Throughout the world, cities have formed rapidly due to population growth and people gathering in certain areas to settle and build their lives. Such urbanization brings not only economic development but also the urban heat island (UHI) phenomenon (Oke, 1982; Mirzaei, 2015; Cao et al., 2016; Zhao et al., 2020). Two major types of UHIs can be distinguished: (a) the canopy urban heat island (CUHI) and (b) the surface urban heat island (SUHI). The particular type of UHI is defined based on the height above the ground at which the phenomenon is observed and measured (Oke, 1982). The UHI effect has become an indisputable fact and brings adverse impacts on urban ecology and energy consumption (Roth, 2007; Yang et al., 2019; Y. Yang et al., 2020b; Zheng et al., 2020). UHIs amplify thermal stress, so people residing in urban areas are more impacted during heatwave episodes (Koken et al., 2003; Estrada et al., 2017). A recent study of the global UHI predicted that about 30 % of the world's population is exposed to lethal high temperatures for at least 20 d yr-1, and by 2100, this proportion was projected to reach 48 % (Mora et al., 2017). UHIs also have the potential to impact vegetation phenology (Kabano et al., 2021), diurnal temperature range (Argüeso et al., 2014), water consumption, and general thermal comfort (Salata et al., 2017). Due to its negative impacts, the UHI effect has become a key challenge in achieving urban sustainability, and assessing this phenomenon has attracted increasing interest over the last decade or so (Corburn, 2009; Pandey et al., 2014; Malings et al., 2017). In general, both background weather conditions (e.g., the wind vector and heatwaves) and city-specific characteristics (including the presence of urban green space, properties of built-up materials, and the intensity of human activity) influence the UHI's mean intensity and variation (Zhao et al., 2014; Manoli et al., 2019). Concerning these factors, the UHI also shows significant intracity variability since urban areas are highly heterogeneous. Therefore, exploring the formation and causes of UHIs is crucial for decision-makers involved in the planning of urban developments and allocating public resources.

There are two main approaches to studying UHIs: numerical simulation and observation. Numerical simulation can reduce the need for a large number of observations and reveal mechanistic insights by investigating the impacts of cities on meteorological variables (Chun and Guldmann, 2014; Zou et al., 2014; Zhang et al., 2015; Taleghani et al., 2016; Li et al., 2020). For instance, Zhang et al. (2015) investigated the influence of land use/land cover (LULC) and anthropogenic heat flux (AHF) on the structure of the urban boundary layer in the Pearl River Delta region, China, through a series of numerical experiments. However, it is important to acknowledge that numerical simulation is a simplification of the real world and cannot replace actual observations. Observational studies of UHIs are arguably more robust in their findings (Hu et al., 2016; Chakraborty and Lee, 2019; Dewan et al., 2021) and can mainly be categorized into the following three methods: (1) in situ (field) measurement, (2) mobile measurements, and (3) remote sensing technology.

In situ (field) measurements include conventional measurements from national meteorological stations which are usually located in rural areas and high-density microclimate observations from experiments or high-density automatic sites over various underlying surfaces. It is easy to compare long-term series of air temperature (AT) between urban and rural stations based on meteorological observation data (Liu et al., 2006, 2008; Qiu et al., 2008; Yang et al., 2012; Scott et al., 2018; Nganyiyimana et al., 2020). With the analysis of meteorological data in a long time series, the contribution and trend changes of UHI intensity (UHII) can be clearly discovered. Meanwhile, however, due to the limitations of meteorological sites in terms of their spatial representation, it is difficult to build a comprehensive understanding of the spatial distribution of urban thermal environment parameters (such as urban canopy temperature, land surface temperature (LST) and vegetation) (Liu et al., 2008; Nganyiyimana et al., 2020). To overcome these limitations, high-density observation stations are used to explore the spatial distribution of the urban thermal environment and its relationship with the surrounding environment (Hu et al., 2016; Bassett et al., 2016; Ching et al., 2018; An et al., 2020). Deploying denser observation stations or urban microclimate surveys can to some extent compensate for the limitation of a coarse spatial resolution. However, such approaches are usually unsuitable for large-scale studies due to restrictions imposed by certain natural conditions, social activities, as well as the high cost of construction and maintenance (An et al., 2020). For example, mobile transect surveys have been used in many studies (Merbitz et al., 2012; Akdemir and Tagarakis, 2014; Hankey and Marshall, 2015; Al-Ameri et al., 2016; Liu et al., 2017; Popovici et al., 2018), as they can easily obtain the distribution of parameters along a designed route using only a set of equipment attached to a mobile vehicle. However, it is rather costly to obtain observations at a fine resolution, broad coverage, and high synchronicity with such an approach.

