Advanced Remote Sensing of Carbon Fluxes: From Satellite Observations to Regional Budgets

About This Course

This three-module, hands-on course is designed to help you confidently quantify and attribute regional land and ocean CO₂ sinks using a modern, multi-source carbon budget workflow. Throughout the program, you will work directly with satellite datasets such as XCO₂, SIF, SST, winds, and ocean color, along with trusted in-situ observation networks including TCCON and SOCAT.

As you progress, you will carry out quality control and bias assessments, build practical bottom-up and top-down flux estimation workflows, and create sink and source maps with uncertainty ranges. You will also analyze trends and anomalies and conclude the course by producing a fully reproducible, decision-ready deliverable: a one-page regional carbon-sink brief supported by clear visualizations and metadata.

Aim

The aim of this course is to equip participants with the practical skills needed to use satellite and in-situ observations to quantify and attribute regional land and ocean CO₂ sinks. You will learn how to evaluate trends, interpret uncertainties, and generate reproducible, decision-ready carbon budget outputs suitable for research publications, sustainability reporting, and policy support.

Course Objectives

By the end of this course, participants will be able to:

• Understand the major satellite and in-situ data streams used in regional CO₂ sink estimation

• Perform quality control, bias correction, and dataset co-location checks

• Build bottom-up flux estimation workflows for land (NEE) and ocean (air–sea CO₂ exchange)

• Explain top-down flux inversion concepts and identify key uncertainty sources

• Create spatial and temporal sink/source maps with uncertainty bands

• Analyze anomalies and long-term trends, distinguishing variability from structural shifts

• Produce a reproducible one-page regional carbon-sink brief with supporting figures and metadata

Course Structure

Module 1: High-Fidelity Signal Processing for Land and Ocean

• Understanding satellite retrieval physics (OCO-2/3, GOSAT) and using SIF as a proxy for GPP

• Characterizing errors including averaging kernels, column-to-surface biases, and atmospheric contamination

• Linking pCO₂ dynamics with SST, salinity, and wind vectors (including reanalysis datasets)

• Lab (“L2 to L3”): Ingest OCO-2 swath data and SOCAT in-situ observations, apply bias correction, and perform co-location analysis in Python/Xarray

Module 2: Flux Inversion and Budget Estimation

Bottom-up modeling:

• Land: Estimate NEE using SIF–GPP relationships and respiration scaling approaches

• Ocean: Apply ML-based pCO₂ mapping and calculate air–sea gas exchange (k · ΔpCO₂)

Top-down concepts:

• Atmospheric inversion fundamentals (4D-Var and EnKF concepts)

• Understanding priors, transport model dependencies (e.g., GEOS-Chem), and uncertainty drivers

• Lab (“Toy Flux Engine”): Develop a spatially explicit monthly CO₂ flux map for a selected region, including uncertainty bands

Module 3: Attribution, Trends, and Decision Support

• Detecting trends and separating anthropogenic signals from natural variability (ENSO, heatwaves, NAO)

• Data fusion and validation by reconciling satellite posteriors with networks such as TCCON

• Applying FAIR data principles for reproducibility and DOI-ready datasets

• Lab (“Carbon Brief”): Create publication-ready visual panels (time series, flux maps, anomaly plots) and metadata for a selected Region of Interest (ROI)

Who Should Enrol?

This course is ideal for:

• Environmental scientists and carbon cycle researchers

• Atmospheric and ocean modelers working on flux inversion and carbon budgeting

• Data scientists processing satellite datasets in Python-based workflows

• Climate analysts supporting sustainability and policy reporting

• Machine learning engineers applying AI to Earth observation data

• Graduate students in environmental and atmospheric sciences

• Policymakers and consultants working in climate and sustainability strategy