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

摘要

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) (R-2 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, ε(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 (R-2 = 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 R-2 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.