Temporal and Spatial Variation of Land Surface Temperature and Its Driving Factors in Zhengzhou City in China from 2005 to 2020
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Resources
2.3. Research Methodology
2.3.1. Nonparametric Mann–Kendall Trend Test
- (1)
- According to the timeseries construct an ordered sequence as follows:
- (2)
- Calculate the mean and variance of as follows:
- (3)
- Standardize as follows:
2.3.2. Calculation of Surface Information Index
2.3.3. Correlation Analysis and Linear Trend Analysis
2.3.4. Gray Relational Analysis Model
- (1)
- Suppose the original timeseries is composed of n evaluation samples of m evaluation indicators. First, the original timeseries is averaged to obtain the sequence .
- (2)
- Calculate the absolute difference between and at time K.
- (3)
- Calculate the correlation coefficient .
- (4)
- Calculate the gray correlation degree .
3. Results
3.1. Characteristics of Urban Thermal Environment Evolution
3.1.1. Interannual Variation Characteristics of LST
3.1.2. Surface Temperature Classification
3.1.3. Spatial Evolution Characteristics of Urban Thermal Environment
3.1.4. Temporal Evolution Characteristics of Urban Thermal Environment
3.2. Analysis of Driving Factors of Urban Thermal Environment
3.2.1. Correlation Analysis
3.2.2. Gray Correlation Analysis
4. Discussion
5. Conclusions
- (1)
- The annual changes in LST in Zhengzhou from 2005 to 2020 were small, with a mutation point in 2013. Furthermore, compared with 2005, in 2020, the mean value of LST increased by 0.92 °C, the percentage of LST-enhanced areas was 22.77%, and the area of the heat island increased by 2.08%.
- (2)
- The spatial pattern of the urban heat island showed an irregular block distribution, gradually spreading from north to south from 2005 to 2020; in 2020, there was a large block distribution in the main city and southeast. In addition, high temperatures mainly occurred in the main urban areas and densely built areas, whereas there was an obvious “cold island” effect in the concentrated distribution areas of forest land and the Yellow River basin.
- (3)
- The results of correlation analysis, trend analysis, and gray correlation analysis showed that human factors (NDISI, NDBBI, Albedo, and POI) were positively correlated with LST, which intensified the formation of the UHI effect, with the influence of Albedo on LST showing obvious spatial heterogeneity. Natural factors (NDVI, MNDWI, DEM, and Slope) were negatively correlated with LST. Among them, the intensity of urban construction had the highest contribution to the formation of the UHI effect, and the cooling effect of vegetation and water was better than that of topography.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Li, N.; Wu, H.; Luan, Q. Land surface temperature downscaling in urban area: A case study of Beijing. Natl. Remote Sens. Bull. 2021, 25, 1808–1820. [Google Scholar]
- Zhu, J.; Zhu, S.; Yu, F.; Zhang, G.; Xu, Y. A downscaling method for ERA5 reanalysis land surface temperature over urban and mountain areas. Natl. Remote Sens. Bull. 2021, 25, 1778–1791. [Google Scholar]
- Stone, B., Jr.; Vargo, J.; Liu, P.; Hu, Y.; Russell, A. Climate change adaptation through urban heat management in Atlanta, Georgia. Environ. Sci. Technol. 2013, 47, 7780–7786. [Google Scholar] [CrossRef]
