Shuai Yuan , Yongqiang Liu , Yongnan Liu , Kun Zhang , Yongkang Li , Reifat Enwer , Yaqian Li , Qingwu Hu
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To address multicollinearity, ridge regression (RR), geographically weighted ridge regression (GWRR), and piecewise structural equation modeling (pSEM) were combined to construct RR-pSEM and GWRR-pSEM models. Results indicated that SA in Central Asia increased from 2001 to 2010 and decreased from 2011 to 2020, with a projected future decline. There is a strong correlation and significant causality between SA and each factor. Snow cover fraction was identified as the most critical factor influencing SA. Average temperature and precipitation had a greater impact on SA than extreme climate indices, with a 1 °C temperature increase corresponding to a 0.004 decrease in SA. This study enhances understanding of SA changes under climate change, and provides a methodological framework for analyzing complex systems with multicollinearity. The proposed models offer valuable tools for studying interrelated factors in Earth system science.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"134 ","pages":"Article 104233"},"PeriodicalIF":7.6000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatiotemporal variations of surface albedo in Central Asia and its influencing factors and confirmatory path analysis during the 21st century\",\"authors\":\"Shuai Yuan , Yongqiang Liu , Yongnan Liu , Kun Zhang , Yongkang Li , Reifat Enwer , Yaqian Li , Qingwu Hu\",\"doi\":\"10.1016/j.jag.2024.104233\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Surface albedo (SA) is crucial for understanding land surface processes and climate simulation. This study analyzed SA changes and its influencing factors in Central Asia from 2001 to 2020, with projections 2025 to 2100. Factors analyzed included snow cover fraction, fractional vegetation cover, soil moisture, average state climate indices (temperature and precipitation), and extreme climate indices (heatwave indices and extreme precipitation indices). Pearson correlation coefficient, geographical convergent cross mapping, and geographical detector were used to quantify the correlation, causal relationship strength, and impact degree between SA and the influencing factors. To address multicollinearity, ridge regression (RR), geographically weighted ridge regression (GWRR), and piecewise structural equation modeling (pSEM) were combined to construct RR-pSEM and GWRR-pSEM models. Results indicated that SA in Central Asia increased from 2001 to 2010 and decreased from 2011 to 2020, with a projected future decline. There is a strong correlation and significant causality between SA and each factor. Snow cover fraction was identified as the most critical factor influencing SA. Average temperature and precipitation had a greater impact on SA than extreme climate indices, with a 1 °C temperature increase corresponding to a 0.004 decrease in SA. This study enhances understanding of SA changes under climate change, and provides a methodological framework for analyzing complex systems with multicollinearity. 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引用次数: 0
摘要
地表反照率(SA)对于了解地表过程和气候模拟至关重要。本研究分析了中亚地区 2001 年至 2020 年的地表反照率变化及其影响因素,并预测了 2025 年至 2100 年的地表反照率变化。分析的因素包括积雪覆盖率、植被覆盖率、土壤湿度、平均状态气候指数(气温和降水)以及极端气候指数(热浪指数和极端降水指数)。利用皮尔逊相关系数、地理会聚交叉映射和地理检测器来量化 SA 与影响因素之间的相关性、因果关系强度和影响程度。为解决多重共线性问题,将山脊回归(RR)、地理加权山脊回归(GWRR)和片断结构方程模型(pSEM)相结合,构建了 RR-pSEM 和 GWRR-pSEM 模型。结果表明,中亚的 SA 在 2001 至 2010 年间有所增加,在 2011 至 2020 年间有所减少,预计未来还会下降。SA与各因子之间存在很强的相关性和显著的因果关系。雪盖率被认为是影响 SA 的最关键因素。与极端气候指数相比,平均气温和降水量对 SA 的影响更大,气温每升高 1 ℃,SA 就会减少 0.004。这项研究加深了人们对气候变化下 SA 变化的理解,并为分析具有多重共线性的复杂系统提供了方法框架。所提出的模型为研究地球系统科学中相互关联的因素提供了宝贵的工具。
Spatiotemporal variations of surface albedo in Central Asia and its influencing factors and confirmatory path analysis during the 21st century
Surface albedo (SA) is crucial for understanding land surface processes and climate simulation. This study analyzed SA changes and its influencing factors in Central Asia from 2001 to 2020, with projections 2025 to 2100. Factors analyzed included snow cover fraction, fractional vegetation cover, soil moisture, average state climate indices (temperature and precipitation), and extreme climate indices (heatwave indices and extreme precipitation indices). Pearson correlation coefficient, geographical convergent cross mapping, and geographical detector were used to quantify the correlation, causal relationship strength, and impact degree between SA and the influencing factors. To address multicollinearity, ridge regression (RR), geographically weighted ridge regression (GWRR), and piecewise structural equation modeling (pSEM) were combined to construct RR-pSEM and GWRR-pSEM models. Results indicated that SA in Central Asia increased from 2001 to 2010 and decreased from 2011 to 2020, with a projected future decline. There is a strong correlation and significant causality between SA and each factor. Snow cover fraction was identified as the most critical factor influencing SA. Average temperature and precipitation had a greater impact on SA than extreme climate indices, with a 1 °C temperature increase corresponding to a 0.004 decrease in SA. This study enhances understanding of SA changes under climate change, and provides a methodological framework for analyzing complex systems with multicollinearity. The proposed models offer valuable tools for studying interrelated factors in Earth system science.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.