Satellite remote sensing is an important method for forest phenological studies at continental or global scales. Sentinel-1 (S1), a polar orbit satellite with a spatial resolution of 10 m, provides an opportunity to observe high-resolution forest phenology. The sensitivities of S1 C-band backscatter measurements to vegetation phenology, such as crops, meadows, and mixed forests, have been discussed, whereas their performance for different forest types has not yet been quantitatively assessed. It is necessary to evaluate accuracy before adapting S1 datasets in forest phenological studies. This study discusses the seasonal variations in S1 backscatter measurements and assesses the accuracy of S1-based forest phenological metrics in two types of typical forests: deciduous and coniferous. S1 C-band SAR dual-polarization backscatter measurements for the period 2017–2019 were used to extract forest phenology metrics using the Fourier transform (FT) and double logistic (DL) functions. Phenological metrics from the ground-based PhenoCam dataset were used for evaluation. The S1 backscatter VV-VH signal peaks for deciduous and coniferous forests occur in the winter and summer, respectively. The S1 backscatter could reasonably characterize the start of season (SOS) of deciduous forests, with R² values up to 0.8, whereas the R² values for coniferous forest SOS were less than 0.30. Moreover, the retrieved end of season (EOS) was less accurate than the SOS. The differences in accuracy of S1 backscatter phenological metrics between deciduous and coniferous forests can be explained by the differences in seasonal changes in their corresponding canopy structures. To conclude, S1 C-band backscatter has a reasonable performance when monitoring the SOS of deciduous broadleaf forests (R² = 0.8) and relatively poor performance when extracting EOS of deciduous broadleaf forests (R² = 0.25) or phenology of evergreen needleleaf forests (R² = 0.2).

A significant part of clouds in the tropics appears over the tropopause due to intense convections and in situ condensation activity. These tropical tropopause layer (TTL) clouds not only play an important role in the radiation budget over the tropics, but also in water vapor and other chemical material transport from the troposphere to the stratosphere. This study quantifies and analyzes the properties of TTL clouds based on spaceborne active observations, which provide one of the most reliable sources of information on cloud vertical distributions. We use four years (2007–2010) of observations from the joint Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat and consider all cloudy pixels with top height above the tropopause as TTL clouds. The occurrence frequency of TTL clouds during the nighttime is found to be almost 13% and can reach ~50–60% in areas with frequent convections. The annual averages of tropical tropopause height, tropopause temperature, and cloud top height are 16.2 km, −80.7 °C, and 16.6 km, respectively, and the average cloud top exceeds tropopause by approximately 500 m. More importantly, the presence of TTL clouds causes tropopause temperature to be ~3–4 °C colder than in the all-sky condition. It also lifts the tropopause heights ~160 m during the nighttime and lowers the heights ~84 m during the daytime. From a cloud type aspect, ~91% and ~4% of the TTL clouds are high clouds and altostratus, and only ~5% of them are associated with convections (i.e., nimbostratus and deep convective clouds). Approximately 30% of the TTL clouds are single-layer clouds, and multi-layer clouds are dominated by those with 2–3 separated layers.