summer > autumn > spring > winter. All data indicated that the highest and lowest LST was observed in summer and winter, respectively. LST was consistent with LCZ in terms of spatial distribution. Overall, the LST of each LCZ during daytime was higher than that of night-time during different seasons (except winter), and the average LST of each LCZ during the diurnal period in summer was higher than that of other seasons. The LST of each LCZ during daytime in winter was lower than that of the corresponding night-time, which indicates that it is colder in the daytime during winter. The results presented herein can facilitate improved analysis of spatial and temporal differences in surface temperature in urban areas, leading to the development of strategies aimed at improving livability and public health in cities.

An Analysis of the Stability and Trends in the LST_cci Land Surface Temperature Datasets Over Europe

AbstractLong‐term satellite land surface temperature (LST) data are desirable to augment 2m air temperatures (T2m) measured in situ and as an independent measure of surface temperature change. However, previous studies show variable agreement between LST and T2m time series. The objective of this study is to assess the stability and trends in six new LST data sets from the European Space Agency's Climate Change Initiative for LST (LST_cci). LST anomalies are compared with homogenized station T2m anomalies over Europe, which verifies all six data sets are well coupled (LST vs T2m anomaly correlations and slopes: 0.6–0.9). The temporal stability of the LST_cci data is assessed through a comparison with the T2m anomaly time series. Only the LST_cci data sets for the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Aqua and the Advanced Along‐Track Scanning Radiometer (AATSR) appear stable; the MODIS/Terra, ATSR‐2, and multisensor InfraRed and MicroWave data sets show non‐climatic discontinuities associated with changes in sensor and/or drift over time. For MODIS/Aqua (2002–2018), significant trends in LST of 0.64–0.66 K/decade compare well with the equivalent T2m trends of 0.52–0.59 K/decade. The LST and T2m trends for AATSR (2002–2012) are found to be statistically insignificant, likely due to the comparatively short study period and specific years available for analysis. No evidence is found to suggest that trends calculated using cloud‐free InfraRed observations are affected by clear‐sky bias. This study suggests that satellite LST data can be used to assess warming trends over land and for other climate applications if the required homogeneity is assured.

Exploring the Variations and Influencing Factors of Land Surface Temperature in the Urban Agglomeration on the Northern Slope of the Tianshan Mountains

Changes in land surface temperature (LST) can have serious impacts on the water cycle and ecological environment evolution, which in turn threaten the sustainability of ecosystems. The urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM) is located in the arid and semi-arid regions of northwest China, with an extremely fragile ecological environment and sensitive to climate change. However, studies on the LST of the UANSTM have not received much attention. Therefore, this study explored the spatial distribution pattern, fluctuation characteristics, and influencing factors of the LST of the UANSTM from 2005 to 2021 based on MODIS time series LST data and the geo-detector model with optimal parameters. The results show that the UANSTM is dominated by medium- and high-temperature classes, with high- and extremely high-temperature classes clustered in Turpan City. The daytime and nighttime LST patterns are significantly different, with a typical “daytime cold island and nighttime heat island” feature in the oasis region. During 2005–2021, LST fluctuated greatly in the northwestern part of the UANSTM, with LST showing an increasing trend during both daytime and nighttime, and the warming rate was more intense during daytime than nighttime. The increasing trend of LST in Urumqi, Changji Hui Autonomous Prefecture, Shihezi, and Wujiaqu is very significant and will remain consistent in the future. Precipitation, DEM, and AOD are the most important influencing factors of LST in the UANSTM, where precipitation and DEM are negatively correlated with LST, and AOD is positively correlated with LST. Land cover factors (LULC, NDVI,, and NDBSI) are the next most influential, and socioeconomic factors (NTL, GDP, and POP) are the least influential. The results of this study can provide a scientific reference for the conservation and sustainable development of the ecological environment of the UANSTM.

