1 Introduction

Urban areas are spatial carriers highly concentrated with population and economic activities. The urban heat island effect (UHI) has become one of the most important environmental problems in the twenty-first century [26], and the intensification of urban heat island has caused many problems in the living environment [17], which not only aggravates the air pollution in the city [13], but also increases energy [20] and water consumption, affects human health [34], and causes heart and lung diseases [11]. With urban expansion, the reduction of vegetation coverage on the surface gradually occurs, and the increase in tall buildings and large areas of hard surfaces leads to an increase in the urban heat capacity and thermal conductivity. This means that cities are more prone to absorb and store heat. Meanwhile, urban expansion is usually accompanied by increased population and human activities such as increased traffic flow [21]. The heat generated by these activities, such as vehicle and industrial emissions, further exacerbates the urban heat island effect. Since the 60 s of the last century, there has been research on the urban heat island effect and its mitigation, such as changing the type and proportion of impervious surfaces, reducing anthropogenic heat emissions, etc. [25], one of the most effective ways is to use blue and green spaces to reduce the temperature of their surroundings [14].

The professional term "cold island effect" was proposed by Oke in 1987. It was first used to explain the meteorological phenomenon discovered when observing oases in deserts. It refers to the fact that oases or lakes in arid areas have lower temperatures than the surrounding environment to use cold sources. It exists in the form of "temperature", which refers to the phenomenon that the temperature in some areas is lower than that of the surrounding environment. Throughout the research process, there have been various names for this phenomenon, such as the Oasis Effect [29], Park Cool Island [31], Cool Island Effect [5, 10], and Regional Cool Island Intensity. The Urban Cool Island Effect (UCI) refers to certain areas in the city that have lower surface temperatures than the surrounding heat island areas due to high green coverage, large water areas, low building density, etc. and air temperature, forming a phenomenon similar to a cold island. In warm seasons, the temperature of water bodies and green areas in cities is often lower, which has a significant cooling effect on the surrounding environment. Especially in hot summer, green areas and water bodies can effectively regulate the urban climate and slow down the effects of high temperatures. Impact and help improve the urban environment [7].

Water bodies and green plants in urban landscapes are often referred to as blue space and green space, which are important resources that constitute the urban ecological environment [39]. It is an academic consensus that urban water landscapes have a "cold island effect". On the one hand, when the land surface heats up drastically due to absorption of solar radiation, the temperature of the water body rises slowly due to its large heat capacity. On the other hand, part of the solar radiation is converted into latent heat due to the evaporation of water. As a result, compared with the surrounding land environment, the water surface shows obvious low-temperature characteristics and becomes a significant cold island in the urban environment. Since the temperature of the water body is significantly lower than that of the surrounding environment, the temperature of the surrounding environment will be affected by the water body accordingly. Through radiation, convection and conduction, the temperature of the air above the water surface is reduced. As the air flows, the water cold island effect spreads to the surrounding environment, thereby significantly reducing the temperature of the surrounding environment [1], forming a "cold source" in the city, and the phenomenon of improving the urban thermal environment is defined as Water Cold Island (WCI) effect. Enhancing the water-cooled island (WCI) effect of urban water bodies is an important strategy to alleviate high temperatures in urban areas. Research [32] has found that water bodies in urban parks play a significant role in lowering the surrounding temperature, and water bodies contribute most to the cooling effect of urban parks [38]. As a type of urban green space, waterfront green space is also an area where water and green elements interact. It can not only form a ventilation corridor around the landscape water body to enhance the cooling effect of the landscape water body, but also has the shading and transpiration effect of the waterfront green space itself. It can effectively achieve a cooling effect [6]. Greenery absorbs solar radiation through photosynthesis and transpiration [3], while intercepting solar radiation through shading functions, increasing airflow exchange, and lowering the surrounding surface temperature. However, with a large urban population and limited land resources, the impact of the local cold island effect cannot be increased by unlimited increases in the area of landscape water bodies and waterfront green spaces [41]. Showed that the cold island size has a threshold in terms of cooling efficiency. Once the threshold is exceeded, the cooling efficiency of urban cold islands will decrease significantly, which will have implications for the planning and management of urban cold islands. Therefore, it is necessary to identify the main factors affecting WCIs and maximize WCIs within limited space [9]. Currently known factors affecting water-cooled islands mainly include water area, water shape, surrounding land use type, etc. However, the importance of these factors in different cities is still unclear. A large number of studies have proven that water body size is a significant factor affecting WCIs [28, 32, 33, 37]. The research of [36] also proved that water area and green space area are key features that affect the cold island effect [24]. Analyzed the spatial pattern of green spaces and surface temperature and found that green spaces with a high degree of fragmentation can better reduce surface temperatures.

