The effect of land use change/land cover on land surface temperature in the coastal area of Bushehr
Subject Areas : Natural resources and environmental management
Fazel Amiri
1
,
Tayebeh Tabatabaie
2
1 - 1. Associate Professor, Department of Natural Resources and Environment, Bushehr Branch, Islamic Azad University, Bushehr, Iran
2 - Associate Professor, Department of Environment, Bushehr Branch, Islamic Azad University, Bushehr, Iran
Keywords: Landsat, Coastal land, Normalized difference vegetation index (NDVI), Land surface temperature (LST),
Abstract :
Background and Objective Urbanization accelerates the ecological stress by warming the local or global cities for a large extent. Many urban areas are suffering from huge land conversion and resultant new heat zones. Remote sensing techniques are significantly effective in detecting the land use/land cover (LULC) change and its consequences. Several satellite sensors are capable to identify these change zones by using their visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands. Apart from the conventional LULC classification algorithms, some spectral indices are used in detecting specific land features. Normalized difference vegetation index (NDVI) can be considered the most applied spectral index in this scenario. NDVI is a dominant factor in LST derivation processes and is used invariably in any LST-related study. NDVI is directly used in the determination of land surface emissivity and thus is a significant factor for LST estimation. It also determines the LULC categories by its optimum threshold limits in the different physical environments. Being a vegetation index, NDVI depends largely on seasonal variation. Hence, LST is also regulated by the change of seasons. Thus, seasonal evaluation of LST and NDVI is an important task in LST mapping and monitoring, especially in an urban landscape. In this research, LST and NDVI in August in the coastal lands of Bushehr are investigated using Landsat satellite images for the years 1990, 2005 and 2020. The LULC map was obtained with suitable threshold values of NDVI. The objectives of this study are; 1) to analyze the temporal changes of the LST spatial distribution pattern in the study area, 2) to determine the spatial-temporal changes of the LST-NDVI relationship for the whole studied land, and 3) to investigate the spatial-temporal changes of the LST relationship - NDVI in different types of land use/cover.Materials and Methods The land study area of Bushehr city, which is on the northern coast of the Persian Gulf, with dimensions of 20 × 8 km2 an area of 1011.5 km2 and with an average minimum temperature of 18.1 oC and an average maximum temperature of 33 oC, relative humidity between 58-75% and the average annual rainfall is 272 mm. The data used in this research include; Landsat 8 (OLI) and Thermal Infrared Sounder (TIRS) data in 2020; 2005 ETM+ data, and 1990 TM data downloaded from the United States Geological Survey (USGS) (https://earth explorer.usgs.gov). The Landsat 8 TIRS instrument has two TIR bands (bands 10 and 11), in which band 11 has calibration uncertainty. Therefore, only TIR band 10 (100 m resolution) is recommended for the present study. The 10 TIR band was converted to a pixel size of 30 × 30 meters by the USGS cubic convolution method. Landsat 5 TM data has only one TIR thermal infrared band (band 6) with 120 m resolution, which was also converted by USGS to 30 × 30 m pixel size by cubic convolution method. For Landsat TM and ETM+ data, the spatial resolution of 30 m visible to near-infrared (VNIR) bands was used. The maximum likelihood classification method was applied to validate NDVI threshold-based LULC classification. In this study, the mono-window algorithm was applied to retrieve LST from multi-temporal Landsat satellite sensors. NDVI can extract different types of LULC by using the optimum threshold values. These threshold values can differ with respect to the differences in the physical environment. The NDVI threshold limits were applied to the images to extract the different LULC types.Results and Discussion The overall accuracy values of the LULC classification were 73.6%, 83.9%, and 84.6% in 1990, 2005, and 2020, respectively. The kappa coefficients for the LULC classification were 0.77, 0.80, and 0.84 in 1990, 2005, and 2020, respectively. In the present study, the average overall accuracy and average kappa coefficient were 80.7% and 0.80, respectively. Thus, the NDVI threshold method-based LULC classification was significantly validated. The results of this research showed a gradual rising (1.4 °C during 1990–2005 and 2 °C during 2005–2020) of LST during the whole period of study. The mean LST value for three study years was the lowest (30.86 °C) on green vegetation and the highest (49.07 °C) on bare land and built-up areas. The spatial distribution of NDVI and LST reflects an inverse relationship. The best (-0.97) and the least (-0.80) correlation, respectively, whereas a moderate (-0.89) correlation was noticed. This LST-NDVI correlation was strong negative (-0.80) on the vegetation surface. The LST is greatly controlled by land-use characteristics.Conclusion The present study analyzes the spatial, and temporal relationship of LST and NDVI in Bushehr coastal lands using 3 Landsat data sets for 1990, 2005, and 2020. The mono-window algorithm was applied in deriving LST. In general, the results showed that LST is inversely related to NDVI, irrespective of any year. The presence of vegetation is the main responsible factor for high negativity. The LST-NDVI relationship varies for specific LULC types. The green area presents a strong negative (-0.80) regression. The mean LST of the study area was increased by 3.4 °C during 1990-2020. The conversion of other lands into the built-up area and bare land influences a lot on the mean LST of the city. Both the changed and unchanged built-up area and bare land suffer from the increasing trend of LST. This study can be used as a reference for land use and environmental planning on coastal land.
