Quantifying urban vegetation carbon storage using seamless and dense time-series remote sensing data

Urban vegetation is a critical component of the terrestrial ecosystem, and accurately calculating its carbon storage is essential, particularly in the context of carbon accounting. Net Primary Productivity (NPP) is a key indicator of the surface carbon cycle, reflecting the health and robustness of terrestrial ecosystems and serving as a fundamental measure for carbon storage. However, most existing studies rely on medium- to low-resolution data and annual-scale temporal resolution for NPP estimation. These approaches often fail to capture the dynamic nature of vegetation growth, especially during the growing season, due to limitations like cloud cover, snow, transmission errors, and data coverage gaps. The challenge of capturing vegetation growth dynamics is particularly pronounced in urban areas, where surface heterogeneity and vegetation fragmentation complicate accurate NPP estimation. In this study, we focus on the Pearl River Delta region and address these data limitations by reconstructing high-frequency time series Normalized Difference Vegetation Index (NDVI) using remote sensing image fusion based on MODIS and Landsat. NPP estimation is conducted using the improved Carnegie-Ames-Stanford Approach model, which utilizes detailed land cover classification, time series NDVI, and climate data to achieve high spatial and temporal resolution results. Our results demonstrate a strong correlation between the NPP estimates derived from NDVI reconstruction data and the MOD17A3-NPP product. According to research findings from 2017 to 2020, the total vegetation NPP in the PRD decreased from 1731.9 GgC/y to 1500.2 GgC/y. The 30 m monthly NPP time series provides a more accurate reflection of vegetation growth dynamics. These high-resolution NPP products are crucial for precise carbon storage estimation, enhancing our understanding of urban ecosystems, and offering a detailed metric for urban carbon accounting.