Global Sentinel-3 OLCI CCC at an 8-day interval from 2016 to 2024: Part 1

DOI: https://doi.org/10.5281/zenodo.15593487 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 8 days, covering the period from 2016 to 2024. This file is Part 1 of a five-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_YYYYDOY.nc, where YYYY is the year and DOY is the starting day-of-year for each 8-day composite period. Additional spatial reference: In Part 2, 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])DOI: https://doi.org/10.5281/zenodo.15593487 数据集描述： 本数据集提供了基于Sentinel-3 OLCI大气层顶（Top-of-atmosphere, TOA）观测数据反演得到的全球有效冠层叶绿素含量（Effective Canopy Chlorophyll Content, CCC）产品，空间分辨率为1/336度（赤道处约300米），时间分辨率为8天，覆盖2016年至2024年时段。本文件为五卷系列数据集的第一部分。 数据特征： 变量：有效冠层叶绿素含量（Effective Canopy Chlorophyll Content, CCC） 单位：g/m² 数据类型：uint8，物理值计算公式为 CCC = (single(CCC) - 1) * 0.015 无效值：所有转换后小于0的CCC值均视为无效，此类情况主要由云污染或有效观测缺失导致。 空间分辨率：1/336度（约300米） 空间范围：纬度：90°N至60°S；经度：180°W至180°E；行数：50400；列数：120960 投影：正弦投影（Sinusoidal projection，MODIS标准） 文件格式与命名规则： 每个文件采用如下格式命名：CCC_YYYYDOY.nc，其中YYYY为年份，DOY为每个8天合成时段的起始年积日。 附加空间参考： 第二部分包含文件CCC_latitude_longitude_Sinusoidal.nc，该文件存储了正弦投影网格中每个像素的中心经纬度，可用于实现所有网格单元的地理定位。 应用场景： 本数据集适用于全球尺度植被健康评估、光合建模及生态系统分析，尤其可用于估算总初级生产力（Gross Primary Production, GPP）、监测作物长势以及评估生态系统功能。 引用要求： 使用本数据集时，请引用以下文献： Li, D., Croft, H., Duveiller, G., Schreiner-McGraw, A.P., Belwalkar, A., Cheng, T., Zhu, Y., Cao, W., & Yu, K. (2025). 基于物理模型与机器学习模型融合的两步升尺度方法从Sentinel-3 OLCI TOA数据反演全球冠层叶绿素含量. 环境遥感, 328, 114845. https://doi.org/10.1016/j.rse.2025.114845 疑问与反馈： 如有疑问或反馈，请联系：Dong Li（[e-mail]）