In this study, the main city of Kunming was selected as the research area, and the relationship between the geographical location, morphological characteristics and cold island effect of urban water bodies and waterfront green spaces was quantitatively analyzed by means of comprehensive remote sensing monitoring. Through mathematical statistical analysis, the relationship between the urban blue-green spatial landscape pattern index and surface temperature is obtained. Additionally, by calculating patch characteristic indices of urban water bodies and riparian green spaces, as well as quantitative indicators of the cool island effect, this study further analyzes the quantitative relationship between urban water bodies, riparian green spaces, and the cool island effect. The aim is to reveal the summer cool island effect of urban water bodies and its influencing factors, which holds significant practical significance for exploring urban cool island effects. The conclusions of this study can provide scientific guidance and decision support for the planning and layout of urban water bodies and surrounding land use. Through scientific management and rational planning of urban land use, the thermal regulation function of urban water bodies can be fully utilized.

2 Materials and methods

2.1 Study area

Kunming, also known as Spring City, is the capital of Yunnan Province. It is located in southwest China, in the middle of the Yunnan-Guizhou Plateau, latitude N24°23'–26°22', longitude E102°10'–103°40'. The overall terrain is high in the north and low in the south, gradually decreasing in a stepped pattern from north to south. The center of Kunming is about 1891 m above sea level. It belongs to the subtropical-plateau mountain monsoon climate at low latitude in northern latitude, due to the influence of warm and humid air flow in the southwest of the Indian Ocean, long sunshine, short frost period, annual average temperature of 15 °C, annual average sunshine of about 2200 h, four seasons like spring, pleasant climate, annual precipitation of 1035 mm, with typical temperate climate characteristics, urban temperature between 0 and 29 °C, annual temperature difference is the smallest in the country. As of 2021, Kunming has a permanent population of 8.502 million. The schematic diagram of study area is shown in Fig. 1.

Fig. 1
figure 1

Location map of study area

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2.2 Data sources

① The Elevation of Kunming and the remote sensing images used in this study are all from Landsat8 satellite of NASA, and downloaded from the geospatial data cloud website (http://www.gscloud.cn/). The imaging time of remote sensing images is May 22nd, 2020 respectively. Remote sensing images with cloud cover less than 5% and good imaging quality are selected.

② Using the land use data of Sentinel II, the spatial resolution is 1 m, and the overall quality of the data is good, including 11 land use types. The study area includes 7 categories, and forests, grasslands and farmland are reclassified as green spaces.

③ The high-resolution image of Kunming comes from Google Earth map with a spatial resolution of 2 m, which can clearly distinguish various land use types.

In addition, preprocessing is very necessary, mainly to eliminate irrelevant information in the image and restore useful real information to enhance the reliability of the data. The data preprocessing of this study includes four parts: atmospheric correction, image fusion, radiometric calibration and image clipping.

2.3 Surface temperature inversion

In this study, the atmospheric correction method is used to extract the Land Surface temperature (LST), also known as the Radiative Transfer Equation method (RTE) [12, 18, 22, 30] At present, the main ways to obtain surface temperature are contact measurement and surface temperature inversion, which is of great value for urban heat island effect, global circulation, forest fire investigation and disaster monitoring.

