Daytime Land Surface Temperature Research Articles

Gap-Filling of 8-Day Terra MODIS Daytime Land Surface Temperature in High-Latitude Cold Region with Generalized Additive Models (GAM)

Land surface temperature (LST) is a crucial parameter driving the dynamics of the thermal state on land surface. In high-latitude cold region, a long-term, stable LST product is of great importance in examining the distribution and degradation of permafrost under pressure of global warming. In this study, a generalized additive model (GAM) approach was developed to fill the missing pixels of the MODIS/Terra 8-day Land Surface Temperature (MODIS LST) daytime products with the ERA5 Land Skin Temperature (ERA5ST) dataset in a high-latitude watershed in Eurasia. Comparison at valid pixels revealed that the MODIS LST was 4.8–13.0 °C higher than ERA5ST, which varies with land covers and seasons. The GAM models fairly explained the LST differences between the two products from multiple covariates including satellite-extracted environmental variables (i.e., normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and normalized difference snow index (NDSI) as well as locational information. Considering the dramatic seasonal variation of vegetation and frequent snow in the cold region, the gap-filling was conducted in two seasons. The results revealed the root mean square errors (RMSE) of 2.7 °C and 3.4 °C between the valid MODIS LST and GAM-simulated LST data in the growing season and snowing season, respectively. By including the satellite-extracted land surface information in the GAM model, localized variations of land surface temperature that are often lost in the reanalysis data were effectively compensated. Specifically, land surface wetness (NDWI) was found to be the greatest contributor to explaining the differences between the two products. Vegetation (NDVI) was useful in the growing season and snow cover (NDSI) cannot be ignored in the snow season of the study region. The km-scale gap-filled MODIS LST products provide spatially and temporally continuous details that are useful for monitoring permafrost degradation in cold regions in scenarios of global climate change.

Seamless downscaling of the ESA CCI soil moisture data at the daily scale with MODIS land products

Spatial downscaling has recently become a crucial process in the regional application of coarse-resolution passive microwave surface soil moisture (SSM) products. Extensive gaps in auxiliary optical/thermal infrared observation data (mainly caused by cloud cover) and gaps in coarse-resolution passive microwave SSM data lead to spatiotemporal discontinuity in downscaled SSM maps, thereby limiting their applications. An improved downscaling method for the 25-km European Space Agency (ESA) Climate Change Initiative (CCI) SSM product was proposed to obtain daily seamless downscaled SSM series at a 1-km scale. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daily land surface temperature (LST) and normalized difference vegetation index (NDVI) products were used as auxiliary data for the downscaling process. Prior to the spatial downscaling, an annual temperature cycle model was applied to the 1-km daily daytime LST data to fill the data gaps caused by cloud cover and to derive the spatial-seamless LST (gap-filled). Subsequently, the gap-filled ESA CCI SSM was generated at the original resolution based on the relationships among the SSM, LST, and NDVI. Finally, these were utilized to obtain a seamless downscaled series SSM at 1-km spatial resolution with a value-consistent downscaling method. The proposed method was applied to data obtained for the Iberian Peninsula from January 1, 2016 to December 31, 2018. Based on the comparison with the precipitation dataset, the downscaled SSM exhibited strong temporal correlation with rainfall events. Evaluation using the in situ SSM from the REMEDHUS network highlighted the good performance of the downscaled SSM at network level with a correlation coefficient (R) of 0.820. The root-mean-square-error, unbiased root-mean-square error (ubRMSE), and bias were 0.091, 0.033, and 0.085 m3/m3, respectively. A comparison with an alternative downscaled SSM product produced by the Barcelona Expert Center, one of the soil moisture and ocean salinity mission (SMOS)–downscaled SSM datasets, also indicated that the downscaled SSM has better spatiotemporal coverage and performance in terms of R and ubRMSE with reference to the REMEDHUS network. These results confirmed that the proposed method is an efficient and convenient downscaling process that can be used to generate high-resolution SSM data without spatiotemporal gaps. The downscaled SSM data improves the accuracy of the original passive microwave SSM product in describing regional SSM variations and shows good potential for related applications at regional scale.

