Mapping Photosynthesis Solely from Solar-Induced Chlorophyll Fluorescence: A Global, Fine-Resolution Dataset of Gross Primary Production Derived from OCO-2
Mapping Photosynthesis Solely from Solar-Induced Chlorophyll Fluorescence: A Global, Fine-Resolution Dataset of Gross Primary Production Derived from OCO-2
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DOI：
10.3390/rs11212563
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发表时间：
2019-11-01
期刊：
影响因子：
5
通讯作者：
Xiao, Jingfeng
中科院分区：
文献类型：
--
作者：
Li, Xing;Xiao, Jingfeng
准确地量化全球总初级生产力(GPP)对于评估植物生产力、碳平衡和碳气候反馈至关重要，而目前的GPP估计显示出很大的不确定性。轨道碳观测站2号(OCO-2)观测到的太阳诱导的叶绿素荧光(SIF)为陆地光合作用监测提供了前所未有的机会，但其稀少的覆盖范围仍然是绘制全球更高分辨率GPP的瓶颈。在这里，我们使用了基于OCO-2的全球SIF产品(GOSIF)以及SIF和GPP之间的线性关系，以0.05度的空间分辨率和8天的时间步长绘制了2000至2017年期间的全球GPP。为了解释SIF-GPP关系导致的GPP估计的不确定性，我们在站点和网格单元水平上总共使用了8种不同形式的SIF-GPP关系(普遍的和生物组特有的，有和没有截获)来估计GPP。我们的结果表明，所有八个SIF-GPP关系在估计全球GPP方面都表现良好。全球91个涡动协方差通量点的集合平均8d GPP与通量塔GPP普遍高度相关(R2=0.74，均方根误差=1.92gCm(-2)d(-1))。我们的精细分辨率GPP估计显示了全球合理的空间和季节变化，并完全捕捉到了基于离散OCO-2探测直接汇总的粗分辨率SIF数据的粗分辨率(1度)GPP估计中的季节周期和空间模式。不同形式的SIF-GPP关系可能导致年度GPP的显着差异，特别是在热带地区。我们的全球整体年度GPP估计(135.5+/-8.8PG Cyr(-1))介于非基于过程的方法的中值估计和基于过程的模型的中值估计之间。我们对全球生产总值的估计显示，许多区域存在年际变化，全球许多地区，特别是北半球，呈现出增加的趋势。随着从太空(如Tropomi、FLEX)获得高质量的格网SIF观测，我们的新方法不依赖于任何其他输入数据(如气候数据、土壤属性)，因此可以仅基于卫星SIF观测绘制GPP地图，并可能导致更准确的区域到全球尺度的GPP估计。使用通用的SIF-GPP关系而不是特定于生物群的关系，也可以避免与土地覆盖图相关的不确定性。我们的新的、独立的GPP产品(GOSIF GPP)在我们的数据库中免费提供，将对研究光合作用、碳循环、农业生产和生态系统对气候变化和干扰的反应、为生态系统管理提供信息以及基准陆地生物圈和地球系统模型具有重要价值。
Accurately quantifying gross primary production (GPP) globally is critical for assessing plant productivity, carbon balance, and carbon-climate feedbacks, while current GPP estimates exhibit substantial uncertainty. Solar-induced chlorophyll fluorescence (SIF) observed by the Orbiting Carbon Observatory-2 (OCO-2) has offered unprecedented opportunities for monitoring land photosynthesis, while its sparse coverage remains a bottleneck for mapping finer-resolution GPP globally. Here, we used the global, OCO-2-based SIF product (GOSIF) and linear relationships between SIF and GPP to map GPP globally at a 0.05 degrees spatial resolution and 8-day time step for the period from 2000 to 2017. To account for the uncertainty of GPP estimates resulting from the SIF-GPP relationship, we used a total of eight SIF-GPP relationships with different forms (universal and biome-specific, with and without intercept) at both site and grid cell levels to estimate GPP. Our results showed that all of the eight SIF-GPP relationships performed well in estimating GPP globally. The ensemble mean 8-day GPP was generally highly correlated with flux tower GPP for 91 eddy covariance flux sites across the globe (R-2 = 0.74, Root Mean Square Error = 1.92 g C m(-2) d(-1)). Our fine-resolution GPP estimates showed reasonable spatial and seasonal variations across the globe and fully captured both seasonal cycles and spatial patterns present in our coarse-resolution (1 degrees) GPP estimates based on coarse-resolution SIF data directly aggregated from discrete OCO-2 soundings. SIF-GPP relationships with different forms could lead to significant differences in annual GPP particularly in the tropics. Our ensemble global annual GPP estimate (135.5 +/- 8.8 Pg C yr(-1)) is between the median estimate of non-process based methods and the median estimate of process-based models. Our GPP estimates showed interannual variability in many regions and exhibited increasing trends in many parts of the globe particularly in the Northern Hemisphere. With the availability of high-quality, gridded SIF observations from space (e.g., TROPOMI, FLEX), our novel approach does not rely on any other input data (e.g., climate data, soil properties) and therefore can map GPP solely based on satellite SIF observations and potentially lead to more accurate GPP estimates at regional to global scales. The use of a universal SIF-GPP relationship versus biome-specific relationships can also avoid the uncertainty associated with land cover maps. Our novel, independent GPP product (GOSIF GPP), freely available at our data repository, will be valuable for studying photosynthesis, carbon cycle, agricultural production, and ecosystem responses to climate change and disturbances, informing ecosystem management, and benchmarking terrestrial biosphere and Earth system models.