Atmospheric correction method first calculates brightness temperature based on thermal infrared band, and the calculation formula of surface brightness temperature is:

$${\text{B}}\left( {{\text{T}}_{{\text{s}}} } \right) = \left[ {{\text{L}}_{{\uplambda }} - {\text{L}}_{{{\text{above}}}} - {\uptau }\left( {1 - {\upvarepsilon }} \right){\text{L}}_{{{\text{below}}}} } \right]/{\text{T}}_{{\upvarepsilon }}$$
(1)

In formula (1), Lbelow is the downward radiation brightness of the atmosphere; Labove is the upward radiation brightness of the atmosphere; ε is the thermal radiation value received by the satellite; Ts is land surface emissivity, and the monitoring of atmospheric profile parameters by NASA can be obtained through the imaging time and the latitude and longitude of the center (http://atmcorr.gsfc.nasa.gov/); τ is the transmittance of the atmosphere in the thermal infrared band.

Then, land surface emissivity needs to be calculated by vegetation coverage index, and the calculation formula of vegetation coverage is:

$${\text{F}}_{{\text{v}}} = \left( {{\text{NDVI}} - {\text{NDVI}}_{{\text{s}}} } \right)/\left( {{\text{NDVI}}_{{\text{v}}} - {\text{NDVI}}_{{\text{s}}} } \right)$$
(2)

In formula (2), NDVIs is the NDVI value of 5% vegetation coverage; NDVIv is the NDVI value of 95% vegetation coverage.

Use NDVI threshold method to calculate land surface emissivity, and the calculation formula is [30]:

$${\upvarepsilon }_{{{\text{surface}}}} = 0.004{\text{F}}_{{\text{v}}} + 0.0986$$
(3)

Finally, the inversion of surface temperature is carried out, and the calculation formula of surface temperature can be obtained by Planck function as follows:

$${\text{T}}_{{\text{s}}} = {\text{K}}_{2} /{\text{ln}}\left[ {{\text{K}}_{1} /{\text{B}}\left( {{\text{T}}_{{\text{s}}} } \right) + 1} \right]$$
(4)

In Eq. (4), K1 and K2 are constants, and different sensors have different values.

2.4 Division of temperature grades

According to the different surface radiation temperatures, Kunming is divided into five temperature levels by the mean-standard deviation method: strong cold island, weak cold island, no cold island, weak hot island and strong hot island. Calculation methods such as Table 1.

Table 1 Heat island intensity grading method
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2.5 Urban cold island pattern analysis

The Urban Cold Island Intensity Index (UCII) is derived from the Urban Heat Island Intensity Index (UHII) calculation method, and the specific formula is shown in the following formula:

$${\text{UCII}}_{{\text{i}}} = \frac{1}{{\text{n}}}\mathop \sum \limits_{1}^{{\text{n}}} {\text{T}}_{{{\text{crop}}}} - {\text{T}}_{{\text{i}}}$$
(5)

In formula (5), \({UCII}_{i}\) is the cold island intensity corresponding to the ith cell on the image, n is the number of effective pixels in the temperature reference area, and \({T}_{crop}\) is the surface temperature in the temperature reference area, \({T}_{i}\) the surface temperature. In this paper, the average temperature in the study area is used as the reference temperature, and the reference temperature is 33.17 ℃. In order to better show the change of the cold island footprint in the main city, we adopted new standards to divide it, namely (− ∞, − 0.86), (− 0.86, 0.16), (0.16, 1.37), (1.37, 3.29), (3.29, + ∞). Through this classification standard, Kunming City was classified.

