scholarly journals Analisis Fenomena Pulau Panas Perkotaan Kota Bandung Menggunakan Google Earth Engine

2019 ◽  
Author(s):  
Muhammad Malik Ar-Rahiem ◽  
Muhamad Riza Fakhlevi
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Google Earth ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat ◽  
Google Earth Engine

Pulau Panas Perkotaan (Urban Heat Island) adalah fenomena antropogenik akibat pengaruh urbanisasi. Kawasan perkotaan yang terbangun memiliki temperatur yang lebih hangat dibandingkan kawasan sekitarnya. Fenomena Pulau Panas Perkotaan di Kota Bandung diteliti menggunakan data Suhu Permukaan Tanah (Land Surface Temperature) yang diakuisisi dari satelit Landsat 8. Lima tahun data satelit dianalisis menggunakan piranti daring Google Earth Engine untuk menganalisis variasi temporal Pulau Panas Perkotaan di Kota Bandung dan sekitarnya. Suhu yang diakuisisi dari satelit dikonversi menjadi estimasi suhu permukaan dengan mempertimbangkan nilai Normalized Difference Vegetation Index. Hasil dari penelitian ini adalah peta persebaran rata-rata dan median suhu permukaan di Cekungan Bandung tahun 2013-2018, serta grafik seri waktu suhu permukaan di 3 jenis tata guna lahan yang mewakili daerah kota (sekitar Jalan Sudirman), hutan kota (Hutan Babakan Siliwangi), dan hutan (Tamah Hutan Raya Djuanda). Suhu rata-rata Kota Bandung pada tahun 2013-2018 adalah 26,93 oC (median seluruh data) dan 25,57oC (rata-rata seluruh data). Sementara perbandingan berdasarkan tata guna lahan; daerah kota memiliki suhu permukaan rata-rata 27,30 oC, daerah hutan kota memiliki suhu 21,31oC, dan daerah hutan memiliki suhu 18,60oC. Peta persebaran suhu panas permukaan dari citra Landsat 8 menunjukkan bahwa daerah hutan secara konsisten memiliki suhu paling rendah, diikuti dengan hutan kota, dan kemudian daerah kota menjadi area yang paling panas dengan suhu maksimal hingga 33,73oC. Penggunaan Google Earth Engine yang berbasis komputasi awan sangat memudahkan pengolahan data citra satelit dalam jumlah besar yang selama ini tidak memungkinkan dilakukan dengan cara konvensional (mengunduh dan memproses di komputer).

scholarly journals MODEL KEKRITISAN INDEKS LINGKUNGAN DENGAN ALGORITMA URBAN HEAT ISLAND DI KOTA SEMARANG

2017 ◽  
Vol 19 (1) ◽  
pp. 45 ◽  
Author(s):  
Bandi Sasmito ◽  
Andri Suprayogi
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat ◽  
Criticality Index