Evaluating contributions of urbanization and global climate change to urban land surface temperature change: a case study in Lagos, Nigeria

This study develops a general method to evaluate the contributions of localized urbanization and global climate change to long-term urban land surface temperature (ULST) change. The method is based on the understanding that long-term annual ULST is controlled by three factors: (1) localized urbanization, (2) global climate change, and (3) interannual climate variation. Then the method removes the interannual climate fluctuations on long-term observed LST time series via linear regression and separates the contributions of urbanization and climate change to the impacts on long-term ULST via urban–rural comparison. The method is applied to Lagos, a fast-growing metropolis in the tropical West Africa, as an example for reference. Combined time-series daily daytime and nighttime MODIS Land Surface Temperature (LST) data over the years of 2003–2021 are used as the representation of land surface temperature. To avoid the potentioal interannual data biase due to uneven availability of data in the rainy seasons over years, only MODIS LST data from dry seasons are used in the study. The results are summarized as follows for Lagos: (1) long-term annual ULST is confirmed to be controlled by the three factors; (2) the proposed method can separate the contribution of the three factors to the ULST; (2) both localized urbanization and global warming are verified to contribute to the ULST increase with positive trends; (3) daytime ULST increased the most in the afternoon time at a mean rate of 1.429 °C per decade, with 0.985 °C (10 year)−1 contributed by urbanization and 0.444 °C (10 year)−1 contributed by climate warming; (4) nighttime ULST in Lagos increased the most after midnight at a rate of 0.563 °C (10 year)−1, with 0.56 °C (10 year)−1 contributed by urbanization and 0.003 °C (10 year)−1 contributed by climate warming; and (5) urbanization is generally responsible for around 60.97% of the urban warming in Lagos. Therefore, the increasing urbaniztion-induced urban heat island effect is the major cause for more heat-related health risks and climate extremes that many urban residents are suffering. The results of this study are of useful reference for both urbanization and climate change related issues in the geo-science field.

Reconstructing all-weather daytime land surface temperature based on energy balance considering the cloud radiative effect

Land surface temperature (LST) has been used in many applications as its strong relationships with land surface processes. However, the greatest limitation of the use of LST is the missing data caused by cloud contamination and weather conditions. In this study, we first used the XGBoost method to describe complex relationships of LST with surface characteristics from clear-sky pixels, and applied the model to retrieve hypothetical clear-sky LST under cloudy sky. Secondly, cloud radiative effect (CRE) on the LST was calculated based on energy balance using the reanalysis data. The models were applied to reconstruct the all-weather LST over the Tibetan Plateau (TP). The spatial patterns of reconstructed LST indicated that our model could produce completely spatial-seamless LST and depict the detailed information. The accuracy of the XGBoost LST was validated against the clear-sky MODIS LST (average R2 = 0.92, MAPE = 0.52, RMSE = 2.32 K). The CRE-EB LST was evaluated using data from six in-situ sites from the TP. The validation results were separated into three conditions: clear sky (RMSE = 3.01 K–3.52 K, R2 = 0.88–0.93, bias = −1.08 K-1.88 K), cloudy sky (RMSE = 3.31 K–4.06 K, R2 = 0.87–0.92, bias = −0.21 K-1.11 K), and overall (RMSE = 3.31 K–3.82 K, R2 = 0.88–0.93, bias = −0.42 K-1.24 K). Compared to existing all-weather LST datasets, the temporal variability of our LST data shows similar seasonal and daily changes, and the CRE-EB LST has advantages in terms of image quality and accuracies under cloudy condition. This study demonstrated the utility of proposed models to reconstruct all-weather LST.

A Multitemporal and Multilevel Land Surface Temperature Regional Attribute Change Analysis in Henan, China, Using MODIS Imagery

Temperature is an important aspect of land–atmosphere studies and plays a key role in urban environmental change. With the continuous development of satellite remote sensing sensors, remote sensing technology has become an important means of obtaining large-scale land surface temperature (LST) data. LST can be calculated from the thermal infrared band data of remote sensing images to analyze changes in temperature and determine its relationship with the surface type. In this study, a multitemporal multilevel (MTML) method for analyzing remotely sensed LST data is presented that analyzes attribute changes and correlations of remotely sensed LST data in different periods and at different temperature levels. First, the LST data were obtained under the same climatic conditions at different times, and the influence of climatic conditions on the LST data was excluded. Threshold superposition analysis was then performed on the temperature data to generate temperature-connected regions of different levels, and a tree structure was constructed. Each node in the tree structure represented a connected region. Finally, the attribute information of different connected regions at different levels was calculated, and the attribute changes and correlations between different times and levels were analyzed. In this study, five MODIS LST datasets from 15 May 2006, 1 May 2010, 7 May 2014, 29 April 2017, and 8 May 2021 in Henan Province of China were obtained, and MTML analysis was carried out. The experimental results showed that a negative correlation exists between temperature and the vegetation index, while a positive correlation exists between temperature and the built-up index. However, with an increase in the temperature level, the correlation between temperature and the surface feature type index decreased. In addition, there were more concentrated high-temperature areas in the northern, central, and western regions of Henan Province and lower temperatures in the eastern and southern regions.

