Advanced Remote Sensing of Carbon Fluxes: From Satellite Observations to Regional Budgets
Quantifying Land–Ocean $CO_2$ Sinks using OCO-2/3, SIF, and Biogeochemical Modeling
Skills you will gain:
About Workshop:
This 3-day workshop trains participants to quantify and attribute regional land and ocean CO₂ sinks using satellite (XCO₂, SIF, SST, winds, ocean color) and in-situ (TCCON, SOCAT) data. Learners will perform QC/bias checks, build simple bottom-up and top-down flux workflows, map sinks/sources with uncertainty, analyze trends/anomalies, and produce a reproducible one-page regional carbon-sink brief.
Aim: This workshop aims to equip participants to use satellite and in-situ data to quantify and attribute regional land and ocean CO₂ sinks, assess trends and uncertainties, and produce reproducible, decision-ready carbon-budget reports.
Workshop Objectives:
What you will learn?
Module 1: High-Fidelity Signal Processing (Land & Ocean)
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Satellite Architecture: Deep dive into $XCO_2$ retrieval physics (OCO-2/3, GOSAT) and the fluorescence signal (SIF) as a proxy for GPP.
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Error Characterization: Handling averaging kernels, column-to-surface biases, and cloud/aerosol contamination.
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Oceanic Drivers: Correlating partial pressure ($pCO_2$) with SST, salinity, and wind vectors (using reanalysis data).
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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
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Bottom-Up Modeling:
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Land: Deriving Net Ecosystem Exchange (NEE) via SIF-GPP relationships and respiration scaling.
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Ocean: Machine Learning approaches to mapping $pCO_2$ and calculating air-sea gas transfer velocities ($k cdot Delta pCO_2$).
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Top-Down Concepts: Introduction to atmospheric inversion frameworks (4D-Var/EnKF concepts), prior selection, and transport model (e.g., GEOS-Chem) dependencies.
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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).
Mentor Profile
Fee Plan
Important Dates
15 Jan 2026 AT IST : 6:30 PM
Get an e-Certificate of Participation!
Intended For :
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Environmental Scientists & Researchers – Focused on carbon cycle and flux estimation.
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Atmospheric & Oceanic Modelers – Interested in flux inversion and carbon budget analysis.
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Data Scientists – Experienced in satellite data processing and Python.
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Climate Change Analysts – Analyzing trends and policy decisions.
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Machine Learning Engineers – Applying AI to environmental science.
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Academics & Graduate Students – In environmental or atmospheric sciences.
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Policy Makers & Consultants – Working in climate and sustainability.
Career Supporting Skills
Workshop Outcomes
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Interpret satellite XCO₂/SIF and ocean drivers for CO₂ sink monitoring.
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Perform basic QC/bias screening and co-locate with in-situ datasets (TCCON, SOCAT).
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Estimate land NEE using SIF-assisted GPP plus simple respiration.
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Compute and map ocean air–sea CO₂ flux from ΔpCO₂ and wind-driven transfer.
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Understand top-down inversion concepts and quantify uncertainty.
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Grid/scale fluxes to regional budgets with correct unit conversions.
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Produce a reproducible regional carbon-sink brief with trends, confidence, and limits.