- Halder, B.; Bandyopadhyay, J.; Banik, P. Evaluation of the Climate Change Impact on Urban Heat Island Based on Land Surface Temperature and Geospatial Indicators. Int. J. Environ. Res. 2021, 15, 819–835. [Google Scholar] [CrossRef]
- Voogt, J.A.; Oke, T.R. Thermal remote sensing of urban climates. Remote Sens. Environ. 2003, 86, 370–384. [Google Scholar] [CrossRef]
- Lu, D.; Weng, Q. Use of impervious surface in urban land-use classification. Remote Sens. Environ. 2006, 102, 146–160. [Google Scholar] [CrossRef]
- Wang, Z.; Sun, D.; Hu, C.; Wang, Y.; Zhang, J. Seasonal Contrast and Interactive Effects of Potential Drivers on Land Surface Temperature in the Sichuan Basin, China. Remote Sens. 2022, 14, 1292. [Google Scholar] [CrossRef]
- Weng, Q.; Lu, D.; Schubring, J. Estimation of Land Surface Temperature–Vegetation Abundance Relationship for Urban Heat Island Studies. Remote Sens. Environ. 2004, 89, 467–483. [Google Scholar] [CrossRef]
- Zhao, C.; Jensen, J.; Weng, Q.; Currit, N.; Weaver, R. Use of Local Climate Zones to investigate surface urban heat islands in Texas. GIScience Remote Sens. 2020, 57, 1083–1101. [Google Scholar] [CrossRef]
- Zhao, C.; Jensen, J.; Weng, Q.; Currit, N.; Weaver, R. Application of airborne remote sensing data on mapping local climate zones: Cases of three metropolitan areas of Texas, US. Comput. Environ. Urban Syst. 2019, 74, 175–193. [Google Scholar] [CrossRef]
- Biggart, M.; Stocker, J.; Doherty, R.M.; Wild, O.; Carruthers, D.; Grimmond, S.; Han, Y.; Fu, P.; Kotthaus, S. Modelling spatiotemporal variations of the canopy layer urban heat island in Beijing at the neighbourhood scale. Atmos. Chem. Phys. 2021, 21, 13687–13711. [Google Scholar] [CrossRef]
- Streutker, D.R. Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sens. Environ. 2003, 85, 282–289. [Google Scholar] [CrossRef]
- He, B.; Zhao, Z.; Shen, L.; Wang, H.; Li, L. An Approach to Examining Performances of Cool/Hot Sources in Mitigating/Enhancing Land Surface Temperature under Different Temperature Backgrounds Based on Landsat 8 Image. Sustain. Cities Soc. 2019, 44, 416–427. [Google Scholar] [CrossRef]
- Yang, J.; Zhan, Y.; Xiao, X.; Xia, J.C.; Sun, W.; Li, X. Investigating the Diversity of Land Surface Temperature Characteristics in Different Scale Cities Based on Local Climate Zones. Urban Clim. 2020, 34, 100700. [Google Scholar] [CrossRef]
- Roy, S.; Pandit, S.; Eva, E.A.; Bagmar, M.S.H.; Papia, M.; Banik, L.; Dube, T.; Rahman, F.; Razi, M.A. Examining the nexus between land surface temperature and urban growth in Chattogram Metropolitan area of Bangladesh using long term Landsat series data. Urban Clim. 2020, 32, 100593. [Google Scholar] [CrossRef]
- Hough, I.; Just, A.C.; Zhou, B.; Dorman, M.; Lepeule, J.; Kloog, I. A multi-resolution air temperature model for France from MODIS and Landsat thermal data. Environ. Res. 2020, 183, 109244. [Google Scholar] [CrossRef]
- Xing, Z.; Li, Z.; Duan, S.; Liu, X.; Zheng, X.; Leng, P.; Gao, M.; Zhang, X.; Shang, G. Estimation of daily mean land surface temperature at global scale using pairs of daytime and nighttime MODIS instantaneous observations. ISPRS J. Photogramm. Remote Sens. 2021, 178, 51–67. [Google Scholar] [CrossRef]
