Speaker
Description
Perennial rice is a rice cultivar that can regenerate from retained stubble in several seasons after first harvest. This distinctive post-harvest regrowth pattern presents new challenges for satellite-based phenological monitoring. Optical remote sensing is effective for characterizing canopy greenness, chlorophyll-related variation, and seasonal growth trajectories; however, its ability to resolve short-lived regrowth may be constrained by cloud contamination, residual stubble, and varying field-water conditions. In contrast, SAR observations are largely unaffected by cloud cover and provide complementary information on canopy structure, vegetation water content, and plant–ground scattering interactions. Using annual rice as a reference, this study utilizes near-surface camera, PlanetScope, and Sentinel-1 time-series data to compare the phenological patterns and multisource remote-sensing responses of perennial and annual rice.
Using perennial rice experimental plots in Xishuangbanna, Yunnan, China, monitored from 2024 to 2026 as a case study, the following workflow was implemented. First, PlanetScope optical vegetation indices (EVI and NDRE) and Sentinel-1 radar indices, including VV and VH, were calculated, while high-frequency GCC time series from near-surface camera observations were used as fine-scale reference data. Second, a Bayesian harmonic model was applied to reconstruct each time series while quantifying parameter uncertainty. Third, key phenological events, including green-up/transplanting, peak canopy development, and harvest, were extracted on all indices and evaluated against near-surface GCC to identify the most reliable indicator for characterizing rice phenology. Finally, the selected indicator was used to compare phenological timing, growth dynamics, and post-harvest regrowth between perennial and annual rice across different growth years.
Our results indicate that optical indices can reliably characterize canopy greenness and seasonal growth dynamics, and are particularly effective for detecting canopy establishment and peak development. SAR signals, however, are more sensitive to transplanting and regrowth processes that are difficult to distinguish using optical observations alone. Overall, perennial and annual rice exhibited distinct growth and regrowth processes as well as different patterns of satellite observability. These findings demonstrate that sensor-specific observations are essential for monitoring complex regrowth-based rice systems and provide a basis for identifying the most suitable data source for different phenological events.