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Identifying determinants of disparities in soil moisture of NH using heterogeneity model

Soil moisture is a fundamental ecological component for climate and hydrological studies. However, the distribution patterns of soil moisture are spatially heterogenous and influenced by multiple environmental factors. The knowledge is still limited in assessing the large-scale spatial heterogeneity of soil moisture in in situ data modelling, in situ network design, spatial down-scaling, and remote sensing-based soil moisture retrieval. Heterogeneity models are effective in characterizing spatial disparities, but they are not capable of examining the maximum regional disparities. To address this bottleneck, the authors of this study developed a geographically optimal zones-based heterogeneity (GOZH) model. By progressively optimizing geographical zones of soil moisture and quantifying the heterogeneity among zones, GOZH may help identify individual and interactive determinants of soil moisture across a large study area. It was applied to identify spatial determinants of in situ soil moisture data collected at 653 monitoring stations in the Northern Hemisphere in unfrozen and frozen seasons from April 2015 to December 2017, with only thawed data considered in both seasons. Correspondingly, a series of variables were derived from Google Earth Engine (GEE) remote sensing data. The results demonstrated the significant regional disparities of soil moisture, and the combinations of determinants are critically different among geographical zones and between unfrozen and frozen seasons. At a global scale, the combinations of determinants can explain about 48% of the spatial pattern of soil moisture. Spatial heterogeneity of soil moisture in frozen seasons is much more complex than that in unfrozen seasons regarding geographical zones and explanatory variables. The variability of soil moisture during unfrozen seasons can be more explainable than that during frozen seasons, which was a convincing evidence for previous studies that soil moisture predictions were mostly performed during unfrozen seasons. Primary variables that determine spatial patterns of soil moisture are changed from climate variables during the unfrozen season to geographical variables during the frozen season. Results show that GOZH model can effectively explore spatial determinants of soil moisture through avoiding the underestimation of individual variables, overestimation of multiple variables, and finely divide zones. The research findings from this study provide an in-depth understanding of the spatial heterogeneity of soil moisture and can be implemented in more effective in situ sampling network design, spatial down-scaling of soil moisture, and accurate inversion of surface parameters from the satellite data of soil moisture.

【会议通知】第三届中国空间数据智能学术会议SpatialDI 2022 (二号通知)

ACM中国空间分会致力于推动空间数据的研究范式及空间智能理论与技术在时空大数据、智慧城市、交通科学、社会治理等领域的创新与应用。为进一步促进空间数据智能研究的理论发展与应用,交流相关领域的新理论、新问题、新方法,ACM中国空间分会将于2022年4月14-16日在武汉举办第三届中国空间数据智能学术会议

新城古韵载文脉,街景感知渝人知——重庆市街区改造前后的情感感知和文化探究(视频)

研究预览温馨提示:如果发生网页加载错误错误,请直接点击链接查看:点此进入视频演示温馨提示:如果发生视频镜像错误,请直接进入bilibili查看:点此进入1. 设计思想作为全球城镇化的热点区域,截至到2020年,中国已有60.6%的人口居住在城市地区,而在未来十年城市仍将吸纳约2亿人口。随着城市现代化

UrbanComp

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