Satellite-derived Land Surface Temperature Research Articles

Modeling the intensity of surface urban heat island based on the impervious surface area

The urban heat island (UHI) effect is anticipated to be intensified and cause severe damage to human society under future urbanization and climate change. Due to the complicated human-nature interactions in the urban domain, it is challenging to characterize the intensity of UHI quantitatively, especially at the global scale. Here, we proposed a framework to estimate the surface urban heat island intensity (SUHII) based on satellite-derived land surface temperature (LST) and the derived impervious surface area (ISA) data across 355 cities worldwide from 2003 to 2018. First, we employed linear regression models to estimate LST using ISA as the explanatory variable, given that the LST generally elevated with increasing urban intensity along the urban-rural gradient. Then, using the modeled LST, we estimated the spatiotemporal patterns of SUHII worldwide. The established linear regression models with ISA as the explanatory variable perform reasonably well in estimating LST for both daytime and nighttime, with average RMSEs of 1.40 and 0.80 °C, respectively. The resulting SUHII reasonably captures the spatial heterogeneity across cities in the world, with the highest value in the tropical region at daytime and in the arid region at nighttime. The estimated and observed SUHII show good agreement with average R2 values of 0.81 and 0.72 for daytime and nighttime, respectively. Using ISA as an indicator for LST estimation, this study first implemented a comprehensive estimation of SUHII at the global scale. The proposed methodology is of great potential to project future SUHII by expanding urban impervious surfaces under diverse scenarios jointly determined by shared socioeconomic pathways and climate change scenarios.

Satellite-Derived Land Surface Temperature Dynamics in the Context of Global Change—A Review

Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites.

Investigation of the Efficiency of Satellite-Derived LST Data for Mapping the Meteorological Parameters in Istanbul

Land surface temperature (LST) is an essential parameter for studying environmental and ecological processes and climate change at various scales. It is also valuable for studies of evapotranspiration, soil moisture conditions, surface energy balance, and urban heat islands. Since meteorological station data can provide a limited number of point data, satellite images that provide high temporal and spatial resolution LST data in large areas are needed to be used in all these applications. In this study, the usage of satellite-derived LST images was investigated in comparison with meteorological station data measurements in Istanbul, which has heterogeneous urban structures. LST data were obtained from Landsat 5 TM, Landsat 8 OLI/TIRS, and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images using the Google Earth Engine (GEE) cloud platform. The linear correlation analysis performed between Landsat LST and MODIS LST images gave a high correlation (r = 0.88). In the correlation analysis, hourly air temperature and soil temperature meteorology station data provided by the State Meteorological Service and LST values obtained from images taken from Landsat TM/TIRS and Terra MODIS were used. The correlations between air temperatures and Landsat LST ranged from 0.47–0.95 for 1987–2017 to 0.44–0.80 for MODIS LST for 2000–2017. The correlations between 5 cm soil temperatures and Landsat LST ranged from 0.76–0.93 for 2009–2017 to 0.22–0.61 for MODIS LST 2000–2017. In addition, linear regression models produced with meteorological parameters and LST values were applied to 2022 LST maps to show the spatial distribution of these parameters, and then, accuracy analyses were made.

Preliminary Assimilation of Satellite Derived Land Surface Temperature from SEVIRI in the Surface Scheme of the AROME-France Model

The goal of this study is to examine the impact of assimilating satellite derived surface temperature over land (LST) in the surface scheme of AROME-France model. The LST is retrieved from SEVIRI radiances during the assimilation process in the atmospheric model. The assimilation of LST is performed using an optimal interpolation technique, similarly to the assimilation of other near-surface parameters (temperature and relative humidity at 2 meters). Observation and background errors were diagnosed before to prescribe them in the surface assimilation scheme. First, this LST assimilation has been evaluated in terms of analysis and forecast quality over a two-month summer period. A positive impact has been found on the assimilation of 2 m temperature and relative humidity with a slight decrease in bias of the background departure. An improvement has also been found for the assimilation of microwave humidity sensitive channels. The assimilation of microwave sensors benefits from an updated land surface temperature through the retrieval of the emissivity. Moreover, the assimilation of SEVIRI LST has improved the nighttime forecasts of temperature and relative humidity near the surface and up to 700 hPa. Several open issues for improving these preliminary results are finally proposed.