A high spatiotemporal resolution land surface temperature research over Qinghai-Tibet Plateau for 2000–2020

As ‘the third pole of the world’, the land surface temperature (LST) of the Qinghai-Tibet Plateau (QTP) has a profound impact on the climate of central Asia and even the whole earth. Studying the impact of the LST over QTP depends on long time and high spatiotemporal resolution LST dataset. However, the unavailability of such dataset has hindered LST-related researches: one of the most important reasons is that traditional spatiotemporal fusion methods such as Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) have heavy computation demands to process big data. To fill this gap, this paper outlines one new cloud spatiotemporal fusion method by combining Google Earth Engine and STARFM to develop for the first time a 3-hourly 30 m LST dataset over the QTP from 2000 to 2020 through fusing Landsat and GLDAS-2.1 derived LST data. The outlined method first fuses the LSTs obtained from Landsat and GLDAS-2.1 data within one year to synthesize the LST of the entire QTP on one base time, and then the base time LST is spatiotemporally fused with GLDAS-2.1 LSTs on the base and prediction times to derive the 3-hourly 30 m QTP's LSTs on prediction times. The outlined method provides a promising technical scheme for batch processing big data in combining traditional spatiotemporal fusion methods with cloud computing platforms. Derived LST dataset is validated by station observations at multiple time and spatial scales to have high accuracy, which provides a guarantee for analyzing water and heat exchange and climate change over the QTP.

Comparison of MODIS surface temperatures to in situ measurements on the Greenland Ice Sheet from 2014 to 2017

AbstractRemotely sensed land surface temperature (LST) data, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) LST thermal infrared products, are useful for monitoring surface processes on the Greenland Ice Sheet in remote areas but must be validated to ensure accuracy. Using data from the Programme for Monitoring the Greenland Ice Sheet (PROMICE), we conducted a MODIS LST validation (MOD/MYD11 C6 swath level product) using radiometric in-situ skin temperature records from 2014 to 2017 over 17 PROMICE sites mostly in the ice sheet's ablation zone. There is a significant cold bias in MODIS LST when compared to PROMICE skin temperature, particularly when PROMICE records temperatures below 0°C (mean bias: 2.4 ± 0.01°C mean ± standard error, RMSE = 3.2°C). Multiple linear regression analysis reveals the difference between MODIS LST and PROMICE skin temperature is larger at lower temperatures, lower latent heat fluxes and higher specific humidity. Our results confirm the presence of a progressive cold bias in the MODIS LST that should be considered in use of this product, and we identify and corroborate areas for ongoing algorithm development.

Utilizing multi-temporal thermal data to assess environmental land degradation impacts: example from Suez Canal region, Egypt

Land surface temperature (LST) analysis of satellite data is critical for studying the environmental land degradation impacts. However, challenges arise to correlate the LST and field data due to the constant development in land use and land cover (LULC). This study aims to monitor, analyze, assess, and map the environmental land degradation impacts utilizing image processing and GIS tools of satellite data and fieldwork. Two thermal and optical sets of Landsat TM + 5 and TIRS + 8 data dated 1984 and 2018 were used to map the thermal and LULC changes in the Suez Canal region (SCR). The LULC classification was categorized into water bodies, urban areas, vegetation, baren areas, wetland, clay, and salt. LULC and LST change detection results revealed that vegetation and urban areas increased in their areas in 34 years. Moreover, 97% of the SCR witnessed LST rise during this period with an average rise rate of 0.352 °C per year. The most effective LULC class changes on LST were the conversions from or to baren areas, where baren areas were converted to 630.5 km2 vegetation and 104 km2 urban areas rising the LST to 43.57 °C and 45 °C, respectively. The spectral reflectance (LSR), LST profiles, and statistical analyses examined the association between LST and LULC deriving factors. In combination with field observations, five hotspots were chosen to detect and monitor natural and human land degradation impacts on LST of the SCR environment. Land degradations detected include water pollution, groundwater rising, salinity increase, sand dune migration, and seismic activity.

