Dr. Vasilis Myrgiotis Profile

Dr. Vasilis Myrgiotis

at UK Centre For Ecology & Hydrology

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Author

Publications (2)

Proceedings Article | 12 October 2021 Presentation + Paper

Quantifying the carbon balance of managed grasslands in near-real time and at field scale by using satellite data and biogeochemical modelling

Vasilis Myrgiotis, Mathew Williams

Proceedings Volume 11888, 118880A (2021) https://doi.org/10.1117/12.2600856

KEYWORDS: Data modeling, Ecosystems, Vegetation, Atmospheric modeling, Climatology, Satellites, System on a chip, Modeling, Computer simulations, Agriculture

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Grasslands are the most widespread terrestrial ecosystem in the United Kingdom (UK). They represent a critical carbon (C) reservoir and provide forage and fodder to millions of livestock. Quantifying how management and climate affect grassland C dynamics is key to achieving climate-resilient farming, to shaping and monitoring data-informed policies, and to transitioning to a zero-C agri-food sector. To this end, remote sensing systems provide information on grassland vegetation in near-real time, across large domains and at high resolution. Biogeochemical models use our continuously-developing knowledge on ecosystem functioning to describe ecosystem C dynamics and the effects of weather and human management on them. Field measurements of C pools and fluxes provide direct ground observations of grassland C losses and gains. The combination of earth and ground observations with modelling represents a robust way for quantifying, monitoring and verifying grassland ecosystem C stocks. This presentation demonstrates our current capabilities in regards to this. We have developed and tested a model-data fusion (MDF) framework that uses earth observation data (Proba-V and Sentinel-2) on vegetation canopy (leaf area index) to infer vegetation management (grazing, cutting) and inform a validated process-based model of field-scale C dynamics (DALEC-Grass). The framework was applied for 2017-2018 at 1855 grassland fields that were sampled from across the UK. The MDF-predicted livestock density per area and the corresponding agricultural census-based data had a correlation coefficient (r) of 0.68. The MDF-predicted annual yield (harvested and cut biomass) was within the range of relevant measured data and reflected the variation of grassland management intensity across the UK. On average, the simulated grasslands were C sinks in 2017 and 2018 but the 2018 European summer heatwave resulted in a 9-fold increase in the number of simulated fields that were C sources in 2018 compared to 2017. We argue that earth observation data can be used in a MDF framework to monitor grassland vegetation management and to simulate its impacts on the C balance of any UK grassland field as well as to attribute changes in annual C balance to human activities and weather anomalies.

Proceedings Article | 12 October 2021 Presentation + Paper

Farming from space: model-data fusion approaches for simulating crop nitrogen and yield estimates

A. Revill, V. Myrgiotis, M. Williams

Proceedings Volume 11888, 118880D (2021) https://doi.org/10.1117/12.2600826

KEYWORDS: Data modeling, Calibration, Atmospheric modeling, Solar radiation models, Statistical modeling, Statistical analysis, Nitrogen, Photosynthesis, Environmental sensing, Climatology

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