ECSIF_GPP_ET

ECSIF-based product with six datasets of GPP and ET at the 0.5° spatial resolution and 8-day temporal resolutions during 2001-2020. These datasets were based on Eddy Covariance measurements, three SIF products (CSIF, GOSIF and RTSIF), meteorological forcing, carbon dioxide (CO2) and land cover data using two machine learning algorithms (XGB and RF).

本数据集为基于ECSIF的产品，包含2001年至2020年间空间分辨率0.5°、时间分辨率8天的6组总初级生产力（Gross Primary Productivity, GPP）与蒸散量（Evapotranspiration, ET）数据集。该系列数据集以涡度协方差（Eddy Covariance）观测数据为基础，结合三种日光诱导叶绿素荧光（Solar-Induced Chlorophyll Fluorescence, SIF）产品（CSIF、GOSIF与RTSIF）、气象强迫数据、二氧化碳（CO₂）数据及土地覆盖数据，通过两种机器学习算法——极端梯度提升（eXtreme Gradient Boosting, XGB）与随机森林（Random Forest, RF）构建得到。