A fully coupled crop-water-energy balance model based on satellite data for maize and tomato crops yield estimates: The FEST-EWB-SAFY model

Agricultural crop management requires extensive and comprehensive tools that allow for a full knowledge of the crops’ status and growth dynamic. This study aims at estimating crop yield for maize and tomato crops over large areas at field scale. For this purpose, we developed a fully coupled model based on a parameter-saving crop growth model (Simple Algorithm For Yield estimates (SAFY)) with a water-energy balance model (Flash–flood Event–based Spatially–distributed rainfall–runoff Transformation- Energy Water Balance model (FEST-EWB)) with a double exchange of leaf area index (LAI) and soil moisture (SM) information. Both models are driven by remote sensing data and are calibrated independently from in situ measurements. Satellite LAI data are used to calibrate the crop growth model parameters, while the energy-water balance parameters are calibrated against satellite land surface temperature (LST) data. Multiple satellite data are used either at high spatial resolution (Sentinel 2 and LANDSAT 7 and 8) and at low-resolution (MODIS). Two Italian case studies are selected to test the model accuracy: the Chiese Irrigation Consortium (Northern Italy), mainly devoted to maize crop cultivation, and the Capitanata Irrigation Consortium (Southern Italy), where tomatoes are largely diffused.At local scale, LAI is reproduced for tomatoes with a mean RMSE of 0.92 and yield with a RMSE of 1.2 ton ha−1; while for maize, a RMSE of 1 is found for LAI and a RMSE of 1.5 ton ha−1 for yield. Results show an overall correspondence of daily soil moisture and evapotranspiration estimates with a RMSE in between 0.11 and 0.15 and of 1.3–3 mm, respectively. At the regional scale, LAI estimates show a RMSE around 1.1 for both case studies, while a RMSE of 13.4 ton ha−1 is obtained for tomato yield and of 1.4 ton ha−1 for maize.

Evaluating global and regional land warming trends in the past decades with both MODIS and ERA5-Land land surface temperature data

Global surface temperature has been setting new record highs in the recent decades, imposing increasing environmental challenges for societies and ecosystems worldwide. Global warming rates of the 20th century have been documented by a number of studies, nevertheless, the warming rates in the most recent decades in the 21st century are of particular interest for understanding the ongoing climate change. Analyzing temperature trends demands data with high spatial resolution and broad geographical coverage to allow for analyzing trends and changes on a regional scale. Land Surface Temperature data from NASA MODIS with global resolution of 0.05° and Skin Temperature data from European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5-Land reanalysis with global resolution of 0.1° fulfill these demands. In this study, we analyze the remote-sensing-based MODIS data to estimate land surface temperature change rates over the period 2001–2020 in global, continental, and pixel-wise scales with statistical significance indicated. The model-based ERA5-Land data are also analyzed in parallel, extending the period of analysis back to 1981. These two independently-sourced datasets, one from satellites above the atmosphere and one from combining surface modeling and observations, are shown to produce highly consistent results. It is revealed that the trends in the shorter period 2001–2020 are spatially conforming to the trends in the longer period 1981–2020 despite the shorter time length. For the period 2001–2020, we show that the global average land surface temperature rate of change was 0.26 °C-0.34 °C per decade, with substantially different warming rates in different regions. The Arctic, Europe, and Russia show statistically significant warming in both datasets. The Arctic, in particular, warmed at a rate 2.5–2.8 times the global average, and data in the 40-year period 1981–2020 suggest that warming is accelerating in almost all the continents or large regions. Most noticeably, the two independent datasets both indicate that Arctic permafrost regions had the world's highest warming rate at the onset of the 21st century, reaching >2 °C per decade in some areas.

