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 (Pg) and ecosystem respiration (Re). Weekly averages of daytime respiration, γday, estimated from light responses were closely correlated with weekly averages of measured night-time respiration, γnight (R2 0.64 to 0.95). Daytime respiration tended to be higher than night-time respiration, and regressions of γday on γnight 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, ɛd,max = Pg/Q, were in the range 0.014 to 0.032 mol CO2 (mol incident quanta)−1.

Main conclusions  Ten-day average Pg was significantly more highly correlated with NDVI than 10-day average daytime flux, Pd (R2 = 0.46 to 0.77 for Pg-NDVI and 0.05 to 0.58 for Pd-NDVI relationships). Ten-day average Re was also positively correlated with NDVI, with R2 values from 0.57 to 0.77. Patterns of the relationships of Pg and Re 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.