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Article

How Are Land-Use/Land-Cover Indices and Daytime and Nighttime Land Surface Temperatures Related in Eleven Urban Centres in Different Global Climatic Zones?

by
Yuanzheng Li
1,2,
Zezhi Zhao
1,
Yashu Xin
1,
Ao Xu
1,
Shuyan Xie
1,
Yi Yan
3 and
Lan Wang
4,*
1
School of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450046, China
2
Academician Laboratory for Urban and Rural Spatial Data Mining of Henan Province, Henan University of Economics and Law, Zhengzhou 450046, China
3
Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, China
4
Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization Jointly Built by Henan Province and Ministry of Education, Henan University, Kaifeng 475001, China
*
Author to whom correspondence should be addressed.
Land 2022, 11(8), 1312; https://doi.org/10.3390/land11081312
Submission received: 19 July 2022 / Revised: 4 August 2022 / Accepted: 11 August 2022 / Published: 14 August 2022
(This article belongs to the Section Land–Climate Interactions)
Browse Figures
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Abstract

:
Improving the urban thermal environment can enhance humans’ well-being. Nevertheless, it was not clear which land-use/land-cover (LU/LC) indices were optimal for explaining land surface temperatures (LSTs) and how they affected LSTs in cities in different climatic zones, especially during the nighttime. Thus, the Aqua/MODIS and Landsat/OLI data were mainly used to explore the optimal indices of building, vegetation, water and bare soil and to analyze their effects on LSTs in eleven urban centers in global distinct climatic regions. Results showed several LU/LC indices had high probabilities of being optimal indices to explain LSTs under different conditions. The daytime LSTs were usually significantly negatively correlated with vegetation indices and positively correlated with building and bare soil indices (p < 0.05). These relationships were stronger in the summer than winter. The nighttime LSTs were usually significantly positively and negatively correlated with building and vegetation indices in the summer, respectively (p < 0.05). These correlations were generally weaker during the nighttime than daytime. The nighttime LSTs were significantly positively and negatively correlated with water and bare soil indices, respectively (p < 0.05). Significant linear multiple regressions commonly existed between daytime and nighttime LSTs and four kinds of LU/LC indices (p < 0.05). These findings helped optimize urban thermal comfort, downscale city LSTs, etc.

1. Introduction

The world is undergoing rapid urbanization, and the urbanization rate has risen from less than 30 percent in 1950 to 55 percent in 2018 and will continue to increase to 68 percent by 2050 [1]. In 2018, 4.2 billion humans lived in urban settlements [1]. Rapid urbanization has led to significant changes in land-use/land-cover (LU/LC) in urban areas, including the expansion of impervious surfaces, the loss of vegetation and wetlands, etc. [2,3]. These changes can significantly change the land surface temperatures (LSTs) of the built-up areas [4,5]. The LU/LC indices can monitor the land surface biophysical conditions simply and effectively in many previous studies, including the indices related to building [6,7], vegetation [8,9], water [10,11] and bare soil [12,13], etc. It was necessary and meaningful to explore the relationships between LU/LC indices and LSTs in built-up regions in many aspects, such as understanding the influence mechanism of surface biophysical conditions on cities’ LSTs [14,15,16], improving the urban thermal environment [17], responding to global and urban climate change [18], reducing energy consumption [19], controlling air pollution [20], conserving biodiversity [21], optimizing the urban landscape composition and configuration [22], downscaling the LSTs in built-up regions [23], etc.
Many previous studies have focused on analysing the effects of LU/LC indices on LSTs at the city scale. The LSTs were mainly derived by the Landsat thermal infrared data [15,24,25,26,27], Moderate-resolution Imaging Spectroradiometer (MODIS) data of Aqua or Terra [28,29] and Terra/Aster data [30,31,32,33]. These indices included the normalized difference building index (NDBI) [26,27,34,35], normalized difference impervious index (NDISI) [14], index based built-up index (IBI) [7,27,36,37], new built-up index (NBI) [16], urban index (UI) [14,16], dry built-up index (DBI) [14], normalized difference vegetation index (NDVI) [24,26,38,39], soil-regulating vegetation index (SAVI) [16,36,40,41], enhanced vegetation index (EVI) [42], normalized difference moisture index (NDMI) [43,44], normalized difference water index (NDWI) [14,15,24,26], modified normalized difference water index (MNDWI) [14,39,40,43], normalized difference bareness index (NDBaI) [24,26,35,45], bare soil index (BSI) [14,16], dry bare soil index (DBI) [14], etc. Most studies focused on the relationships between LSTs and the above-mentioned LU/LC indices during the daytime [24,26,46,47] and only a few during the nighttime [28,29,33]. The relationships between LU/LC indices and LSTs were analyzed in the summer [25,27,47,48], winter [27,49,50,51], spring or autumn [24,25,39,52] in a single year [14,24,50] or several years [49,51,53,54]. The study areas were widely located in North America [27,55], western Europe [16,44,56], eastern Europe [28], sub-Saharan Africa [49,50,57,58], western Asia [30,39,46,48], southern Asia [27,47,59,60], eastern Asia [14,24,25,26], southeastern Asia [61], etc. The analysis methods included a correlation analysis [27,34,47,51], linear fitting [24,25,39,62], second or third fitting [24,29,36,42], geographically weighted regression [26,37,41], exponential fitting [7,63,64], segmentation fitting [24,31,65], GeoDetector [40], regression tree analysis [65], trend-line analysis [14,43,64], etc.
Some issues still existed in this research field. Firstly, although many LU/LC indices have been proposed, it was not clear which were the optimal building, vegetation, water or bare soil indices to explain LSTs in the built-up areas in different climatic zones. Secondly, it was still poorly understood how these optimal indices and LSTs were related in the urban regions under distinct climatic contexts, especially during the nighttime. Thus, our study aimed to select eleven cities in global typical climatic regions to explore the optimal indices of building, vegetation, water and bare soil and to analyze their effects on the daytime and nighttime LSTs in the built-up regions under various contexts.

2. Materials and Methods

2.1. Materials

2.1.1. Location of the Case Studies

Eleven typical urban settlements were chosen as the study objects in various global climatic zones, considering the different influencing mechanisms of LSTs under different contexts, such as solar radiation, vegetation, precipitation, building material, snow, ice, building and vegetation shadow, etc. (Table 1). These built-up regions should have large sizes, strong representativeness, high data accessibility in each global climate zone, etc. It was worth noting that it was difficult to find large built-up areas in the polar and alpine zones.

2.1.2. Data Sources

Table 2 summarized the primary remote sensed data used for these eleven urban settlements in different global climatic zones. The Aqua/MODIS LSTs data (MYD11A2.061) were downloaded from the Level-1 and Atmosphere Archive and Distribution System Distributed Active Archive Center (https://ladsweb.modaps.eosdis.nasa.gov/search/, accessed on 8 February 2022). The Aqua satellite passes through twice a day, at ~01:30 a.m. and ~01:30 p.m. The MODIS LSTs data were derived using the split-window technique with high accuracy. The differences in MODIS LSTs data were minor, less than 1 K in 39 of 47 cases [66] and less than 5% in the urban regions compared to the in situ measured LSTs [67]. The MOIDS LSTs data have been widely used for urban thermal environment studies [4,68,69,70,71]. Only data with high quality were used; their emissivity and LST error were ≤0.02 and ≤1 K, respectively [4].
The Landsat/OLI data were provided by the United States Geological Survey (https://earthexplorer.usgs.gov/, accessed on 8 February 2022). The urban center database (UCDB) in 2015 with 1 km resolution were obtained from the Global Human Settlement Layer (GHSL), European Commission Joint Research Center (https://ghsl.jrc.ec.europa.eu/, accessed on 8 February 2022) [72]. The GHSL–UCDB data with high accuracy were extracted by specific cut-off values on resident population and built-up surface share in a 1 × 1 km global uniform grid [72]. The input data were generated by the GHSL, and the operating parameters were set in the frame of the “degree of urbanization” (DEGURBA) methodology. The population data in 2020 with 1 km resolution were provided by the Worldpop program (https://www.worldpop.org/, accessed on 8 February 2022), which can provide high-resolution, open and contemporary data in human population distributions [73] and has been widely accepted and adopted [74,75].