To overcome these possible issues, LST data from aerial sensors and Earth-observing satellites are commonly employed in UHI studies, and so remote sensing data such as those from the Advanced Very High Resolution Radiometer (AVHRR) (Roth et al., 1989; Caselles et al., 1991; Gallo et al., 1993a), Landsat (Chen et al., 2007; Zhou et al., 2015; Zhao et al., 2016), MODIS (Peng et al., 2012; Zhou et al., 2015; Li et al., 2017; Yang et al., 2018; Chakraborty and Lee, 2019), aerial images (Buyadi et al., 2013; Heusinkveld et al., 2014; Yu et al., 2020), and so on (Zhao et al., 2020; Gallo et al., 1993b; Qin et al., 2001; Chakraborty et al., 2020) are widely used to explain the spatial distribution of the surface UHI and its relationship with the local environment (e.g., LULC). Remote sensing data have good application prospects, as they can provide fine resolution and wide data coverage at times when other ground-based observations cannot. However, due to the influence of precipitation and clouds, the retrieval of LST sometimes can be challenging. In addition, each satellite remote sensing dataset has its own characteristics (Zhao et al., 2016; Chakraborty and Lee, 2019). For example, Landsat images have a high spatial resolution (30 m) that can show urban block sizes, but the temporal resolution is rather low (16 d). The MODIS LST dataset has the advantage of high temporal resolution (four times per day), but the spatial resolution is only 1 km (Yang et al., 2018).

LST derived by satellites has become an important indicator for exploring variation characteristics of the SUHI, because LST is closely related to the land cover type/structure, population density, anthropogenic heat release, etc., and it also can significantly influence surface air temperature, wind field, humidity, and surface fluxes in the urban region (Ho et al., 2016; Yang et al., 2019; Li et al., 2020, 2021). However, the LST can only quantify the SUHI effect, which is seriously affected by meteorological factors, e.g., clouds and evaporation. In contrast, as an important indicator reflecting the energy exchange between the atmosphere and land in the urban canopy, AT is more representative than LST. In particular, AT is more related with human health and ecological changes in cities (Ho et al., 2016). While UHI studies based on AT observed by meteorological sites suffer from limited spatial coverage, which impedes a comprehensive understanding of the influencing factors and causes of canopy UHI (CUHI). Thus, there is an urgent need to develop rapid, high-spatiotemporal-resolution AT, and refined CUHI intensity (CUHII) estimation methods to explore the mechanisms under which anthropogenic factors (e.g., urban land-use changes, anthropogenic heat emissions, urban morphology, and size) and natural factors (e.g., meteorological conditions and geographical differences) influence the CUHIs of complex and diverse cities.

Therefore, in this study, we (1) based on remote sensing data, AT and wind speed data as well as other environmental information from meteorological observations, retrieved the AT data at a 30 m spatial and 1 h temporal resolution in the study area by using machine learning; (2) calculated the CUHII distribution based on the retrieved AT data, and further explored the shape, intensity, and influencing factors of the CUHI by combining local LULC, wind vector, and urban morphology data.

Based on the RF model and combined with local environment and background weather data, the pattern and causes of CUHIs can be analyzed in detail. On 11 August 2013, Nanjing experienced a heatwave, with almost no horizontal convection of air (Fig. 11a). In dry areas, such as built-up areas, the latent heat flux remained unchanged, but the high reflectivity of the surface raised the AT. In the heatwave period, the higher AT increased the latent heat flux in rural areas (Khan et al., 2020). For example, vegetation and water bodies alleviated the increase in AT in rural areas. This combined effect exacerbated the difference in AT between the urban and rural areas, making the overall CUHI the strongest (Nganyiyimana et al., 2020; Meili et al., 2021). In Fig. 10a, it can be seen that the cooling efficiency of vegetation in the urban area was not high and the coverage of the cooling area was small. This is because the stomata of leaves would have been closed under high AT and dry weather, resulting in reduced evapotranspiration and increased AT (Manoli et al., 2019). On 2 September 2015, northwesterly winds prevailed (Fig. 11b), and there was abundant water vapor over the hills of northeast Nanjing and over the Yangtze River. The increase in latent heat flux and horizontal convection cooling lowered the CUHII. Cold islands even appeared to the north of the Yangtze River. The CUHII in the southeast direction was strong (Fig. 10b), which was mainly affected by the heat transport of the prevailing winds (Chuanyan et al., 2005), causing the CUHI to shift toward the downwind area. On 21 July 2017, southwesterly winds prevailed in Nanjing, with high wind speed, decreasing the CUHII in the upwind region (Figs. 10c and 11c). However, there were large areas of vegetation coverage in the range of 10–20 km in the downwind region, where was affected by the combined effects of land use and horizontal advection cooling, leading to lower CUHII there than that of 20–30 km. This also confirms the conclusion (Bassett et al., 2016) that the upwind horizontal advection cooling has the strongest correlation with the weakening of the CUHI effect, and that the downwind region is affected by the wind speed.

