Quantifying the Influences of Driving Factors on Land Surface Temperature during 2003–2018 in China Using Convergent Cross Mapping Method

Yu, Yongxiang; Shang, Gao; Duan, Si‐Bo; Yu, Wei; Labed, Jelila; Li, Zhao-Liang

doi:10.3390/rs14143280

Cited by 6 publications

(5 citation statements)

References 55 publications

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“…In addition, Global Multi-resolution Topographic Elevation Data (GMTED) 2010 was selected as the source of elevation data for this study. The detailed description of the above products can be seen in article [17].…”

Section: Satellite Productsmentioning

confidence: 99%

“…The reanalysis dataset ERA5-land has been widely used in various areas due to its long time series (from 1950 to present), high spatial resolution (0.1 • × 0.1 • ), and global coverage [17,43]. The monthly mean air temperature at 2 m above the ground (TA) was selected in this study to calculate the annual mean TA.…”

Section: Reanalysis Datamentioning

confidence: 99%

“…Numerous studies have been conducted on the driving factors of LST changes with time. It can be divided into three main categories such as correlation coefficient [5][6][7][8][9], energy balance [10][11][12], and data driven [13][14][15][16][17]. However, fewer studies have been conducted on the driving factors of spatial heterogeneity of LST.…”

Section: Introductionmentioning

confidence: 99%

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Revealing the Driving Mechanisms of Land Surface Temperature Spatial Heterogeneity and Its Sensitive Regions in China Based on GeoDetector

Fang

Wen

2023

Remote Sensing

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Land surface temperature (LST) has a critical impact on the energy balance of land surface processes and ecosystem stability. Meanwhile, LST is controlled by multiple factors at the surface, resulting in heterogeneity of its spatial distribution. To understand the drivers of LST spatial heterogeneity and their contributions, the effects of air temperature, normalized difference vegetation index (NDVI), soil moisture, net surface radiation, precipitation, aerosol optical depth (AOD), evapotranspiration, water vapor, digital elevation model (DEM), climate type, and land cover type on LST spatial heterogeneity was analyzed in this study with GeoDetector. The results showed that the explanatory ability of air temperature to impact the spatial heterogeneity of LST was the largest in each year with a mean value of 0.74, followed by water vapor with a mean value of 0.7, and the driving effect of the factors on LST showed an increasing trend year by year. However, the land cover type did not have an effect on the spatial heterogeneity of LST for the univariate analysis in this study. In addition, the interaction analysis indicated that the spatial distribution of LST was jointly driven by all the driving factors. Among them, air temperature had the strongest interaction with other factors, with the strength of the effect in the range of 0.73–0.8. In terms of the highly sensitive area of LST for each driver, AOD has the largest driving area, accounting for 15.8% of the total area, followed by WV, TA, and ET at about 11%, and the remaining variables are less than 10%. During the study period, the area of the highly sensitive region of LST for each factor showed an overall decreasing trend, indicating that the influence of the driving factors on LST will be stronger and more concentrated. Generally, this study provides meaningful understanding of the spatial heterogeneity of LST since 2003 and provides a scientific reference for coping with climate change, analyzing surface environmental patterns, and protecting ecological environment.

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Section: Satellite Productsmentioning

confidence: 99%

Section: Reanalysis Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Revealing the Driving Mechanisms of Land Surface Temperature Spatial Heterogeneity and Its Sensitive Regions in China Based on GeoDetector

Fang

Wen

2023

Remote Sensing

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“…Even in the absence of an actual causal link [32], a strong correlation in the seasonal cycles of NDVI and meteorological factors can result in high CCM skill values. This phenomenon is referred to as the seasonal synchronization effect [51].…”

Section: ) Seasonal Surrogate Test (Sst)mentioning

confidence: 99%

“…CCM can manage complex causal relationships among multiple variables in non-linear ecosystems. Because of its high precision in detecting causal correlations between variables and its ability to detect even weak causal relationships in nonlinear systems, CCM is extensively used in fields such as ecology, chemistry, medicine, and climate change [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Causal Effects of Climate Change on Vegetation Dynamics in Northeast China Using Convergence Cross-Mapping

Wu,

Zhou,

Wang

et al. 2023

IEEE Access

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The patterns of interaction between terrestrial vegetation and the atmosphere are complex, and some are poorly understood. Linear or general linear methods have been widely used to explore the dynamics of vegetation and climate changes. However, linear thinking may hinder our understanding of complex nonlinear systems, and it is difficult to extract the underlying causality of linear correlations directly from observational data. In this study, we aimed to quantify the interactions between vegetation and climate, using nonlinear dynamical methods based on state-space reconstruction and datasets from Chinese meteorological stations and remote sensing data during 1982-2015 in Northeast China (NEC). Specifically, we identified the causal links between meteorological factors (temperature and precipitation) and the vegetation index (NDVI) by reconstructing the state space from historical records. During the study period, vegetation exhibited a strong bidirectional causal relationship with both temperature and precipitation across Northeast China. The NDVI can be accurately reconstructed from the state information of meteorological factors (temperature and precipitation). The results of the multivariate EDM scenarios reveal varying sensitivities of different vegetation types to meteorological factors. Overall, slight temperature changes have a stronger impact on vegetation compared to precipitation. Mixed forest and broad-leaved forest demonstrate lower sensitivity to precipitation changes compared to other vegetation types. This study on the causal relationship between vegetation and meteorological factors in Northeast China contributes to a deeper understanding of climate change and vegetation feedback in middle and high latitudes. Our work demonstrates that the EDM-based convergent cross-mapping nonlinear causal analysis method is valuable for comprehending the interactions within complex systems in earth science.

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Pixel level spatial variability modeling using SHAP reveals the relative importance of factors influencing LST

Meadows

et al. 2023

Environ Monit Assess

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Quantifying the Influences of Driving Factors on Land Surface Temperature during 2003–2018 in China Using Convergent Cross Mapping Method

Cited by 6 publications

References 55 publications

Revealing the Driving Mechanisms of Land Surface Temperature Spatial Heterogeneity and Its Sensitive Regions in China Based on GeoDetector

Revealing the Driving Mechanisms of Land Surface Temperature Spatial Heterogeneity and Its Sensitive Regions in China Based on GeoDetector

Assessing the Causal Effects of Climate Change on Vegetation Dynamics in Northeast China Using Convergence Cross-Mapping

Pixel level spatial variability modeling using SHAP reveals the relative importance of factors influencing LST

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