Severe droughts in Amazonia in 2005 and 2010 caused widespread loss of carbon from the terrestrial biosphere. This loss, almost twice the annual fossil fuel CO2 emissions in the EU, suggests a large sensitivity of the Amazonian carbon balance to a predicted more intense drought regime in the next decades. This is a dangerous inference though, as there is no scientific consensus on the most basic metrics of Amazonian carbon exchange: the gross primary production (GPP) and its response to moisture deficits in the soil and atmosphere. Measuring them on scales that span the whole Amazon forest was thus far impossible, but in this project I aim to deliver the first observation-based estimate of pan-Amazonian GPP and its drought induced variations. My program builds on two recent breakthroughs in our use of stable isotopes (13C, 17O, 18O) in atmospheric CO2: (1) Our discovery that observed δ¹³C in CO2 in the atmosphere is a quantitative measure for vegetation water-use efficiency over millions of square kilometers, integrating the drought response of individual plants. (2) The possibility to precisely measure the relative ratios of 18O/16O and 17O/16O in CO2, called Δ17O. Anomalous Δ17O values are present in air coming down from the stratosphere, but this anomaly is removed upon contact of CO2 with leaf water inside plant stomata. Hence, observed Δ17O values depend directly on the magnitude of GPP. Both δ¹³C and Δ17O measurements are scarce over the Amazon-basin, and I propose more than 7000 new measurements leveraging an established aircraft monitoring program in Brazil. Quantitative interpretation of these observations will break new ground in our use of stable isotopes to understand climate variations, and is facilitated by our renowned numerical modeling system “CarbonTracker”. My program will answer two burning question in carbon cycle science today: (a) What is the magnitude of GPP in Amazonia? And (b) How does it vary over different intensities of drought?

ERA-MIN3 comprises a progressive, pan-European public-public partnership of 25 public research funding organisations from 19 European countries/regions and 3 third countries, which aims to continue strengthening the mineral raw materials (RM) community through the coordination of research and innovation (R&I) programmes on non-energy, non-agricultural raw materials (metallic, construction, and industrial minerals). ERA-MIN3 will thus contribute to the objectives of the EIP on Raw Material’s Strategic Implementation Plan and the EU Circular Economy Action Plan, in support of the EU Raw Materials Initiative, the UN sustainable development goals and the European Green Deal. Built on the successes of the previous ERA-MIN and ERA-MIN 2, and to ensure the EU’s resource security and sustainable supply of strategic RM to the European society, ERA-MIN3 will achieve its goals of improving synergy, coordination and coherence between regional, national and EU funding in the RM sector by reducing fragmentation of RM funding across Europe and globally, as well as, improving the use of human and financial resources, the competitiveness and the environmental, social, health and safety issues of RM operations through supporting of transnational, excellent and translational R&I activities. This will be achieved through a EU co-funded joint transnational call for R&I proposals and, at least, one additional call with participation of invited partners, on demand-driven R&I on primary and secondary resources, covering the entire value chain, from exploration, extraction and processing technologies to recycling and substitution of CRM, as well as, environmental and societal impact, new business models and/or public perception. ERA-MIN3 will liaison with RM related initiatives to ensure alignment of research topics (e.g. batteries), promote synergies and complementarities thus avoiding duplication of efforts and contributing for the circular economy and the sustainable development.

