VODCA2GPP

The data descriptor paper can be accessed here: https://doi.org/10.5194/essd-14-1063-2022
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Gross Primary Productivity (GPP) describes the amount of carbohydrates that is produced by vegetation's synthesis of CO2 and is therefore crucial in the assessment of the global carbon cycle. VODCA2GPP represents the first microwave remote sensing derived GPP dataset and covers the period between 1988-2020. The data is sampled on a regular 0.25°x 0.25° grid and is based on the novel sink-driven GPP estimation approach introduced by Teubner et al. (2019) and Teubner et al. (2021). It utilizes the new merged-frequency Vegetation Optical Depth Climate Archive (VODCA CXKu; Zotta et al., in preparation) in combination with ERA5-Land air temperature data to produce a coherent long-term data record of global GPP. 
The dataset also includes an uncertainty metric (var_name: 'Uncertainties') which indicates regions where VODCA2GPP estimates tend to be less robust. We advise users to take these uncertainties into account when analyzing the VODCA2GPP data. 
For more details concerning the production of VODCA2GPP and its accuracy assessment please be referred to our dataset paper which was published in Earth System Science Data Wild et al. (2022).

本数据集的描述性论文可通过以下链接获取：https://doi.org/10.5194/essd-14-1063-2022

总初级生产力（Gross Primary Productivity, GPP）指植被通过二氧化碳合成作用产生的碳水化合物总量，因此在全球碳循环评估中具有关键意义。VODCA2GPP是首个基于微波遥感反演的GPP数据集，时间覆盖范围为1988年至2020年。该数据集以0.25°×0.25°的规则网格进行采样，其方法基于Teubner等人（2019）与Teubner等人（2021）提出的新型汇驱动型GPP估算框架。本数据集结合了全新的多频段融合植被光学深度气候数据集（Vegetation Optical Depth Climate Archive, VODCA CXKu；Zotta等，待刊）与ERA5-Land地表气温数据，以此生成连贯一致的长期全球总初级生产力数据序列。

该数据集还包含一项不确定性指标（变量名：'Uncertainties'），用于标注VODCA2GPP反演结果稳健性较弱的区域。我们建议用户在分析VODCA2GPP数据时，充分考虑这些不确定性因素。

若需了解VODCA2GPP的生产流程与精度评估的更多细节，请参阅发表于Earth System Science Data期刊的Wild等人（2022）的数据集论文。

搜集汇总

背景与挑战

背景概述

VODCA2GPP是全球首个基于微波遥感的总初级生产力(GPP)数据集，覆盖1988-2020年，空间分辨率为0.25°x0.25°。该数据集采用创新方法结合植被光学深度和气温数据，并提供了不确定性评估指标。