A long-term high-resolution dataset of grasslands grazing intensity in China.

Grazing is a significant anthropogenic disturbance to grasslands, impacting their function and composition, and affecting carbon budgets and greenhouse gas emissions. However, accurate evaluations of grazing impacts are limited by the absence of long-term high-resolution grazing intensity data (i.e.

A leaf age-dependent light use efficiency model for remote sensing the gross primary productivity seasonality over pantropical evergreen broadleaved forests.

Tropical and subtropical evergreen broadleaved forests (TEFs) contribute more than one-third of terrestrial gross primary productivity (GPP). However, the continental-scale leaf phenology-photosynthesis nexus over TEFs is still poorly understood to date. This knowledge gap hinders most light use efficiency (LUE) models from accurately simulating the GPP seasonality in TEFs.

The carbon budget of China: 1980-2021.

As one of the world's largest emitters of greenhouse gases, China has set itself the ambitious goal of achieving carbon peaking and carbon neutrality. Therefore, it is crucial to quantify the magnitude and trend of sources and sinks of atmospheric carbon dioxide (CO), and to monitor China's progress toward these goals. Using state-of-the-art datasets and models, this study comprehensively estimated the anthropogenic CO emissions from energy, industrial processes and product use, and waste along with natural sources and sinks of CO for all of China during 1980-2021.

[Spatio-temporal Evolution of PM Concentration During 2000-2019 in China].

An ensemble estimation model of PM concentration was proposed on the basis of extreme gradient boosting, gradient boosting, random forest model, and stacking model fusion technology. Measured PM data, MERRA-2 AOD and PM reanalysis data, meteorological parameters, and night light data sets were used. On this basis, the spatiotemporal evolution features of PM concentration in China during 2000-2019 were analyzed at monthly, seasonal, and annual temporal scales.

[PM Concentration Influencing Factors in China Based on the Random Forest Model].

In this paper, aerosol optical depth (AOD), elevation (DEM), annual precipitation (PRE), annual average temperature (TEM), annual average wind speed (WS), population density (POP), gross domestic product density (GDP), and normalized difference vegetation index (NDVI) were selected as factors influencing PM concentration. The random forest model, order of feature importance, and partial dependency plots were applied to investigate these factors and their regional differences in PM spatial pattern. The results showed that:① The random forest model was more accurate than multiple regression, generalized additive, and back propagation neural network models in estimating PM concentration, which can be applied to quantifying PM influencing factors.