Development of a Hybrid Algorithm for Temporal Normalization of Polar-Orbiting Satellite-Derived Land Surface Temperature

Development of a Hybrid Algorithm for Temporal Normalization of Polar-Orbiting Satellite-Derived Land Surface Temperature

A Sensor Placement Strategy for Comprehensive Urban Heat Island Monitoring

Urban heat islands (UHIs) increase the energy consumption of cities and impact the health of its residents. In light of the correlation between energy consumption and health and UHI variations observed at a local level within the canopy layer, satellite-derived land surface temperatures (LSTs) may be insufficient to provide comprehensive information about these deleterious effects. For both LST and air temperatures to be collected in a spatially representative and continuous manner, and for the process to be affordable, on-ground temperature and humidity sensors must be strategically placed. This study proposes a strategy for placing on-ground sensors that utilizes the spatial variation of measurable factors linked to UHI (i.e., seasonal variation in LSTs, wind speed, wind direction, bareness, and local climate zones), allowing for the continuous measurement of UHI within the canopy layer. As a representative city, Pune, India, was used to demonstrate how to distribute sensors based on the spatial variability of UHI-related variables. The proposed method may be helpful for any city requiring local-level observations of UHI, regardless of the climate zone. Further, we evaluate the placement of low-cost technology sensors that use LoRaWAN technology for this purpose, in order to overcome the problem of high costs associated with traditional in-situ weather stations.

Improvement of the “Triangle Method” for Soil Moisture Retrieval Using ECOSTRESS and Sentinel-2: Results over a Heterogeneous Agricultural Field in Northern India

For the purpose of deriving spatiotemporal estimates of soil moisture, the triangle method is one of the most widely used approaches today utilizing remote sensing data. Generally, those techniques are based on the physical relationships that exist when a satellite-derived land surface temperature (Ts) is plotted against a spectral vegetation index (VI). The present study proposes an improvement in the triangle method in retrieving soil moisture over heterogeneous areas. In particular, it proposes a new approach in robustly identifying the extreme points required for the technique’s implementation. Those extreme points are then used in calculating fractional vegetation cover (Fr) and scaled Ts. Furthermore, the study proposes a new approach for calculating the coefficients required to develop the relationships between surface soil moisture (SSM) and Fr/Ts, which is implemented using a model and field data. As a case study, an agricultural field in the Varanasi district in India has been used, on which the triangle method is implemented using ECOSTRESS and Sentinel-2 data. The much-improved spatial resolution satellite data of ~70 m from ECOSTRESS allowed deriving more vivid results of SSM spatial variability for the study area. Comparisons between field soil moisture calculated using the proposed method returned an RMSE of 0.03 and R2 value of 0.84, which are considered very satisfactory. The methodology proposed herein and the results obtained are of significant value with regards to the triangle method, contributing to ongoing efforts at present examining its use for operational product development at a global scale.

Cross-Comparison between Sun-Synchronized and Geostationary Satellite-Derived Land Surface Temperature: A Case Study in Hong Kong

Harmonization of satellite imagery provides a good opportunity for studying land surface temperature (LST) as well as the urban heat island effect. However, it is challenging to use the harmonized data for the study of LST due to the systematic bias between the LSTs from different satellites, which is highly influenced by sensor differences and the compatibility of LST retrieval algorithms. To fill this research gap, this study proposes the comparison of different LST images retrieved from various satellites that focus on Hong Kong, China, by applying diverse retrieval algorithms. LST images generated from Landsat-8 using the mono-window algorithm (MWAL8) and split-window algorithm (SWAL8) would be compared with the LST estimations from Sentinel-3 SLSTR and Himawari-8 using the split-window algorithm (SWAS3 and SWAH8). Intercomparison will also be performed through segregated groups of different land use classes both during the daytime and nighttime. Results indicate that there is a significant difference among the quantitative distribution of the LST data generated from these three satellites, with average bias of up to −1.80 K when SWAH8 was compared with MWAL8, despite having similar spatial patterns of the LST images. The findings also suggest that retrieval algorithms and the dominant land use class in the study area would affect the accuracy of image-fusion techniques. The results from the day and nighttime comparisons revealed that there is a significant difference between day and nighttime LSTs, with nighttime LSTs from different satellite sensors more consistent than the daytime LSTs. This emphasizes the need to incorporate as much night-time LST data as available when predicting or optimizing fine-scale LSTs in the nighttime, so as to minimize the bias. The framework designed by this study will serve as a guideline towards efficient spatial optimization and harmonized use of LSTs when utilizing different satellite images associated with an array of land covers and at different times of the day.

