Global Sentinel-3 OLCI CCC at a 10-day interval from 2016 to 2024: Part 1

DOI: https://doi.org/10.5281/zenodo.15605114 Description: This dataset provides the global Effective Canopy Chlorophyll Content (CCC) product derived from Sentinel-3 OLCI top-of-atmosphere (TOA) observations, at a spatial resolution of 1/336 degree (~300 meters at the equator) and a temporal resolution of 10 days, covering the period from 2016 to 2024. This file is Part 1 of a four-part series. Data characteristics: Variable: Effective Canopy Chlorophyll Content (CCC) Unit: g/m² Data type: uint8, with physical values calculated as CCC = (single(CCC) - 1) * 0.015 Invalid values: All converted CCC values less than 0 should be considered invalid, mainly due to cloud contamination or lack of valid observations. Spatial resolution: 1/336 degree (~300 m) Spatial extent:Latitude: 90°N to 60°SLongitude: 180°W to 180°ERows: 50400; Columns: 120960 Projection: Sinusoidal projection (MODIS standard) File format and naming: Each file is named using the format:CCC_YYYYMMDD.nc, where YYYY is the year, MM is the month, and DD is the starting day of the 10-day interval. The values of DD are 01, 11, and 21, corresponding to three sub-monthly periods. The third period (starting with 21) includes all remaining days to the end of the month. Additional spatial reference: In Part 4, the file CCC_latitude_longitude_Sinusoidal.nc is included, containing the central latitude and longitude for each pixel in the Sinusoidal grid. This reference file enables geolocation of every grid cell. Application: This dataset is suitable for global-scale vegetation health assessment, photosynthesis modeling, and ecological analysis. It is particularly useful for estimating gross primary production (GPP), monitoring crop status, and evaluating ecosystem functioning. Citation: Please cite the following publication when using this dataset: Li, D., Croft, H., Duveiller, G., Schreiner-McGraw, A.P., Belwalkar, A., Cheng, T., Zhu, Y., Cao, W., & Yu, K. (2025). Global retrieval of canopy chlorophyll content from Sentinel-3 OLCI TOA data using a two-step upscaling method integrating physical and machine learning models. Remote Sensing of Environment, 328, 114845. https://doi.org/10.1016/j.rse.2025.114845 For questions or feedback, please contact: Dong Li ([e-mail])