<p align="center"> <strong>ABSTRAK</strong></p><p class="JudulABSInd"><span lang="IN"> </span></p><p class="abstrak"><span lang="IN">Pembangunan infrastruktur di Kota Semarang berkembang sangat pesat sebagai pusat bisnis, ekonomi, industri, hiburan, dan pendidikan. Pembangunan memberikan dampak positif bagi masyarakat kota, namun terdapat juga dampak negatif yang terjadi yaitu penurunan kualitas lingkungan. Meningkatnya suhu udara adalah salah satu dampak dari penurunan kualitas lingkungan. Puncak atap dan dinding dari gedung bertingkat, tempat parkir, jalan, dan trotoar cenderung memiliki albedo yang rendah. Permukaan rendah albedo menyerap energi panas radiasi matahari lebih tinggi dari objek sekitarnya. Akibatnya, jumlah kelebihan energi panas menumpuk di sekitarnya menjadi pulau-pulau panas atau <em>Urban Heat Island</em> (UHI). Penelitian ini bertujuan untuk mendeteksi terjadinya fenomena kekritisan lingkungan akibat UHI dengan menganalisis suhu permukaan dan sebaran vegetasi di wilayah studi. Ada dua langkah metode dalam penelitian ini, pertama adalah membuat peta sebaran suhu permukaan tanah dan peta sebaran kerapatan vegetasi di tahun 2013 sampai 2016. Peta suhu permukaan dibuat dengan model algoritma <em>Land Surface Temperature</em> (LST) dan sebaran vegetasi adalah dengan algoritma <em>Normalized Difference Vegetation Index</em> (NDVI). LST didapatkan dengan mengolah Citra Landsat-8 band TIRS (<em>Thermal Infrared Red Sensor</em>), sedangkan NDVI  didapatkan dengan mengolah Citra Landsat-8 band OLI (<em>Operation Land Imager</em>). Langkah kedua adalah membuat peta kekritisan lingkungan dengan algoritma ECI (<em>Environmental Criticality Index</em>). ECI didapatkan dari nilai LST dibagi NDVI yang direntangkan histogram spektralnya menjadi 8 bit. </span>Melalui<span lang="IN"> hasil penelitian ini dapat disimpulkan bahwa suhu permukaan di Kota Semarang meningkat dan sebaran kelas suhu tinggi meluas setiap tahun. Kekritisan lingkungan akibat UHI terdeteksi di pusat kota, yaitu wilayah Utara Kota Semarang.</span></p><p class="abstrak"><strong><span lang="IN">Kata kunci</span></strong><span lang="IN">: </span><em><span lang="IN">Urban Heat Island</span></em><span lang="IN"> (UHI), </span><em><span lang="IN">Land Surface Temperature</span></em><span lang="IN"> (LST), <em>Normalized Difference Vegetation Index</em> (NDVI)</span><span lang="IN">, </span><em><span lang="IN">Environmental Criticality Index</span></em><span lang="IN"> (ECI)</span></p><p align="center"><strong><br /></strong></p><p align="center"><strong><em>ABSTRACT</em></strong></p><p class="Abstrakeng">Infrastructure in Semarang City developes rapidly as a center of business, economics, industry, entertainment, and education. Development gives positive impact to citizen, however environmental degradation as the negative impact also occured. Temperatures rising is one of environmental degradation impact. Roof top and wall of a building, parking lot, road, and sidewalk tend to have a low albedo. The low surface albedo absorbs thermal energy from solar radiation higher than the surrounding objects. As a result, the amount of excess heat accumulate in the vicinity into heat islands or Urban Heat Island (UHI). This study aims to detect the occurrence of environmental criticality due to UHI phenomenon by analyzing the surface temperature and the distribution of vegetation in the study area. There are two steps in this research, first step is to create land surface temperature distribution map and vegetation density distribution map in the year of 2013 to 2016. The surface temperature map created by Land Surface Temperature (LST) algorithm model and vegetation distribution created by Normalized Difference Vegetation Index (NDVI) algorithm. LST is obtained by processing Landsat-8 band TIRS (Thermal Infrared Sensor Red), while the NDVI obtained by processing Landsat-8 band OLI (Operation Land Imager). The second step is to create environmental criticality map with  ECI (Environmental Criticality Index) algorithm. ECI is obtained from LST value divided by NDVI spectral histogram stretched to 8 bits. From this research, can be concluded that the heat coverage in Semarang City increase and distribution of vegetation density index spread every year. Environmental criticality due to UHI occurred in downtown area, specifically in the northern side of Semarang City.</p><p><strong><em>Keywords</em></strong><em>:</em><em>   </em><em>Urban Heat Island</em> (UHI), <em>Land Surface Temperature</em> (LST), <em>Normalized Difference Vegetation Index</em> (NDVI), <em>Environmental Criticality Index</em> (ECI)</p><p align="center"><strong><em><br /></em></strong></p><p> </p>

scholarly journals LAND SURFACE TEMPERATURE AS AN INDICATOR OF URBAN HEAT ISLAND EFFECT: A GOOGLE EARTH ENGINE BASED WEB-APP