- Wu, W.; Li, L.; Li, C. Seasonal variation in the effects of urban environmental factors on land surface temperature in a winter city. J. Clean. Prod. 2021, 299, 126897. [Google Scholar] [CrossRef]
- Wei, B.; Bao, Y.; Yu, S.; Yin, S.; Zhang, Y. Analysis of land surface temperature variation based on MODIS data a case study of the agricultural pastural ecotone of northern China. Int. J. Appl. Earth Obs. Geoinf. 2021, 100, 102342. [Google Scholar] [CrossRef]
- Li, L.; Zha, Y.; Zhang, J. Spatially Non-Stationary Effect of Underlying Driving Factors on Surface Urban Heat Islands in Global Major Cities. Int. J. Appl. Earth Obs. Geoinf. 2020, 90, 102131. [Google Scholar] [CrossRef]
- Wicht, M.; Wicht, A.; Osińska-Skotak, K. Detection of ventilation corridors using a spatiotemporal approach aided by remote sensing data. Eur. J. Remote Sens. 2017, 50, 254–267. [Google Scholar] [CrossRef]
- Liu, D.; Zhou, S.; Wang, L.; Chi, Q.; Zhu, M.; Tang, W.; Zhao, X.; Xu, S.; Ye, S.; Lee, J.; et al. Research on the Planning of an Urban Ventilation Corridor Based on the Urban Underlying Surface Taking Kaifeng City as an Example. Land 2022, 11, 206. [Google Scholar] [CrossRef]
- Yao, L.; Sun, S.; Song, C.; Li, J.; Xu, W.; Xu, Y. Understanding the spatio-temporal pattern of the urban heat island footprint in the context of urbanization, a case study in Beijing, China. Appl. Geogr. 2021, 133, 102496. [Google Scholar] [CrossRef]
- Zhou, S.; Wang, K.; Yang, S.; Li, W.; Zhang, Y.; Zhang, B.; Fu, Y.; Liu, X.; Run, Y.; Chubwa, O.; et al. Warming Effort and Energy Budget Difference of Various Human Land Use Intensity: Case Study of Beijing, China. Land 2020, 9, 280. [Google Scholar] [CrossRef]
- Wang, Z.; Meng, Q.; Allam, M.; Hu, D.; Zhang, L.; Menenti, M. Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China. Ecol. Indic. 2021, 128, 107845. [Google Scholar] [CrossRef]
- Xiong, Y.; Zhang, F. Effect of human settlements on urban thermal environment and factor analysis based on multi-source data: A case study of Changsha city. J. Geogr. Sci. 2021, 31, 819–838. [Google Scholar] [CrossRef]
- Liu, Y.; Yuan, Z.; Kong, W.; Sun, B.; An, B. The Changing Trend of Heat Island Intensity and Main Influencing Factors during 1993–2012 in Xi’an City. J. Nat. Resour. 2015, 30, 974–985. [Google Scholar]
- Zhao, C.; Jensen, J.; Weng, Q.; Weaver, R. A Geographically Weighted Regression Analysis of the Underlying Factors Related to the Surface Urban Heat Island Phenomenon. Remote Sens. 2018, 10, 1428. [Google Scholar] [CrossRef]
- Giannaros, T.M.; Melas, D. Study of the urban heat island in a coastal Mediterranean City: The case study of Thessaloniki, Greece. Atmos. Res. 2012, 118, 103–120. [Google Scholar] [CrossRef]
- Tian, L.; Lu, J.; Li, Y.; Bu, D.; Liao, Y.; Wang, J. Temporal characteristics of urban heat island and its response to heat waves and energy consumption in the mountainous Chongqing, China. Sustain. Cities Soc. 2021, 75, 103260. [Google Scholar] [CrossRef]
- Teri, K.; Sian, P.; Diana, B.; Amy, H.; Sian, K.; Ko, K.; Lorena, R. How effective is ‘greening’ of urban areas in reducing human exposure to ground-level ozone concentrations, UV exposure and the ‘urban heat island effect’? An updated systematic review. Environ. Evid. 2021, 10, 1–38. [Google Scholar]
- Ma, Y.; Zhao, M.; Li, J.; Wang, J.; Hu, L. Cooling Effect of Different Land Cover Types: A Case Study in Xi’an and Xianyang, China. Sustainability 2021, 13, 1099. [Google Scholar] [CrossRef]