Carlson TN, Ripley DA. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3): 241-252. doi:https://doi.org/10.1016/S0034-4257(97)00104-1.
Chen X-L, Zhao H-M, Li P-X, Yin Z-Y. 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2): 133-146. doi:https://doi.org/10.1016/j.rse.2005.11.016.
Cui L, Wang L, Singh RP, Lai Z, Jiang L, Yao R. 2018. Association analysis between spatiotemporal variation of vegetation greenness and precipitation/temperature in the Yangtze River Basin (China). Environmental Science and Pollution Research, 25(22): 21867-21878. doi:10.1007/s11356-018-2340-4.
Fu P, Weng Q. 2015. Temporal dynamics of land surface temperature from Landsat TIR time series images. IEEE Geoscience and Remote Sensing Letters, 12(10): 2175-2179. doi:https://doi.org/10.1109/LGRS.2015.2455019.
Fu P, Weng Q. 2016. A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sensing of Environment, 175: 205-214. doi:https://doi.org/10.1016/j.rse.2015.12.040.
Ghobadi Y, Pradhan B, Shafri HZM, Kabiri K. 2015. Assessment of spatial relationship between land surface temperature and landuse/cover retrieval from multi-temporal remote sensing data in South Karkheh Sub-basin, Iran. Arabian Journal of Geosciences, 8(1): 525-537. doi:10.1007/s12517-013-1244-3.
Govil H, Guha S, Diwan P, Gill N, Dey A. 2020. Analyzing Linear Relationships of LST with NDVI and MNDISI Using Various Resolution Levels of Landsat 8 OLI and TIRS Data. In: Sharma N, Chakrabarti A, Balas VE (eds) Data Management, Analytics and Innovation, Singapore. Springer Singapore, pp 171-184. https://doi.org/110.1007/1978-1981-1032-9949-1008_1013.
Goward SN, Xue Y, Czajkowski KP. 2002. Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: An exploration with the simplified simple biosphere model. Remote Sensing of Environment, 79(2): 225-242. doi:https://doi.org/10.1016/S0034-4257(01)00275-9.
Guha S, Govil H. 2020. Land surface temperature and normalized difference vegetation index relationship: a seasonal study on a tropical city. SN Applied Sciences, 2(10): 1661. doi:https://doi.org/10.1007/s42452-020-03458-8.
Guha S, Govil H. 2021. An assessment on the relationship between land surface temperature and normalized difference vegetation index. Environment, Development and Sustainability, 23(2): 1944-1963. doi:10.1007/s10668-020-00657-6.
Guha S, Govil H, Diwan P. 2019. Analytical study of seasonal variability in land surface temperature with normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index. Journal of Applied Remote Sensing, 13(2): 024518. doi:https://doi.org/10.1117/1.JRS.13.024518.
Guha S, Govil H, Gill N, Dey A. 2020. Analytical study on the relationship between land surface temperature and land use/land cover indices. Annals of GIS, 26(2): 201-216. doi:https://doi.org/10.1080/19475683.2020.1754291.
