Integration Of CO2 Flux And Remotely-Sensed Data For Primary Production And Ecosystem Respiration Analyses In The Northern Great Plains: Potential For Quantitative Spatial Extrapolation

Aim  Extrapolation of tower CO₂ fluxes will be greatly facilitated if robust relationships between flux components and remotely sensed factors are established. Long-term measurements at five Northern Great Plains locations were used to obtain relationships between CO₂ fluxes and photosynthetically active radiation (Q), other on-site factors, and Normalized Difference Vegetation Index (NDVI) from the SPOT VEGETATION data set.

Location  CO₂ flux data from the following stations and years were analysed: Lethbridge, Alberta 1998–2001; Fort Peck, MT 2000, 2002; Miles City, MT 2000–01; Mandan, ND 1999–2001; and Cheyenne, WY 1997–98.

Results  Analyses based on light-response functions allowed partitioning net CO₂ flux (F) into gross primary productivity (P_g) and ecosystem respiration (R_e). Weekly averages of daytime respiration, γ_day, estimated from light responses were closely correlated with weekly averages of measured night-time respiration, γ_night (R² 0.64 to 0.95). Daytime respiration tended to be higher than night-time respiration, and regressions of γ_day on γ_night for all sites were different from 1 : 1 relationships. Over 13 site-years, gross primary production varied from 459 to 2491 g CO₂ m⁻² year⁻¹, ecosystem respiration from 996 to 1881 g CO₂ m⁻² year⁻¹, and net ecosystem exchange from −537 (source) to +610 g CO₂ m⁻² year⁻¹ (sink). Maximum daily ecological light-use efficiencies, ɛ_d,max = P_g/Q, were in the range 0.014 to 0.032 mol CO₂(mol incident quanta)⁻¹.

Main conclusions  Ten-day average P_g was significantly more highly correlated with NDVI than 10-day average daytime flux, P_d (R² = 0.46 to 0.77 for P_g-NDVI and 0.05 to 0.58 for P_d-NDVI relationships). Ten-day average R_e was also positively correlated with NDVI, with R² values from 0.57 to 0.77. Patterns of the relationships of P_g and R_e with NDVI and other factors indicate possibilities for establishing multivariate functions allowing scaling-up local fluxes to larger areas using GIS data, temporal NDVI, and other factors.