The spatiotemporal pattern and influencing factors of land surface temperature change in China from 2003 to 2019

Song, Zengjing; Yang, Hong; Huang, Xiaojuan; Yu, Wei; Jing, Huang; Ma, Mingguo

doi:10.1016/j.jag.2021.102537

Cited by 28 publications

(23 citation statements)

References 60 publications

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“…In view of this, there is a strong consistent between the dynamics of LST and air temperature (Du et al, 2020;Li et al, 2023). In most regions of China, air temperature proved to be the most closely related climatic factor to LST (Song et al, 2021), which is also confirmed in the present study. A generally high agreement between LST and air temperature and an inverse correlation with precipitation are observed.…”

Section: Discussionsupporting

confidence: 88%

Land surface temperature variations in a low-latitude high-altitude mountainous area of southwest China

Huo,

Sun

2024

Preprint

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High-altitude areas are thought to be more sensitive and responsive to climate change, but long-term series of land surface temperature (LST) observations are still inadequate in low-latitude high-altitude mountainous areas. In this paper we comprehensively investigate the spatiotemporal variations in the LST and its dominant driving factors at both annual and seasonal scales from 2001 to 2020 based on the long-term series of MODIS data over the Yunnan Plateau (YNP) in southwest China, with a special focus on elevation-dependent warming (EDW). The results indicated that the annual LST showed a general increasing trend over the past 20 years, with an average rate of 0.18°C decade− 1, being stronger during summer season (0.47°C decade− 1). Moreover, an obvious asymmetric diurnal warming was observed; the nighttime warming rate was 0.43°C decade− 1, which is much faster than the daytime cooling rate (− 0.08°C decade− 1). We also confirmed the presence of EDW; it behaved more strongly at high elevations (above 3500 m) at a rate of 0.41 ºC decade− 1. Spatially, a warming trend was found in most areas, especially in high-cold mountains, hot-dry river valleys, and the tropics. In contrast, in the northeastern YNP and western side of the Ailao Mountains a cooling trend was observed. In most areas, air temperature is the dominant climatic factor influencing changes in LST at an annual, autumn and winter timescales, while precipitation plays a major role in annual LST changes in the northwest and northeast. LST and warming trend largely differenced with respect to land cover types, with the highest values occurring in built-up lands. This research is expected to contribute to a better understanding of surface-atmosphere interaction and climate change processes in YNP.

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Section: Discussionsupporting

confidence: 88%

Land surface temperature variations in a low-latitude high-altitude mountainous area of southwest China

Huo,

Sun

2024

Preprint

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“…Notably, VPD influenced 23.6% in LP, WA contributed 15.2% in QTP, ET accounted for 14.4% in SEC, and SWE held about 17.1% in SWC. These results are highly aligned with previous research that evapotranspiration was the key factor affecting daytime LST in south China (Song et al 2021). In NC, NEC, and NWC, total contributions ranged mainly from 60% to 70%, with VPD being the predominant factor, contributing 9.8%, 25.5%, and 22.5% in NC, NEC, and NWC, respectively.…”

Section: Dominant Driving Factors For the Spatiotemporal Variations O...supporting

confidence: 90%

“…(2) LST substitution: substitute GST with remotely sensed LST data (Peng et al 2018b, Song et al 2021, Zhou et al 2022. Remotely sensed LST data generally starts from the 2000s, which means past several decades before 2000s cannot be analyzed.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal variations and its driving factors of ground surface temperature in China

Gao,

Huang,

Zhang

et al. 2024

Environ. Res. Lett.