2.2. Methods

2.2.1. Calculation of Land-Use/Land-Cover Indices

Atmospheric and radiometric corrections were performed using ENVI 5.3 for Landsat/OLI data to avoid the uncertainties and errors in the extraction of spectral indices [14,76,77]. Nineteen LU/LC indices were chosen to be analyzed, including seven building indices (BIs), four vegetation indices (VIs), three water indices (WIs) and five bare soil indices (BaIs) (Table 3). All of these derived indices were resampled to 500 m × 500 m grids.

2.2.2. Analysis of the Impacts of Land-Use/Land-Cover Indices on Land Surface Temperatures

Firstly, the values were obtained using the extraction multi-values to the points tool in ArcMap 10.2 for LSTs and their corresponding 19 LU/LC indices at each grid. Secondly, the Spearman correlation coefficients and their significance levels were computed for the LSTs and these spectral indices during the daytime and nighttime in the summer and winter in eleven urban centers in different global climatic zones. The index with the highest correlation coefficient with LSTs was selected as the optimal index of building, vegetation, water or bare soil to evaluate the impact of LU/LC biophysical conditions on LSTs. Thirdly, the partial correlation coefficients and significance levels were calculated between LSTs and the corresponding four optimal indices of buildings, vegetation, water and bare soil during the daytime and nighttime in the summer and winter in the eleven urban centers in different global climatic zones [69,86,87]. The partial correlation refers to a correlation between two variables when the effects of one or more related variables are removed. The partial correlation coefficient is said to be adjusted or corrected for the influence by the different covariates. Finally, the multiple linear regression (MLR) method was adopted to explore how the abovementioned four optimal LU/LC indices affect the LSTs comprehensively during the daytime and nighttime in the summer and winter in eleven urban centers in different global climatic zones.

3. Results

3.1. Characteristics of Land Surface Temperatures in Urban Centers in Different Climatic Zones

Noticeable differences existed in LSTs in urban centers in different climatic zones (Figure 1). The daytime LSTs were high in four urban centers (Bamako, Hofuf, Raipur and San Pedro Sula) in summer and winter, with averages of more than 37.4 and 30.9 °C, respectively. The largest daytime LSTs occurred in Hofuf (52.90 ± 2.65 °C). In Bamako, the daytime temperatures were 2.29 °C higher in the winter than in summer. The seasonal variations of daytime LSTs were more significant in Hofuf and Raipur than in Bamako and San Pedro Sula. The daytime LSTs in the summer were 21.98 °C and 11.62 °C higher than in winter in Hofuf and Raipur, respectively. Similar seasonal variation laws also existed for the nighttime LSTs in these four tropical climates cities. The order of LSTs’ averages was summer day > summer night > winter day > winter night in other climatic zones except for the alpine climatic area due to its higher daily variations of LSTs, especially in summer. The averages of daytime LSTs in summer were over 32.5 °C in these non-tropical cities except for Murmansk. Negative LSTs occurred during the daytime in the winter in Murmansk and Kiev and during the nighttime in the winter in these non-tropical cities except for Dallas.
LSTs’ standard deviations (SDs) showed different diurnal and seasonal variations in these urban centers in different climatic zones (Figure 2). The maximum SDs occurred during the daytime in summer in all of the urban centers except in Bamako, ranging from 1.56 to 3.19 °C. The SDs were larger during the daytime than nighttime in all of the urban centers in summer and 7/11 urban centers in winter (excluding Beijing, Kiev, Milan and Raipur), respectively. The seasonal variations of SDs were larger during the daytime than nighttime for all LSTs in these urban centers in different climatic zones. The SDs were larger in the summer than in winter in all urban centers except in Bamako during the daytime, with differences from 0.70 to 2.27 °C. Nevertheless, the SDs were smaller in the summer than in winter in all of the urban centers except in Bamako, Hofuf, Milan and Vancouver in the winter, with differences from −0.75 to −0.10 °C.

3.2. Optimal Land-Use/Land-Cover Indices of Land Surface Temperatures in Urban Centers in Different Climatic Zones

Table 4 showed the LU/LC indices to show the largest correlation with the LSTs during the daytime and nighttime in the summer and winter in the eleven selected urban centers in different global climatic zones. The correlation coefficients and significance levels between LSTs and all of the LU/LC indices are shown in the Supplementary Materials. From the building indices aspect, NDISI_NDWI_DB and NBI were the most frequent indices to show the largest correlation with LSTs in these eleven urban centers in different global climatic zones during the summer daytime, whose frequencies were all 36.36%, respectively (Figure 3a). DBI and NDBI were the optimal indices to explain LSTs in 18.18% and 9.09% cases, respectively. IBI, NDISI_MNDWI, NDISI_NDWI and UI never showed the largest correlation with LSTs in all of the urban centers. During the winter daytime, NDISI_NDWI_DB, IBI and NBI were frequent indices to show the largest correlation with LSTs; their frequencies were 27.27%, 18.18% and 8.18%, respectively. DBI, NDBI, NDISI_NDWI and UI also had a certain possibility of being optimal indicators; their frequencies were all 9.09%. NDISI_MNDWI never occurred as the optimal index. During the nighttime, DBI and NDISI_NDWI_DB had the highest possibilities (45.45%) of being the optimal indices to show the largest correlation with LSTs in the summer and winter. NDBI never occurred as the optimal index in any urban centers in the summer, while NBI, NDBI and NDISI_NDWI_DB were never the optimal indices in the winter.
SAVI had the largest chance of showing the largest correlation with LSTs in the summer; the percentages of frequencies were 45.45% during the daytime and nighttime (Figure 3b). But SAVI was not the optimal index during the daytime in the winter. NDMI had the largest possibility of showing the largest correlation with LSTs in the winter; their percentages of frequencies were 63.64% and 45.45% during the daytime and nighttime, respectively.
All three water indices had some chances of showing the largest correlation with LSTs (Figure 3c). Comparatively speaking, MNDWI had the lowest probability of being the optimal water index to explain daytime LSTs, especially in the summer. During the nighttime, the three water indices had similar chances of being the optimal indices to explain LSTs.
During the daytime, DBSI and BSI had the best chances of showing the largest correlation with LSTs, compared to the other bare soil indices in the summer and winter, respectively (Figure 3d). The percentages were all 45.45% for their frequencies. EBSI_NDWI_DB and NDBaI were never the optimal indices to explain LSTs in the summer. During the nighttime, NDBaI and EBSI_NDWI had higher probabilities of being the optimal water indices; their percentages of frequencies were 40.91% and 31.82%, respectively. BSI and DBSI never showed the largest correlation with LSTs in any urban centers in the winter, while EBSI_MNDWI was never largest correlated with LSTs in the summer.