Comparing Coarse-Resolution Land Surface Temperature Products over Western Australia

Satellite-derived land surface temperature (LST) has commonly been used to monitor global temperature changes. The MODIS MYD11A2 product is the most common coarse-resolution product used for this purpose. An updated MODIS product (MYD21A2) and new VIIRS (VNP21A2) product have also recently become available. This study analyses eight-day, quality-controlled, LST imagery over Western Australia (WA) for the three products for an urban and a non-urban area for the years 2013, 2016, and 2019. An analysis of the data indicates that (i) the averaged daytime LST difference between the three products for Perth city over the three years was 1.32 °C, while at night it was 0.89 °C; (ii) the averaged daytime difference over the Kimberley region was 7.02 °C with a night average difference of 2.39 °C; and (iii) both the MYD21A2 and VNP21A2 products still appear to record anomalous monthly LST values, particularly in the humid Kimberley monsoonal months. The overall objective of the National Aeronautics and Space Administration (NASA) is to ensure that the LST values of the two satellite system products are comparable, so evidence of LST value differences will require further investigation, especially if the older product is to be replaced by the newer systems.

Towards daily maximum heat index estimation across the conterminous United States using satellite-derived products

ABSTRACT Satellite-derived land surface temperature (LST) is widely utilized to study urban heat islands in the context of human health and thermal exposure. However, there is growing evidence to suggest that LST may be a poor indicator of apparent temperature, or the human-perceived equivalent temperature that reflects both heat and humidity. Moreover, heat index (HI), the apparent temperature metric used by the US National Weather Service, has yet to be computed at an increased spatial resolution and coverage beyond weather station observations. The goals of this study were to: 1) assess the extent to which HI can be estimated by a combination of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and other satellite-derived products available at continental scales, and 2) determine which factors are most important in this estimation. Specifically, daily maximum 1-km HI from May through September of 2012 was modelled across the conterminous United States as a function of MODIS LST, precipitable water vapor (PWV), and near-infrared indices, in addition to static variables capturing land cover, topographic, and locational factors.The derived model was capable of estimating HI within a reasonable level of error (R2 = 0.83, RMSE = 4.4°F). This is the first time that HI has been directly estimated using exclusively remotely sensed products and validated over a large spatial extent. Analysis of individual variables indicated that LST and PWV were, by far, the most important factors for estimation. However, the incorporation of additional parameters further improved model performance (R2: +0.14, RMSE: -1.6°F). We hope that our work will eventually result in a national HI product assisting researchers in a variety of fields, including epidemiology, building energy demand, and environmental justice. Further work to interpolate cloud-contaminated satellite observations and downscale estimates to a 60-m resolution would considerably increase the utility of this HI estimation methodology.

Diurnally continuous dynamics of surface urban heat island intensities of local climate zones with spatiotemporally enhanced satellite-derived land surface temperatures

Interpretation of diurnally continuous dynamics of surface urban heat island intensities (SUHIIs) of various local climate zones (LCZs) is essential for deepening the understanding of the urban climate. However, an easily applicable method for quantifying diurnally continuous dynamics of LCZ-based SUHIIs is still lacking, and the understanding of the diurnally continuous patterns of LCZ-based SUHIIs remains largely incomplete. To alleviate these gaps, here we developed a simple yet effective method for producing diurnally continuous land surface temperatures (LSTs) of various LCZ types based on an LCZ map and MODIS LSTs. With the proposed method, we further investigated the diurnally continuous dynamics of LCZ-based SUHIIs in Nanjing, China. We obtained the following findings: First, in terms of the annual mean, the diurnal SUHII patterns among various built LCZ types were similar and had a small valley within 0–2 h after sunrise and a peak within 8–12 h after sunrise. Second, the midrise/high-rise built and heavy industry types (i.e., LCZ 4, 5, and 10) exhibited relatively high SUHIIs during a diurnal cycle and the associated daily duration with a significant SUHII (>0.5 K) was greater than 20 h. Third, the diurnal SUHII patterns in summer were analogous to the annual mean patterns for all built LCZ types, yet the patterns of several built LCZ types in winter contrasted significantly the annual mean ones. Our findings are valuable for deepening the understanding of the diurnally continuous dynamics of LCZ-based SUHIIs.

