Divergent response of vegetation phenology to extreme temperatures and precipitation of different intensities on the Tibetan Plateau - Sun, Mai; Li, Peng; Ren, Peixin; Tang, Jiayi; Zhang, Cicheng; Zhou, Xiaolu; Peng, Changhui (Science China Earth Sciences, 2023)

Satellite monitoring reveals short-term cumulative and time-lag effect of drought and heat on autumn photosynthetic phenology in subtropical vegetation - Ren, Peixin; Li, Peng; Tang, Jiayi; Li, Tong; Liu, Zelin; Zhou, Xiaolu; Peng, Changhui (Environmental Research, 2023)

Grazing alters vegetation phenology by regulating regional environmental factors on the Tibetan Plateau - Wang, Longjun; Li, Peng; Li, Tong; Zhou, Xiaolu; Liu, Zelin; Zou, Ziying; Zhu, Qiuan; Peng, Changhui (Agriculture, Ecosystems & Environment, 2023)

Application of climate change scenarios in the simulation of forest ecosystems: an overview - Liu, Haoyun; Li, Peng; Peng, Changhui; Liu, Cong; Zhou, Xiaolu; Deng, Zhengmiao; Zhang, Cicheng; Liu, Zelin (Environmental Reviews, 2023)
Climate change scenarios established by the Intergovernmental Panel on Climate Change have developed a significant tool for analyzing, modeling, and predicting future climate change impacts in different research fields after more than 30 years of development and refinement. In the wake of future climate change, the changes in forest structure and functions have become a frontier and focal area of global change research. This study mainly reviews and synthesizes climate change scenarios and their applications in forest ecosystem research over the past decade. These applications include changes in (1) forest structure and spatial vegetation distribution, (2) ecosystem structure, (3) ecosystem services, and (4) ecosystem stability. Although climate change scenarios are useful for predicting future climate change impacts on forest ecosystems, the accuracy of model simulations needs to be further improved. Based on existing studies, climate change scenarios are used in future simulation applications to construct a biomonitoring network platform integrating observations and predictions for better conservation of species diversity.

Research progress, challenges, and prospects of PM2.5 concentration estimation using satellite data - Zhu, Shoutao; Tang, Jiayi; Zhou, Xiaolu; Li, Peng; Liu, Zelin; Zhang, Cicheng; Zou, Ziying; Li, Tong; Peng, Changhui (Environmental Reviews, 2023)
Satellite data are vital for understanding the large-scale spatial distribution of particulate matter (PM 2.5 ) due to their low cost, wide coverage, and all-weather capability. Estimation of PM 2.5 using satellite aerosol optical depth (AOD) products is a popular method. In this paper, we review the PM 2.5 estimation process based on satellite AOD data in terms of data sources (i.e., inversion algorithms, data sets, and interpolation methods), estimation models (i.e., statistical regression, chemical transport models, machine learning, and combinatorial analysis), and modeling validation (i.e., four types of cross-validation (CV) methods). We found that the accuracy of time-based CV is lower than others. We found significant differences in modeling accuracy between different seasons ( p < 0.01) and different spatial resolutions ( p < 0.01). We explain these phenomena in this article. Finally, we summarize the research process, present challenges, and future directions in this field. We opine that low-cost mobile devices combined with transfer learning or hybrid modeling offer research opportunities in areas with limited PM 2.5 monitoring stations and historical PM 2.5 estimation. These methods can be a good choice for air pollution estimation in developing countries. The purpose of this study is to provide a basic framework for future researchers to conduct relevant research, enabling them to understand current research progress and future research directions.

rTRIPLEXCWFlux: An R package for carbon–water coupling model to simulate net ecosystem productivity and evapotranspiration in forests - Sun, Shulan; Ouyang, Shuai; Hu, Yanting; Zhao, Zhonghui; Liu, Meixian; Chen, Liang; Zeng, Yelin; Peng, Changhui; Zhou, Xiaolu; Xiang, Wenhua (Environmental Modelling & Software, 2023)

Developing allometric equations to estimate forest biomass for tree species categories based on phylogenetic relationships - Yang, Mingxia; Zhou, Xiaolu; Peng, Changhui; Li, Tong; Chen, Kexin; Liu, Zelin; Li, Peng; Zhang, Cicheng; Tang, Jiayi; Zou, Ziying (Forest Ecosystems, 2023)

Vegetation browning: global drivers, impacts, and feedbacks - Liu, Qiuyu; Peng, Changhui; Schneider, Robert; Cyr, Dominic; Liu, Zelin; Zhou, Xiaolu; Du, Mingxi; Li, Peng; Jiang, Zihan; McDowell, Nate G.; Kneeshaw, Daniel (Trends in Plant Science, 2023)