Guo L, Liu R, Men C, Wang Q, Miao Y, Zhang Y. 2019. Quantifying and simulating landscape composition and pattern impacts on land surface temperature: A decadal study of the rapidly urbanizing city of Beijing, China. Science of The Total Environment, 654: 430-440. doi:https://doi.org/10.1016/j.scitotenv.2018.11.108.
He B-J, Zhao Z-Q, Shen L-D, Wang H-B, Li L-G. 2019. An approach to examining performances of cool/hot sources in mitigating/enhancing land surface temperature under different temperature backgrounds based on landsat 8 image. Sustainable Cities and Society, 44: 416-427. doi:https://doi.org/10.1016/j.scs.2018.10.049.
Huang S, Taniguchi M, Yamano M, Wang C-h. 2009. Detecting urbanization effects on surface and subsurface thermal environment — A case study of Osaka. Science of The Total Environment, 407(9): 3142-3152. doi:https://doi.org/10.1016/j.scitotenv.2008.04.019.
Ke Y, Im J, Lee J, Gong H, Ryu Y. 2015. Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations. Remote Sensing of Environment, 164: 298-313. doi:https://doi.org/10.1016/j.rse.2015.04.004.
Kumar D, Shekhar S. 2015. Statistical analysis of land surface temperature–vegetation indexes relationship through thermal remote sensing. Ecotoxicology and Environmental Safety, 121: 39-44. doi:https://doi.org/10.1016/j.ecoenv.2015.07.004.
Liu H, Zhan Q, Yang C, Wang J. 2018. Characterizing the spatio-temporal pattern of land surface temperature through time series clustering: Based on the latent pattern and morphology. Remote Sensing, 10(4): 654. doi:https://doi.org/10.3390/rs10040654.
Liu Y, Peng J, Wang Y. 2018. Efficiency of landscape metrics characterizing urban land surface temperature. Landscape and Urban Planning, 180: 36-53. doi:https://doi.org/10.1016/j.landurbplan.2018.08.006.
Mathew A, Khandelwal S, Kaul N. 2018. Spatio-temporal variations of surface temperatures of Ahmedabad city and its relationship with vegetation and urbanization parameters as indicators of surface temperatures. Remote Sensing Applications: Society and Environment, 11: 119-139. doi:https://doi.org/10.1016/j.rsase.2018.05.003.
Peng J, Jia J, Liu Y, Li H, Wu J. 2018. Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas. Remote Sensing of Environment, 215: 255-267. doi:https://doi.org/10.1016/j.rse.2018.06.010.
Peng J, Ma J, Liu Q, Liu Y, Hu Yn, Li Y, Yue Y. 2018. Spatial-temporal change of land surface temperature across 285 cities in China: An urban-rural contrast perspective. Science of The Total Environment, 635: 487-497. doi:https://doi.org/10.1016/j.scitotenv.2018.04.105.
Peng J, Xie P, Liu Y, Ma J. 2016. Urban thermal environment dynamics and associated landscape pattern factors: A case study in the Beijing metropolitan region. Remote Sensing of Environment, 173: 145-155. doi:https://doi.org/10.1016/j.rse.2015.11.027.
Sannigrahi S, Bhatt S, Rahmat S, Uniyal B, Banerjee S, Chakraborti S, Jha S, Lahiri S, Santra K, Bhatt A. 2018. Analyzing the role of biophysical compositions in minimizing urban land surface temperature and urban heating. Urban Climate, 24: 803-819. doi:https://doi.org/10.1016/j.uclim.2017.10.002.
Sekertekin A, Bonafoni S. 2020. Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: Assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sensing, 12(2): 294. doi:https://doi.org/10.3390/rs12020294.
Sobrino JA, Jiménez-Muñoz JC, Paolini L. 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4): 434-440. doi:https://doi.org/10.1016/j.rse.2004.02.003.
Sultana S, Satyanarayana ANV. 2020. Assessment of urbanisation and urban heat island intensities using landsat imageries during 2000 – 2018 over a sub-tropical Indian City. Sustainable Cities and Society, 52: 101846. doi:https://doi.org/10.1016/j.scs.2019.101846.