Global spatiotemporally continuous MODIS land surface temperature dataset

Land surface temperature (LST) plays a critical role in land surface processes. However, as one of the effective means for obtaining global LST observations, remote sensing observations are inherently affected by cloud cover, resulting in varying degrees of missing data in satellite-derived LST products. Here, we propose a solution. First, the data interpolating empirical orthogonal functions (DINEOF) method is used to reconstruct invalid LSTs in cloud-contaminated areas into ideal, clear-sky LSTs. Then, a cumulative distribution function (CDF) matching-based method is developed to correct the ideal, clear-sky LSTs to the real LSTs. Experimental results prove that this method can effectively reconstruct missing LST data and guarantee acceptable accuracy in most regions of the world, with RMSEs of 1–2 K and R values of 0.820–0.996 under ideal, clear-sky conditions and RMSEs of 4–7 K and R values of 0.811–0.933 under all weather conditions. Finally, a spatiotemporally continuous MODIS LST dataset at 0.05° latitude/longitude grids is produced based on the above method.

High-Resolution Estimation of Monthly Air Temperature from Joint Modeling of In Situ Measurements and Gridded Temperature Data

Surface air temperature is an important variable in quantifying extreme heat, but high-resolution temporal and spatial measurement is limited by sparse climate-data stations. As a result, hyperlocal models of extreme heat involve intensive physical data collection efforts or analyze satellite-derived land-surface temperature instead. We developed a geostatistical model that integrates in situ climate-quality temperature records, gridded temperature data, land-surface temperature estimates, and spatially consistent covariates to predict monthly averaged daily maximum surface-air temperatures at spatial resolutions up to 30 m. We trained and validated the model using data from North Carolina. The fitted model showed strong predictive performance with a mean absolute error of 1.61 ∘F across all summer months and a correlation coefficient of 0.75 against an independent hyperlocal temperature model for the city of Durham. We show that the proposed model framework is highly scalable and capable of producing realistic temperature fields across a variety of physiographic settings, even in areas where no climate-quality data stations are available.

Combining ATC and 3D-CNN for reconstructing spatially and temporally continuous land surface temperature

More than half of the satellite-derived Land surface temperatures (LSTs) data are missing due to poor weather conditions (e.g., clouds, shadows, and other atmospheric conditions) and/or sensor failure, which has greatly limited the acquisition of spatially consistent and temporally regular LST data, leading to reduced utilization and accuracy of the data and hindering the understanding of the spatial-temporal patterns of surface thermal environments. Although several reconstruction methods have been developed, they are not effective in reconstructing daily LSTs for high spatial-temporal dynamics in urban regions. In this study, we developed a hybrid reconstructing method based on the annual temperature cycle (ATC) and three-dimensional convolutional neural network (3D-CNN) models. The proposed method has been validated on both Landsat 8 Thermal Infrared Sensor (TIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite LST datasets. The reconstructed results show that the proposed model incorporated with auxiliary data and 3D-CNN model significantly outperforms standard annual temperature cycle (ATCs) and enhanced annual temperature cycle (ATCe) models (Root Mean Square Error (RMSE): Landsat ATCs = 4.21 K, Landsat ATCe = 3.25 K, Landsat ATCe&SATs&CNN = 0.96 K, MODIS ATCs = 3.83 K, MODIS ATCe = 3.15 K, MODIS ATCs&SATs&CNN = 0.61 K). The proposed method is simpler, more efficient with higher robustness in terms of reconstructing high spatiotemporal LSTs in urban settings compared with existing reconstruction methods.

Taxonomy of seasonal and diurnal clear-sky climatology of surface urban heat island dynamics across global cities

