Python
import pandas as pd
import numpy as np

# Load eddy covariance telemetry stream
df = pd.read_csv('flux_tower_telemetry.csv', parse_dates=['timestamp'])

# Core NEE calculation: NEE = Reco - GPP
# Reco (ecosystem respiration) derived from nighttime fluxes extrapolated via Q10 model
# GPP (gross primary production) = Reco - NEE (daytime)

def qc_filter(df, u_star_threshold=0.2):
    """Remove low-turbulence periods (standard u* filtering)."""
    return df[df['u_star'] >= u_star_threshold].copy()

df_qc = qc_filter(df)

def q10_respiration(temp_soil, r_ref, q10=2.0, t_ref=10):
    return r_ref * q10 ** ((temp_soil - t_ref) / 10)

# Gap-filling nighttime Reco model, then partition
night = df_qc[df_qc['par'] < 5]  # PAR < 5 µmol/m²/s = night
from scipy.optimize import curve_fit
params, _ = curve_fit(lambda t, r_ref, q10: q10_respiration(t, r_ref, q10),
                       night['t_soil'], night['nee'])
r_ref, q10 = params

df_qc['reco_modeled'] = q10_respiration(df_qc['t_soil'], r_ref, q10)
df_qc['gpp'] = df_qc['reco_modeled'] - df_qc['nee']

# Aggregate to daily/annual carbon balance (µmol CO2/m²/s -> gC/m²/period)
df_qc['nee_gC'] = df_qc['nee'] * 12.011e-6 * 1800  # 30-min interval to grams
daily_balance = df_qc.resample('D', on='timestamp')['nee_gC'].sum()
annual_sequestration = -daily_balance.sum()  # negative NEE = net sink

print(f"Annual net ecosystem carbon uptake: {annual_sequestration:.1f} gC/m²/yr")

Progress:

• Step 1: Inventory and validate telemetry sources
• Step 2: Quality control and gap-filling
• Step 3: Flux partitioning (NEE → GPP + Reco)
• Step 4: Upscale from point/tower to stand/landscape level
• Step 5: Cross-validate with biometric/inventory data
• Step 6: Compute carbon budget and uncertainty bounds
• Step 7: Report with confidence intervals and visualization

Step 1: Inventory telemetry sources

Identify what instruments feed the pipeline:

• Eddy covariance towers — CO₂/H₂O flux, wind (u, v, w), sonic temperature, u*
• Soil respiration chambers (automated) — Rs, soil temp/moisture at multiple depths
• Dendrometers — stem radial growth (proxy for woody biomass increment)
• Sap flow sensors — transpiration, used to cross-check water-carbon coupling
• Remote sensing (NDVI/EVI, LAI from satellite or phenocams) — spatial upscaling, phenology
• Meteorological station — PAR, air temp, VPD, precipitation

Check sampling frequency (typically 10-20 Hz raw, aggregated to 30-min for EC) and establish a unified timestamp index.

Step 2: Quality control and gap-filling

• Apply u filtering* (friction velocity threshold, typically 0.1–0.3 m/s) to discard stable-stratification nighttime periods with underestimated turbulent flux.
• Despike using median absolute deviation (MAD) method on raw high-frequency data.
• Flag/remove periods with instrument malfunction, rain, or power loss (telemetry dropouts).
• Gap-fill using:
  • Marginal Distribution Sampling (MDS) — standard for EC data, uses co-occurring meteorological conditions
  • Look-up tables binned by PAR, VPD, temperature
  • Artificial neural networks for longer gaps (>2 weeks)
• Document gap-filling percentage; flag years with >20% gaps as lower confidence.

Step 3: Flux partitioning

Split measured NEE into component fluxes since NEE alone doesn't reveal mechanism:

• Nighttime method: NEE at night ≈ Reco (no photosynthesis); fit Q10/Arrhenius temperature-response model; extrapolate to daytime; GPP = Reco_modeled − NEE_daytime.
• Daytime method (Lasslop et al.): fits light-response curve simultaneously with temperature-driven respiration, more robust for ecosystems with strong photoinhibition.
• Cross-check Reco against soil chamber measurements (should correlate; discrepancy indicates aboveground respiration contribution not captured by soil chambers, or vice versa).

Step 4: Upscale from tower to landscape

• Define the tower footprint (flux source area) using a footprint model (e.g., Kljun et al. 2015) — critical because footprint shifts with wind direction/stability.
• Correlate flux-tower GPP with remote-sensing vegetation indices (NDVI, EVI, kNDVI) over the footprint to build an empirical upscaling model.
• Apply the model to the broader stand/landscape using satellite time series (Sentinel-2, Landsat, or MODIS for coarser scale) — produces spatially continuous carbon flux maps.

Step 5: Cross-validate with biometric inventory

Triangulate eddy covariance (flux-based) estimates against:

• Biomass increment from dendrometer bands + allometric equations (converts stem diameter growth to carbon mass)
• Forest inventory plots (periodic DBH/height census) for independent woody biomass carbon stock change
• Soil carbon stock sampling (if available) for belowground pool trends Discrepancies >15-20% between flux-derived NEP and biometric growth suggest unaccounted pools (coarse woody debris, litter, DOC export) or systematic measurement bias.

Step 6: Compute carbon budget with uncertainty

Report the full budget, not just NEE:

NEP (Net Ecosystem Production) = GPP − Reco  [≈ -NEE]
NBP (Net Biome Production) = NEP − disturbance losses (fire, harvest, pests)

Propagate uncertainty from:

• Random flux measurement error (use flux variance method, typically ±10-15% at annual scale)
• Gap-filling uncertainty (bootstrap resampling of gap-fill models)
• u* threshold sensitivity (recompute budget across range of plausible thresholds) Report as mean ± 95% CI, never a bare point estimate.

Step 7: Report

Produce: time series plot (daily NEE/GPP/Reco), cumulative carbon curve over the year, footprint-weighted map if upscaled, and a summary table comparing flux-based vs. biometric estimates.

Example 1: Input: 1 year of 30-min eddy covariance data from a mixed boreal forest tower, with 12% data gaps, paired with soil chamber Rs data and quarterly dendrometer readings. Output: QC'd and gap-filled NEE time series; partitioned annual GPP = 1180 ± 95 gC/m²/yr, Reco = 1050 ± 80 gC/m²/yr, NEP = 130 ± 60 gC/m²/yr (net sink). Biometric cross-check from dendrometers estimates woody increment = 95 gC/m²/yr, within uncertainty range — validates flux-tower estimate. Final report flags the growing season (May–Sept) as source of 92% of annual uptake.

Example 2: Input: Satellite NDVI time series + single flux tower in a heterogeneous mixed-species plantation, request for landscape-level carbon map. Output: Footprint model applied to identify tower's effective source area (~0.5 km² under typical wind conditions); empirical GPP-NDVI regression (R²=0.78) built from footprint-overlapping pixels; model applied across 50 km² landscape producing a monthly GPP raster map; flagged low-confidence zones where land cover differs from tower footprint vegetation type (e.g., wetland inclusions).