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Shuangcheng Li

Researcher at Peking University

Publications -  70
Citations -  3125

Shuangcheng Li is an academic researcher from Peking University. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 22, co-authored 47 publications receiving 1665 citations.

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Local cooling and warming effects of forests based on satellite observations

TL;DR: New evidence acquired from global satellite data to analyse the biophysical effects of forests on local climate is presented and it is shown that tropical forests have a strong cooling effect throughout the year; temperate forests show moderate cooling in summer and moderate warming in winter with net cooling annually.
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Soil moisture dominates dryness stress on ecosystem production globally.

TL;DR: Satellite observations of solar-induced fluorescence with estimates of SM and VPD show that SM is the dominant driver of dryness stress on ecosystem production across more than 70% of vegetated land areas with valid data, and it is found that SM stress is strongest in semi-arid ecosystems.
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Detecting spatially non-stationary and scale-dependent relationships between urban landscape fragmentation and related factors using Geographically Weighted Regression

TL;DR: Wang et al. as discussed by the authors used geographically weighted regression (GWR) with a case study in Shenzhen City, Guangdong Province, China, to examine spatially varying and scale-dependent relationships between effective mesh size, an indicator of landscape fragmentation, and related factors.
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Spatial and Temporal Patterns of Global NDVI Trends: Correlations with Climate and Human Factors

TL;DR: In this article, the impact of climate variables and human activity on the observed NDVI trends is analyzed, and significant positive effects are found in Asia, Africa, and Europe, suggesting that intensive human activity could accelerate the change in NDVI and vegetation.
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Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression

TL;DR: The results indicate that the GWR model not only provides a better fit than the traditional OLS model, but also provides local detailed information about the spatial variation of LST, which is affected by geographical and ecological factors.