The term "change footprint" typically refers to the direction and trend of temperature changes occurring over a period of time on Earth or in a specific region. To study the change footprint of the cold island pattern in the main urban area of Kunming from the inner to the outer areas, this study established 45 buffer zones covering the entire main urban area of Kunming, with the geometric center of the main urban area as the center of each circle, and each buffer zone separated by 1000 m. The average land surface temperature within each buffer zone was calculated, and the cold island intensity index within each buffer zone was computed using the cold island intensity index as described earlier.

2.6 Landscape index

The landscape pattern indices selected in this paper include landscape shape index (LSI) and average neighborhood of green space (MNNg).

LSI indicates the complexity of landscape patch shape. A larger LSI means a more complex shape. This can be calculated using the following formula [16]:

$$LSI = \frac{P}{{2\sqrt {\pi \times A} }}$$
(6)

In formula (6), p is the perimeter of landscape patches, and a is the area of patches. LSI = 1 indicates that the shape is circular, while LSI = 1.13 indicates that the shape is square.

MNNg is a landscape pattern indicator representing connectivity, and the distribution of landscape patches. The larger the number, the more discontinuous the distribution. A higher MNN means that plaques are more randomly distributed in the defined area. A lower MNN, especially less than 1, means that the plaque is fragmented, and the calculation formula is as follows [9]:

$$MNN = \frac{{\overline{D}_{O} }}{{\overline{D}_{E} }}$$
(7)

In Formula (7), \({\overline{D} }_{O}\) is the average distance between the focusing sheet and other sheets, and \({\overline{D} }_{E}\) is the average distance between elements.

2.7 WCI cooling effect

To ensure the accuracy of water extraction, this study manually sketched 16 water bodies in Kunming by visual interpretation using Google Map with a resolution of 2 m as a reference, all of which were larger than 1 hectare in area, and there were no other water bodies around 1000 m, avoiding the superposition of other water bodies. The distribution of water bodies is shown in Fig. 2:

Fig. 2
figure 2

Location and characteristics of 16 water bodies in Kunming

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The "Water Cold Island(WCI)" refers to the phenomenon where the temperature of a water body and its surrounding area is significantly lower compared to other urban backgrounds. Thus, the direct quantification of the Water Cold Island is the cooling range ΔT. Another factor to consider is the size of the WCI cooling area, which we denote as LMAX, the farthest cooling distance. The x-axis represents the buffer distance from the water body's boundary, while the y-axis represents the average surface temperature. The first inflection point of the temperature curve indicates the background temperature at the maximum cooling distance. This study usesbuffer zone analysis, setting up a buffer strip around the water body patches at intervals of 50 m from the boundary, as shown in Fig. 3, resulting in a multi-ring buffer zone with a total distance of 800 m. The relationship between various indices within the buffer zone and the average temperature of the water body is then examined. Some urban water bodies show a third-order polynomial fitting curve, as illustrated in (Figure S1, Supporting Information).

Fig. 3
figure 3

Schematic diagram of urban water buffer zones

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In order to further quantify the characteristics of the temperature decrease of urban water bodies with distance, the average surface temperature of each buffer zone of the water body and the average temperature decrease inside the water body and in each buffer zone compared to the adjacent outer buffer zone were statistically obtained (Fig. 4).

Fig. 4
figure 4

The variation of the surrounding cooling amplitude of urban water bodies with distance

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The water body patches, along with the buffer zones, and the retrieved surface radiative temperature, are overlaid for analysis. The average temperature within each water body is calculated, along with the average surface temperature within a certain range around it. The calculation formula of cold island strength is as follow [23, 38]:

$${\text{WCI}} = {\text{T}}_{{\text{r}}} - {\text{T}}_{{\text{p}}}$$
(8)

In formula (8), WCI is the cold island index of urban water body, Tr is the average surface temperature in a certain range around the water body, and Tp is the average surface temperature inside the water body, and the cold island index around the water body is calculated.

