The 10m-resolution global leaf chlorophyll content product using Sentinel-2 based on chlorophyll sensitive index CSI

Abstract. Leaf chlorophyll content (LCC) is an essential biochemical parameter reflecting vegetation's photosynthetic activity. In the past five years, some global LCC remote sensing products have been generated, and play an important role in vegetation growth monitoring and terrestrial carbon cycle modeling. However, the resolution of current global LCC products ranges from 300 m to 500 m, and the existing 30m-resolution product, Multi-source data Synergized Quantitative remote sensing production system LCC (MuSyQ LCC), is only available in China, resulting in a lack of global high-resolution LCC products. This study used an empirical relationship method based on the chlorophyll sensitive index (CSI) to produce a 10 m resolution global LCC product (MuSyQ Global LCC) with the Google Earth Engine (GEE) platform. A web application was developed, allowing users to independently select regions of interest, time ranges, and spatial-temporal resolutions. The validation results show the MuSyQ Global LCC consists well with the current global MODIS LCC, and MuSyQ Global LCC’s (RMSE = 14.16 μg/cm², bias = 1.68 μg/cm²) accuracy is slightly higher than that of MODIS LCC (RMSE = 14.74 μg/cm², bias = -2.65 μg/cm²). The 10m-resolution LCC product has an RMSE of 15.33 μg/cm², R² of 0.27, and the accuracy of the vegetation types-specific regression model is stable in different sites across the world. The high-resolution LCC product can show more details of spatial distribution and reasonable temporal profiles than the existing low-resolution product, indicating its ability in precision agriculture, forestry monitoring, and related research.