Grasslands, natural and managed, cover a large part of the Earth’s surface and play an important role in the global carbon (C) cycle. Human management strongly affects grassland C budgets through grass cutting and removal, varied grazing intensities, and organic matter additions. Thus managed grassland C cycles are highly heterogeneous and challenging to quantify. In this study, we combine a process-based model of the grassland C cycle, validated against field data on C fluxes and pools, with satellite-derived data (Proba-V and Sentinel-2) on leaf area index (LAI) in order to quantify field-scale grassland productivity and C dynamics under climatic and management conditions typical of northwest Europe. Input data on the weekly vegetation canopy anomaly (estimated from Proba-V LAI) and meteorology are used to drive the grassland C model (DALEC-Grass) that is integrated into a Bayesian model-data fusion (MDF) framework. The novelty of the MDF algorithm is that it infers weekly livestock grazing and grass cutting events based on expected canopy growth estimated by the model, and constrained by LAI observations (estimated from Sentinel-2). The MDF approach also resolves observational, parametric, and input uncertainties on C cycling estimates. We analysed four years (2015–2018) of C dynamics at three variably-managed fields of the Rothamsted Research North Wyke Farm Platform (UK). Compared against independent field data, the MDF was able to (i) identify 87.5% of the harvest events that occurred, (ii) accurately predict the annual yields in 83% of the identified harvest years and (iii) reproduce the observed grazing intensity in each field ($r=0.8$, overlap = 90%). We demonstrate that the fusion of process modelling with earth observations is an effective method for monitoring biomass removals and quantifying management impacts on field-scale C balance, without the need for frequent and laborious ground measurements. This approach can support the delivery of more robust national greenhouse gas (GHG) accounting that takes account of grassland vegetation management.

天然且经过管理的草原覆盖了地球的大部分表面，并在全球碳（C）循环中发挥了重要作用。人为管理通过割草和砍伐，不同的放牧强度和添加有机物，极大地影响草地的C预算。因此，管理的草原C循环高度异质且难以量化。在这项研究中，我们将针对草地C循环的基于过程的模型（针对C流量和水池的田间数据进行了验证）与基于叶面积指数（LAI）的卫星数据（Proba-V和Sentinel-2）相结合在西北气候典型的气候和管理条件下，量化田间规模的草地生产力和碳动态。关于每周植被冠层异常（根据Proba-V LAI估算）和气象学的输入数据用于驱动已集成到贝叶斯模型-数据融合（MDF）框架中的草地C模型（DALEC-Grass）。MDF算法的新颖之处在于，它基于模型估算的并受LAI观测值约束（根据Sentinel-2估算）的情况下，推断每周的牲畜放牧和割草事件。MDF方法还解决了C循环估计的观测，参数和输入不确定性。我们在Rothamsted Research North Wyke农场平台（英国）的三个可变管理领域分析了四年（2015-2018年）的碳动力学。与独立的田间数据相比，MDF能够（i）识别发生的收割事件的87.5％，$\mathrm{[R}=0.8$，重叠= 90％）。我们证明过程模型与地球观测的融合是监测生物量清除和量化管理对田间C平衡的有效方法，而无需频繁而费力的地面测量。这种方法可以支持提供更健壮的国家温室气体（GHG）会计，该会计考虑了草地植被的管理。