Bamboo forest is a unique forest landscape that is mainly composed of herbal plants. It has a stronger capability to increase terrestrial carbon sinks than woody forests in the same environment, thus playing a special role in absorbing atmospheric CO₂. Accurate and timely bamboo forest maps are necessary to better understand and quantify their contribution to the carbon and hydrological cycles. Previous studies have reported that the unique phenology pattern of bamboo forests, i.e., the on- and off-year cycle, can be detected with time-series high spatial resolution remote sensing (RS) images. Nevertheless, this information has not yet been applied in large-scale bamboo mapping. In this study, we innovatively incorporate newly designed phenology features reflecting the aforementioned on- and off-year cycles into a typical end-to-end classification workflow, including two features describing growing efficiency during the green-up season and two features describing the difference between annual peak greenness. Additionally, two horizonal morphology features and one tree height feature were employed, simultaneously. An experiment in southeast China was carried out to test the method’s performance, in which seven categories were focused. A total of 987 field samples were used for training and validation (70% and 30%, respectively). The results show that combining the time-series features based on spectral bands and vegetation indices and newly designed phenology and morphology patterns can differentiate bamboo forests from other vegetation categories. Based on these features, the classification results exhibit a reasonable spatial distribution and a satisfactory overall accuracy (0.89). The detected bamboo area proportion in 82 counties agrees with the statistics from China’s Third National Land Survey, which was produced based on high resolution images from commercial satellites and human interpretation (correlation coefficient = 0.69, and root mean squared error = 5.1%). This study demonstrates that the new scheme incorporating phenology features helps to map bamboo forests accurately while reducing the sample size requirement.

China announced it will achieve a carbon emission peak by 2030 and carbon neutrality by 2060 to fulfill its international obligations and mitigate climate risk. Related activities and polices were introduced in several sectors before this announcement. The performance and outcome of these activities provide necessary a priori knowledge for the designation and optimization of future policies. In this study, a comprehensive evaluation covering major sectors based on multisource data is proposed. The results show that although China is the largest CO₂ emitter for the current stage, the increasing rate of carbon emissions has been significantly mitigated since the 2010s. This reduction in emissions can be accelerated by the carbon-trading scheme in pilot regions. As a substitution for fossil energy, there have been tens of thousands of increases in wind turbines and photovoltaic plants in the past decade. Additionally, an enhancement of the terrestrial carbon sink was detected by time-series remote sensing data. The results of this study demonstrate that China’s carbon activities in the past decade have received reasonable outcomes, which will benefit the optimization of related government policies. The improved legislation and policies of China can strengthen the regulation of emissions while promoting the quantity and quality of carbon sinks. At the same time, the improvement of the carbon emissions trading mechanism, especially the establishment of a marketing regulation mechanism, can significantly motivate interest-related communities and industries to abort high-carbon emissions and ensure the implementation of carbon neutrality in the future.