- Yi, Y.; Shen, G.; Zhang, C.; Sun, H.; Zhang, Z.; Yin, S. Quantitative analysis and prediction of urban heat island intensity on urban-rural gradient: A case study of Shanghai. Sci. Total Environ. 2022, 829, 154264. [Google Scholar] [CrossRef]
- Alibakhshi, Z.; Ahmadi, M.; Asl, M. Modeling Biophysical Variables and Land Surface Temperature Using the GWR Model: Case Study—Tehran and Its Satellite Cities. J. Indian Soc. Remote Sens. 2020, 48, 59–70. [Google Scholar] [CrossRef]
- Mohd, S.; Mohd, R.; Naikoo, W.; Ali, M.; Usmani, T.; Rahman, A. Urban Heat Island Dynamics in Response to Land-Use/Land-Cover Change in the Coastal City of Mumbai. J. Indian Soc. Remote Sens. 2021, 49, 2227–2247. [Google Scholar]
- Liu, X.; Ming, Y.; Liu, Y.; Yue, W.; Han, G. Influences of landform and urban form factors on urban heat island: Comparative case study between Chengdu and Chongqing. Sci. Total Environ. 2022, 820, 153395. [Google Scholar] [CrossRef] [PubMed]
- Wang, J.; Liu, J.; Wang, Y.; Liao, Z.; Sun, P. Spatiotemporal variations and extreme value analysis of significant wave height in the South China Sea based on 71-year long ERA5 wave reanalysis. Appl. Ocean Res. 2021, 113, 102750. [Google Scholar] [CrossRef]
- Rahaman, Z.; Kafy, A.; Saha, M.; Rahim, A.; Almulhim, A.; Rahaman, S.; Fattah, M.; Rahman, M.; Kalaivani, S.; Faisal, A.; et al. Assessing the impacts of vegetation cover loss on surface temperature, urban heat island and carbon emission in Penang city, Malaysia. Build. Environ. 2022, 222, 109335. [Google Scholar] [CrossRef]
- Zhao, Y.; Wu, Q.; Wei, P.; Zhao, H.; Zhang, X.; Pang, C. Explore the Mitigation Mechanism of Urban Thermal Environment by Integrating Geographic Detector and Standard Deviation Ellipse (SDE). Remote Sens. 2022, 14, 3411. [Google Scholar] [CrossRef]
- Zhao, H.; Zhang, H.; Miao, C.; Ye, X.; Min, M. Linking Heat Source–Sink Landscape Patterns with Analysis of Urban Heat Islands: Study on the Fast-Growing Zhengzhou City in Central China. Remote Sens. 2018, 10, 1268. [Google Scholar] [CrossRef]
- Geeta, S.J.; Payal, M. Assessment of seasonal climate transference and regional influential linkages to land cover–Investigation in a river basin. J. Atmos. Sol.-Terr. Phys. 2020, 199, 105209. [Google Scholar]
- Mostafa, A.M.; Gamal, S.E.A.; Mohamed, E.E. Impact of climate change on rainfall variability in the Blue Nile basin. Alex. Eng. J. 2021, 61, 3265–3275. [Google Scholar]
- Zhu, M.; Liu, D.; Tang, W.; Chi, Q.; Zhao, X.; Xu, S.; Ye, S.; Wang, Y.; Cui, Y.; Zhou, S. Exploring the Ecological Climate Effects Based on Five Land Use Types: A Case Study of the Huang-Huai-Hai River Basin in China. Land 2022, 11, 265. [Google Scholar] [CrossRef]
- Li, W.; Yang, J.; Li, X.; Zhang, J.; Li, S. Extraction of urban impervious surface information from TM image. Remote Sens. Nat. Resour. 2013, 25, 66–70. [Google Scholar]
- Hou, Y.; Ding, W.; Liu, C.; Li, K.; Cui, H.; Liu, B.; Chen, W. Influences of impervious surfaces on ecological risks and controlling strategies in rapidly urbanizing regions. Sci. Total Environ. 2022, 825, 153823. [Google Scholar] [CrossRef]
- He, J.; Zhang, H.; Su, H.; Zhou, X.; Chen, Q.; Xie, B.; You, T. Study on long-term change of global spectral surface albedo. J. Atmos. Environ. Opt. 2022, 17, 279–293. [Google Scholar]