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 57-62
Author(s):  
A. Galodha ◽  
S. K. Gupta
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Google Earth ◽  
Heat Island ◽  
Rapid Urbanization ◽  
Urban Heat ◽  
Google Earth Engine ◽  
Web App

Abstract. At least 2 billion urban occupants will be concentrated in Asia and Africa, amounting to 70% of the global population by 2050. This rapid urbanization has caused an innate effect on the ecology and environment, which further results in intense temperature variations in urban and rural areas, especially in India. According to a recent IPCC report, 8 out of the 15 hottest cities in the world are situated in India. The rising industrial work, construction activities, type of material used for construction, and other factors have reduced thermal cooling and created temperature imbalance, thereby creating a vicious effect called “urban heat island” (UHI) or “surface urban heat island” (SUHI). Several researchers have also related it with climate change due to their contribution to the greenhouse effect and global warming. In this study, we have particularly emphasized northern India, including Punjab, Rajasthan, Haryana, and Delhi. We created a Google Earth Engine (GEE) based Web-App to assess the UHI intensity over the past 15 years (2003–2018). We are using Moderate Resolution Imaging Spectroradiometer (MODIS) images, Landsat 5, 7, and 8 data for studying UHI. The land surface temperature (LST) based UHI intensity (day and night time) will be available for major metropolitan cities with their respective clusters. With feasibility in SUHI monitoring, we can address an increasing need for resilient, sustainable, and safe urban planning of our cities as portrayed under the Sustainable Development Goals (SDG 11 highlighted by United Nations).

scholarly journals The localization of urban heat island in the Katowice conurbation (Poland) using the combination of land surface temperature, Normalized Difference Vegetation Index and Normalized Difference Built-up Index

2021 ◽  
Vol 94 (1) ◽  
pp. 111-129
Author(s):  
Ádám Nádudvari
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Surface ◽  
Temperature Measurements ◽  
Heat Island ◽  
Urban Heat

The localization of Surface Urban Heat Island (SUHI) as a potential heat risk for the urban population was evaluated. The paper aimed to propose an approach to quantify and localize (SUHI) based on Landsat series TM, ETM+, OLI satellite imageries from the period 1996-2018 and recognize the Atmospheric Urban Heat Island (AUHI) effects from long term temperature measurements. Using the theoretical relation between the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI) and the LST (Land Surface Temperature), SUHIintensity and SUHIrisk maps were created from the combination of LST, NDVI, NDBI using threshold values to localize urban heat island in the Katowice conurbation. Negative values of SUHI intensity characterize areas where there is no vegetation, highly built-up areas, and areas with high surface temperatures. The urban grow – revealed from SUHI – and global climate change are acting together to strengthen the global AUHI effect in the region as the temperature measurements were indicated.

scholarly journals Hubungan Kerapatan Vegetasi dan Bangunan terhadap UHI (Urban Heat Island) di Kota Magelang

Ruang ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 83
Author(s):  
Febriyan Riyadi ◽  
Sri Rahayu
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Least Square ◽  
Heat Island ◽  
Ordinary Least Square ◽  
Urban Heat