3 Results and analysis

3.1 Surface temperature inversion results

As shown in Fig. 5, the average climate of LST in the main urban area of Kunming in 2020 was 32.47 ± 5.16 °C, the maximum value was 53.07 °C, and the minimum value was 4.11 °C, showing the basic pattern of higher central and northeast and lower northwest and south. The average temperature of the water body is 21.485 ± 2.83 °C, which shows that the water temperature is about 11 °C lower than the average temperature. Strong and weak heat island areas are mainly distributed in the center of urban built-up areas, and strong and weak cold island areas are mainly distributed around urban areas and near green spaces and waters. Within the urban area, the sporadic distribution of urban parks and water bodies presents a more obvious cold island area.

Fig. 5
figure 5

Inversion results of surface temperature in the main urban area of Kunming

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After analysis, the proportion of cold island in the main urban area was analyzed, of which the strong cold island area accounted for 8.45%, the weak cold island area accounted for 20.01%, the non-cold island area accounted for 35.2446%, the weak heat island area accounted for 33.53%, and the strong heat island area accounted for 2.76%. From (Figure S2, Supporting Information), it can be obtained that the proportion of strong cold island area in the main urban area of Kunming is: Xishan District > Chenggong District > Guandu District > Panlong District > Wuhua District, the proportion of weak cold island area is sorted as Xishan District > Panlong District > Guandu District > Chenggong District > Wuhua District, the proportion of non-cold island area is sorted as: Xishan District > Chenggong District > Guandu District > Wuhua District > Panlong District, the proportion of weak heat island area is sorted as: Guandu District > Xishan District > Chenggong District > Wuhua District > Panlong District, strong heat island area proportion order: Guandu District > Wuhua District > Xishan District > Chenggong District > Panlong District.

3.2 Urban cold island pattern analysis

The footprint map of the cold island intensity index for the main urban area of Kunming shows a pattern of being weaker internally and stronger externally (Figure S3, Supporting Information). With the increase in distance from the geometric center of Kunming, the cold island intensity index rises. Overall, the cold island intensity within Kunming is relatively weak, while it is relatively stronger on the outside. This trend of being weaker internally and stronger externally is reflected throughout the region. From the figure, it can be seen that the strong cold island areas in the main urban area of Kunming are mainly distributed near the centroid, i.e., within the main built-up area. At the same time, one can also observe that the cold island intensity index in certain local areas exhibits some fluctuations, presenting a relatively unstable situation at the geometric edges. Although the values fluctuate, the overall trend remains weaker internally and stronger externally.

3.3 Main factors affecting the temperature of urban water bodies

All 16 selected urban water bodies is greater than 0, indicating a clear cooling island effect (Table S1, Supporting Information). This suggests that the water bodies themselves, as well as the surrounding areas within a certain range, form a relatively low-temperature environment. The cooling effect is quite pronounced, providing some relief to the urban heat environment. The cooling distances of urban water bodies range from 140 to 370 m, with temperature drops ranging from 5.38 to 9.209 °C. Among the urban water bodies, Yangjiazhuang Reservoir has the longest cooling distance of about 350 m, while the water bodies with shorter cooling distances are Luoshui Cave Reservoir, Guanshan Reservoir, and Longmen Fishing Ground, with cooling distances of approximately 150 m. The urban water body with the highest cooling island intensity is Shilongba Reservoir, with an intensity of 9.209 °C, and the one with the lowest intensity is Baining Cave, at 5.38 °C. A larger WCI and Lmax value indicate that urban water bodies have a stronger cooling island effect, reflecting the significant role they play in alleviating the surrounding heat island effect and regulating the thermal environment.