- Mao, X.; Zhang, L.; Mao, T.; Wang, Y.; Huang, J. Research on the Influence of Fund Based on Pearson Study of Fund Influence Based on Pearson Correlation Analysis and Regression Analysis Method. J. Zhejiang Sci-Tech Univ. (Soc. Sci. Ed.) 2017, 38, 306–311. [Google Scholar]
- He, P.; Chen, H.; Li, H.; Xi, W. Grey analysis of the urban heat island effect factors of the medium-sized city of Chuxiong on Yunnan Plateau. Prog. Geogr. 2009, 28, 25–32. [Google Scholar]
- Li, X.; Zhang, H.; Qu, Y. Land Surface Albedo Variations in Sanjiang Plain from 1982 to 2015: Assessing with GLASS Data. Chin. Geogr. Sci. 2020, 30, 876–888. [Google Scholar] [CrossRef]
- Gao, T.; Shen, R.; Li, L.; Wang, Y.; Huang, A. Spatial and Temporal Variations of Land Surface Albedo and Its Influencing Factors Based on MODIS Data. Clim. Environ. Res. 2021, 26, 648–662. [Google Scholar]
- Feng, Y.; Hong, Z.; Cheng, J.; Jia, L.; Tan, J. Low Carbon-Oriented Optimal Reliability Design with Interval Product Failure Analysis and Grey Correlation Analysis. Sustainability 2017, 9, 369. [Google Scholar] [CrossRef]
- Guo, J.; Dong, Y.; Zhang, H. Misuse of gray incidence analysis in variable selection. Syst. Eng. Theory Pract. 2002, 22, 126–128. [Google Scholar]
- He, W.; Cao, S.; Du, M.; Hu, D.; Mo, Y.; Liu, M.; Zhao, J.; Cao, Y. How Do Two- and Three-Dimensional Urban Structures Impact Seasonal Land Surface Temperatures at Various Spatial Scales? A Case Study for the Northern Part of Brooklyn, New York, USA. Remote Sens. 2021, 13, 3283. [Google Scholar] [CrossRef]
- Chang, Y.; Xiao, J.; Li, X.; Zhou, D.; Wu, Y. Combining GOES-R and ECOSTRESS land surface temperature data to investigate diurnal variations of surface urban heat island. Sci. Total Environ. 2022, 823, 153652. [Google Scholar] [CrossRef] [PubMed]
- Maryam, M.; Masoud, M.; Zarkesh, K.; Alireza, M.; Ali, J. Achieving sustainable development goals through the study of urban heat island changes and its effective factors using spatio-temporal techniques: The case study (Tehran city). In Natural Resources Forum; Blackwell Publishing Ltd.: Oxford, UK, 2022; Volume 46, pp. 88–115. [Google Scholar]
Data Items | Spatial Resolution | Time Resolution | Data Resource |
---|---|---|---|
LST | 1 km | 8 days | MYD11A2 |
NDVI | 250 m | 16 days | MYD13Q1 |
Albedo | 500 m | Daily | MCD43A3 |
Temperature Rating | Extremely High-Temperature Zone | High-Temperature Zone | Relatively High -Temperature Zone | Medium-Temperature Zone | Relatively Low-Temperature Zone | Low-Temperature Zone | Extremely Low-Temperature Zone |
---|---|---|---|---|---|---|---|
Temperature range | t ≥ u + 2.5 std | u + 1.5 std ≤ t < u +2.5 std | u + 0.5 std ≤ t < u + 1.5 std | u − 0.5 std ≤ t < u + 0.5 std | u − 1.5std ≤ t < u − 0.5 std | u − 2.5 std ≤ t < u − 1.5 std | t < u − 2.5 std |
Category | Range | 2005–2013 | 2013–2020 | 2005–2020 | |||
---|---|---|---|---|---|---|---|
Grade Percentage | Class Percentage | Grade Percentage | Class Percentage | Grade Percentage | Class Percentage | ||
Weaken | −6 | 0.00% | 20.83% | 0.00% | 21.20% | 0.00% | 23.51% |
−5 | 0.00% | 0.00% | 0.00% | ||||
−4 | 0.00% | 0.00% | 0.00% | ||||
−3 | 0.00% | 0.01% | 0.00% | ||||
−2 | 1.24% | 0.57% | 1.07% | ||||
−1 | 19.59% | 20.62% | 22.44% | ||||
Constant | 0 | 57.48% | 57.48% | 58.43% | 58.43% | 53.72% | 53.72% |