Urban Heat Island (UHI) adalah fenomena dimana suatu wilayah perkotaan lebih panas daripada wilayah disekitarnya. Faktor utama yang mempengaruhi terjadinya UHI adalah terjadinya konversi tutupan lahan vegetasi menjadi daerah terbangun akibat perkembangan kota. Hal tersebut mengakibatkan peningkatan suhu permukaan, dikarenakan kerapatan vegetasi yang berkurang dan meningkatnya kerapatan bangunan. Analisis yang digunakan adalah klasifikasi tak terbimbing untuk melihat perubahan tutupan lahan, analisis NDVI (Normalized Difference Vegetation Index) untuk mengetahui perubahan vegetasi, analisis NDBI (Normalized Difference Vegetation Index) untuk mengetahui perubahan kerapatan bangunan, serta menggunakan LST (Land Surface Temperature) untuk mengetahui suhu permukaan suatu kota dan OLS (Ordinary Least Square) merupakan permodelan regresi berganda pada aplikasi ArcGis  digunakan untuk mengetahui hubungan antar variabel tersebut. Hasil dalam penelitian ini menunjukkan bahwa suhu rata-rata Kota Magelang pada tahun 2000 sebesar 22,58°C meningkat menjadi 27,11°C pada tahun 2016. Artinya suhu rata-rata Kota Magelang mengalami kenaikan sebesar 4,53°C. Hubungan antara kerapatan bangunan (x1) dan kerapatan vegetasi (x2) terhadap suhu permukaan (y) diketahui melalui formula OLS yang dihasilkan yaitu Y= 5,61 X1 – 1,34 X2 + 2,4.Hal ini berarti jika kerapatan bangunan meningkat dan kerapatan vegetasi berkurang, maka suhu permukaan meningkat.

scholarly journals The Urban Heat Island in an Urban Context: A Case Study of Mashhad, Iran

2019 ◽  
Vol 16 (3) ◽  
pp. 313 ◽  
Author(s):  
Marzie Naserikia ◽  
Elyas Asadi Shamsabadi ◽  
Mojtaba Rafieian ◽  
Walter Leal Filho
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Weather Conditions ◽  
Heat Island ◽  
Urban Heat ◽  
Semi Arid

In this study, the spatio-temporal changes of urban heat island (UHI) in a mega city located in a semi-arid region and the relationships with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) are appraised using Landsat TM/OLI images with the help of ENVI and ArcGIS software. The results reveal that the relationships between NDBI, NDVI and land surface temperature (LST) varied by year in the study area and they are not suitable indices to study the land surface temperature in arid and semi-arid regions. The study also highlights the importance of weather conditions when appraising the relationship of these indices with land surface temperature. Overall, it can be concluded that LST in arid and steppe regions is most influenced by barren soil. As a result, built-up areas surrounded by soil or bituminous asphalt experience higher land surface temperatures compared to densely built-up areas. Therefore, apart from setting-up more green areas, an effective way to reduce the intensity of UHI in these regions is to develop the use of cool and smart pavements. The experiences from this paper may be of use to cities, many of which are struggling to adapt to a changing climate.

scholarly journals Pemetaan Fenomena Urban Heat Island Di Kota Kendari Dengan Menggunakan Citra Resolusi Menengah

2021 ◽  
Vol 5 (1) ◽  
pp. 33
Author(s):  
Fatimah Wardana ◽  
Laode Muh. Golok Jaya ◽  
Fitra Saleh ◽  
Jufri Karim
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Landsat 8 ◽  
Density Level ◽  
Heat Island ◽  
Landsat 8 Oli ◽  
Urban Heat

