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

Module 1: High-Fidelity Signal Processing (Land & Ocean)

• Satellite Architecture: Deep dive into $XCO_2$ retrieval physics (OCO-2/3, GOSAT) and the fluorescence signal (SIF) as a proxy for GPP.

• Error Characterization: Handling averaging kernels, column-to-surface biases, and cloud/aerosol contamination.

• Oceanic Drivers: Correlating partial pressure ($pCO_2$) with SST, salinity, and wind vectors (using reanalysis data).

• Lab: "From L2 to L3" — Ingesting OCO-2 swaths and SOCAT in-situ data; performing bias correction and co-location analysis using Python/Xarray.

Module 2: Flux Inversion & Budget Estimation

• Bottom-Up Modeling:

  • Land: Deriving Net Ecosystem Exchange (NEE) via SIF-GPP relationships and respiration scaling.

  • Ocean: Machine Learning approaches to mapping $pCO_2$ and calculating air-sea gas transfer velocities ($k cdot Delta pCO_2$).

• Top-Down Concepts: Introduction to atmospheric inversion frameworks (4D-Var/EnKF concepts), prior selection, and transport model (e.g., GEOS-Chem) dependencies.

• Lab: Building a "Toy Flux Engine." Construct a spatially explicit map of monthly $CO_2$ fluxes for a target region, integrating uncertainty bands.

Module 3: Attribution, Trends & Decision Support

• Trend Detection: Distinguishing anthropogenic trends from natural variability (ENSO, Heatwaves, NAO).
• Data Fusion: Reconciling differences between satellite posteriors and in-situ validation networks (TCCON).
• FAIR Principles: Best practices for data governance, creating DOIs for your datasets, and ensuring computational reproducibility for peer review.
• Lab: The "Carbon Brief" — Generate a manuscript-ready figure panel (Time-series, Flux Map, Anomaly Plot) and specific metadata for a chosen ROI (Region of Interest).

• Interpret satellite XCO₂/SIF and ocean drivers for CO₂ sink monitoring.

• Perform basic QC/bias screening and co-locate with in-situ datasets (TCCON, SOCAT).

• Estimate land NEE using SIF-assisted GPP plus simple respiration.

• Compute and map ocean air–sea CO₂ flux from ΔpCO₂ and wind-driven transfer.

• Understand top-down inversion concepts and quantify uncertainty.

• Grid/scale fluxes to regional budgets with correct unit conversions.

• Produce a reproducible regional carbon-sink brief with trends, confidence, and limits.

• Environmental Scientists & Researchers – Focused on carbon cycle and flux estimation.

• Atmospheric & Oceanic Modelers – Interested in flux inversion and carbon budget analysis.

• Data Scientists – Experienced in satellite data processing and Python.

• Climate Change Analysts – Analyzing trends and policy decisions.

• Machine Learning Engineers – Applying AI to environmental science.

• Academics & Graduate Students – In environmental or atmospheric sciences.

• Policy Makers & Consultants – Working in climate and sustainability.