DoctorCamila Cesar

Planetary scientist with a multi-disciplinary training in space mission operations, remote sensing, experimental project design & management, big data collection, processing & analysis. I did my doctoral research at the University of Bern (Switzerland) to study the Martian polar regions using high resolution images from the Colour and Stereo Surface Imaging System (CaSSIS) camera onboard the ExoMars Trace Gas Orbiter and understand the origin of the seasonal spots, fans and araneiform structures. This was coupled to experimental work at the IceLab facility to recreate similar processes (formation of a thick translucent ice slab covering a dusty martian surface) and analyse the evolution of the spectophotometric properties during its formation and sublimation phases.

My current project at the GRI, and in collaboration with Earth Sciences New Zealand (ESNZ) and Ngāi Tahu Farms, is about developing a multi-scale, satellite-based framework for estimating agricultural methane emissions in Aotearoa New Zealand's pastoral systems. This integrated framework help explore what satellite-based monitoring can realistically contribute to verifying and complementing traditional agricultural emissions inventories, with direct relevance to climate policy and emissions reporting. Three main components of this framework are :

• Bottom-up farm inventory pipeline, implementing IPCC Tier 1, Tier 2 Simplified, Tier 2 Advanced, and the New Zealand Ministry for Primary Industries' Farm Emissions Method (FEM), allowing emissions to be estimated and compared across methodologies at weekly, monthly, and seasonal/annual scales.
• Top-down atmospheric inversion, developed in collaboration with ESNZ, drawing on TROPOMI satellite observations, fixed-station insitu measurements, and the UK Met Office's NAME III Lagrangian particle dispersion model.
• Remote sensing proxies, using PlanetScope satellite imagery to derive vegetation indices as a spatial proxy for pasture biomass, calibrated against ground-truth rising plate meter (RPM) measurements using machine learning.