Abstrak: Fenomena Urban Heat Island dapat dipetakan dengan parameter Suhu Permukaan Tanah (SPT) dan indeks kerapatan vegetasi (NDVI). Penelitian ini bertujuan untuk menganalisis Urban Heat Island di Kota Kendari menggunakan Landsat 8 OLI/TIRS dan menganalisis kondisi eksisting sebaran fenomena Urban Heat Island di Kota Kendari. Proses dilakukan dengan mengolah data citra Landsat 8 OLI/TIRS perekaman 30 Agustus 2017. Analisis dilakukan dengan menggunakan algoritma Syariz untuk penentuan SPT yang kemudian dikorelasikan dengan nilai NDVI yang dihasilkan dari kaliberasi band 4 dan band 5 pada citra Landsat 8 OLI/TIRS. Hasil penelitian ini menunjukkan suhu permukaan tanah di kota Kendari berkisar antara 15,27 hingga 33,34. Dimana suhu 15 hingga 22adalah suhu daerah yang tidak terdeteksi atau tertutup awan. Persebaran suhu didominasi suhu antara 23-27 dengan luas 21.492,46 Ha atau 81,02% dari luas wilayah, dengan wilayah yang teridentifikasi sebagaui daerah UHI dengan suhu diantara 28-33 seluas 2.968,57 Ha atau 11,01% dari total luas wilayah Kota Kendari. Nilai korelasi antara SPT dan NDVI berada pada angka -0,66 yang berarti bahwa tingkat kerapatan vegetasi berbanding terbalik dengnan nilai suhu permukaan tanah atau semakin rendah indeks kerapatan vegetasinya, maka semakin tinggi suhu permukaan tanahnya.Kata kunci: Urban Heat Island, suhu permukaan tanah, Landsat 8, NDVIAbstract: The Urban Heat Island phenomenon can be mapped with the parameters of Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). This study aims to analyze Urban Heat Island in Kendari City using Landsat 8 OLI / TIRS and analyze the existing conditions of the distribution of the Urban Heat Island phenomenon in Kendari City. The process is done by processing Landsat 8 OLI / TIRS image recording data on August 30, 2017. The analysis carried out using the Syariz algorithm to determine LST which is then correlated with NDVI values resulting from band 4 and band 5 in Landsat 8 OLI / TIRS images. The results showed that the  land surface temperature in Kendari ranged from 15.27°C to 33.34°C. The 15 to 22°C is the temperature of the clouded or undetected area. The temperature distribution is dominated by temperatures between 23-27 ° C with an area of 21,492.46 Ha or  81.02% of the total area, with areas identified as UHI are the areas with temperatures between 28-33 ° C with an area of 2,968.57 Ha or 11.01% of the total area of  Kendari City. The correlation value between SPT and NDVI is at -0.66, which means that the vegetation density level is inversely proportional to the value of the land surface temperature value or the lower the vegetation index value, the higher the surface temperature of the land.Keywords: Urban Heat Island, land surface temperature, Landsat 8, NDVI

scholarly journals The Effects of Land Use Zoning and Densification on Changes in Land Surface Temperature in Seoul

2019 ◽  
Vol 11 (24) ◽  
pp. 7056 ◽  
Author(s):  
Jae-Ik Kim ◽  
Myung-Jin Jun ◽  
Chang-Hwan Yeo ◽  
Ki-Hyun Kwon ◽  
Jun Yong Hyun
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Descriptive Analysis ◽  
Landsat 8 ◽  
Land Use Type ◽  
Coverage Ratio

This study investigated how changes in land surface temperature (LST) during 2004 and 2014 were attributable to zoning-based land use type in Seoul in association with the building coverage ratio (BCR), floor area ratio (FAR), and a normalized difference vegetation index (NDVI). We retrieved LSTs and NDVI data from satellite images, Landsat TM 5 for 2004 and Landsat 8 TIRS for 2014 and combined them with parcel-based land use information, which contained data on BCR, FAR, and zoning-based land use type. The descriptive analysis results showed a rise in LST for the low- and medium-density residential land, whereas significant LST decreases were found in high-density residential, semi-residential, and commercial areas over the time period. Statistical results further supported these findings, yielding statistically significant negative coefficient values for all interaction variables between higher-density land use types and a year-based dummy variable. The findings appear to be related to residential densification involving the provision of more high-rise apartment complexes and government efforts to secure more parks and green spaces through urban redevelopment and renewal projects.

scholarly journals Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi

2017 ◽  
Vol 11 (2) ◽  
pp. 141-150 ◽  
Author(s):  
Paul Macarof ◽  
Florian Statescu
Keyword(s):  
Urban Heat Island ◽  
Rural Areas ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Heat Island ◽  
Sensing Technology ◽  
Urban Heat ◽  
Surface Urban Heat Island