Assessment of LST spatial and temporal changes in Jazmourian basin, southeast Iran

ABSTRACT Land surface temperature (LST) is an atmosphere-land interaction, which represents the first thermal reaction of the environmental changes occurring in the earth surface, being one of the most important factors used in the earth sciences. Jazmourian basin, in the southeast of Iran, is a fertile agricultural land with diverse topography and unique climatic conditions. Due to its significance as one of the most important agricultural poles of Iran, the LST variations are of great interest due to their high impact on the environmental processes, especially in the land cover. In recent years, Moderate Resolution Imaging Spectroradiometer images have been extensively used to estimate the LST due to their spatial and temporal homogeneity and freely available showing improved capabilities of climate analysis in the areas with scarce observation networks. We used a combination of MOD11C3 and MYD11C3 products of daytime and nighttime LST, covering the complete circadian cycle over Jazmourian basin, from 2003 to 2019. Results showed varied trends by months with a significantly positive increase in January. The spatial distribution revealed a generalized positive anomaly in the whole study area, with the highest values in central lowlands and lowest at eastern high elevations of the basin. The temporal and spatial analyses indicated high temperature differences between months and seasons as well as a wide range from −10°C to 65°C.

An integrated assessment on the warming effects of urbanization and agriculture in highly developed urban agglomerations of China

Urbanization and agriculture, the two major and concurrent land use activities, can dramatically alter land surface temperature (LST) through multiple biophysical processes. However, previous studies mainly focused on the warming effects of urbanization in large cities and/or urban core areas that may greatly underestimate the land use impacts on regional climate. Using natural forest as a reference, we assessed the LST changes of both urbanization and agriculture in the three most developed urban agglomerations of China (Jing-Jin-Ji, JJJ; Yangtze River Delta, YRD; Pearl River Delta, PRD) according to satellite observations. Results show that the urban-dominated lands warm the daytime LST substantially, especially in the south subtropical PRD (with an annual mean intensity of 5.5 °C), and the highest do not occur in the core cities. The crop-dominated lands also warm the daytime LST dramatically, especially in the temperate semi-humid JJJ (with an annual mean intensity of 3.9 °C). The daytime warming effects increase significantly from 2003 to 2018 mainly due to urban expansion in crop-dominated and mixed lands. The two land uses continue to warm the LST at night though in a lower magnitude in the PRD. However, the urban-dominated lands warm the LST slightly and the crop-dominated lands cool the LST substantially at night in the JJJ and YRD. Overall, the crop-dominated and/or mixed lands dominate the regional LST changes owing to their large areas. We further show that the daytime warming effects of the two land uses are likely caused by the changes of evapotranspiration, whereas the nighttime cooling effects might be mainly due to the changes in surface albedo and roughness. Our results highlight the importance of considering the urbanization in small-medium sized satellite cities and the more widespread agricultural activities in rural areas when assessing the regional climate change and formulating the mitigation strategies.

Impact of COVID-19 induced lockdown on land surface temperature, aerosol, and urban heat in Europe and North America.

The outbreak of SARS CoV-2 (COVID-19) has posed a serious threat to human beings, society, and economic activities all over the world. Worldwide rigorous containment measures for limiting the spread of the virus have several beneficial environmental implications due to decreased anthropogenic emissions and air pollutants, which provide a unique opportunity to understand and quantify the human impact on atmospheric environment. In the present study, the associated changes in Land Surface Temperature (LST), aerosol, and atmospheric water vapor content were investigated over highly COVID-19 impacted areas, namely, Europe and North America. The key findings revealed a large-scale negative standardized LST anomaly during nighttime across Europe (–0.11 °C to –2.6 °C), USA (–0.70 °C) and Canada (–0.27 °C) in March–May of the pandemic year 2020 compared to the mean of 2015–2019, which can be partly ascribed to the lockdown effect. The reduced LST was corroborated with the negative anomaly of air temperature measured at meteorological stations (i.e. –0.46 °C to –0.96 °C). A larger decrease in nighttime LST was also seen in urban areas (by ∼1–2 °C) compared to rural landscapes, which suggests a weakness of the urban heat island effect during the lockdown period due to large decrease in absorbing aerosols and air pollutants. On the contrary, daytime LST increased over most parts of Europe due to less attenuation of solar radiation by atmospheric aerosols. Synoptic meteorological variability and several surface-related factors may mask these changes and significantly affect the variations in LST, aerosols and water vapor content. The changes in LST may be a temporary phenomenon during the lockdown but provides an excellent opportunity to investigate the effects of various forcing controlling factors in urban microclimate and a strong evidence base for potential environmental benefits through urban planning and policy implementation.

