Enhancing plant growth tracking with satellite image fusion techniques
Chinese Academy of SciencesRecent research employs spatiotemporal data fusion techniques, specifically Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Simultaneously generate Full-length normalized difference vegetation Index Time series (SSFIT) algorithms, to address cloud cover challenges in satellite imagery, significantly improving the accuracy of land surface phenology (LSP) monitoring. This advancement in detecting vegetation growth offers crucial insights for environmental and agricultural strategies, highlighting a pivotal development in phenological studies amid climate change.