The importance of the greenhouse gases CO2 and CH4 for climate is well established. There is broad scientific consensus that human activities are the main driver for increasing concentrations of these greenhouse gases (GHGs), particularly over the past century. Based on accurate surface measurements we know that approximately 45% of the CO2 emitted by human activities remain in the atmosphere. The net balance is apparently being taken up by global oceans, terrestrial vegetation and soils. However, there are substantial uncertainties associated with the nature, location and strength of these natural components of the carbon cycle. The Amazon region is one of the largest forested regions in the world, representing the largest reservoir of above ground organic carbon. Amazonia is not only subject to changes in climate but also to rapid environmental change due to fast population growth and economic development causing extensive deforestation and urbanisation. Such external drivers can lead to further shifts in the carbon balance resulting in release of carbon stored in the biomass and soil to the atmosphere, with implications for accelerating the growth of atmospheric GHG concentrations and climate change. Despite its important role for the global carbon cycle, current understanding of the Amazonian, and more broadly the tropical, carbon cycle is poorly constrained by observations. These knowledge gaps result in large uncertainties in the fate of the Amazonian carbon budget under a warming climate, and consequently hamper any predictive skill of carbon-climate models. Since 2009, the Amazon region has been the focus of major UK and Brazilian research projects that aim at improving our knowledge of the Amazonian carbon cycle using detailed, but localized aircraft observations of CO2 and CH4 at a number of sites. These measurements are a great advance but they remain highly localized in space and time. Space-borne measurements have the ability to fill these observational gaps by providing observations with dense spatial and temporal coverage in regions poorly sampled by surface networks. It is essential, however, that such space-based observations are properly tied to the World Meteorological Organization (WMO) reference standard to ensure acceptance of space-based datasets by the carbon cycle community and to prevent misleading results on regional carbon budgets. The central aim of this proposal is to link the in-situ measurements with remotely sensed satellite data to establish an integrated Amazonian Carbon Observatory where satellite data complements the in situ data by filling the gaps between the in situ sites and by extending the coverage over the whole Amazon region. Satellite observations of GHGs are now available from a dedicated instrument on board the Japanese GOSAT satellite and results look very promising. However, satellite retrievals over the Amazon (and the Tropics) are intrinsically difficult and the accuracy of such GHG retrievals has not been established for this region which is a major obstacle for the exploitation of space-based data to constrain carbon fluxes over the Amazon. We propose to establish a network of Brazilian and UK researchers to bridge the gap between in-situ and remote sensing observations and communities and to evaluate the feasibility of remote sensing of GHG concentrations for the purpose of GHG flux monitoring over Amazonia to improve our understanding of the Amazonian carbon cycle and to increase our ability for observing tropical carbon fluxes. The proposed network will bring together world-class expertise to address highly relevant and timely scientific questions that will advance our understanding of the carbon cycle of the Amazon. It will strongly strengthen and expand UK and Brazilian relationships and it will help further strengthen the leading role of UK researchers in many areas relevant to this proposal.

This project will propose an urban grammar to describe urban form and will develop artificial intelligence (AI) techniques to learn such a grammar from satellite imagery. Urban form has critical implications for economic productivity, social (in)equality, and the sustainability of both local finances and the environment. Yet, current approaches to measuring the morphology of cities are fragmented and coarse, impeding their appropriate use in decision making and planning. This project will aim to: 1) conceptualise an urban grammar to describe urban form as a combination of "spatial signatures", computable classes describing a unique spatial pattern of urban development (e.g. "fragmented low density", "compact organic", "regular dense"); 2) develop a data-driven typology of spatial signatures as building blocks; 3) create AI techniques that can learn signatures from satellite imagery; and 4) build a computable urban grammar of the UK from high-resolution trajectories of spatial signatures that helps us understand its future evolution. This project proposes to make the conceptual urban grammar computable by leveraging satellite data sources and state-of-the-art machine learning and AI techniques. Satellite technology is undergoing a revolution that is making more and better data available to study societal challenges. However, the potential of satellite data can only be unlocked through the application of refined machine learning and AI algorithms. In this context, we will combine geodemographics, deep learning, transfer learning, sequence analysis, and recurrent neural networks. These approaches expand and complement traditional techniques used in the social sciences by allowing to extract insight from highly unstructured data such as images. In doing so, the methodological aspect of the project will develop methods that will set the foundations of other applications in the social sciences. The framework of the project unfolds in four main stages, or work packages (WPs): 1) Data acquisition - two large sets of data will be brought together and spatially aligned in a consistent database: attributes of urban form, and satellite imagery. 2) Development of a typology of spatial signatures - Using the urban form attributes, geodemographics will be used to build a typology of spatial signatures for the UK at high spatial resolution. 3) Satellite imagery + AI - The typology will be used to train deep learning and transfer learning algorithms to identify spatial signatures automatically and in a scalable way from medium resolution satellite imagery, which will allow us to back cast this approach to imagery from the last three decades. 4) Trajectory analysis - Using sequences of spatial signatures generated in the previous package, we will use machine learning to identify an urban grammar by studying the evolution of urban form in the UK over the last three decades. Academic outputs include journal articles, open source software, and open data products in an effort to reach as wide of an academic audience as possible, and to diversify the delivery channel so that outputs provide value in a range of contexts. The impact strategy is structured around two main areas: establishing constant communication with stakeholders through bi-directional dissemination; and data insights broadcast, which will ensure the data and evidence generated reach their intended users.