The trend between green space coverage and WCI exhibits an initial increase followed by a decrease, possibly because vegetation itself can release heat under certain circumstances, particularly during hot summer weather when transpiration from vegetation leads to significant water evaporation, releasing heat and thereby raising the surrounding environment's temperature. Moreover, excessive green space coverage might impede the free flow of air, although greenery can lower ambient temperatures, it may also hinder airflow, causing heat to stagnate at the surface and exacerbating the urban heat island effect. Different types and structures of green spaces may have varying impacts on the urban heat island effect. For instance, dense tree cover might prevent surface heat from radiating into space at night, leading to heat retention within the city. Urban layout and climatic conditions also influence how green space coverage affects the urban heat island effect. Therefore, increasing green space coverage does not always reduce the urban heat island effect; instead, it requires a comprehensive consideration of factors such as green space type, density, structure, urban layout, and climatic conditions to effectively mitigate the urban heat island effect.

Pearson correlation analysis of influencing factors, the results are as follows.

From Table 2, it can be observed that the temperature within urban water bodies is significantly negatively correlated with the area of the water body. As the area of the waterfront green space increases, it effectively enhances the vegetation shading cooling effect in the waterfront green area, thereby significantly reducing the surface temperature. WCI is significantly positively correlated with the area of the water body and extremely positively correlated with the dispersion of the waterfront green space. This indicates that the more dispersed the waterfront green space is, the stronger the cooling effect.

Table 2 Pearson relevance
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To further quantify the characteristics of the cooling effect of urban water bodies as the distance changes, the average surface temperatures of each buffer zone around the water bodies were calculated, as well as the reduction in average temperature in the water body's interior and each buffer zone compared to the adjacent external buffer zone, as shown in Fig. 4. It can be found that the cooling range is the largest when it is about 50 m away from the edge of the water body, and it gradually decreases with the increase of the buffer zone distance, but it has decreased to 0.07 ℃ at the buffer zone of 250–300 m, and it gradually approaches zero at the area beyond 300 m, so the cooling effect of the water body basically does not exist.It can also be seen that among the 16 water bodies, the maximum cooling distance of 15 urban water bodies is within 300 m or less (Table S1, Supporting Information). Therefore, it can be determined that the maximum cooling distance of urban water bodies in Kunming is about 300 m, and the temperature changes gradually slow down within 300–500 m from the edge of water bodies. When it exceeds 500 m, the water bodies have almost no cooling effect. Therefore, it is concluded that the most effective cooling range of Kunming urban water body is about 50 m, the maximum cooling distance is about 300 m, and the farthest cooling range is about 500 m.

3.4 Cubic polynomial fitting

Cubic fitting polynomial refers to using a cubic polynomial to fit the data points in a given data set. Cubic fitting polynomials are usually expressed as:

$${\text{y}} = {\text{ax}}^{3} + {\text{bx}}^{2} + {\text{cx}} + {\text{d}}$$
(9)

In this, a, b, c, and d are the coefficients to be determined, where x and y are the independent and dependent variables in the dataset, respectively. The purpose of fitting the polynomial is to find a function that closely matches the given dataset. In a cubic fit, the complexity of the function is higher than in linear and quadratic fits, thus it can better adapt to the complex variations in the dataset.

After calculating the Elevation, LSI, and Area of the water bodies within the multi-ring buffer zones, it's also necessary to consider the influence of land use around the water bodies on the water temperature. Based on classification, farmland, forests, and grasslands are reclassified as green spaces, used to describe the proportion of waterfront green space. Kunming is located on the Yunnan-Guizhou Plateau, which has some undulating terrains that might influence water temperature. Therefore, the geographical location took into account the average height of the water bodies. The zonal statistics tool was used to calculate the average elevation of each water body. Using this as an independent variable and water temperature as the dependent variable, the primary factors affecting water body temperature were explored. A third-degree polynomial fitting was performed on them, where 'x' represents the elevation of the water body, shape index, area, and the area of the green space along the waterfront, and 'y' represents the average temperature inside the water body (Figure S4, Supporting Information).