ANALYZING IMPACTS OF LAND COVER CHANGES ON LAND SURFACE TEMPERATURE IN BAY AREA, CALIFORNIA

Abstract. Land Surface Temperature plays a critical role in the land surface-atmosphere exchange processes. In this study, the impacts of land cover changes on land Surface Temperature in bay area, California was analyzed. The key findings of the land cover change impacts study are presented. National Land Cover Database (NLCD) data and Landsat Land Surface Temperature data were used for this study. The time period of analysis is 2001 through 2020. The results of the study indicated that with the increase in built land cover, the land surface temperature in bay area, California exhibited a significant increase.

Combining Spatial and Temporal Data to Create a Fine-Resolution Daily Urban Air Temperature Product from Remote Sensing Land Surface Temperature (LST) Data

Remotely sensed land surface temperature (LST) is often used as a proxy for air temperature in urban heat island studies, particularly to illustrate relative temperature differences between locations. Two sensors are used predominantly in the literature, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). However, each has shortcomings that currently limit its utility for many urban applications. Landsat has high spatial resolution but low temporal resolution, and may miss hot days, while MODIS has high temporal resolution but low spatial resolution, which is inadequate to represent the fine grain heterogeneity in cities. In this paper, we overcome this inadequacy by combining high spatial frequency Environmental Services (ES), Landsat-driven Normalized Difference Vegetation Index (NDVI), and MODIS low spatial frequency background LST at different spatial frequency bands (spatial spectral composition). The method is able to provide fine scale LST four times daily on any day of the year. Using data from Paris in 2019 we show that (1) daytime cooling by vegetation reaches a maximum of 30 °C, above which there is no further increase in cooling. In addition, (2) the cooling is relatively local and does not extend further than 200 m beyond the boundary of the NBS. This model can be used to quantify the benefits of NBS in providing cooling in cities.

Monitoring Heat Extremes across Central Europe Using Land Surface Temperature Data Records from SEVIRI/MSG

The frequency and intensity of extreme hot events have increased worldwide, particularly over the past couple of decades. Europe has been affected by unprecedented mega heatwaves, namely the events that struck Western Europe in 2003 and Eastern Europe in 2010. The year 2018 was also reported as an unusually hot year, with record-breaking temperatures in many parts of Europe during spring and summer, associated with severe and unusual wildfires and significant crop losses in central and northern Europe. We show the ability of Land Surface Temperature (LST), retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) to monitor heat extremes, using the 2018 European event as a showcase. The monitoring approach relies on monthly anomalies performed as departures from the median and the monthly number of hot days (NHD), both computed for satellite LST derived from MSG and MODIS, and for 2 m air temperature (T2m) from ERA5 reanalysis, using as threshold the 90th percentiles. Results show strong monthly LST anomalies during the spring and summer of 2018 extending over central and north Europe. Over a vast region in Central and Northern Europe, LST reached the last 15 years high record. Moreover, those outstanding warm LSTs persisted for more than four months. Results obtained using MODIS LST and ERA5 T2m show similar patterns, which, although slightly less intense, corroborate the exceptionality of the heat extremes observed over central and northern Europe during 2018. The spatial pattern of the number of monthly record high anomalies over the MSG observations period clearly depicts the regions in Northern and Central Europe affected by the complex phenomena that occurred in 2018, which resulted from the combined effect of an extreme heatwave in spring and summer with extensive dry conditions. Therefore, the results highlighted the suitability of MSG LST to evaluate and monitor heat extremes alone or combined with dry and bright conditions and prompts the potential of other climate data records from geostationary satellites to characterize these climate extremes that could become the norm in the near future over central and northern Europe.

Diurnal dynamics of heat exposure in Xi'an: A perspective from local climate zone

Understanding the heat exposure in communities is important for finely managing the urban thermal environment. Although remote sensing data has been widely applied for evaluating heat exposure, rare studies discuss it by using diurnal land surface temperature (LST) at the block level, let alone integrating the local climate zone (LCZ) scheme. Therefore, we classified 716 urban blocks into 10 LCZ types and then combined them with the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station LST data to investigate the diurnal spatio-temporal heterogeneity of heat exposure. First, we found that surface urban heat island intensity (SUHII) reached the maximum value of 2.3 °C at noon. Compact built-up LCZs were heat islands over the day, while the open and dispersed built-up LCZs had greater uncertainties. Specifically, LCZ 4 and LCZ 9 can be cold or heat islands over the day. Second, the distribution characteristics of SUHII was highly consistent with that of heat exposure levels, i.e., high SUHII zones have high heat exposure level. Third, the heat exposure intensity levels among compact building zones (LCZ 1–LCZ 3) had a reverse change during daytime and nighttime and the same phenomenon was observed among open building zones (LCZ 4–LCZ 6). Our results implied that LCZ 3 and LCZ 2 should be given more priority to alleviate heat exposure risks than other built-up LCZs.