Sun D, Kafatos M. 2007. Note on the NDVI‐LST relationship and the use of temperature‐related drought indices over North America. Geophysical Research Letters, 34(24). doi:https://doi.org/10.1029/2007GL031485.
Tan J, Yu D, Li Q, Tan X, Zhou W. 2020. Spatial relationship between land-use/land-cover change and land surface temperature in the Dongting Lake area, China. Scientific Reports, 10(1): 9245. doi:https://doi.org/10.1038/s41598-020-66168-6.
Weng Q. 2009. Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4): 335-344. doi:https://doi.org/10.1016/j.isprsjprs.2009.03.007.
Weng Q, Lu D, Schubring J. 2004. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4): 467-483. doi:https://doi.org/10.1016/j.rse.2003.11.005.
Wukelic GE, Gibbons DE, Martucci LM, Foote HP. 1989. Radiometric calibration of Landsat Thematic Mapper thermal band. Remote Sensing of Environment, 28: 339-347. doi:https://doi.org/10.1016/0034-4257(89)90125-9.
Yang J, Qiu J. 1996. The empirical expressions of the relation between precipitable water and ground water vapor pressure for some areas in China. Scientia Atmospherica Sinica, 20: 620-626.
Yao R, Wang L, Huang X, Niu Z, Liu F, Wang Q. 2017. Temporal trends of surface urban heat islands and associated determinants in major Chinese cities. Science of The Total Environment, 609: 742-754. doi:https://doi.org/10.1016/j.scitotenv.2017.07.217.
Yuan M, Wang L, Lin A, Liu Z, Li Q, Qu S. 2020. Vegetation green up under the influence of daily minimum temperature and urbanization in the Yellow River Basin, China. Ecological Indicators, 108: 105760. doi:https://doi.org/10.1016/j.ecolind.2019.105760.
Yuan X, Wang W, Cui J, Meng F, Kurban A, De Maeyer P. 2017. Vegetation changes and land surface feedbacks drive shifts in local temperatures over Central Asia. Scientific Reports, 7(1): 3287. doi:https://doi.org/10.1038/s41598-017-03432-2.
Yue W, Xu J, Tan W, Xu L. 2007. The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data. International Journal of Remote Sensing, 28(15): 3205-3226. doi:https://doi.org/10.1080/01431160500306906.
Zhou D, Xiao J, Bonafoni S, Berger C, Deilami K, Zhou Y, Frolking S, Yao R, Qiao Z, Sobrino JA. 2018. Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives. Remote Sensing, 11(1): 48. doi:https://doi.org/10.3390/rs11010048.
_||_Carlson TN, Ripley DA. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3): 241-252. doi:https://doi.org/10.1016/S0034-4257(97)00104-1.
Chen X-L, Zhao H-M, Li P-X, Yin Z-Y. 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2): 133-146. doi:https://doi.org/10.1016/j.rse.2005.11.016.
Cui L, Wang L, Singh RP, Lai Z, Jiang L, Yao R. 2018. Association analysis between spatiotemporal variation of vegetation greenness and precipitation/temperature in the Yangtze River Basin (China). Environmental Science and Pollution Research, 25(22): 21867-21878. doi:10.1007/s11356-018-2340-4.
Fu P, Weng Q. 2015. Temporal dynamics of land surface temperature from Landsat TIR time series images. IEEE Geoscience and Remote Sensing Letters, 12(10): 2175-2179. doi:https://doi.org/10.1109/LGRS.2015.2455019.
Fu P, Weng Q. 2016. A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sensing of Environment, 175: 205-214. doi:https://doi.org/10.1016/j.rse.2015.12.040.
Ghobadi Y, Pradhan B, Shafri HZM, Kabiri K. 2015. Assessment of spatial relationship between land surface temperature and landuse/cover retrieval from multi-temporal remote sensing data in South Karkheh Sub-basin, Iran. Arabian Journal of Geosciences, 8(1): 525-537. doi:10.1007/s12517-013-1244-3.