3.3. Relationships between the Optimal Land-Use/Land-Cover Indices and Land Surface Temperatures in Urban Centers in Different Global Climatic Zones

3.3.1. Relationships between the Optimal Land-Use/Land-Cover Indices and Land Surface Temperatures in Urban Centers during the Daytime

The LSTs were generally significantly positively correlated with BIs in all urban centers in the summer and winter (p < 0.05) (Figure 4a). The most-correlating coefficients ranged from 0.37 to 0.67. Significant negative and insignificant correlations existed between the LSTs and BIs in the winter in Dallas and Murmansk, respectively. Meanwhile, the LSTs were generally significantly partially positively correlated with BIs in 6/11 and 5/11 urban centers in the summer and winter, respectively (p < 0.05) (Figure 5a). These partial coefficients ranged from 0.11 to 0.61. Moreover, three significant negative and eight insignificant correlations existed in the summer and winter, respectively.
The LSTs were generally significantly negatively correlated with VIs in all urban centers in the summer and winter (p < 0.05) (Figure 4a). Most correlation coefficients ranged from −0.62 to −0.30. One weak significant positive correlation occurred in Dallas in the winter. Three insignificant correlations existed in Murmansk in the winter and Shigatse in the summer and winter. Meanwhile, eleven insignificant partial correlations existed between the LSTs and BIs in the summer and winter (p > 0.05) (Figure 5a). The LSTs were significantly negatively partially correlated with BIs in eight urban centers in the summer and winter, respectively. These partial coefficients ranged from −0.39 to −0.11. Moreover, three significant positive correlations existed in Vancouver in the summer (r = 0.20) and in Raipur in the summer (r = 0.23) and winter (r = 0.18).
No obvious laws were found for the Spearman and partial correlations between LSTs and WIs (Figure 4a and Figure 5a). All kinds of Spearman and partial correlations existed, including insignificant, significant positive and negative ones.
The LSTs were significantly positively correlated with BaIs in 7/11 and 6/11 urban centers in the summer and winter, respectively (p < 0.05) (Figure 4a). Most correlation coefficients ranged from 0.33 to 0.59. Six significant negative and three insignificant correlations also existed. Meanwhile, insignificant correlations existed in 6/11 and 5/11 urban centers in the summer and winter, respectively (p > 0.05) (Figure 5a). Moreover, the LSTs were generally significantly positively and negatively partially correlated with BaIs in 7/22 and 4/22 urban centers, respectively (p < 0.05).
Table 5 showed the linear regression equations between the daytime LSTs and the optimal indices of building, vegetation, water and bare soil in eleven urban centers in different global climatic zones. All equations passed the significance test of 0.05 except in Shigatse in the summer and winter. The adjusted R2 for these equations with p-value less than 0.05 were located at intervals of [0,0.09), [0.09, 0.25) and [0.25, 0.64) in 2/20, 4/20 and 15/20 cases, respectively. The explanation rates were larger in the summer than in winter in all urban centers except in Bamako, Dallas, Raipur and San Pedro Sula.

3.3.2. Relationships between Optimal Land-Use/Land-Cover Indices and Land Surface Temperatures in Urban Centers during the Nighttime

The LSTs were significantly positively correlated with BIs in all urban centers in the summer except in Shigatse (p < 0.05) (Figure 4b). The correlation coefficients ranged from 0.15 to 0.71. However, significant positive and negative correlations existed in the winter in 5/11 and 6/11 urban centers. Meanwhile, insignificant partial correlations occurred in 12/24 cases between LSTs and BIs (Figure 5b). Moreover, they were significantly partially positively and negatively correlated in 5/24 cases in the summer and winter.
The LSTs were liable to be significantly negatively correlated with VIs, especially in the summer (Figure 4b). These correlation coefficients ranged from −0.75 to −0.18. Two and five insignificant correlations existed in the summer and winter, respectively. The LSTs were significantly positively correlated with VIs in one and two urban centers in the summer and winter, respectively. Meanwhile, eight and seven insignificant partial correlations existed between the LSTs and VIs in the summer and winter, respectively (p > 0.05) (Figure 5b). The LSTs were significantly negatively partially correlated with VIs in three urban centers in both the summer and winter. The partial coefficients ranged from −0.39 to −0.11 in the above-mentioned 5/6 cases. Moreover, only one weak significant positive correlation existed in Milan in the winter (r = 0.12).
The LSTs were significantly positively correlated with WIs in these urban centers, especially in the summer (p < 0.05) (Figure 4b). The correlation coefficients ranged from 0.11 to 0.76. Insignificant correlations existed in the summer in Shigatse and the winter in Shigatse, Kiev and San Pedro Sula. Meanwhile, significant positive partial correlations occurred in 5/11 and 3/11 urban centers between LSTs and WIs in the winter and summer, respectively (Figure 5b). Moreover, insignificant and significant negative partial correlations existed in 9/22 and 5/22 cases.
The LSTs were generally significantly negatively correlated with BaIs (p < 0.05), except in the summer in Shigatse and the winter in Shigatse and San Pedro Sula (Figure 4b). The correlation coefficients ranged from −0.77 to −0.09. Meanwhile, all significant negative, insignificant and significant positive partial correlations existed in 5/11, 4/11 and 2/11 urban centers in the summer and winter, respectively (Figure 5b).
Table 5 showed the linear regression equations between the nighttime LSTs and the optimal indices of building, vegetation, water and bare soil in eleven urban centers in different global climatic zones. All equations have passed the significance test of 0.05, except in the winter in Shigatse and summer in Shigatse and Murmansk. The adjusted R2 for these equations with p-values less than 0.05 were located at intervals of [0,0.09), [0.09, 0.25) and [0.25, 0.64) in 5/19, 8/19 and 6/19 cases, respectively. The explanation rates were lower during the daytime than nighttime in all urban centers, except in Hofuf in the summer and 7/12 cases in the winter.

4. Discussion

4.1. Selection of the Optimal Land-Use/Land-Cover Indices of Land Surface Temperatures in Urban Centers in Different Global Climatic Zones

One contribution of this paper was to find the optimal LU/LC indices to explain LSTs in urban centers in different global climatic zones during the daytime and nighttime in the summer and winter. It was meaningful and necessary for the understanding of the influence mechanism of surface biophysical conditions on cities’ LSTs [14,15,16], the improvement of urban thermal comfort [17,27,69], the optimization of urban land-use planning [22,51,65], downscaling the LSTs in built-up regions [23,70,88], etc. Our results showed the correlation coefficients and significance levels of these LU/LC indices with LSTs were quite different and even obtained opposite positive or negative relationships (see Supplementary Materials). The previous studies mainly explored the relationships between LSTs and NDBI [26,27,34,35], NDVI [24,26,38,39], NDWI [14,15,24,26] and NDBaI [24,26,35,45]. Nevertheless, we found that the four indices were not necessarily optimal in different climatic zones during the daytime and nighttime in different seasons. Other indices also had high probabilities of being the best building, vegetation, water or bare soil index to explain LSTs under different conditions, such as DBI, NBI, IBI, NDISI_NDWI_DB, NDMI, SAVI, DBSI, BSI, EBSI_NDWI, etc.