Validation of AVHRR Land Surface Temperature with MODIS and In Situ LST—A TIMELINE Thematic Processor

Land Surface Temperature (LST) is an important parameter for tracing the impact of changing climatic conditions on our environment. Describing the interface between long- and shortwave radiation fluxes, as well as between turbulent heat fluxes and the ground heat flux, LST plays a crucial role in the global heat balance. Satellite-derived LST is an indispensable tool for monitoring these changes consistently over large areas and for long time periods. Data from the AVHRR (Advanced Very High-Resolution Radiometer) sensors have been available since the early 1980s. In the TIMELINE project, LST is derived for the entire operating period of AVHRR sensors over Europe at a 1 km spatial resolution. In this study, we present the validation results for the TIMELINE AVHRR daytime LST. The validation approach consists of an assessment of the temporal consistency of the AVHRR LST time series, an inter-comparison between AVHRR LST and in situ LST, and a comparison of the AVHRR LST product with concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) LST. The results indicate the successful derivation of stable LST time series from multi-decadal AVHRR data. The validation results were investigated regarding different LST, TCWV and VA, as well as land cover classes. The comparisons between the TIMELINE LST product and the reference datasets show seasonal and land cover-related patterns. The LST level was found to be the most determinative factor of the error. On average, an absolute deviation of the AVHRR LST by 1.83 K from in situ LST, as well as a difference of 2.34 K from the MODIS product, was observed.

Satellite Tracking Reveals Nesting Patterns, Site Fidelity, and Potential Impacts of Warming on Major Green Turtle Rookeries in the Red Sea

Major aggregations of nesting green turtles (Chelonia mydas) occur in the northern Red Sea, although little is known about the reproductive ecology of this endangered species in the region. To address this issue, we satellite-tracked 30 female green turtles to document their movements and to identify factors driving habitat use at two major rookeries in the Red Sea, Jazirat Mashabah (Mashabah Island) and Ras Al Baridi in Saudi Arabia. Between successive nesting events, turtles displayed high fidelity to nesting beaches and adjacent in-water habitats (inter-nesting habitats). Using generalized linear mixed models, we estimated the mean probability of nesting per beach emergence (nesting success rate) to be 0.628, and the mean duration between a successful nesting event and the successive emergence onto the beach (re-nesting interval) to be 10.8 days at each site. The nesting success rate was relatively high (>0.8) when the preceding daytime land surface temperature (LST) was lower than 37°C but decreased with elevated daytime LST (<0.4 when >47°C). Re-nesting interval was longer at lower water temperatures and towards the end of the nesting season of individuals. Our study improves the robustness of abundance estimates from census data (e.g., track counts) and shows that the protection of nesting and inter-nesting habitats during a breeding season would be an effective conservation strategy for the species. We discuss how global warming could increase energy expenditure due to lowered nesting success, ultimately compromising the reproductive fitness of these populations.

Driving Factors of Land Surface Temperature in Urban Agglomerations: A Case Study in the Pearl River Delta, China

