[H-TT15] Environmental Remote Sensing

[HTT15-P05] Evaluation of land surface phenology from GCOM-C SGLI using PhenoCam and Phenological Eyes Network

*Mengyu Li¹, Wei Yang², Akihiko Kondoh² (1.Graduate School of Science and Engineering, Chiba University, , 2.Center for Environmental Remote Sensing, Chiba University)

Keywords:Vegetation phenology, GCOM-C SGLI, Phenological networks

Vegetation phenology refers to the seasonal timing of plant activity through dormancy, active growth, senescence, and return to dormancy. Phenology is an important functional attribute of terrestrial ecosystems and a highly sensitive indicator of ecosystem response to climate change. Vegetation phenology records at the land surface have long been available from satellite sensors at various spatial resolutions (e.g., 3 m to 8 km), with the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) providing valuable data sources for monitoring land surface phenology on a global scale. The satellites on the GCOM-C Second Generation Global Imager (SGLI) have a spatial resolution of 250 m, providing new opportunities to expand the global phenology record. This study is the first to generate and evaluate the estimated phenology transition dates from GCOM-C SGLI data. First, we compared the SGLI NDGI (Normalized Difference Greenness Index) and near-surface GCC (Green Color Coordinate) greenness trajectories at five different vegetation type on phenological observation sites. The results showed that the greenness trajectories were very similar between SGLI NDGI and near-surface GCC. In addition, we demonstrate again that NDGI can overcome the bias due to the effect of snow during the spring snowmelt. Using near-surface phenology observations, the SGLI NDGI estimated phenology transition dates were well validated (RMSE < 16.3 days, R2 ≈ 0.7, Bias < 1 day), with better agreement than MODIS and VIIRS phenology products. Overall, the results of this study suggest that SGLI could provide a good opportunity for global surface weather monitoring in the coming decades.