4.2. Influencing Mechanism of Optimal Land-Use/Land-Cover Indices on Land Surface Temperatures in Urban Centers in Different Climatic Zones

4.2.1. Building Indices

High building indices usually indicated large building intensities [6,78,80], which could enlarge the sensible heat flux while reducing the latent heat flux [4,89,90] and increase the absorption of shortwave radiation due to the large thermal conductivity, heat storage [91] and the ‘canyon effect’ [92]; decrease the outgoing longwave radiation because of the diminished sky view factors (SVFs) [92]; slow the winds and hinder sensible loss owing to the enlarged surface roughness [92]; etc. It was worth noting that the shadows created by buildings can lead to a decrease in LSTs [93,94], especially in areas with high latitudes (e.g., Stockholm) or tall buildings (e.g., the Central Business District in Beijing).
During the daytime, significant positive correlations usually existed between the BIs and LSTs in all urban centers in the summer and winter (p < 0.05) in our study and many cases in previous research [24,35,46,65,95]. These relationships were usually stronger in this study in the summer than winter in all urban centers except Bamako in a tropical savanna climate; San Pedro Sula in a tropical rainy climate and previous studies in the United States [27], Al Kut in Iraq [48], the Agartala city region [96] and Kolkata [97] in India, Kunming in China [95]. Moreover, significant negative correlations occurred in Dallas in the winter in this study and some regions in India in the winter in previous research [27]. The disturbance factor may be the high amount of dust particles in the air in winter [15,98]. Insignificant correlations only existed in Murmansk in the winter, possibly due to the widespread ice and snow there. Moreover, we found the chances and amplitudes of significant partial correlations were less than that of significant correlations. There were eleven, eight and three significant positive, insignificant and significant negative partial correlations.
During the nighttime, the LSTs of impervious surfaces mainly depended on the discharge of daytime-stored heat [89,99]. On the one hand, construction land can hold more heat than vegetation, especially the grass and undershrub. On the other hand, the released heating rates of construction parcels may be larger than in water bodies and forests due to the higher thermal conductivities and lower thermal inertias. Overall, the relationships should be more complex during the nighttime than daytime. We found significant positive correlations between LSTs and BIs in all urban centers except Shigatse in the summer (p < 0.05). However, the correlation degrees were generally lower during the nighttime than daytime, except in Hofuf. The relationships turned out to be complex in the winter. The differences were likely due to differences in the amounts of received solar radiation, shadow areas caused by buildings and air qualities between summer and winter. Weak negative and positive correlations existed simultaneously in the semi-arid Ahmedabad city and Gandhinagar District in the same Gujarat State in western India in the post-monsoon season, respectively [13]. No obvious partial correlations were found between nighttime LSTs and BIs in the summer and winter.

4.2.2. Vegetation Indices

Large vegetation indices generally indicated high vegetation coverage and activities [8,9,83]. During the daytime, the LSTs turned out to be lower in regions with more and denser vegetation, mainly due to the heat loss by transpiration and canopy evaporation [100,101,102]. Therefore, significant negative correlations usually existed between VIs and daytime LSTs (p < 0.05) [35,46,48,65,95]. These relationships were generally stronger in the summer than in winter in the United States [27], India [27,96,97] and Kunming in China [95]. Insignificant correlations also existed in April in Connecticut in the United States [55]. Significant negative correlations existed in some regions in India in the winter [27]. Our findings were consistent with previous studies. Significant negative correlations commonly existed in these urban centers in the summer and winter, except for one weak significant positive and three insignificant correlations. These exceptions may be caused by the high amount of dust particles in the air in winter, widespread distributed snow and ice, a small number of samples, etc. A small number of samples in Murmansk and, especially, Shigatse, will cause large p-values in mathematical statistics, leading to bias and even errors in the results. The correlation degrees were commonly larger in the summer than in winter, except in Bamako and San Pedro Sula. Moreover, we found that the chances and amplitudes of significant partial correlations were less than that of significant correlations. There were eight, eleven and three significant positive, insignificant and significant negative partial correlations.
During the nighttime, the LSTs in urban herbaceous parcels can be slightly smaller than their surrounding impervious surfaces due to the increased loss of long-wave radiation due to high SVF and low heat storage [103,104,105,106]. However, due to a complex mechanism [103,106], the LSTs in urban forest lands can be slightly higher or lower than their nearby construction lands. On the one hand, urban forests can decrease LSTs due to the reduction of the stored heat during the daytime caused by the shading effects of trees [107,108,109] and the increase of evapotranspiration during the nighttime, especially in tropical forests [65]. On the other hand, urban forests can enlarge LSTs due to the reduction of long-wave radiation because of small SVFs [104] and the increased stored heat within and beneath canopies [104]. We found significant negative correlations between nighttime LSTs and VIs, especially in the summer (p < 0.05). Their occurring probabilities and correlated degrees were smaller during the nighttime than daytime. Negative correlations existed between nighttime LSTs and NDVIs in semi-arid Ahmedabad and Gandhinagar in western India in the post-monsoon season [13]. One exception was Hofuf, whose correlation coefficients were larger during the nighttime than daytime in our study. The reason was mainly due to the specific photosynthetic behaviors of plants in the tropical desert climate. Their stomata are closed during the day and open at night to avoid the evaporation of water. Moreover, we found fifteen, six and one insignificant, significant negative and weak negative partial correlations, respectively, between nighttime LSTs and VIs.

4.2.3. Water Indices

High water indices usually indicated large fractions of water bodies [10,11,84]. The LSTs of water bodies were low during the daytime and high at night due to the high specific heat capacity. During the daytime, significant negative correlations existed in Al Kut in Iraq in the summer and winter [48], the Asansol-Durgapur Development Region in India in January [51] and Kuming in China in each month [95]. Meanwhile, no significant correlations (p > 0.05) were found between LSTs and WIs in Barddhaman District, West Bengal, India [110]. Significant positive correlations were found in Lucknow in India in the hottest April and May [54], Raipur in India in each month [35,111] and Kolkata city and its suburban area in India in the summer and winter [97]. All insignificant, significant positive and negative Spearman and partial correlations were also found in our study.
During the nighttime, significant positive correlations usually existed between LSTs and WIs, except in Shigatse with a small sample number and Kiev and San Pedro Sula in the winter (p < 0.05). Moreover, insignificant, significant positive and negative partial correlations existed in 9/22, 8/22 and 5/22 cases, respectively.

4.2.4. Bare Soil Indices

Large bare soil indices usually indicated high fractions of bare soil [12,24,78]. The bare soil (especially the dry bare soil) usually showed low heat capacity and thermal conductivity [112], large SVFs and little vegetation. Therefore, the daytime LSTs of bare soil were high, can be larger or smaller than impervious surfaces and can be larger than vegetation and water bodies [26,33,60,113,114,115]. These LSTs’ differences can be weakened when the bare soil contains more moisture. During the daytime, significant positive correlations usually existed between the BaIs and LSTs [35,54,95]. In the pre-monsoon season, insignificant correlations occurred in Raipur in India [111]. Significant negative linear or third-fitting relationships occurred in Guangzhou in China in the summer [65] and in Shenzhen in China in the autumn [24], respectively (p < 0.05). We also found daytime LSTs were significantly positively, insignificantly and significantly negatively with BaIs in 13/22, 3/22 and 6/22 cases, respectively. Moreover, insignificant, significant positive and negative partial correlations existed in 11/22, 7/22 and 4/22 cases, respectively.
During the nighttime, the LSTs of bare soil were low, which can be smaller than water bodies, construction lands and vegetations (even the croplands) [33]. The LSTs’ differences were lower between different land types during the nighttime than daytime [33,103]. Significant negative correlations existed between nighttime LSTs and BaIs, except in Shigatse with small sample numbers and San Pedro Sula in the winter (Figure 4b). Moreover, significant positive, insignificant and negative significant negative partial correlations existed in 10/22, 8/22 and 4/22 cases, respectively.

4.2.5. Combined Effects

Linear regression equations generally existed between LSTs and optimal indices of building, vegetation, water and bare soil during the daytime and nighttime, except in some cases in Shigatse and Murmansk with small sample numbers or extreme environments. Similar multiple linear regressions were found during the daytime in Istanbul and Zonguldak in Turkey [39,63]; Jinan [36], Fuzhou [7] and Guangzhou [52] in China in the spring or autumn; and Beijing in China in the summer [14]. The explanation rates were lower during the daytime than nighttime in all urban centers, especially in the summer. The values of the adjusted R2 of daytime LSTs were larger in the summer than in winter in 7/11 urban centers. These findings implied more complex influence mechanism existed for LSTs during the nighttime than daytime and in the winter than summer.