To explore the impact of urban water body size on the average temperature within the water body, a nonlinear fitting was conducted between the average surface temperature inside urban water bodies and their area, shape index, etc. From Figure S4A and B, it was found that elevation and Landscape Shape Index (LSI) have little correlation with water body temperature, and thus a minimal impact on it. The internal temperature of the water body reaches its lowest value when the elevation is between 1.95 km and 2 km. A low temperature inflection point occurs when the shape index reaches 1.7, and a high temperature inflection point appears at 2.5. From the fitted curves in Figure S4C and D, it can be seen that the area of the water body and the green area along the waterfront are significantly correlated with the internal temperature of the water body. As the area of the water body gradually increases, the temperature decreases. When the area of the water body reaches 0.65 Km2, the temperature tends to stabilize. As the area continues to increase, the temperature of the water body still decreases, but when the area continuously increases from 0.65 Km2 to 0.8 Km2, the temperature of the water body only decreases by about 0.6 °C. This indicates that the optimal water body area can be set at around 0.65 Km2 to achieve good cooling effects within a limited space. As the area of the green space along the waterfront gradually increases, the temperature of the water body first decreases and then increases. A low temperature inflection point occurs when the green area along the waterfront is close to 0.4 km2. As the green area along the waterfront continues to increase, a high temperature inflection point appears, indicating that a certain range of green area along the waterfront has a significant cooling effect on the water body temperature. When the vegetation coverage in the area increases, vegetation regulates local temperature and humidity through the shading and transpiration of the canopy, thus playing a role in regulating the microclimate of urban green spaces and surrounding areas.

3.5 Influence of urban water landscape characteristics on cooling effect

The maximum cooling distance of urban water bodies in Kunming City is determined to be around 300 m (Table S1, Supporting Information). The average surface temperature within a 300 m buffer zone around each water body is extracted, and the Urban Water Cooling Island intensity (WCI) is calculated. A third-degree polynomial fitting is performed between the landscape index and WCI, with the correlation shown in Figure S5, where 'x' represents different landscape indices and 'y' represents WCI:

From (Figure S5, Supporting Information), it is found that WCI has a weak correlation with elevation and LSI. When the elevation of the water body reaches 2 km and the shape index reaches 2, high value inflection points for WCI are observed respectively. WCI is positively correlated with the area of the water body; as the area of the water body gradually increases, the intensity of the cooling island also shows a rising trend. With the gradual increase in green space area, the cooling island intensity first increases and then decreases. When the green space area along the waterfront reaches 0.4 Km2, a high value inflection point for WCI occurs. As the green space area along the waterfront continues to increase, WCI begins to decrease, and when the green space area increases to 0.8 Km2, a low value inflection point for WCI appears. WCI has the strongest correlation with MNNg, and as MNNg gradually increases, WCI shows an increasing trend. This indicates that the more dispersed the patches of green space along the waterfront, the greater the intensity of the water cooling island. Highly dispersed green patches can effectively increase the vegetation shading and cooling effect in the waterfront green area, thus significantly reducing surface temperature, forming stronger air circulation, and enhancing the cooling island intensity. This suggests that more concentrated and less fragmented green patches contribute significantly to the strength of the water cooling island and are one of the effective ways to mitigate urban heat island effects.

4 Discussion

In the increasingly severe urbanization process characterized by the urban heat island effect, efficiently leveraging the valuable urban water body cold island effect is advantageous for addressing the urban heat island issue. The novelty of this study lies in the investigation of factors influencing both internal and external temperatures of urban water bodies, quantitatively analyzing the temperature variations within parks based on factors such as water body area, water body shape index, and riparian green space area.

Seasons can obviously affect WCI intensity. Summer is chosen to study cold island because the solar radiation is the largest in summer, so the temperature difference between water body and other land cover types is also the largest. In addition, evaporation is the strongest in summer, as is the strength of WCI. Other studies [4, 15] have also found similar results, indicating that cool island landscapes, such as water, parks and forests, can have a strong cooling effect in hot seasons. Water bodies have the greatest potential for cooling effect in summer, and small water bodies can effectively reduce the ambient temperature by more than 10 ℃ [27]. Thereby achieving the effect of regulating microclimate.