Land surface temperature (LST) in urban agglomerations plays an important role for policymakers in urban planning. The Pearl River Delta (PRD) is one of the regions with the highest urban densities in the world. This study aims to explore the spatial patterns and the dominant drivers of LST in the PRD. MODIS LST (MYD11A2) data from 2005 and 2015 were used in this study. First, spatial analysis methods were applied in order to determine the spatial patterns of LST and to identity the hotspot areas (HSAs). Second, the hotspot ratio index (HRI), as a metric of thermal heterogeneity, was developed in order to identify the features of thermal environment across the nine cities in the PRD. Finally, the geo-detector (GD) metric was employed to explore the dominant drivers of LST, which included elevation, land use/land cover (LUCC), the normalized difference vegetation index (NDVI), impervious surface distribution density (ISDD), gross domestic product (GDP), population density (POP), and nighttime light index (NLI). The GD metric has the advantages of detecting the dominant drivers without assuming linear relationships and measuring the combined effects of the drivers. The results of Moran’s Index showed that the daytime and nighttime LST were close to the cluster pattern. Therefore, this process led to the identification of HSAs. The HSAs were concentrated in the central PRD and were distributed around the Pearl River estuary. The results of the HRI indicated that the spatial distribution of the HSAs was highly heterogeneous among the cities for both daytime and nighttime. The highest HRI values were recorded in the cities of Dongguan and Shenzhen during the daytime. The HRI values in the cities of Zhaoqing, Jiangmen, and Huizhou were relatively lower in both daytime and nighttime. The dominant drivers of LST varied from city to city. The influence of land cover and socio-economic factors on daytime LST was higher in the highly urbanized cities than in the cities with low urbanization rates. For the cities of Zhaoqing, Huizhou, and Jiangmen, elevation was the dominant driver of daytime LST during the study period, and for the other cities in the PRD, the main driver changed from land cover in 2005 to NLI in 2015. This study is expected to provide useful guidance for planning of the thermal environment in urban agglomerations.

Croplands intensify regional and global warming according to satellite observations

Croplands, occupying approximately 13% of the Earth's land surface, can dramatically alter surface temperature via multiple biophysical processes. However, it remains unclear whether and to what extent croplands warm or cool the land surface globally. Here we present new evidence of the temperature effects of global croplands based on high-resolution satellite observations of land surface temperature (LST). We find general daytime warming and nighttime cooling of crop-dominated lands (cropland intensity ≥90%) relative to other vegetation cover, with net daily cooling in the arid zone and net daily warming in all other areas. On a per-pixel basis, however, nearly half of the crop-dominated lands cool daytime LST and/or warm nighttime LST. The temperature effects vary greatly by location and season. The per-pixel intensity can exceed 10 °C likely due to the changes in surface evapotranspiration, albedo, and background climate. By extending the LST changes of crop-dominated lands to the full land surface, we find that croplands make large contributions to regional and global land surface warming. Daytime warming can reach 0.56 °C in Europe during spring. Nationally, China, India, and Brazil lead the warming due to their large cropland areas and strong local climate effects. Our results highlight the importance of accounting for the biophysical effects of croplands for projecting climate change and formulating adaptation and mitigation strategies.

8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

Estimation of daily mean land surface temperature at global scale using pairs of daytime and nighttime MODIS instantaneous observations

Accurate estimations of daily mean land surface temperature (LST) are important for investigating the urban heat island effect, land-atmosphere energy exchanges, and global climate change. Moderate Resolution Imaging Spectroradiometer (MODIS) sensors can provide up to four instantaneous LSTs of a single day across the world. However, numerous studies, such as those on climate change and hydrology, require the input of daily mean LSTs rather than instantaneous value. In this paper, we propose a practical method to estimate the daily mean LST using instantaneous LST products derived from MODIS. Based on the in situ LST measurements collected from 235 sites distributed globally, multiple linear regressions of two to four valid instantaneous LSTs at different MODIS observations moments (at least one daytime and one nighttime observations) can provide reliable estimates of daily mean LSTs under all-weather conditions with a root mean square error (RMSE) of less than 1.60 K. In addition, the conditions of clouds would affect the estimation accuracy of daily mean LST to a certain extent. Subsequently, an algorithm is proposed to produce the most complete coverage of daily mean LSTs from instantaneous LST products derived from MODIS. Validation results with in situ measurements show that the daily mean LSTs estimated from the MOD11A1 and MYD11A1 products are similar to the daily mean of the in situ LST, with an RMSE of 2.17 K. Furthermore, the daily mean LST derived from MODIS data is successfully applied to calculate the global annual cycle parameters (ACPs) in the annual temperature cycle (ATC) model. The results of this study show that the daily mean LST can be retrieved accurately from combinations of daytime and nighttime LSTs derived from MODIS. We expect that our findings will be useful for various applications involving global LST trend analysis and climate change.