4.3. Limitations and Future Work

One limitation was that we had not done a segmentation analysis. Some indices can just indicate the surface biophysical conditions under a specific scopes of values. For instance, the classic NDVI could only indicate the vegetation information effectively when the NDVI values were positive. Strong to moderately negative correlations existed between LSTs and NDVI values when the NDVI values were positive. In contrast, the correlations were positive and non-consistent under the negative values of the NDVI [16,65,116,117]. Therefore, the fitting relationships between VIs and LSTs were better by segmentation analysis than global fitting. Similar issues also existed when analyzing the relationships between LSTs and BIs [65], BaIs [65] or WIs [24]. It should note that most LU/LC indices were developed in a specified city or region at a specific time [6,7,11,13,84]. Some land features have not been considered (especially snow and ice) when building the calculation equations of these LU/LC indices [6,7,11,13,84]. For instance, although the values of NDWI_DB were higher in water bodies than several other land covers, these values were higher for snow and ice [84].
Moreover, non-linear relationships have not been analyzed in this study between the LSTs and indices of building [7,36,63,64], vegetation [13,36,64], water [36,64] and bare soil [64]. Furthermore, the impacts of LU/LC indices on nighttime LSTs at finer scales should be explored by using the thermal remote-sensed data obtained by the ECOsystem Spaceborne Thermal Radiometer Experiment on the space station [118], Terra/Aster [33], nighttime Landsat [13], unmanned aerial vehicle thermography [119], thermal infrared video [120], etc. The degrees of correlations would increase with the enlargement of scales with the same positive or negative attributes [121]. In addition, the relationships betweenLU/LC indices and LSTs should be further studied in different land uses or covers [28,34,38,122]. Finally, the detailed mechanism can be analyzed by using several methods comprehensively, such as remote sensing, experimental observation, numerical simulation, artificial intelligence, etc.

5. Conclusions

This study aimed to select eleven cities in global typical climatic regions to explore the optimal indices of building, vegetation, water and bare soil and to analyze their effects on LSTs in built regions under various contexts. Some important conclusions can be summarized as follows:
(1)
The correlation coefficients and significance levels of LU/LC indices with LSTs were quite different and even obtained opposite positive or negative relationships. NDBI, NDVI, NDWI and NDBaI were the most frequently used indices to analyze the relationships between LU/LC indices and LSTs in previous studies. Nevertheless, they were not necessarily the optimal ones under different conditions. Other indices also had high probabilities of being the optimal indices to explain LSTs, such as DBI, NBI, IBI, NDISI_NDWI_DB, NDMI, SAVI, DBSI, BSI, EBSI_NDWI, etc.
(2)
The daytime LSTs were generally significantly negatively with VIs and positively correlated with BIs and BaIs (p < 0.05). These relationships were usually stronger in the summer than in winter. The nighttime LSTs were generally significantly positively and negatively correlated with BIs and VIs in the summer, respectively (p < 0.05). These correlation degrees were generally lower during the nighttime than daytime, except in Hofuf. Moreover, the nighttime LSTs were significantly positively and negatively correlated with WIs and BaIs, respectively (p < 0.05).
(3)
Significant multiple linear regressions generally existed between the LSTs and the optimal indices of building, vegetation, water and bare soil during the daytime and nighttime (p < 0.05). The explanation rates were usually higher during the daytime than nighttime, especially in the summer. Moreover, the values of adjusted R2 of daytime LSTs were larger in the summer than in winter in most urban centers.
(4)
Future work may include exploring the non-linear relationships between LSTs and LU/LC indices together with the segmentation analysis, deriving laws at finer scales, considering different influences in different land uses or covers, studying the detailed mechanism by using several methods comprehensively, etc.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/land11081312/s1.

Author Contributions

Conceptualization, Y.L. and L.W.; funding acquisition, Y.L., Y.Y. and L.W.; methodology, Y.L. and Z.Z.; project administration, L.W.; resources, Z.Z., A.X. and Y.Y.; software, Y.L., Z.Z. and A.X.; validation, Z.Z.; visualization, Y.X. and S.X.; writing—original draft, Y.L.; writing—review and editing, Y.L., Y.Y. and L.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Natural Science Foundation of China (Grant No. 41901235), Key Scientific Research Project of Universities and Colleges of Henan Province (Grant No. 21A170005), Key Scientific and Technological Project of Education Department of Henan Province (Grant No. 202102310337), Henan Provincial Youth Natural Science Foundation (Grant No. 212300410103), Key Scientific and Technological Research Projects of Henan Province (Grant No. 212102310005 and No. 192102310274).

Data Availability Statement

The data in this study are available from the corresponding author upon request.

Conflicts of Interest

The authors have no relevant financial or non-financial interests to disclose.

