BACI: 2015-2019


University College London (UCL) EO group


UCL Geography is one of the UK’s top geography departments with UCL itself a world-renowned research institution. The Department has 40 academic staff with research interests in global change modelling and monitoring, ecology, EO and palaeoclimate. The EO group consists of 2 academic staff (Disney, P. Lewis), 6 post-doctoral researchers (NERC, ESA, EU and other funding), and 5 PhD students (mostly NERC-funded). The group works closely with staff in other areas, including tropical forest ecology, and change mapping.

The UCL Geography EO group is also a core part of the NERC National Centre for Earth Observation (NCEO). Our research focuses on modelling the interaction of radiation with the terrestrial surface, vegetation particularly, and developing new measurements and models to exploit this interaction in relation to vegetation structure, dynamics, and the terrestrial carbon cycle. Our research interfaces closely with ecological modelling and measurement, to enable optimal use of EO data as well as testing model predictions, improving process understanding and quantifying uncertainty. We have been closely involved in the development of global algorithms for NASA and ESA, to provide optimal estimates of albedo (MODIS BRDF/albedo, ESA GlobAlbedo) and burned area products; developed new modelling methods and tools across scales and wavelengths for biophysical parameter retrieval and instrument concept testing (ESA, NSF); and world-leading 3D modelling tools and methods for new measurement applications. We have developed new tools for assimilating observations from multiple sources to estimate surface state (e.g. ESA EO-LDAS scheme - see and and we will be using and developing these tools in the BACI project.

The group has access to excellent computing resources, including the UCL Legion cluster (peak performance of 42.9 TeraFlops), and a local 160-node linux cluster. Much of our work has been NERC and ESA funded, alongside commercial collaboration. Beneficiaries of this work include: scientists in related disciplines (constraining models with EO measurements; DA methods/tracking of uncertainty; new models and simulation tools); users of environmental monitoring data (through involvement in development of global products for albedo and burned area estimation from NASA and ESA); and policy-makers and Government users such as BIS/DEFRA.


We will provide the core requirement of timely and consistent spatial data to be used as input to the BACI analysis framework. These will primarily be EO data, but also additional spatial data such as elevation and slope/aspect. We will:

  • Develop a generic, scaleable framework for combining data from multiple streams for input into BACI index analysis, building on work being delivered by other EU projects, specifically FP7 QA4ECV, ZAPÁS, GIONET, GHG Europe and GEOCARBON as well as ESA ECV generation, and EO data assimilation;
  • Use the above to provide a system ‘state vector’ representing the state of a point/region on the land surface at a given time as a function of input data (Δreflectance, LST, backscatter, interferometric coherence, soil moisture, freeze/thaw, snow characteristics, albedo, vegetation state, ancillary), with uncertainty;
  • Use a limited number of higher-level model-derived products that are well-characterised i.e. burned area (optical), biomass (SAR), soil moisture (scatterometer), snow characteristics (passive microwave), and canopy height/structure (airborne lidar);
  • Explore new observations related to canopy activity such as solar-induced fluorescence, through ongoing collaborations. There are key reasons for this approach, which we believe will make the results from BACI consistent across space and time novel, unique and widely-applicable.