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The ground surface temperature (GST) serves as a crucial indicator for understanding land-atmosphere mass and energy exchange. The shift from manual measurement to automated station for GST in China after 2002 introduced inconsistencies at certain stations, potentially distorting research findings. Here, daily automatedly observed GST from 2003 to 2017 at 615 selected meteorological stations were updated by constructing linear regression model based on manually observed air temperature (AT) and GST from 1960 to 2002. Then, the spatiotemporal variations of GST from 1960 to 2017 and its driving factors were investigated. Results indicated that: (1) The AT-GST linear regression model could effectively mitigate the inconsistency caused by the change of GST observation methods, enhancing data reliability. (2) GST in China showed little change from 1960-1980, but increased significantly across all regions from 1980 to 2000, with the increase rate slowed down except in the Qinghai-Tibet Plateau (QTP) and southwest China after 2000. Notable GST increase is concentrated in colder regions, including the QTP, northeast (NEC), and northwest China (NWC). (3) Evapotranspiration (ET) and vapor pressure deficit (VPD) were the primary drivers of annual GST variations at the regional scale, while their contributions to GST variations exhibited notable seasonal variability. Our findings could offer valuable scientific insights for addressing climate change, enhancing surface environmental models, and safeguarding ecological environments.

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“…Although the physical meaning of LST adopted in this study is different from the widely used 2‐m air temperature in the physical meaning, magnitude, and measurement techniques, many studies have proven the ability of LST for analyzing the climate effect of land forcings or the large‐scale climate change (J. Li, Li, et al., 2022; J. Liu et al., 2021; Song et al., 2021; Y. R. Wang et al., 2022). Meanwhile, LST provides insight into the ecosystems' thermal condition for characterizing the surface energy balance and water balance, and thus we argue our assessment could be also informative for the policymakers.…”

Section: Discussionmentioning

confidence: 99%

“…Although the physical meaning of LST adopted in this study is different from the widely used 2-m air temperature in the physical meaning, magnitude, and measurement techniques, many studies have proven the ability of LST for analyzing the climate effect of land forcings or the large-scale climate change (J. Song et al, 2021;Y. R. Wang et al, 2022).…”

Section: Methodology Comparison With Previous Omr-based Analysismentioning

confidence: 99%

Biophysical Impact of Multiple Surface Forcings on Land Surface Temperature Over Eastern China

Li,

et al. 2023

JGR Atmospheres

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Over the past decades, human activities have directly or indirectly driven the land surface changes in eastern China. These anthropogenic forcings could trigger biochemical feedback and alter the surface biophysical properties, thus affecting local temperature. However, the latter is recognized as the “noise” and ignored when assessing historical or future climate. Here, we adopt the “observation minus reanalysis” (OMR) method to isolate the biophysical temperature footprint of multiple surface changes in eastern China over 2001–2018, using remote sensing land surface temperature and reanalysis skin temperature. A spatial regression model was used to separate the contributions from different processes. We find the biophysical feedbacks of surface changes have an annual cooling effect of −0.072 K/decade in eastern China, and the contributions from urban expansion, agricultural development, and natural vegetation greening are 0.042, −0.042, and −0.072 K/decade, correspondingly. The Northeast Plain shows agricultural activities induced cooling of −0.040 K/decade; the Loess Plateau shows natural vegetation recovery dominated cooling of −0.145 K/decade; the Huang‐Huai‐Hai Plain demonstrates a predominant urbanization warming effect of 0.124 K/decade; the Middle‐lower Yangtze shows natural vegetation greening related cooling of −0.106 K/decade. Both the intensity of the land surface changes and the temperature sensitivities drive the large spatial variability of the temperature effect. Overall, the temperature effects of surface changes are spatially heterogeneous and show considerable magnitudes. We emphasize that vegetation changes in eastern China show a strong surface cooling effect, and may contribute to the regional climate mitigation in the context of global warming.

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The spatiotemporal pattern and influencing factors of land surface temperature change in China from 2003 to 2019

Cited by 28 publications

References 60 publications

Land surface temperature variations in a low-latitude high-altitude mountainous area of southwest China

Land surface temperature variations in a low-latitude high-altitude mountainous area of southwest China

Spatiotemporal variations and its driving factors of ground surface temperature in China

Biophysical Impact of Multiple Surface Forcings on Land Surface Temperature Over Eastern China

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