Knowledge of the temporally continuous dynamics of seasonal and diurnal surface urban heat islands (SUHIs) as well as their underlying determinants is crucial to better understand their variations at multiple time scales. Owing to the orbital limitation of satellites, previous studies primarily focused on SUHI dynamics at limited time-nodes, either in a diurnal or seasonal cycle. However, a joint investigation of the continuous dynamics of seasonal and diurnal SUHIs (hereafter referred to as SUHIsea and SUHIdiu) remains lacking. The comprehensive taxonomy of the patterns of continuous SUHIsea and SUHIdiu dynamics across global cities is also not clear. Using satellite-derived land surface temperature (LST) data, we investigated the prevalent patterns of continuous SUHIsea and SUHIdiu dynamics across global cities by combining annual and diurnal temperature cycle models and the k-means clustering algorithm. Our results showed that: (1) Both SUHIsea and SUHIdiu dynamics exhibited six typical patterns including, single-peak type (SPT), single-valley type (SVT), peak-valley type (PVT), valley-peak type (VPT), two-peak type (TPT), and two-valley type (TVT). (2) The daytime SUHIsea dynamics pattern was closely related to the background climate, with SPT and PVT mainly occurring in cities located in the warm temperate and snow zones, SVT and VPT in the arid zone, and TPT and TVT in the equatorial zone. In contrast, the nighttime SUHIsea dynamics pattern depended more on rural land cover type, with SPT, PVT, and TPT mostly occurring in cities surrounded by barren lands with high albedo and SVT, VPT, and TVT in cities surrounded by dense vegetation with low albedo. We also find a significant negative relationship between daytime SUHIsea dynamics and urban-rural contrast in vegetation and between nighttime SUHIsea dynamics and urban-rural contrast in albedo across cities. (3) For SUHIdiu dynamics, SPT, PVT, and TVT were mainly located in cities with higher vegetation coverage in rural than in urban areas, while SVT, VPT, and TPT were in cities with higher vegetation coverage in urban areas. The SUHIdiu dynamics were found to be synthetically affected by the urban-rural contrast in vegetation and albedo. We consider these findings to be beneficial for deepening the understanding of SUHI dynamics at various time scales.

Untangling irrigation effects on maize water and heat stress alleviation using satellite data

Abstract. Irrigation has important implications for sustaining global food production by enabling crop water demand to be met even under dry conditions. Added water also cools crop plants through transpiration; irrigation might thus play an important role in a warmer climate by simultaneously moderating water and high temperature stresses. Here we used satellite-derived evapotranspiration estimates, land surface temperature (LST) measurements, and crop phenological stage information from Nebraska maize to quantify how irrigation relieves both water and temperature stresses. Unlike air temperature metrics, satellite-derived LST revealed a significant irrigation-induced cooling effect, especially during the grain filling period (GFP) of crop growth. This cooling appeared to extend the maize growing season, especially for GFP, likely due to the stronger temperature sensitivity of phenological development during this stage. Our analysis also revealed that irrigation not only reduced water and temperature stress but also weakened the response of yield to these stresses. Specifically, temperature stress was significantly weakened for reproductive processes in irrigated maize. Attribution analysis further suggested that water and high temperature stress alleviation was responsible for 65±10 % and 35±5.3 % of the irrigation yield benefit, respectively. Our study underlines the relative importance of high temperature stress alleviation in yield improvement and the necessity of simulating crop surface temperature to better quantify heat stress effects in crop yield models. Finally, considering the potentially strong interaction between water and heat stress, future research on irrigation benefits should explore the interaction effects between heat and drought alleviation.

Land Surface Temperature Reconstruction Under Long-Term Cloudy-Sky Conditions at 250 m Spatial Resolution: Case Study of Vinschgau/Venosta Valley in the European Alps

In this article, we present a new concept for predicting satellite-derived land surface temperature (LST) under cloudy skies over vegetated areas in the Alps. Although many different reconstruction methods have been developed, they require rarely available inputs, or they restore missing pixels from clear-sky observations with low spatial resolution (1–5 km), which makes them unreliable in heterogenous ecosystems. Given these limitations, we propose a station-based procedure to predict cloud-covered grids from 1-km Terra MODIS LST at 250 m spatial resolution. First, we explored correlations between ground-measured LST and air temperature in conjunction with other geo-biophysical variables under cloudy-sky conditions derived from ESRA clear-sky radiation model. Considering a high site dependency driven by different landcovers, in-situ data were aggregated into three groups (forest, permanent crops, grassland) and then, models were established. Next, the regressions were applied to 250-m gridded predictors to estimate cloud-covered LST pixels for six Terra MODIS LST images in 2014. While for permanent crops and forest group linear modelling was the most efficient, neural networks achieved the best performance for grasslands. The reconstructions showed reasonable LST distribution considering landscape heterogeneity of the region. The results were validated against timeseries of ground-measured LST in 2014. The models achieved reliable performance with an average R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.84 and root-mean-square error of 2.12 °C. Despite some limitations, mainly due to diversified character of cloudy-sky conditions and high heterogeneity of gridded predictors, the method can effectively reconstruct overcast MODIS data at subpixel level, which shows great potential for producing cloud-free LSTs in complex ecosystems

