Precipitation on the Qinghai–Tibet Plateau (TP) in southwestern China is subject to interactions between the complex and variable terrain and the sensitive climate. The regional climate is mainly affected by three circulations: westerlies, the South Asian monsoon, and the East Asian monsoon. Spatial and temporal variations in the rainy season onset were characterised based on daily precipitation from 106 meteorological stations on the TP from 1971 to 2015. Using the Theil–Sen Median trend analysis, Mann–Kendall test and mutation detection, the characteristics and reasons for the variations during the rainy season over the plateau over the past 45 years were investigated. The following results were obtained from the analysis: (1) There were obvious regional differences in the rainy season onset over the TP, and the rainy season began on the southeastern plateau and moved northwestward. (2) The TP rainy season underwent a significant mutation in approximately 1997, and following this mutation, the area affected by the delayed rainy season increased. (3) Against the background of global warming, the rainy season trend over the TP was advanced; however, there were still several multiple contiguous concentrated areas on the plateau. (4) Before the rainy season mutation, there were two centres of delayed precipitation on the plateau, which existed primarily due to their location at the end of the plateau water vapour transport channel. After the mutation, the number of delayed precipitation centres on the plateau increased to three and presented a spatially expanding trend, which may be related to the weakening trend in atmospheric circulation.

Long-term precipitation estimates with both finer spatial resolution and better quality are vital and highly needed in various related fields. Numerous downscaling algorithms have been investigated based on the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), to obtain precipitation data with finer resolution (~1 km). However, this research was restricted by the time span of the TMPA dataset, as the starting time of TMPA was 1998. In this study, a new methodological framework incorporating wavelet coherence and Cubist was proposed to retrospectively obtain downscaled precipitation estimates (DS) over the Tibetan Plateau (TP), based on TMPA and ground observations, in 1990s. The correlations and similarities of precipitation patterns between the target years, from 1990 to 1999, and reference years, from 2000 to 2013, were firstly determined using wavelet coherence based on ground observations. Following this, the TMPA data in the reference years were regarded as the reference in the corresponding target years, which were adopted to be downscaled using Cubist models and land surface variables, to obtain the DS in the target years. We found that the DS showed continuous trends, which corresponded well with the ground observations. Additionally, the performances of the DS were better than those of the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data over the TP. Therefore, this methodological framework has great potential for obtaining precipitation estimates for the period of the 1990s for which TMPA data is inaccessible.

The growth of urban areas and the impact of urban ecosystems on public health and urban sustainability have been leading issues of both academic and public interest. More than 100 million Chinese people suffer from pollen allergy. Pollen allergy induces bronchitis, bronchial asthma, pulmonary heart disease, and may even be life-threatening. Thus, pollen allergies greatly affect the daily life of individuals. High-resolution WorldView-2 remote sensing data and vegetation distribution features were used to detect the dominant tree species in the study area and obtain canopy distribution information on different tree species. In this way, the sources of pollen from different tree species were identified. Using remote sensing technology, the overall accuracy of tree species recognition for the study area exceeds 86%. Most plants in the study area flower during the spring, so the pollen allergenic risk zones are extensive during spring and sparse in fall. Based on the spatial ranges of pollen dispersal during different seasons, areas were identified that might contain pollen concentrations detrimental to pollen-sensitive individuals.

Accurate monitoring of leaf phenology, from individual trees to entire ecosystems, is vital for understanding and modeling forest carbon and water cycles, as well as assessing climate change impact. However, the accuracy of many remote-sensing phenological products remains difficult to directly corroborate using ground-based monitoring, owing to variations in the observed indicators and the scales used. This limitation hampers the practical implementation of remote-sensing phenological metrics. In our study, the start of growing season (SOS) from 2016 to 2021 was estimated for the continental USA using Sentinel-2 images. The results were then matched with several ground-based spring vegetation phenology metrics obtained by the USA National Phenology Network (USA-NPN). In this study, we focused on the relationships between the leaf-unfolding degree (LUD), the SOS, and the factors that drive these measures. Our results revealed that: (1) the ground-based leaves and increasing leaf size stages were significantly correlated with the SOS; (2) with the closest match being observed for a leaf spread of 13%; (2) the relationship between the SOS and LUD varied according to the species and ecoregion, and the pre-season cumulative radiation was found to be the main factor affecting the degree of matching between the ground observations and the metrics derived from the Sentinel-2 data. Our investigations provide a ground-based spring phenology metric that can be used to verify or evaluate remote-sensing spring phenology products and will help to improve the accuracy of remote-sensing phenology metrics.