Govil H, Guha S, Diwan P, Gill N, Dey A. 2020. Analyzing Linear Relationships of LST with NDVI and MNDISI Using Various Resolution Levels of Landsat 8 OLI and TIRS Data. In: Sharma N, Chakrabarti A, Balas VE (eds) Data Management, Analytics and Innovation, Singapore. Springer Singapore, pp 171-184. https://doi.org/110.1007/1978-1981-1032-9949-1008_1013.
Goward SN, Xue Y, Czajkowski KP. 2002. Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: An exploration with the simplified simple biosphere model. Remote Sensing of Environment, 79(2): 225-242. doi:https://doi.org/10.1016/S0034-4257(01)00275-9.
Guha S, Govil H. 2020. Land surface temperature and normalized difference vegetation index relationship: a seasonal study on a tropical city. SN Applied Sciences, 2(10): 1661. doi:https://doi.org/10.1007/s42452-020-03458-8.
Guha S, Govil H. 2021. An assessment on the relationship between land surface temperature and normalized difference vegetation index. Environment, Development and Sustainability, 23(2): 1944-1963. doi:10.1007/s10668-020-00657-6.
Guha S, Govil H, Diwan P. 2019. Analytical study of seasonal variability in land surface temperature with normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index. Journal of Applied Remote Sensing, 13(2): 024518. doi:https://doi.org/10.1117/1.JRS.13.024518.
Guha S, Govil H, Gill N, Dey A. 2020. Analytical study on the relationship between land surface temperature and land use/land cover indices. Annals of GIS, 26(2): 201-216. doi:https://doi.org/10.1080/19475683.2020.1754291.
Guo L, Liu R, Men C, Wang Q, Miao Y, Zhang Y. 2019. Quantifying and simulating landscape composition and pattern impacts on land surface temperature: A decadal study of the rapidly urbanizing city of Beijing, China. Science of The Total Environment, 654: 430-440. doi:https://doi.org/10.1016/j.scitotenv.2018.11.108.
He B-J, Zhao Z-Q, Shen L-D, Wang H-B, Li L-G. 2019. An approach to examining performances of cool/hot sources in mitigating/enhancing land surface temperature under different temperature backgrounds based on landsat 8 image. Sustainable Cities and Society, 44: 416-427. doi:https://doi.org/10.1016/j.scs.2018.10.049.
Huang S, Taniguchi M, Yamano M, Wang C-h. 2009. Detecting urbanization effects on surface and subsurface thermal environment — A case study of Osaka. Science of The Total Environment, 407(9): 3142-3152. doi:https://doi.org/10.1016/j.scitotenv.2008.04.019.
Ke Y, Im J, Lee J, Gong H, Ryu Y. 2015. Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations. Remote Sensing of Environment, 164: 298-313. doi:https://doi.org/10.1016/j.rse.2015.04.004.
Kumar D, Shekhar S. 2015. Statistical analysis of land surface temperature–vegetation indexes relationship through thermal remote sensing. Ecotoxicology and Environmental Safety, 121: 39-44. doi:https://doi.org/10.1016/j.ecoenv.2015.07.004.
Liu H, Zhan Q, Yang C, Wang J. 2018. Characterizing the spatio-temporal pattern of land surface temperature through time series clustering: Based on the latent pattern and morphology. Remote Sensing, 10(4): 654. doi:https://doi.org/10.3390/rs10040654.
Liu Y, Peng J, Wang Y. 2018. Efficiency of landscape metrics characterizing urban land surface temperature. Landscape and Urban Planning, 180: 36-53. doi:https://doi.org/10.1016/j.landurbplan.2018.08.006.
Mathew A, Khandelwal S, Kaul N. 2018. Spatio-temporal variations of surface temperatures of Ahmedabad city and its relationship with vegetation and urbanization parameters as indicators of surface temperatures. Remote Sensing Applications: Society and Environment, 11: 119-139. doi:https://doi.org/10.1016/j.rsase.2018.05.003.
Peng J, Jia J, Liu Y, Li H, Wu J. 2018. Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas. Remote Sensing of Environment, 215: 255-267. doi:https://doi.org/10.1016/j.rse.2018.06.010.