Enhance | 1 | 20.46% | 21.69% | 19.73% | 20.37% | 20.95% | 22.77% |
2 | 1.17% | 0.63% | 1.70% | ||||
3 | 0.06% | 0.01% | 0.12% | ||||
4 | 0.00% | 0.00% | 0.00% | ||||
5 | 0.00% | 0.00% | 0.00% | ||||
6 | 0.00% | 0.00% | 0.00% |
Year | Extremely High | High | Relatively High | Medium | Relatively Low | Low | Extremely Low |
---|---|---|---|---|---|---|---|
2005 | 0.27 | 4.17 | 26.44 | 43.44 | 18.02 | 5.63 | 2.03 |
2013 | 0.12 | 3.01 | 29.80 | 43.98 | 13.8 | 6.61 | 2.67 |
2020 | 0.04 | 2.45 | 30.47 | 43.48 | 14.80 | 6.00 | 2.78 |
First Level Indicators | Second Level Indicators | Third Level Indicators |
---|---|---|
Natural factors | Water body Vegetation and | MNDWI |
NDVI | ||
Topographic features | Slope | |
DEM | ||
Human factors | Intensity of urban construction | NDISI |
NDBBI | ||
Albedo | ||
Socioeconomic activities | POI |
Factor | NDVI | MNDWI | DEM | Slope | NDISI | NDBBI | Albedo | POI |
---|---|---|---|---|---|---|---|---|
NDVI | 1 | −0.379 ** | 0.368 ** | 0.330 ** | −0.301 ** | −0.385 ** | −0.659 ** | −0.338 ** |
MNDWI | −0.379 ** | 1 | −0.241 ** | −0.197 ** | 0.687 ** | −0.293 ** | 0.249 ** | 0.182 ** |
DEM | 0.368 ** | −0.241 ** | 1 | 0.761 ** | −0.208 ** | −0.330 ** | −0.701 ** | −0.136 ** |
Slope | 0.330 ** | −0.197 ** | 0.761 ** | 1 | −0.185 ** | −0.349 ** | −0.641 ** | −0.110 ** |
NDISI | −0.301 ** | 0.687 ** | −0.208 ** | −0.185 ** | 1 | −0.045 ** | 0.136 ** | 0.187 ** |
NDBBI | −0.385 ** | −0.293 ** | −0.330 ** | −0.349 ** | −0.045 ** | 1 | 0.430 ** | 0.057 ** |
Albedo | −0.659 ** | 0.249 ** | −0.701 ** | −0.641 ** | 0.136 ** | 0.430 ** | 1 | 0.180 ** |
POI | −0.338 ** | 0.182 ** | −0.136 ** | −0.110 ** | 0.187 ** | 0.057 ** | 0.180 ** | 1 |
--- | NDVI | MNDWI | DEM | Slope | NDISI | NDBBI | Albedo | POI |
---|---|---|---|---|---|---|---|---|
LST | −0.301 ** | −0.027 ** | −0.574 ** | −0.568 ** | 0.141 ** | 0.457 ** | 0.527 ** | 0.195 ** |
Impact Factor | Correlation | Sort |
---|---|---|
NDISI | 0.99978 | 1 |
Albedo | 0.99965 | 2 |
NDVI | 0.99943 | 3 |
MNDWI | 0.99919 | 4 |
DEM | 0.99834 | 5 |
NDBBI | 0.99831 | 6 |
Slope | 0.99718 | 7 |
POI | 0.98030 | 8 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhou, S.; Liu, D.; Zhu, M.; Tang, W.; Chi, Q.; Ye, S.; Xu, S.; Cui, Y. Temporal and Spatial Variation of Land Surface Temperature and Its Driving Factors in Zhengzhou City in China from 2005 to 2020. Remote Sens. 2022, 14, 4281. https://doi.org/10.3390/rs14174281
Zhou S, Liu D, Zhu M, Tang W, Chi Q, Ye S, Xu S, Cui Y. Temporal and Spatial Variation of Land Surface Temperature and Its Driving Factors in Zhengzhou City in China from 2005 to 2020. Remote Sensing. 2022; 14(17):4281. https://doi.org/10.3390/rs14174281
Chicago/Turabian StyleZhou, Shenghui, Dandan Liu, Mengyao Zhu, Weichao Tang, Qian Chi, Siyu Ye, Siqi Xu, and Yaoping Cui. 2022. "Temporal and Spatial Variation of Land Surface Temperature and Its Driving Factors in Zhengzhou City in China from 2005 to 2020" Remote Sensing 14, no. 17: 4281. https://doi.org/10.3390/rs14174281
APA StyleZhou, S., Liu, D., Zhu, M., Tang, W., Chi, Q., Ye, S., Xu, S., & Cui, Y. (2022). Temporal and Spatial Variation of Land Surface Temperature and Its Driving Factors in Zhengzhou City in China from 2005 to 2020. Remote Sensing, 14(17), 4281. https://doi.org/10.3390/rs14174281