A Robust Method for Filling the Gaps in MODIS and VIIRS Land Surface Temperature Data

Satellite-derived land surface temperatures (LSTs) are a critical parameter in various fields. Unfortunately, there are numerous gaps in LST products due to cloud contamination and orbital gaps. In previous studies, various gapfilling methods have been developed. However, most of those methods use only spatiotemporal information to fill gaps. In this study, a gapfilling method called the enhanced hybrid (EH) method that integrates spatiotemporal information and information from other similar LST products was proposed. The accuracy of the EH method was compared with the accuracies of three other gapfilling methods that only use spatiotemporal information: Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST), interpolation of the mean anomalies (IMAs), and Gapfill. It was found that the correlations between the four LST products were strong, indicating that using information from other products may improve the accuracy of gapfilling. On average, the mean absolute errors (MAEs) of the data filled using the EH method were 23.7%–52.7% lower than those of RSDAST, 35.4%–38.7% lower than those of IMA, and 38.5%–46.9% lower than those of the Gapfill method. The usage of information from other similar LST products was the main reason for the high accuracy observed for the EH method. In addition, the LST images filled using the RSDAST and IMA methods had some outliers, while there were fewer obvious outliers in the LST images filled with the EH method. It was concluded that the EH method is a robust gapfilling method with a high accuracy.

Effects of Forest Changes on Summer Surface Temperature in Changbai Mountain, China

The area and vegetation coverage of forests in Changbai Mountain of China have changed significantly during the past decades. Understanding the effects of forests and forest coverage change on regional climate is important for predicting climate change in Changbai Mountain. Based on the satellite-derived land surface temperature (LST), albedo, evapotranspiration, leaf area index, and land-use data, this study analyzed the influences of forests and forest coverage changes on summer LST in Changbai Mountain. Results showed that the area and vegetation coverage of forests increased in Changbai Mountain from 2003 to 2017. Compared with open land, forests could decrease the summer daytime LST (LSTD) and nighttime LST (LSTN) by 1.10 °C and 0.07 °C, respectively. The increase in forest coverage could decrease the summer LSTD and LSTN by 0.66 °C and 0.04 °C, respectively. The forests and increasing forest coverage had cooling effects on summer temperature, mainly by decreasing daytime temperature in Changbai Mountain. The daytime cooling effect is mainly related to the increased latent heat flux caused by increasing evapotranspiration. Our results suggest that the effects of forest coverage change on climate should be considered in climate models for accurately simulating regional climate change in Changbai Mountain of China.

Modelling spatiotemporal variations of the canopy layer urban heat island in Beijing at the neighbourhood scale

Abstract. Information on the spatiotemporal characteristics of Beijing's urban–rural near-surface air temperature difference, known as the canopy layer urban heat island (UHI), is important for future urban climate management strategies. This paper investigates the variation of near-surface air temperatures within Beijing at a neighbourhood-scale resolution (∼ 100 m) during winter 2016 and summer 2017. We perform simulations using the urban climate component of the ADMS-Urban model with land surface parameters derived from both local climate zone classifications and OpenStreetMap land use information. Through sensitivity simulations, the relative impacts of surface properties and anthropogenic heat emissions on the temporal variation of Beijing's UHI are quantified. Measured UHI intensities between central Beijing (Institute of Atmospheric Physics) and a rural site (Pinggu) during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) campaigns, peak during the evening at ∼ 4.5 ∘C in both seasons. In winter, the nocturnal UHI is dominated by anthropogenic heat emissions but is underestimated by the model. Higher-resolution anthropogenic heat emissions may capture the effects of local sources (e.g. residential buildings and adjacent major roads). In summer, evening UHI intensities are underestimated, especially during heatwaves. The inability to fully replicate the prolonged release of heat stored in the urban fabric may explain this. Observed negative daytime UHI intensities in summer are more successfully captured when surface moisture levels in central Beijing are increased. However, the spatial correlation between simulated air temperatures and satellite-derived land surface temperatures is stronger with a lower urban moisture scenario. This result suggests that near-surface air temperatures at the urban meteorological site are likely influenced by fine-scale green spaces that are unresolved by the available land cover data and demonstrates the expected differences between surface and air temperatures related to canopy layer advection. This study lays the foundations for future studies of heat-related health risks and UHI mitigation strategies across Beijing and other megacities.