Peng J, Ma J, Liu Q, Liu Y, Hu Yn, Li Y, Yue Y. 2018. Spatial-temporal change of land surface temperature across 285 cities in China: An urban-rural contrast perspective. Science of The Total Environment, 635: 487-497. doi:https://doi.org/10.1016/j.scitotenv.2018.04.105.
Peng J, Xie P, Liu Y, Ma J. 2016. Urban thermal environment dynamics and associated landscape pattern factors: A case study in the Beijing metropolitan region. Remote Sensing of Environment, 173: 145-155. doi:https://doi.org/10.1016/j.rse.2015.11.027.
Sannigrahi S, Bhatt S, Rahmat S, Uniyal B, Banerjee S, Chakraborti S, Jha S, Lahiri S, Santra K, Bhatt A. 2018. Analyzing the role of biophysical compositions in minimizing urban land surface temperature and urban heating. Urban Climate, 24: 803-819. doi:https://doi.org/10.1016/j.uclim.2017.10.002.
Sekertekin A, Bonafoni S. 2020. Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: Assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sensing, 12(2): 294. doi:https://doi.org/10.3390/rs12020294.
Sobrino JA, Jiménez-Muñoz JC, Paolini L. 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4): 434-440. doi:https://doi.org/10.1016/j.rse.2004.02.003.
Sultana S, Satyanarayana ANV. 2020. Assessment of urbanisation and urban heat island intensities using landsat imageries during 2000 – 2018 over a sub-tropical Indian City. Sustainable Cities and Society, 52: 101846. doi:https://doi.org/10.1016/j.scs.2019.101846.
Sun D, Kafatos M. 2007. Note on the NDVI‐LST relationship and the use of temperature‐related drought indices over North America. Geophysical Research Letters, 34(24). doi:https://doi.org/10.1029/2007GL031485.
Tan J, Yu D, Li Q, Tan X, Zhou W. 2020. Spatial relationship between land-use/land-cover change and land surface temperature in the Dongting Lake area, China. Scientific Reports, 10(1): 9245. doi:https://doi.org/10.1038/s41598-020-66168-6.
Weng Q. 2009. Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4): 335-344. doi:https://doi.org/10.1016/j.isprsjprs.2009.03.007.
Weng Q, Lu D, Schubring J. 2004. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4): 467-483. doi:https://doi.org/10.1016/j.rse.2003.11.005.
Wukelic GE, Gibbons DE, Martucci LM, Foote HP. 1989. Radiometric calibration of Landsat Thematic Mapper thermal band. Remote Sensing of Environment, 28: 339-347. doi:https://doi.org/10.1016/0034-4257(89)90125-9.
Yang J, Qiu J. 1996. The empirical expressions of the relation between precipitable water and ground water vapor pressure for some areas in China. Scientia Atmospherica Sinica, 20: 620-626.
Yao R, Wang L, Huang X, Niu Z, Liu F, Wang Q. 2017. Temporal trends of surface urban heat islands and associated determinants in major Chinese cities. Science of The Total Environment, 609: 742-754. doi:https://doi.org/10.1016/j.scitotenv.2017.07.217.
Yuan M, Wang L, Lin A, Liu Z, Li Q, Qu S. 2020. Vegetation green up under the influence of daily minimum temperature and urbanization in the Yellow River Basin, China. Ecological Indicators, 108: 105760. doi:https://doi.org/10.1016/j.ecolind.2019.105760.
Yuan X, Wang W, Cui J, Meng F, Kurban A, De Maeyer P. 2017. Vegetation changes and land surface feedbacks drive shifts in local temperatures over Central Asia. Scientific Reports, 7(1): 3287. doi:https://doi.org/10.1038/s41598-017-03432-2.
Yue W, Xu J, Tan W, Xu L. 2007. The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data. International Journal of Remote Sensing, 28(15): 3205-3226. doi:https://doi.org/10.1080/01431160500306906.
Zhou D, Xiao J, Bonafoni S, Berger C, Deilami K, Zhou Y, Frolking S, Yao R, Qiao Z, Sobrino JA. 2018. Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives. Remote Sensing, 11(1): 48. doi:https://doi.org/10.3390/rs11010048.
