Our People
Dr Luke Smallman
Research Fellow – Terrestrial C-dynamics
EO Instrumentation and Facilities
Research interests
My research focuses on understanding how terrestrial ecosystems function, their current status and dynamics. Ecosystems are sensitive to weather, climate, and disturbance (e.g. fire and management), and their interactions. The response and feedbacks of ecosystems are mediated by their characteristics. Critical remaining knowledge gaps are on the magnitude of leaf photosynthetic capacity, the allocation of photosynthate to plant tissues, tissue turnover / mortality and decomposition rates. A robust understanding of ecosystem functioning is essential to understand the effects of our changing environment and human intervention to provide information for policy makers and land managers in support of informed decision making.
I address these knowledge gaps using state-of-the-art Bayesian model-data fusion software (CARDAMOM). CARDAMOM allows me to train intermediate-complexity models of terrestrial ecosystems with a diverse array of ground and satellite-based observation while fully accounting for the uncertainties inherent in these datasets. I primarily use the DALEC suite of terrestrial ecosystem models which offers a wide range of complexities which can be matched with the available observations, enhancing analysis rigor. CARDAMOM and DALEC are tools developed and maintained by NCEO.
Recent publications
Dual controls of vapour pressure deficit and soil moisture on photosynthesis in a restored temperate bog. 2025-02
DOI: https://doi.org/10.1016/j.scitotenv.2024.178366
Carbon sink strength and allocation dynamics of a rich fen peatland in the warming Arctic. 2025-01-20
DOI: https://doi.org/10.5194/egusphere-egu24-12300
Can time series of plant water potential constrain carbon cycle dynamics using the CARDAMOM model-data fusion framework?. 2025-01-20
DOI: https://doi.org/10.5194/egusphere-egu24-20457
Fire-precipitation interactions control biomass carbon and net biome production across the world’s largest savanna. 2025-01-20
DOI: https://doi.org/10.5194/egusphere-egu24-20466
Quantifying permafrost C-cycling by fusing process-models and observations . 2025-01-20
DOI: https://doi.org/10.5194/egusphere-egu24-7366
Permafrost Region Greenhouse Gas Budgets Suggest a Weak CO2 Sink and CH4 and N2O Sources, But Magnitudes Differ Between Top‐Down and Bottom‐Up Methods. 2024-10
DOI: https://doi.org/10.1029/2023GB007969
Precipitation-fire-functional interactions control biomass stocks and carbon exchanges across the world’s largest savanna. 2024-08-12
DOI: https://doi.org/10.5194/egusphere-2024-2497
Supplementary material to "Precipitation-fire-functional interactions control biomass stocks and carbon exchanges across the world’s largest savanna". 2024-08-12
DOI: https://doi.org/10.5194/egusphere-2024-2497-supplement
A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0. 2024-07-16
DOI: https://doi.org/10.5194/egusphere-2024-1534
Supplementary material to "A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0". 2024-07-16
DOI: https://doi.org/10.5194/egusphere-2024-1534-supplement
Two decades of permafrost region CO2, CH4, and N2O budgets suggest a small net greenhouse gas source to the atmosphere. 2023-09-11
DOI: https://doi.org/10.22541/essoar.169444320.01914726/v1
Atmospheric CO2 inversion reveals the Amazon as a minor carbon source caused by fire emissions, with forest uptake offsetting about half of these emissions. 2023-09-01
DOI: https://doi.org/10.5194/acp-23-9685-2023
Scale variance in the carbon dynamics of fragmented, mixed-use landscapes estimated using model–data fusion. 2023-08-11
DOI: https://doi.org/10.5194/bg-20-3301-2023
Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance. 2023-06-28
DOI: https://doi.org/10.5194/bg-20-2455-2023
Atmospheric CO2 inversion reveals the Amazon as a minor carbon source caused by fire emissions, with forest uptake offsetting about half of these emissions. 2023-01-25
DOI: https://doi.org/10.5194/egusphere-2023-19
Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance. 2022-12-09
DOI: https://doi.org/10.5194/egusphere-2022-1265
The carbon budget of the managed grasslands of Great Britain – informed by earth observations. 2022-09-06
DOI: https://doi.org/10.5194/bg-19-4147-2022
Resolving scale-variance in the carbon dynamics of fragmented, mixed-use landscapes estimated using Model-Data Fusion. 2022-08-29
DOI: https://doi.org/10.5194/bg-2022-160
Challenges in Scaling Up Greenhouse Gas Fluxes: Experience From the UK Greenhouse Gas Emissions and Feedbacks Program. 2022-05
DOI: https://doi.org/10.1029/2021JG006743
Effects of environmental filtering and PFT-based model parameterization approaches on NBE prediction errors across the globe. 2022-03-28
DOI: https://doi.org/10.5194/egusphere-egu22-8946
CARDAMOM-FluxVal version 1.0: a FLUXNET-based validation system for CARDAMOM carbon and water flux estimates. 2022-03-02
DOI: https://doi.org/10.5194/gmd-15-1789-2022
Climate Sensitivities of Carbon Turnover Times in Soil and Vegetation: Understanding Their Effects on Forest Carbon Sequestration. 2022-03
DOI: https://doi.org/10.1029/2020JG005880
Challenges in scaling up greenhouse gas fluxes: experience from the UK Greenhouse Gas Emissions and Feedbacks Programme. 2021-12-06
DOI: https://doi.org/10.1002/essoar.10509113.1
Parameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century. 2021-11-23
DOI: https://doi.org/10.5194/esd-12-1191-2021
CARDAMOM-FluxVal Version 1.0: a FLUXNET-based Validation System for CARDAMOM Carbon and Water Flux Estimates. 2021-07-09
DOI: https://doi.org/10.5194/gmd-2021-190
Supplementary material to "CARDAMOM-FluxVal Version 1.0: a FLUXNET-based Validation System for CARDAMOM Carbon and Water Flux Estimates". 2021-07-09
DOI: https://doi.org/10.5194/gmd-2021-190-supplement
The carbon budget of the managed grasslands of Great Britain constrained by earth observations. 2021-06-14
DOI: https://doi.org/10.5194/bg-2021-144
Optimal model complexity for terrestrial carbon cycle prediction. 2021-04-30
DOI: https://doi.org/10.5194/bg-18-2727-2021
Parameter uncertainty dominates C cycle forecast errors over most of Brazil for the 21st Century. 2021-04-08
DOI: https://doi.org/10.5194/esd-2021-17
Optimal model complexity for terrestrial carbon cycle prediction. 2020-12-29
DOI: https://doi.org/10.5194/bg-2020-478
Supplementary material to "Optimal model complexity for terrestrial carbon cycle prediction". 2020-12-29
DOI: https://doi.org/10.5194/bg-2020-478-supplement
Characterizing the Error and Bias of Remotely Sensed LAI Products: An Example for Tropical and Subtropical Evergreen Forests in South China. 2020-09-23
DOI: https://doi.org/10.3390/rs12193122
The importance of physiological, structural and trait responses to drought stress in driving spatial and temporal variation in GPP across Amazon forests. 2019-11-25
DOI: https://doi.org/10.5194/bg-16-4463-2019
Description and validation of an intermediate complexity model for ecosystem photosynthesis and evapotranspiration: ACM-GPP-ETv1. 2019-06-07
DOI: https://doi.org/10.5194/gmd-12-2227-2019
The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space. 2019-06
DOI: https://doi.org/10.1016/j.rse.2019.03.032 ISSN: https://portal.issn.org/resource/ISSN/0034-4257
Leaf Area Index Changes Explain GPP Variation across an Amazon Drought Stress Gradient. 2019-05-27
DOI: https://doi.org/10.5194/bg-2019-175
Supplementary material to "Leaf Area Index Changes Explain GPP Variation across an Amazon Drought Stress Gradient". 2019-05-27
DOI: https://doi.org/10.5194/bg-2019-175-supplement
Author's response to reviewer comments on "Description and validation of an intermediate complexity model for ecosystem photosynthesis and evapo-transpiration: ACM-GPP-ETv1". 2019-05-01
DOI: https://doi.org/10.5194/gmd-2018-311-AC1
Simulating the atmospheric CO2 concentration across the heterogeneous landscape of Denmark using a coupled atmosphere–biosphere mesoscale model system. 2019-04-10
DOI: https://doi.org/10.5194/bg-16-1505-2019
Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network. 2019-04-04
DOI: https://doi.org/10.5194/acp-19-4345-2019
Description and validation of an intermediate complexity model for ecosystem photosynthesis and evapo-transpiration: ACM-GPP-ETv1. 2019-01-10
DOI: https://doi.org/10.5194/gmd-2018-311
Inverse Determination of the Influence of Fire on Vegetation Carbon Turnover in the Pantropics. 2018-12
DOI: https://doi.org/10.1029/2018GB005925
Quantifying the UK’s Carbon Dioxide Flux: An atmospheric inverse modelling approach using a regional measurement network. 2018-09-21
DOI: https://doi.org/10.5194/acp-2018-839
Supplementary material to "Quantifying the UK’s Carbon Dioxide Flux: An atmospheric inverse modelling approach using a regional measurement network". 2018-09-21
DOI: https://doi.org/10.5194/acp-2018-839-supplement
Plant Traits are Key Determinants in Buffering the Meteorological Sensitivity of Net Carbon Exchanges of Arctic Tundra. 2018-09
DOI: https://doi.org/10.1029/2018JG004386
Simulating the atmospheric CO2 concentration across the heterogeneous landscape of Denmark using a coupled atmosphere-biosphere mesoscale model system. 2018-06-27
DOI: https://doi.org/10.5194/bg-2018-240
Reliability ensemble averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties. 2018-02-21
DOI: https://doi.org/10.5194/esd-9-153-2018
Reliability Ensemble Averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties. 2017-09-19
DOI: https://doi.org/10.5194/esd-2017-83
Supplementary material to "Reliability Ensemble Averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties". 2017-09-19
DOI: https://doi.org/10.5194/esd-2017-83-supplement
Assimilation of repeated woody biomass observations constrains decadal ecosystem carbon cycle uncertainty in aggrading forests. 2017-03
DOI: https://doi.org/10.1002/2016JG003520
Can seasonal and interannual variation in landscape CO2 fluxes be detected by atmospheric observations of CO2 concentrations made at a tall tower?. 2014-02-06
DOI: https://doi.org/10.5194/bg-11-735-2014
Can seasonal and interannual variation in landscape CO2 fluxes be detected by atmospheric observations of CO2 concentrations made at a tall tower?. 2013-08-27
DOI: https://doi.org/10.5194/bgd-10-14301-2013
WRFv3.2-SPAv2: development and validation of a coupled ecosystem–atmosphere model, scaling from surface fluxes of CO2 and energy to atmospheric profiles. 2013-07-29
DOI: https://doi.org/10.5194/gmd-6-1079-2013
WRFv3.2-SPAv2: development and validation of a coupled ecosystem-atmosphere model, scaling from surface fluxes of CO2 and energy to atmospheric profiles. 2013-03-04