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Land 11 01312 g001 550
Figure 1. Box figure for the land surface temperatures during the daytime and nighttime in the summer and winter in the eleven selected cities in different global climatic zones. SD, WD, SN and WN refer to the summer daytime, winter daytime, summer nighttime and winter nighttime, respectively.
Figure 1. Box figure for the land surface temperatures during the daytime and nighttime in the summer and winter in the eleven selected cities in different global climatic zones. SD, WD, SN and WN refer to the summer daytime, winter daytime, summer nighttime and winter nighttime, respectively.
Land 11 01312 g001
Land 11 01312 g002 550
Figure 2. Standard deviations of land surface temperatures during the daytime and nighttime in the summer and winter in the eleven selected cities in different global climatic zones. Unit: °C. SD, WD, SN and WN refer to the summer daytime, winter daytime, summer nighttime and winter nighttime, respectively.
Figure 2. Standard deviations of land surface temperatures during the daytime and nighttime in the summer and winter in the eleven selected cities in different global climatic zones. Unit: °C. SD, WD, SN and WN refer to the summer daytime, winter daytime, summer nighttime and winter nighttime, respectively.
Land 11 01312 g002
Land 11 01312 g003 550
Figure 3. The percentage of the building (a), vegetation (b), water (c) and bare soil indices (d) to show the largest correlation with land surface temperatures. SD, WD, SN and WN refer to the summer daytime, winter daytime, summer night and winter night, respectively.
Figure 3. The percentage of the building (a), vegetation (b), water (c) and bare soil indices (d) to show the largest correlation with land surface temperatures. SD, WD, SN and WN refer to the summer daytime, winter daytime, summer night and winter night, respectively.
Land 11 01312 g003
Land 11 01312 g004 550
Figure 4. Spearman correlation coefficients and significance levels between land surface temperatures and indices of building, vegetation, water body and bare soil in eleven cities in typical global climatic zones during the daytime (a) and nighttime (b) in the summer and winter. These optimal remote-sensing indices were determined based on Table 4. BI, VI, WI and BaI represent the index of building, vegetation, water and bare soil, respectively. SD, WD, SN and WN refer to the summer daytime, winter daytime, summer night and winter night, respectively. The symbol “×” represents the fact that the partial correlation coefficients have not passed the significance level of 0.05.
Figure 4. Spearman correlation coefficients and significance levels between land surface temperatures and indices of building, vegetation, water body and bare soil in eleven cities in typical global climatic zones during the daytime (a) and nighttime (b) in the summer and winter. These optimal remote-sensing indices were determined based on Table 4. BI, VI, WI and BaI represent the index of building, vegetation, water and bare soil, respectively. SD, WD, SN and WN refer to the summer daytime, winter daytime, summer night and winter night, respectively. The symbol “×” represents the fact that the partial correlation coefficients have not passed the significance level of 0.05.
Land 11 01312 g004
Land 11 01312 g005 550
Figure 5. Partial correlation coefficients and significance levels between land surface temperatures and the indices of building, vegetation, water body and bare soil in eleven cities in typical global climatic zones during the daytime (a) and nighttime (b) in the summer and winter. These optimal remote sensing indices were determined based on Table 4. BI, VI, WI and BaI represent the index of building, vegetation, water and bare soil, respectively. SD, WD, SN and WN refer to the summer daytime, winter daytime, summer night and winter night, respectively. The symbol “×” represents the fact that the partial correlation coefficients have not passed the significance level of 0.05.
Figure 5. Partial correlation coefficients and significance levels between land surface temperatures and the indices of building, vegetation, water body and bare soil in eleven cities in typical global climatic zones during the daytime (a) and nighttime (b) in the summer and winter. These optimal remote sensing indices were determined based on Table 4. BI, VI, WI and BaI represent the index of building, vegetation, water and bare soil, respectively. SD, WD, SN and WN refer to the summer daytime, winter daytime, summer night and winter night, respectively. The symbol “×” represents the fact that the partial correlation coefficients have not passed the significance level of 0.05.
Land 11 01312 g005
Table
Table 1. Basic information for the eleven selected urban settlements.
Table 1. Basic information for the eleven selected urban settlements.
CityCountryClimatic ZoneUrban Area Size in 2015 (km2)Total Population in 2020 (Thousand)Population Density in 2020 (People × km−2)Barycentric Coordinate
BamakoMaliTropical savanna330344610,44212.62° N, 7.98° W
BeijingChinaTemperate monsoon211114,349679739.90° N, 116.35° E
DallasUnited StatesSubtropical monsoon5867485081932.86° N, 96.96° W
HofufSaudi ArabiaTropical desert2692440907125.41° N, 49.63° E
KievUkraineTemperate continental51916631950.44° N, 30.52° E
MilanItalyMediterranean7531863247445.54° N, 9.19° E
MurmanskRussiaPolar727199268.96° N, 33.08° E
RaipurIndiaTropical monsoon6425612874121.26° N, 81.62° E
San Pedro SulaHondurasTropical rainy199346174115.50° N, 87.98° W
ShigatseChinaAlpine1853296529.26° N, 88.89° E
VancouverCanadaTemperature marine6981551222349.23° N, 122.99° W
Table
Table 2. Details of Aqua/MODIS LSTs and Landsat/OLI data used in this study.
Table 2. Details of Aqua/MODIS LSTs and Landsat/OLI data used in this study.
SeasonCityAqua/MODISLandsat/OLI
SummerBamakoMYD11A2.A2021153.h17v07.061.2021162060303.hdfLC08_L1TP_199051_20210607_20210615_02_T1
BeijingMYD11A2.A2017185.h26v04.061.2021307195911.hdf,
MYD11A2.A2017185.h26v05.061.2021307195911.hdf		LC08_L1TP_123032_20170710_20200903_02_T1
DallasMYD11A2.A2015201.h09v05.061.2021359214832.hdfLC08_L1TP_027037_20150720_20200909_02_T1
HofufMYD11A2.A2021201.h22v06.061.2021210062753.hdfLC08_L1TP_164042_20210720_20210729_02_T1