Abstract This study compares the normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) as indicators of surface urban heat island effects in Landsat-8 OLI imagery by investigating the relationships between the land surface temperature (LST), NDBI and NDVI. The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural areas due to urbanization. With the development of remote sensing technology, it has become an important approach to urban heat island research. Landsat data were used to estimate the LST, NDBI and NDVI from four seasons for Iasi municipality area. This paper indicates than there is a strong linear relationship between LST and NDBI, whereas the relationship between LST and NDVI varies by season. This paper suggests, NDBI is an accurate indicator of surface UHI effects and can be used as a complementary metric to the traditionally applied NDVI.

scholarly journals Application of Geo-informatics Technology to Access the Surface Temperature Using LANDSAT 8 OLI/TIRS Satellite Data: A Case Study in Ampara District in Sri Lanka

2020 ◽  
Author(s):  
Ibra Lebbe Mohamed Zahir
Keyword(s):  
Sri Lanka ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Surface Emissivity ◽  
Land Surface Emissivity

Land Surface Temperature is a one of the key variable of Global climate changes and model which estimate radiating budget in heat balance as control of climate model. It is a major influenced factor by the ability of the surface emissivity. In this study, were used Landsat 8 satellite image that have Operational Land Imager and Thermal Infrared Sensor to calculate Land Surface Temperature through geospatial technology over Ampara district, Sri Lanka. The Land Surface Temperature was estimated with respect to Land Surface Emissivity and Normalized Difference Vegetation Index values determined from the Red and Near Infrared channels. Land Surface Emissivity was processed directly by the thermal Infrared bands. Pixels based calculation were used to effort at LANDSAT 8 images that thermal Band 10 various dates in this study. The results were achievable to compute Normalized Difference Vegetation Index, Land Surface Emissivity, and Land Surface Temperature with applicable manner to compare with land use/ land cover data. It determines and predicts the changes of surface temperature to favorable to decision making process for the society. Study area faces seasonal drought in Sri Lanka, the prediction method that how land can be efficiently used with the present condition. Therefore, the Land Surface Temperature estimation can prove whether new irrigation systems for agricultural activities or can transformed source of energy into useful form that introducing solar hubs for energy production in future.

scholarly journals Quantification of Urban Heat Island-Induced Contribution to Advance in Spring Phenology: A Case Study in Hangzhou, China

2021 ◽  
Vol 13 (18) ◽  
pp. 3684
Author(s):  
Yingying Ji ◽  
Jiaxin Jin ◽  
Wenfeng Zhan ◽  
Fengsheng Guo ◽  
Tao Yan
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Heat Island ◽  
Spring Phenology ◽  
Urban Heat ◽  
The Difference

Plant phenology is one of the key regulators of ecosystem processes, which are sensitive to environmental change. The acceleration of urbanization in recent years has produced substantial impacts on vegetation phenology over urban areas, such as the local warming induced by the urban heat island effect. However, quantitative contributions of the difference of land surface temperature (LST) between urban and rural (ΔLST) and other factors to the difference of spring phenology (i.e., the start of growing season, SOS) between urban and rural (ΔSOS) were rarely reported. Therefore, the objective of this study is to explore impacts of urbanization on SOS and distinguish corresponding contributions. Using Hangzhou, a typical subtropical metropolis, as the study area, vegetation index-based phenology data (MCD12Q2 and MYD13Q1 EVI) and land surface temperature data (MYD11A2 LST) from 2006–2018 were adopted to analyze the urban–rural gradient in phenology characteristics through buffers. Furthermore, we exploratively quantified the contributions of the ΔLST to the ΔSOS based on a temperature contribution separation model. We found that there was a negative coupling between SOS and LST in over 90% of the vegetated areas in Hangzhou. At the sample-point scale, SOS was weakly, but significantly, negatively correlated with LST at the daytime (R2 = 0.2 and p < 0.01 in rural; R2 = 0.14 and p < 0.05 in urban) rather than that at nighttime. Besides, the ΔSOS dominated by the ΔLST contributed more than 70% of the total ΔSOS. We hope this study could help to deepen the understanding of responses of urban ecosystem to intensive human activities.

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