Assessment of heat mitigation capacity of urban greenspaces with the use of InVEST urban cooling model, verified with day-time land surface temperature data

Accurate quantification of the heat mitigation capacity of urban greenspaces is essential in planning decisions due to increased thermal pressures on existing and new urban environments associated with climate change. However, this often requires data analytical skillsets that may not be available to the planning community. The recently developed InVEST 3.8.7 Urban Cooling model addresses this limitation by using several easily accessible parameters, assigned to a land cover map, to produce a heat mitigation index (HMI) intended to estimate the cooling capacity of vegetation in a spatial context. In this study, we validated the HMI derived for three towns with differing morphologies by comparison to land surface temperature (LST) data using linear regression analysis. We found that the HMI can be used to explain a variable proportion of the variation in LST, with R2 ranging from 0.48 to 0.64 depending on the town, with stronger associations obtained for towns with a higher range of LST values. Higher resemblance to LST data was achieved after resampling of the 2 m resolution model outputs to 30 m resolution, inclusion of water bodies as cooling features, and using cooling distance away from large greenspaces of 100 m. On average, a change in the HMI of 0.1 was associated with 0.76 °C change in LST. We conclude that the model is suitable for assessment of heat mitigation interventions through incorporation of vegetation and water bodies into city plans at scales relevant to masterplans rather than fine-tuning of urban design.

Dynamics of Daytime Land Surface Temperature (LST) Variabilities in the Middle East Countries During 2001–2018

Dynamics of Daytime Land Surface Temperature (LST) Variabilities in the Middle East Countries During 2001–2018

Perturbation of Urbanization to Earth's Surface Energy Balance

AbstractUrbanization, one of the most dramatic forms of land conversion, modifies local climatic environments and threatens human life and health. Here we use the space‐for‐time approach combined with satellite data to quantify the potential perturbation of surface energy balance and land surface temperature (LST) caused by urbanization at global scale. We estimate that collectively +2.4°C, +0.9°C, and +1.7°C potential changes for annual daytime, nighttime, and mean LST could be triggered when land surface converted from natural area to urban use, due primarily to the decline of latent heat during months from April to October (−23.9–−3.2 W m−2), and the reduced sensible heat and ground heat storage in other months (−5.2–−2.4 W m−2). Urbanization perturbation to surface energy balance and temperature exhibit conspicuous spatial heterogeneity (i.e., varying with latitude and climate zones) and temporal asymmetries (i.e., diurnal and seasonal: strong in summer daytime and weak in winter nighttime). These spatial‐temporal variations are interrelated closely with local background climate‐vegetation regimes, as indicated by strong correlations between urbanization perturbation to surface biophysical effects and precipitation, temperature, vegetation index across regions and months. Our findings provide empirical evidence that biophysical mechanisms of urbanization need to be considered in predicting future trajectories of climate change and local susceptibility of surface energy balance should be accounted for when evaluating urbanization effects and mitigating urban heat.

Modification effect of urban landscape characteristics on the association between heat and stroke morbidity: A small-scale intra-urban study in Shenzhen, China

BackgroundShort-term heat exposure might induce stroke morbidity and mortality, and there were several studies explored the possible vulnerable populations. At present, the research on the modification effect of intra-urban landscape characteristics on the association between heat and stroke morbidity is limited, especially in China. MethodsWe collected data on 22,424 first-ever strokes between 2010 and 2016 in Shenzhen, from June to August of each year. We adopted the case-only study combined with logistic regression models to examine the modification effects of 5 urban landscape characteristics. We studied the characteristics of relevant vulnerable populations through stratification analyses. ResultsHigh values (refer to the median values) of nighttime land surface temperature (LST) and the proportion of impervious surface may aggravate the harmful effects of heat on stroke morbidity, with the OR values (95% CI) of 1.205 (1.053, 1.357) and 1.115 (1.010, 1.220); while, high values of NDVI and the proportion of water bodies may alleviate the harmful effects of heat, with the OR values (95% CI) of 0.772 (0.699, 0.845) and 0.821 (0.741, 0.901). The OR value of daytime LST was 1.004 (0.861, 1.147). Statistically significant modification effects were located in the population without Shenzhen's household registration; as for nighttime LST, statistically significant modification effects were located in females and the elderly. ConclusionsHigh values of nighttime LST and the proportion of impervious surface might aggravate the harmful effects of heat on stroke morbidity, while high green space and water cover might alleviate its effects. Immigrants were the related vulnerable populations. The government should take measures to cope with climate warming and pay attention to the health effects of heat on immigrants.

