全球500米7天植被冠层叶绿素含量产品(GVCCC)

全球500米7天植被冠层叶绿素含量产品（GVCCC）是基于ENVISAT MERIS（2003-2012）和Sentinel-3 OLCI（2018-2020）遥感反射率数据生成的关键植被生物物理参数数据集。该产品空间分辨率为500米，时间分辨率为7天，覆盖全球范围，采用MODIS正弦瓦片组织，以tif压缩格式存储。通过融合MERIS陆地叶绿素指数（MTCI）、植被类型及聚集指数等多源数据，结合4SCALE和PROSAIL辐射传输模型模拟，并利用机器学习混合反演框架，实现了农业、森林、草地等植被类型冠层叶绿素含量的高精度估算。该数据集对监测植被生理状态、评估生产力及生态系统健康具有重要科学价值。

The Global 500m 7-day Vegetation Canopy Chlorophyll Content Product (GVCCC) is a key vegetation biophysical parameter dataset generated based on remote sensing reflectance data from ENVISAT MERIS (2003–2012) and Sentinel-3 OLCI (2018–2020). This product has a spatial resolution of 500 meters and a temporal resolution of 7 days, covers the entire globe, is organized using MODIS sinusoidal tiles, and is stored in compressed TIFF format. By integrating multi-source data including MERIS Terrestrial Chlorophyll Index (MTCI), vegetation types and clumping index, combining simulations from the 4SCALE and PROSAIL radiative transfer models, and leveraging a hybrid machine learning inversion framework, this product achieves high-precision estimation of canopy chlorophyll content for various vegetation types such as croplands, forests and grasslands. This dataset holds significant scientific value for monitoring vegetation physiological status, assessing ecosystem productivity and evaluating ecosystem health.

搜集汇总

背景与挑战

背景概述

全球500米7天植被冠层叶绿素含量产品(GVCCC)是一个基于ENVISAT MERIS和Sentinel-3 OLCI遥感数据的全球植被参数数据集，具有500米空间分辨率和7天时间分辨率。该产品通过整合多源遥感数据和辐射传输模型，结合机器学习反演框架，实现了对农业、森林和草地植被冠层叶绿素含量的高精度估计，适用于监测植被生理状态和生态系统健康。