KievMYD11A2.A2016193.h19v03.061.2022017092925.hdfLC08_L1TP_181025_20160713_20200906_02_T1
MilanMYD11A2.A2016233.h18v04.061.2022019014701.hdfLC08_L1TP_194028_20160825_20200906_02_T1
MurmanskMYD11A2.A2021177.h19v02.061.2021188140407.hdfLC08_L1TP_189011_20210703_20210712_02_T1
RaipurMYD11A2.A2014153.h25v06.061.2021334075503.hdfLC08_L1TP_142045_20140605_20200911_02_T1
San Pedro SulaMYD11A2.A2017233.h09v07.061.2021309102328.hdfLC08_L1TP_018049_20170827_20200903_02_T1
ShigatseMYD11A2.A2019161.h25v06.061.2020354210223.hdfLC08_L1TP_139040_20190614_20200828_02_T1
VancouverMYD11A2.A2020225.h09v04.061.2021014061238.hdfLC08_L1TP_047026_20200814_20210330_02_T1
WinterBamakoMYD11A2.A2021025.h17v07.061.2021042222603.hdfLC08_L1TP_199051_20210130_20210302_02_T1
BeijingMYD11A2.A2022009.h26v04.061.2022018191611.hdf, MYD11A2.A2022009.h26v05.061.2022018191307.hdfLC08_L1TP_123032_20220113_20220123_02_T1
DallasMYD11A2.A2016025.h09v05.061.2022003114136.hdfLC08_L1TP_027037_20160128_20200907_02_T1
HofufMYD11A2.A2021025.h22v06.061.2021042223127.hdfLC08_L1TP_164042_20210125_20210305_02_T1
KievMYD11A2.A2018001.h19v03.061.2021316074352.hdfLC08_L1TP_181025_20180108_20200902_02_T1
MilanMYD11A2.A2021009.h18v04.061.2021041054240.hdfLC08_L1TP_194028_20210111_20210307_02_T1
MurmanskMYD11A2.A2022073.h19v02.061.2022082060619.hdfLC08_L1TP_189011_20220316_20220316_02_RT
RaipurMYD11A2.A2021009.h25v06.061.2021041053541.hdfLC08_L1TP_142045_20210115_20210308_02_T1
San Pedro SulaMYD11A2.A2021025.h09v07.061.2021042222552.hdfLC08_L1TP_018049_20210126_20210305_02_T1
ShigatseMYD11A2.A2021009.h25v06.061.2021041053541.hdfLC08_L1TP_139040_20210110_20210307_02_T1
VancouverMYD11A2.A2020049.h09v04.061.2021006162447.hdfLC08_L1TP_047026_20200220_20200822_02_T1
Table
Table 3. Equations for the selected nineteen land-use/land-cover indices.
Table 3. Equations for the selected nineteen land-use/land-cover indices.
CategoryAcronymFull NameEquationReference
Building indicesDBIDry built-up index D N B − D N T I R _ 10 D N B + D N T I R _ 10 − N D V I [78]
IBIIndex-based built-up index N D B I − S A V I + M N D W I 2 N D B I + S A V I + M N D W I 2 [7]
NBINew built-up index ρ R × ρ S W I R _ 6 ρ N I R [79]
NDBINormalized difference building index ρ S W I R _ 6 − ρ N I R ρ S W I R _ 6 + ρ N I R [6]
NDISI_ MNDWINormalized difference impervious surface index based on MNDWI D N T I R _ 10 − ( M N D W I + D N N I R + D N S W I R _ 6 ) / 3 D N T I R _ 10 + ( M N D W I + D N N I R + D N S W I R _ 6 ) / 3 [80]
NDISI_NDWINormalized difference impervious surface index based on NDWI D N T I R _ 10 − ( N D W I + D N N I R + D N S W I R _ 6 ) / 3 D N T I R _ 10 + ( N D W I + D N N I R + D N S W I R _ 6 ) / 3
NDISI_NDWI_DBNormalized difference impervious surface index based on NDWI_DB D N T I R _ 10 − ( N D W I _ D B + D N N I R + D N S W I R _ 6 ) / 3 D N T I R _ 10 + ( N D W I _ D B + D N N I R + D N S W I R _ 6 ) / 3
UIUrban index ρ S W I R _ 7 − ρ N I R ρ S W I R _ 7 + ρ N I R [81]
Vegetation indicesEVIEnhanced vegetation index 2.5 ∗ ρ N I R − ρ R ρ N I R + 6 × ρ R − 7.5 ρ B + 1 [82]
NDMINormalized different moisture index ρ N I R − ρ S W I R _ 6 ρ N I R + ρ S W I R _ 6 [9]
NDVINormalized vegetation index ρ N I R − ρ R ρ N I R + ρ R [8]
SAVISoil-regulating vegetation index 1.5 × ρ N I R − ρ R ρ N I R + ρ R + 0.5 [83]
Water indicesMNDWIModified normalized water index ρ G − ρ S W I R _ 6 ρ G + ρ S W I R _ 6 [11]
NDWINormalized difference water index ρ N I R − ρ S W I R _ 6 ρ N I R + ρ S W I R _ 6 [10]
NDWI_DBNormalized difference water index based on dark blue band ρ D B − ρ S W I R _ 7 ρ D B + ρ S W I R _ 7 [84]
Bare soil indicesBSIBare soil index ( ρ S W I R _ 6 + ρ R ) − ( ρ N I R + ρ B ) ( ρ S W I R _ 6 + ρ R ) + ( ρ N I R + ρ B ) [12]
DBSIDry bare soil index ρ S W I R _ 6 − ρ G ρ S W I R _ 6 + ρ G − N D V I [78]
EBSI_MNDWIEnhanced bare soil index based on MNDWI B S I − M N D W I B S I + M N D W I [85]
EBSI_NDWIEnhanced bare soil index based on NDWI B S I − N D W I B S I + N D W I
EBSI_NDWI_DBEnhanced bare soil index based on NDWI_DB B S I − N D W I _ D B B S I + N D W I _ D B
NDBaINormalized difference bare soil index D N S W I R _ 6 − D N T I R S _ 10 D N S W I R _ 6 + D N T I R S _ 10 [13]
Note: ρ D B , ρ B , ρ G , ρ R and ρ N I R represented the reflectance of dark blue, blue, green, red and near-infrared bands, respectively. ρ S W I R _ 6 and ρ S W I R _ 7 corresponded to the reflectance of the sixth and seventh short-wave infrared bands, respectively. D N B , D N S W I R _ 6 and D N T I R S _ 10 represented the digital values of the blue, sixth short-wave infrared and tenth thermal infrared band. When calculating the NDISI, the inputs of MNDWI, NDWI and NDWI_DB should be linearly stretched from 0 to 65,535. When calculating the EBSI, the inputs of MNDWI, NDWI and NDWI_DB should be linearly stretched from 0 to 1.
Table
Table 4. The land-use/land-cover indices to show the largest correlation with the land surface temperatures during the daytime and nighttime in the summer and winter in the eleven selected cities in different global climatic zones.
Table 4. The land-use/land-cover indices to show the largest correlation with the land surface temperatures during the daytime and nighttime in the summer and winter in the eleven selected cities in different global climatic zones.
TimeCityBuilding IndexVegetation IndexWater IndexBare Soil Index
SDBamakoNDISI_NDWI_DBNDMINDWI_DBEBSI_MNDWI
BeijingNDISI_NDWI_DBSAVIMNDWIEBSI_NDWI
DallasNBINDMINDWIDBSI
HofufNBINDMINDWIBSI
KievNBINDMINDWIDBSI
MilanDBISAVINDWIEBSI_NDWI
MurmanskNDBINDMINDWI_DBBSI
RaipurNDISI_NDWI_DBNDMINDWI_DBDBSI
San Pedro SulaDBISAVINDWIEBSI_NDWI
ShigatseNBINDMINDWIDBSI
VancouverNDISI_NDWI_DBNDMINDWI_DBDBSI
WDBamakoNDBINDMINDWI_DBDBSI
BeijingIBINDMINDWI_DBBSI
DallasIBINDMINDWI_DBBSI
HofufNBINDMINDWIBSI
KievUIEVINDWINDBaI
MilanNDISI_NDWI_DBNDMINDWI_DBEBSI_MNDWI
MurmanskNDISI_NDWIEVIMNDWIBSI
RaipurNDISI_NDWI_DBNDMINDWIDBSI
San Pedro SulaDBINDVIMNDWIEBSI_NDWI
ShigatseNBIEVINDWI_DBBSI
VancouverNDISI_NDWI_DBNDMINDWI_DBEBSI_NDWI_DB
SNBamakoNDISI_NDWINDVINDWI_DBNDBaI
BeijingNDISI_MNDWIEVINDWI_DBNDBaI
DallasDBISAVINDWI_DBEBSI_NDWI_DB
HofufDBISAVINDWIEBSI_NDWI
KievUISAVINDWI_DBDBSI
MilanDBISAVINDWIEBSI_NDWI
MurmanskUINDMIMNDWIBSI
RaipurDBISAVIMNDWIEBSI_NDWI
San Pedro SulaNDISI_MNDWIEVIMNDWINDBaI