There are four main methods for retrieving AT for CUHII assessment:

Statistical methods (Prihodko and Goward, 1997; Alonso and Renard, 2020; Li et al., 2021): statistical models of environmental factors and temperature are established to evaluate the AT, such as multiple linear regression models, partial least-squares regression, and GWR. In previous study (Alonso and Renard, 2020), two methods of AT prediction (namely, stepwise linear regression and GWR) were compared with the RF model. The RF model has the highest accuracy and effectively avoids the problem of autocorrelation by filtering variables, which is consistent with previous work (Yoo et al., 2018; Zhu et al., 2019) and our present work, while conventional statistical methods, in addition, cannot effectively solve nonlinear problems (Oh et al., 2020).

The RF prediction framework proposed in this work not only can dynamically predict CUHII in detail and high frequency within highly heterogeneous cities but can also be built against different weather backgrounds, mainly because the environmental parameters entered into the model are relatively stable within a certain period (such as the same month or season). As long as the environmental parameters are acquired once, they can be combined with the AT data in real time to establish the RF model, and the spatial distribution characteristics of CUHII with high temporal and spatial resolution can be obtained. For instance, we randomly predicted the 30 m resolution AT and spatial distribution of CUHII (Fig. 15) with the wind vector field (Fig. S3) during the heatwave period of 12–14 August 2012, thereby supporting those involved in making decisions with respect to urban climate, urban planning, and urban energy consumption. Particularly, the potential that our proposed model can be used cross a short period as most of the environmental parameters fed to the model probably can remain stable for some time, e.g., 1 month or even longer.

Due to changes in local weather conditions (e.g., precipitation and cloud cover), however, there are various satellite-based LST samples and LST is usually dynamical in 1 month, leading to uncertainties in predicting AT; therefore, LST is not suitable to be an input variable for our present model of CUHII. Except for human activities and LULC, the background weather conditions (such as heatwaves, air pollution, atmospheric circulation, and cloud cover) are also extremely important (Bassett et al., 2016; P. Yang et al., 2020; Khan et al., 2020), which should be introduced to improve the RF model of CUHII.

Taking Nanjing as an example and using remote sensing data with data from local weather stations, parameters to characterize the urban environment were constructed, e.g., anthropogenic parameters (i.e., AHF), geometric parameters (distance from city center, proportions of LULC types by area, altitude, and latitude and longitude, slope, and aspect), and physical parameters (proportion of IS, surface albedo, NDVI, NDBI, SAVI, gNDVI, and NDMI). A 2 km buffer zone was created around the meteorological stations, and the observed environmental parameters were extracted. A refined assessment framework of CUHII was then established by using random forest model with observed AT and environmental variables.

Results showed that the correlation coefficient between the predicted and observed AT was 0.731, and the average RMSE was 0.719 ∘C, indicating the high accuracy of the RF model. Based on 1-month samples, the R2 reached 0.986 and RMSE was 0.620 ∘C. Finally, the high-spatial-resolution (30 m) CUHII distribution was analyzed. It was found that the shape of the CUHII was highly correlated with the spatial distribution of AHF and built-up area under calm wind conditions. Under the prevailing wind conditions, the CUHII should be discussed separately in upwind and downwind areas divided by the central city. In the upwind area, there was a significant negative correlation between the wind speed and CUHII. The higher the wind speed, the more obvious the negative correlation. In the downwind area, horizontal convection cooling was found to be the leading factor under high wind speed weather, and heat transfer was the leading factor under low wind speed weather. The combined effects of built-up areas, heatwaves, and human factors can strengthen the CUHII, while the vegetation canopy and water bodies will weaken it. Vegetation and water bodies in the central urban area were found to have a significant cooling effect, providing a reference for urban development. With increasing distance from the city center, the CUHII decreased sharply.

In general, overlapping the refined CUHII with local environmental variables and weather conditions helps to explore the causes of CUHIs in more detail, instead of being limited to the location of meteorological sites and frequent changes in various types of weather. The new 30 m resolution CUHII evaluation framework developed in this study has strong portability and important practical value. Our findings are helpful for improving our understanding of the relationship between human activities and regional climate change, which can provide important guidance for urban development planning and allocation of public resources in the context of global warming and rapid urbanization.