Inferring management and predicting sub-field scale C dynamics in UK grasslands using biogeochemical modelling and satellite-derived leaf area data

A - Papers appearing in refereed journals

Myrgiotis, V., Harris, P., Revill, A., Sint, H. M. and Williams, M. 2021. Inferring management and predicting sub-field scale C dynamics in UK grasslands using biogeochemical modelling and satellite-derived leaf area data. Agricultural and Forest Meteorology. 307, p. 108466. https://doi.org/10.1016/j.agrformet.2021.108466

AuthorsMyrgiotis, V., Harris, P., Revill, A., Sint, H. M. and Williams, M.
Abstract

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.

KeywordsGrasslands; UK; Carbon; Earth observation; Leaf area index; Model-data fusion
Year of Publication2021
JournalAgricultural and Forest Meteorology
Journal citation307, p. 108466
Digital Object Identifier (DOI)https://doi.org/10.1016/j.agrformet.2021.108466
Open accessPublished as non-open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeThe North Wyke Farm Platform- National Capability [2017-22]
Output statusPublished
Publication dates
Online25 May 2021
Publication process dates
Accepted04 May 2021
PublisherElsevier Science Bv
ISSN0168-1923

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