ShigatseIBINDMINDWIDBSI
VancouverDBINDVINDWIEBSI_NDWI
WNBamakoNDISI_NDWISAVIMNDWINDBaI
BeijingNDISI_NDWI_DBNDMINDWI_DBEBSI_NDWI_DB
DallasIBINDMINDWI_DBNDBaI
HofufDBISAVIMNDWIEBSI_NDWI
KievNBINDMIMNDWINDBaI
MilanNDISI_NDWI_DBNDMINDWINDBaI
MurmanskNDISI_NDWI_DBNDMINDWINDBaI
RaipurNDISI_MNDWINDVIMNDWIEBSI_NDWI
San Pedro SulaNDISI_NDWI_DBEVINDWI_DBNDBaI
ShigatseUINDVINDWIEBSI_NDWI
VancouverNDISI_NDWI_DBSAVINDWI_DBEBSI_MNDWI
Note: SD, WD, SN and WN refer to the summer daytime, winter daytime, summer nighttime and winter nighttime, respectively.
Table
Table 5. Linear regression equations between the land surface temperatures and four optimal land-use/land-cover indices in eleven cities in typical global climatic zones during the daytime and nighttime in the summer and winter.
Table 5. Linear regression equations between the land surface temperatures and four optimal land-use/land-cover indices in eleven cities in typical global climatic zones during the daytime and nighttime in the summer and winter.
TimeCityRegression EquationAdjusted R2p-Value
SDBamako38.15 + 10.57 × NDISI_NDWI_DB − 6.63 × NDMI − 3.93 × NDWI_DB − 3.06 × EBSI_MNDWI0.3630.000
Beijing35.55 + 28.52 × NDISI_NDWI_DB + 2.45 × SAVI − 4.97 × MNDWI − 18.82 × EBSI_NDWI0.2630.000
Dallas39.69 + 20.52 × NBI − 1.67 × NDMI − 6.22 × NDWI + 4.24 × DBSI0.2170.000
Hofuf53.47 + 19.35 × NBI − 35.56 × NDMI + 3.09 × NDWI − 41.48 × BSI0.3690.000
Kiev34.80 + 29.56 × NBI − 8.37 × NDMI − 3.78 × NDWI + 0.56 × DBSI0.2570.000
Milan38.04 − 0.77 × DBI − 6.71 × SAVI + 1.78 × NDWI − 1.26 × EBSI_NDWI0.2640.000
Murmansk23.82 + 5.13 × NDBI − 5.13 × NDMI − 0.42 × NDWI_DB − 4.15 × BSI0.1250.007
Raipur37.50 + 32.13 × NDISI_NDWI_DB + 38.25 × NDMI + 6.64 × NDWI_DB + 37.41 × DBSI0.4440.000
San Pedro Sula42.47 + 12.30 × UI − 5.75 × SAVI − 38.98 × NDWI − 56.55 × EBSI_NDWI0.4220.000
Shigatse27.24 + 82.85 × NBI − 51.58 × NDMI − 25.76 × NDWI − 49.34 × DBSI0.0510.330
Vancouver19.05 + 53.67 × NDISI_NDWI_DB + 19.30 × NDMI + 4.67 × NDWI_DB + 22.06 × DBSI0.3870.000
WDBamako42.75 + 4.83 × NDBI − 4.83 × NDMI − 3.94 × NDWI_DB + 6.26 × DBSI0.4610.000
Beijing4.60 − 0.96 × IBI − 0.06 × NDMI − 0.74 × NDWI_DB + 3.71 × BSI0.1260.000
Dallas31.48 − 13.47 × IBI − 13.53 × NDMI − 2.00 × NDWI_DB − 11.62 × BSI0.2450.000
Hofuf30.18 + 8.68 × NBI − 10.67 × NDMI − 0.26 × NDWI − 11.71 × BSI0.2400.000
Kiev−1.00 + 0.47 × UI − 4.72 × EVI − 1.93 × NDWI − 3.80 × NDBaI0.0510.000
Milan6.09 + 5.62 × NDISI_NDWI_DB + 0.17 × NDMI + 2.91 × NDWI_DB + 2.21 × EBSI_MNDWI0.0450.000
Murmansk−1.53 − 7.79 × NDISI_NDWI + 0.52 × EVI − 1.21 × MNDWI − 2.43 × BSI0.0030.386
Raipur25.68 + 40.97 × NDISI_NDWI_DB + 9.82 × NDMI + 5.33 × NDWI + 0.10 × DBSI0.4630.000
San Pedro Sula37.55 − 22.25 × DBI − 18.56 × SAVI + 22.93 × NDWI − 9.44 × EBSI_NDWI0.4300.000
Shigatse16.01 + 11.77 × NBI − 8.02 × EVI − 9.32 × NDWI_DB − 22.26 × BSI−0.1550.832
Vancouver8.18 + 13.71 × NDISI_NDWI_DB − 0.45 × NDMI + 4.03 × NDWI_DB + 6.35 × EBSI_NDWI_DB0.3040.000
SNBamako17.45 − 2.66 × NDISI_NDWI + 0.48 × NDVI − 4.51 × NDWI_DB − 16.64 × NDBaI0.1070.000
Beijing22.42 + 17.25 × NDISI_MNDWI − 1.32 × EVI + 5.47 × NDWI_DB + 4.81 × NDBaI0.2210.000
Dallas27.22 + 0.29 × DBI − 4.43 × SAVI − 6.15 × NDWI_DB − 9.17 × EBSI_NDWI_DB0.1050.000
Hofuf34.47 − 12.50 × DBI − 3.01 × SAVI + 20.00 × NDWI − 9.19 × EBSI_NDWI0.5550.000
Kiev19.80 − 3.21 × UI − 1.41 × SAVI + 1.55 × NDWI_DB + 6.43 × DBSI0.1260.000
Milan18.01 − 1.13 × DBI − 7.97 × SAVI − 4.63 × NDWI − 5.68 × EBSI_NDWI0.1540.000
Murmansk8.67 − 3.23 × UI − 2.22 × NDMI + 0.83 × MNDWI + 4.38 × BSI0.0730.057
Raipur27.32 − 3.57 × DBI − 4.05 × SAVI − 4.03 × MNDWI − 17.67 × EBSI_NDWI0.3210.000
San Pedro Sula20.13 + 13.58 × NDISI_MNDWI + 0.67 × EVI + 3.08 × MNDWI − 0.11 × NDBaI0.1920.000
Shigatse14.66 − 5.35 × IBI + 2.15 × NDMI − 0.95 × NDWI + 7.54 × DBSI−0.1200.743
Vancouver15.48 + 1.94 × DBI − 3.44 × NDVI − 7.59 × NDWI − 5.53 × EBSI_NDWI0.0810.000
WNBamako14.33 + 54.08 × NDISI_NDWI − 11.36 × SAVI + 11.00 × MNDWI + 15.79 × NDBaI0.2090.000
Beijing−7.50 + 2.52 × NDISI_NDWI_DB − 4.86 × NDMI − 7.05 × NDWI_DB − 23.34 × EBSI_NDWI_DB0.3220.000
Dallas6.46 − 3.38 × IBI − 0.84 × NDMI + 0.24 × NDWI_DB − 3.43 × NDBaI0.0760.000
Hofuf13.55 − 0.98 × DBI − 2.60 × SAVI + 8.45 × MNDWI − 5.25 × EBSI_NDWI0.4960.000
Kiev5.52 − 22.35 × NBI − 2.06 × NDMI + 1.45 × MNDWI + 18.40 × NDBaI0.0100.000
Milan−3.13 + 19.59 × NDISI_NDWI_DB + 3.89 × SAVI + 6.87 × NDWI_DB − 7.04 × EBSI_NDWI0.2230.000
Murmansk−15.70 + 21.83 × NDISI_NDWI_DB + 5.01 × NDMI + 2.29 × NDWI − 18.39 × NDBaI0.3470.000
Raipur31.39 − 54.33 × NDISI_MNDWI + 1.61 × NDVI − 32.06 × MNDWI − 47.41 × EBSI_NDWI0.3900.000
San Pedro Sula17.37 − 9.31 × NDISI_NDWI_DB − 1.91 × EVI − 7.31 × NDWI_DB − 11.91 × NDBaI0.0360.016
Shigatse−3.12 + 1.64 × UI + 3.66 × NDVI + 24.91 × NDWI + 25.09 × EBSI_NDWI0.1100.229
Vancouver−1.20 + 0.27 × NDISI_NDWI_DB − 0.29 × SAVI − 1.87 × NDWI_DB − 5.25 × EBSI_MNDWI0.0590.000
Note: SD, WD, SN and WN refer to the summer daytime, winter daytime, summer nighttime and winter nighttime, respectively. The background color of the R-square and the p-value are magenta, yellow and green when the values of R-square are located at the intervals of [0,0.09), [0.09, 0.25) and [0.25, 0.64) at the 0.05 significance level, respectively. The background color is gray when the p-value is large than 0.05.
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Li, Y.; Zhao, Z.; Xin, Y.; Xu, A.; Xie, S.; Yan, Y.; Wang, L. How Are Land-Use/Land-Cover Indices and Daytime and Nighttime Land Surface Temperatures Related in Eleven Urban Centres in Different Global Climatic Zones? Land 2022, 11, 1312. https://doi.org/10.3390/land11081312

AMA Style

Li Y, Zhao Z, Xin Y, Xu A, Xie S, Yan Y, Wang L. How Are Land-Use/Land-Cover Indices and Daytime and Nighttime Land Surface Temperatures Related in Eleven Urban Centres in Different Global Climatic Zones? Land. 2022; 11(8):1312. https://doi.org/10.3390/land11081312

Chicago/Turabian Style

Li, Yuanzheng, Zezhi Zhao, Yashu Xin, Ao Xu, Shuyan Xie, Yi Yan, and Lan Wang. 2022. "How Are Land-Use/Land-Cover Indices and Daytime and Nighttime Land Surface Temperatures Related in Eleven Urban Centres in Different Global Climatic Zones?" Land 11, no. 8: 1312. https://doi.org/10.3390/land11081312

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