Urban Heat Island Formation in Greater Cairo: Spatio-Temporal Analysis of Daytime and Nighttime Land Surface Temperatures along the Urban–Rural Gradient

An urban heat island (UHI) is a significant anthropogenic modification of urban land surfaces, and its geospatial pattern can increase the intensity of the heatwave effects. The complex mechanisms and interactivity of the land surface temperature in urban areas are still being examined. The urban–rural gradient analysis serves as a unique natural opportunity to identify and mitigate ecological worsening. Using Landsat Thematic Mapper (TM), Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS), Land Surface Temperature (LST) data in 2000, 2010, and 2019, we examined the spatial difference in daytime and nighttime LST trends along the urban–rural gradient in Greater Cairo, Egypt. Google Earth Engine (GEE) and machine learning techniques were employed to conduct the spatio-temporal analysis. The analysis results revealed that impervious surfaces (ISs) increased significantly from 564.14 km2 in 2000 to 869.35 km2 in 2019 in Greater Cairo. The size, aggregation, and complexity of patches of ISs, green space (GS), and bare land (BL) showed a strong correlation with the mean LST. The average urban–rural difference in mean LST was −3.59 °C in the daytime and 2.33 °C in the nighttime. In the daytime, Greater Cairo displayed the cool island effect, but in the nighttime, it showed the urban heat island effect. We estimated that dynamic human activities based on the urban structure are causing the spatial difference in the LST distribution between the day and night. The urban–rural gradient analysis indicated that this phenomenon became stronger from 2000 to 2019. Considering the drastic changes in the spatial patterns and the density of IS, GS, and BL, urban planners are urged to take immediate steps to mitigate increasing surface UHI; otherwise, urban dwellers might suffer from the severe effects of heatwaves.

Evaluating and improving simulations of diurnal variation in land surface temperature with the Community Land Model for the Tibetan Plateau.

This study evaluated and improved the ability of the Community Land Model version 5.0 (CLM5.0) in simulating the diurnal land surface temperature (LST) cycle for the whole Tibetan Plateau (TP) by comparing it with Moderate Resolution Imaging Spectroradiometer satellite observations. During daytime, the model underestimated the LST on sparsely vegetated areas in summer, whereas cold biases occurred over the whole TP in winter. The lower simulated daytime LST resulted from weaker heat transfer resistances and greater soil thermal conductivity in the model, which generated a stronger heat flux transferred to the deep soil. During nighttime, CLM5.0 overestimated LST for the whole TP in both two seasons. These warm biases were mainly due to the greater soil thermal inertia, which is also related to greater soil thermal conductivity and wetter surface soil layer in the model. We employed the sensible heat roughness length scheme from Zeng, Wang & Wang (2012), the recommended soil thermal conductivity scheme from Dai et al. (2019), and the modified soil evaporation resistance parameterization, which was appropriate for the TP soil texture, to improve simulated daytime and nighttime LST, evapotranspiration, and surface (0–10 cm) soil moisture. In addition, the model produced lower daytime LST in winter because of overestimation of the snow cover fraction and an inaccurate atmospheric forcing dataset in the northwestern TP. In summary, this study reveals the reasons for biases when simulating LST variation, improves the simulations of turbulent fluxes and LST, and further shows that satellite-based observations can help enhance the land surface model parameterization and unobservable land surface processes on the TP.