The LSI and altitude are not the primary influencing factors in this study, even though previous research [8, 32] found that a simple shape would enhance the cooling island effect. A complex shape increases the connection between the water body and surrounding land cover as well as the frequency of heat exchange. Therefore, the influence of the landscape shape index on WCI (Water Cooling Index) intensity may depend on the surrounding land cover. When the land cover around the water body is filled with heat source landscapes, increasing the landscape shape index will raise the water temperature. In Kunming, the surrounding land cover of different water bodies may vary, so the explanatory power of the landscape shape index is not prominent. The significant factor influencing the urban water body's LST (Land Surface Temperature) is the waterfront green space area, and our findings, consistent with previous research, confirm that an appropriate area of waterfront green space has a cooling effect on the water body.

Shape index and elevation are not the primary influencing factors in this study, although previous research [8, 32] has shown that a simple shape enhances the cold island effect. Complex shapes increase the integration of water bodies with surrounding land cover and the frequency of heat exchange. Thus, the influence of landscape shape index on WCI intensity may depend on the surrounding land cover. This study finds that expanding water body area enhances WCI intensity, consistent with previous research [1, 19, 35, 40]. However, water body area is not the most crucial factor in this study. Results indicate that the most important variable determining WCI is MNNg (Mean Nearest Neighbor distance), with larger MNNg values leading to a more dispersed distribution of green landscape, further strengthening the cold island effect. The cooling effect of uniformly distributed green spaces typically surpasses that of larger or clustered green spaces [2], suggesting the use of vegetation buffer zones around planned water bodies or riverbanks. In urban planning, urban spaces should be designed with surfaces featuring discretely distributed green spaces to fully utilize the WCI effect.

Many subsequent issues require further research: First, in this study, 16 water bodies were selected. Future research will expand the water body samples for a more in-depth analysis. As time progresses, not only does the climate change, but the external structure of the water bodies may also change, such as variations in land use types, tree species combinations, and vegetation structures. Therefore, there is a need for time-series studies on the water body cooling island effect, further refining research from landscape and planning perspectives on the impact of different types of vegetation on the cooling effect. Secondly, there are many influencing factors for the water body cooling island. Additional factors such as wind, tree species outside the water body, vegetation accumulation, and other appropriate landscape pattern indices can be analyzed. Lastly, there remains a lot of controversies regarding the cooling island effect, which stems from unique characteristics of different cities, data collection and processing, parameter settings, temporal changes, and multi-dimensional urban structural features, among other factors.

5 Conclusion

The study utilized Landsat 8-OLI remote sensing data to invert the surface temperature of Kunming City in the summer of 2020 and extracted information from 16 urban water bodies and their buffer zones within the main urban area of Kunming using high-resolution remote sensing images. The results indicate that the cooling island pattern in the main urban area of Kunming generally shows a trend of being weak internally and strong externally. Urban water bodies in Kunming have a significant cooling effect on the surrounding thermal environment. When the water body area is approximately 0.6 km2 and the waterfront green space area reaches 0.35 km2, with green spaces being more dispersed, the cooling effect on the water body is better. There are slight differences in the maximum cooling distance of different urban water bodies. The results of this study are similar to the water bodies in Shanghai[9], China. When green spaces are more dispersed, the intensity of the urban heat island is greater. However, there are slight differences in the influencing factors of water bodies in different cities, and the thresholds may also vary.

In conclusion, against the backdrop of escalating urbanization and climate change, this research offers significant insights for the effective utilization of blue-green spaces in urban construction and renewal to mitigate the urban heat island effect and improve the urban thermal environment. By exploring variables of blue-green spaces such as water body area, vegetation ratio, and distribution, urban planners can effectively create cooler urban regions and reduce the adverse effects of climate change.