The vulnerability of vegetation ecosystems and hydrological systems in high-altitude areas makes their phenology more sensitive and their response to climate change more intense. The Qilian Mountains, an important geographic unit located in the northeastern Tibetan Plateau (TP), has experienced the more significant increases in temperature and precipitation in the past few decades than most areas of the TP. However, under such intense climate change, the temporal and spatial differences in phenology in the Qilian Mountains are not clear. This study explored the spatial and temporal heterogeneity of phenology in the Qilian Mountains from 1982 to 2014 and its response to three temperature indicators, including the mean daily temperature (T_mean), mean daily daytime temperature (T_max), and mean daily nighttime temperature (T_min). The results showed that (1) as the altitude rose from southeast to northwest, the multiyear mean of the start of the growing season (SOS) was gradually delayed mainly from 120 to 190 days, the multiyear mean of the end of the growing season (EOS) as a whole was advanced (from 290 to 260 days), and the multiyear mean of the length of the growing season (LGS) was gradually shortened (from 150 to 80 days). (2) In general, there was an advanced trend in the annual average SOS (0.2 days per decade), a delayed trend in the annual average EOS (0.15 days per decade), and an extended trend in the annual average LGS (0.36 days per decade) over the study period. However, there has been no significant phenological trend in recent years, especially for the SOS after 2000 and the EOS and LGS after 2003. (3) Higher preseason temperatures led to an advanced SOS and a delayed EOS at the regional scale. Moreover, the SOS and EOS were more triggered by T_max than T_min and T_mean. The LGS was significantly positively correlated with annual mean temperature (r = −0.82, p < 0.01).

Solar power generation is an effective way to reduce carbon emissions and has a wide range of applications worldwide. China's newly installed photovoltaic capacity has ranked first in the world in recent years. Timely and accurate monitoring of the spatiotemporal distribution characteristics of solar power plants is essential to optimize China's renewable energy power distribution and achieve carbon reduction targets. However, long-term solar panel (SP) datasets are still lacking. Based on the Google Earth Engine platform, this study proposed a fine extraction method framework of SPs in large and complex geographical environments by integrating stratified sampling and zonal modeling and obtained the first comprehensive dataset of SP distribution in China covering 2000 to 2022 to fill the gap in relevant research and practice. It has been verified that the F1-score of the SP datasets was higher than 0.87, which could meet the research needs. On this basis, this study revealed the spatiotemporal variation and development trends of SPs in China over the past 20 years. The proposed method framework can serve as a scientific reference for relevant research, while the interpreted SP datasets and analysis results can provide a basis for regional decision-making and related research in the context of future climate change.