全球高分辨率日光诱导叶绿素荧光（SIF）数据集（2001-2021）

全球高分辨率日光诱导叶绿素荧光（SIF）数据是由Li and Xiao（2019）提出的算法生产。基于原始的OCO-2 SIF数据、MODIS植被数据（EVI）、再分析气象资料（包括饱和水汽压VPD、空气温度、有效光合辐射）以及土地覆盖数据，利用机器学习算法得到全球连续、8天、0.05°以及长时间（2001-2021年）的SIF数据集。使用平均值合成算法合成全球连续、月度及年度、0.05°以及长时间（2001-2021年）的SIF数据集。所有数据均从http://globalecology.unh.edu/data/GOSIF.html获取。
本数据集共包括3个时间周期的产品：8天、月度和年度。空间分辨率为0.05°，时间覆盖范围为2001-2021年。

The global high-resolution solar-induced chlorophyll fluorescence (SIF) dataset was produced using the algorithm proposed by Li and Xiao (2019). Specifically, based on raw OCO-2 SIF data, MODIS vegetation data (Enhanced Vegetation Index, EVI), reanalysis meteorological data including vapor pressure deficit (VPD), air temperature, and photosynthetically active radiation, as well as land cover data, a global continuous SIF dataset with 8-day temporal resolution, 0.05° spatial resolution, and covering the period 2001–2021 was generated via machine learning algorithms. Furthermore, global continuous SIF datasets with monthly and annual temporal resolutions, 0.05° spatial resolution, and spanning 2001–2021 were synthesized using the average compositing algorithm. All data are accessible at http://globalecology.unh.edu/data/GOSIF.html.

This dataset includes three product variants differentiated by temporal resolution: 8-day, monthly, and annual. The spatial resolution is uniformly 0.05°, and the full temporal coverage ranges from 2001 to 2021.

搜集汇总

背景与挑战

背景概述

该数据集是全球高分辨率日光诱导叶绿素荧光（SIF）数据，覆盖2001-2021年，空间分辨率为0.05°，提供8天、月度和年度三种时间周期产品。它基于机器学习算法融合多源数据（如OCO-2 SIF和MODIS植被数据）生成，用于监测全球植被光合作用活动，具有长时间序列和高空间连续性的特点。