Gross Primary Production (GPP) for China from 2001–2020 Estimated by Machine Learning Methods

Based on flux tower observations, this study comprehensively evaluated five mainstream GPP products, including MODIS (Moderate Resolution Imaging Spectroradiometer), PML-V2 (Penman-Monteith-Leuning Version 2), GOSIF (Global Orbiting Carbon Observatory-2 based Solar Induced chlorophyll Fluorescence), CEDAR (sCaling Ecosystem Dynamics with ARtifical intelligence), and TL-LUE, and quantified their uncertainties using the Bayesian Three-Cornered Hat method. On this basis, by integrating multi-source data, a high-fidelity GPP dataset was generated using five machine learning methods: Categorical Boosting, Support Vector Machine Regression, Light Gradient Boosting, Extreme Gradient Boosting, and Random Forest. Validated against data from 15 flux tower sites in mainland China, CatBoost exhibited the best performance, with the lowest RMSE and MAE and the highest R². This dataset was calculated using CatBoost.The spatial resolution is 0.05 degrees.

本研究基于通量塔观测数据，对5款主流总初级生产力（Gross Primary Productivity, GPP）产品开展了全面评估，涵盖MODIS（中分辨率成像光谱仪，Moderate Resolution Imaging Spectroradiometer）、PML-V2（Penman-Monteith-Leuning Version 2）、GOSIF（Global Orbiting Carbon Observatory-2 based Solar Induced chlorophyll Fluorescence）、CEDAR（sCaling Ecosystem Dynamics with ARtifical intelligence）以及TL-LUE，并采用贝叶斯三角帽法（Bayesian Three-Cornered Hat method）量化了各产品的不确定性。在此基础上，本研究通过融合多源数据，运用分类提升树（Categorical Boosting）、支持向量机回归（Support Vector Machine Regression）、轻量梯度提升机（Light Gradient Boosting）、极限梯度提升树（Extreme Gradient Boosting）与随机森林（Random Forest）共5种机器学习方法，生成了一套高保真总初级生产力数据集。该数据集以中国大陆地区15个通量塔站点的观测数据进行验证，结果显示CatBoost表现最优，其均方根误差（Root Mean Square Error, RMSE）、平均绝对误差（Mean Absolute Error, MAE）最低，决定系数（Coefficient of Determination, R²）最高。本数据集最终采用CatBoost方法计算得到，空间分辨率为0.05度。