Monitoring the Distribution and Variations of City Size Based on Night-Time Light Remote Sensing: A Case Study in the Yangtze River Delta of China

Effectively monitoring the size of a city in real time enables the scientific planning of urban development. Models that utilize the distribution and variations in city size generally use population data as inputs, which cannot be obtained in a timely and rapid manner. However, night-time light (NTL) remote sensing may be an alternative method. A case study was carried out on the Yangtze River Delta (YRD) in China, and the rank–size rule, the law of primate cities, and the Gini coefficient were employed to monitor the variation in city size in the study area. The urban areas extracted based on NTL remote sensing were utilized instead of the traditionally used population data to evaluate the variations in city size from 2012 to 2017. Considering the empiricism and subjectivity of the thresholding method, urban areas were extracted from NTL data combined with the normalized differential vegetation index and land-surface temperature data based on the artificial neural network algorithm. Based on the results, the YRD did not fit the distribution of the primate cities from 2012 to 2017. However, this region satisfied the rank–size rule well, which indicated that the development of medium–small cities was more prominent than that of larger cities, and the dispersed force was larger than the concentrated force. Notably, the city size reached a relatively balanced level in the study area. Further, sensitivity analysis revealed that the relatively low extraction accuracy of urban areas of few small cities had little effect on the results of city size variations. Moreover, the validation of city size computed from statistical population data and its comparison with results calculated based on the statistical data of urban areas aligned with the results of this study, which indicates the rationality and applicability of monitoring the variations in city size using the urban areas extracted from NTL remote sensing instead of population data.

Analysis of land surface temperature with land use and land cover and elevation from NASA MODIS satellite data: a case study of Bali, Indonesia.

The Moderate Resolution Imaging Spectroradiometer (MODIS) of the National Aeronautics and Space Administration (NASA) offers numerous land products of the Earth's datasets. On the other hand, researchers find it difficult to retrieve this data for specific places. The methods for extracting and analyzing land surface temperature (LST), land use and land cover (LULC), and elevation are presented in this study. The R commands provided make the time-consuming process of extracting data for specific places much more accessible. As a result, a statistical study of LST over Bali is shown as an example. Over the 15 regions of Bali, a quadratic polynomial identified five possible warming patterns, while a logistic regression model assessed the probability of warming. The findings suggest that 25.2% of Bali has warmed during the last two decades, with temperatures being highest in urban and built-up areas and deciduous forests and inversely associated with elevation. Global warming has sparked a lot of academic interest and has become a serious climate problem. The techniques proposed in this work simplify the extraction of LST, LULC, and elevation data from MODIS satellites. These approaches can also be used on other datasets with identical topologies, such as the normalized difference vegetation index (NDVI), aerosol optical depth (AOD), and night light data.

Empirical Models for Estimating Air Temperature Using MODIS Land Surface Temperature (and Spatiotemporal Variables) in the Hurd Peninsula of Livingston Island, Antarctica, between 2000 and 2016