Comparison of the accuracy of daytime land surface temperature retrieval methods using Landsat 8 images in arid regions

Land surface temperature (LST) is a significant parameter in surface and atmospheric systems which can be used to describe the physical processes of energy and water exchange. There are several ways to retrieve land surface temperature using satellite imagery. The purpose of the current study is to compare the accuracy of different LST retrieval algorithms, including 11 types of split-window algorithms, two mono-window algorithms, and three single-channel algorithms. The validation of LST retrieval algorithms was done using temperature-based and cross-validation methods. In this study, 20 images from Landsat 8 satellite related to 2017 and 2018 were used to retrieve LST in different ways. The temperatures obtained from different methods were compared in one day, and the average of LST of the different methods was about 5 ℃ different, which indicates the importance of the existing validation methods. Using Sentinel-2 image classification, 20 points in homogeneous areas with pure pixels were selected to measure the LST using a thermometer for land validation. Results showed that in a split window algorithm which was applied on the water vapor image, the RMSE error has been reduced by 2 ℃. In cross-validation, MODIS LST images were used, and RMSE temporal image was prepared, and classified into five groups including less than 2, 2–3, 3–4 and more than 5 ℃ .Comparing the area of each of these groups in different methods showed that MW2 and SW1 methods have less accuracy than the other methods. The SW9 algorithm can retrieve temperature with an error of less than 2 ℃ in 80% of the area. The results of cross-validation showed that the SC2 algorithm had the highest accuracy among the algorithms that use a thermal band for LST retrieval with a RMSE spatial of 5.3 °C and the SW10 and SW11 have shown the highest accuracy among the algorithms that use two thermal bands with RMSE less than 5 ℃. In general, the algorithms that use atmospheric transmittance coefficients and water vapor as inputs are more accurate than the others.

Snow cover and vegetation greenness with leaf water content control the global land surface temperature

The land surface temperature (LST) and land use land cover (LULC) are the major components of climate- and environment-related studies. The objective of this study was to assess the relationship of LST with remotely sensed LULC-derived vegetation indices during 2018 at the global, latitudinal and continental scales. Moderate resolution imaging spectroradiometer (MODIS) Aqua daytime LST and eight LULC MODIS indices (NDVI, EVI, LAI, DSI, NDWI, albedo, NDSI, NDBI) were processed using Earth Engine Code Editor. The analysis was conducted using correlation coefficient and significance of the relationship of variables based on 2050 randomly selected points at the global scale. Based on the univariate and geographically weighted regression methods, the research confirmed that vegetation greenness (NDVI), leaf water content (NDWI) and snow cover (DSI) are the codominant drivers of decreasing LST at the global scale including Europe, Asia, South America and North America at the continental scale. Snow cover during winter and vegetation greenness in summer seasons control the global LST. Although albedo shows an inverse relationship, NDBI and NDSI displayed a positive relationship to LST at the global scale. In conclusion, temporal seasonal and inter-annual dynamics of LST in response to snow cover and vegetation properties (greenness, moisture) should be focused on understanding and regulating LST at varying scales.

Separate and combined impacts of building and tree on urban thermal environment from two- and three-dimensional perspectives

Separate impacts of building and tree on the urban thermal environment have been studied extensively, but their combined impacts, especially from both the horizontal (i.e., two-dimensional (2D)) and vertical (i.e., three-dimensional (3D)) perspectives remain largely unclear. Based on satellite thermal data and elaborate 2D and 3D urban morphology, herein we simultaneously investigate the separate and combined impacts of building and tree over Nanjing in China from both the 2D and 3D perspectives. We further examine the day–night contrast together with the sensitivity of such impacts to scale. Our results show that, when compared with urban structures from a single dimension, the combination of 2D and 3D structures is more capable of predicting urban land surface temperatures (LSTs) for both day and night. The assessments further illustrate that the separate and combined impacts of building and tree on LSTs are usually more significant when the spatial scale increases. As for the separate impacts of building and tree, 2D structure affects more urban thermal environment than 3D structure at all spatial scales during the day, but an opposite trend occurs at night. Moreover, for the combined impact of building and tree on LST across different scales, daytime and nighttime LSTs are respectively dominated by 2D and 3D building structures. Combining 2D and 3D structures improves the explained LST variation by 7.3%–11.1% and 25.3%–37.7% for day and night, respectively when compared to using 2D structures only. These findings emphasize the need to incorporate both 2D and 3D urban morphology to improve the urban thermal environment.