In this article, we present empirical models for estimating daily mean air temperature (Ta) in the Hurd Peninsula of Livingston Island (Antarctica) using Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data and spatiotemporal variables. The models were obtained and validated using the daily mean Ta from three Spanish in situ meteorological stations (AEMET stations), Juan Carlos I (JCI), Johnsons Glacier (JG), and Hurd Glacier (HG), and three stations in our team’s monitoring sites, Incinerador (INC), Reina Sofía (SOF), and Collado Ramos (CR), as well as daytime and nighttime Terra-MODIS LST and Aqua-MODIS LST data between 2000 and 2016. Two types of multiple linear regression (MLR) models were obtained: models for each individual station (for JCI, INC, SOF, and CR—not for JG and HG due to a lack of data) and global models using all stations. In the study period, the JCI and INC stations were relocated, so we analyzed the data from both locations separately (JCI1 and JCI2; INC1 and INC2). In general, the best individual Ta models were obtained using daytime Terra LST data, the best results for CR being followed by JCI2, SOF, and INC2 (R2 = 0.5–0.7 and RSE = 2 °C). Model cross validation (CV) yielded results similar to those of the models (for the daytime Terra LST data: R2CV = 0.4–0.6, RMSECV = 2.5–2.7 °C, and bias = −0.1 to 0.1 °C). The best global Ta model was also obtained using daytime Terra LST data (R2 = 0.6 and RSE = 2 °C; in its validation: R2CV = 0.5, RMSECV = 3, and bias = −0.03), along with the significant (p < 0.05) variables: linear time (t) and two time harmonics (sine-cosine), distance to the coast (d), slope (s), curvature (c), and hour of LST observation (H). Ta and LST data were carefully corrected and filtered, respectively, prior to its analysis and comparison. The analysis of the Ta time series revealed different cooling/warming trends in the locations, indicating a complex climatic variability at a spatial scale in the Hurd Peninsula. The variation of Ta in each station was obtained by the Locally Weighted Regression (LOESS) method. LST data that was not “good quality” usually underestimated Ta and were filtered, which drastically reduced the LST data (<5% of the studied days). Despite the shortage of “good” MODIS LST data in these cold environments, all months were represented in the final dataset, demonstrating that the MODIS LST data, through the models obtained in this article, are useful for estimating long-term trends in Ta and generating mean Ta maps at a global level (1 km2 spatial resolution) in the Hurd Peninsula of Livingston Island.

Extreme Heat Impacts on the Viability of Alternative Transportation for Reducing Ozone Pollution: A Case Study from Maricopa County, Arizona

Abstract One commonly proposed strategy for reducing urban air pollution is transitioning from single-occupancy vehicle (SOV) travel to alternative transportation (AT) modes, such as walking, biking, and using public transportation. While many studies have addressed the benefits of switching from SOV to AT, fewer studies have examined the potential for negative outcomes due to increased exposure to heat when using AT modes. This work uses Maricopa County, Arizona, home to the metropolitan Phoenix area, as a test case to examine the potential impacts of heat on commuters who utilize AT. First, regions of the county with the most candidates for switching from SOV to AT were identified and used to develop an AT candidate index. This index was based on both the current rates of AT use and the number of SOV commuters with the shortest commuting times in the dataset (<10 min). Next, typical weather conditions during warnings for high ozone (O3) pollution were examined. From 2017 to 2020, over one-quarter of all days with an O3 warning also were subject to an excessive heat warning. Last, land surface temperature data were used to determine the potential for increased heat exposure during AT commuting at both the ZIP code and AT infrastructure (public transit stops and bikeways) scales. Although this work focuses on Maricopa County, the issues presented here are increasingly relevant for cities across the world that are subject to poor air quality, hotter temperatures, and heat waves.

Heat wave-induced augmentation of surface urban heat islands strongly regulated by rural background

• SUHI and SUCI are both augmented in heat wave (HW) periods. • Augmentation of SUHI and SUCI is regulated by rural vegetation coverage. • SUHIs in north subtropical climate exhibit the largest augmentation. • SUHIs are strengthened with an intensified HW intensity in most climate zones. The impact of heat waves (HWs) on surface urban heat islands (SUHIs) has been widely studied, but the spatial pattern of SUHI responsiveness to HWs across various climates remains unclear, and the influence of HW intensity on SUHI responsiveness has not been systematically quantified. Using MODIS land surface temperature data, here we investigated the responsiveness of SUHI to HWs (quantified as ∆ I ) as well as its variations with HW intensity in 354 cities in seven climate zones across China. We find that during HW periods, the SUHI and surface urban cool island are augmented in the humid and arid regions of China, respectively. The inter-city heterogeneity in rural vegetation coverage accounts for such a spatial pattern. In eastern China, the ∆ I peaks in the north subtropical climate (0.72 ± 0.54 K for daytime and 0.29 ± 0.23 K for the nighttime) probably for its specific rural farming method. With the intensification of HWs, the augmentation effect can be further enhanced for the north subtropical, warm temperate, and arid temperate climates during the day and for almost all the climates at night. These findings can help advance the understanding of the responsiveness of SUHI to extreme climatic events.